diff --git a/.github/actions/install-pnl/action.yml b/.github/actions/install-pnl/action.yml
index 8571a3293b0..cd4dca7dbe9 100644
--- a/.github/actions/install-pnl/action.yml
+++ b/.github/actions/install-pnl/action.yml
@@ -48,6 +48,17 @@ runs:
[[ ${{ runner.os }} = Windows* ]] && pip install "pywinpty<1" "terminado<0.10"
fi
+ - name: Install updated package
+ if: ${{ startsWith(github.head_ref, 'dependabot/pip') && matrix.pnl-version != 'base' }}
+ shell: bash
+ id: new_package
+ run: |
+ python -m pip install --upgrade pip wheel
+ export NEW_PACKAGE=`echo '${{ github.head_ref }}' | cut -f 4 -d/ | sed 's/-gt.*//' | sed 's/-lt.*//'`
+ echo "::set-output name=new_package::$NEW_PACKAGE"
+ pip install "`echo $NEW_PACKAGE | sed 's/[-_]/./g' | xargs grep *requirements.txt -h -e | head -n1`"
+ pip show "$NEW_PACKAGE" | grep 'Version' | tee new_version.deps
+
- name: Python dependencies
shell: bash
run: |
@@ -66,3 +77,11 @@ runs:
pip cache remove -v $P || true
fi
done
+
+ - name: Check updated package
+ if: ${{ startsWith(github.head_ref, 'dependabot/pip') && matrix.pnl-version != 'base' }}
+ shell: bash
+ run: |
+ pip show ${{ steps.new_package.outputs.new_package }} | grep 'Version' | tee installed_version.deps
+ cmp -s new_version.deps installed_version.deps || echo "::error::Package version restricted by dependencies: ${{ steps.new_package.outputs.new_package }}"
+ diff new_version.deps installed_version.deps
diff --git a/.github/workflows/compare-comment.yml b/.github/workflows/compare-comment.yml
index 61bf6896a5d..15f5e85cf6d 100644
--- a/.github/workflows/compare-comment.yml
+++ b/.github/workflows/compare-comment.yml
@@ -18,7 +18,7 @@ jobs:
steps:
- name: 'Download docs artifacts'
id: docs-artifacts
- uses: actions/github-script@v5
+ uses: actions/github-script@v6
with:
script: |
var artifacts = await github.rest.actions.listWorkflowRunArtifacts({
@@ -70,7 +70,7 @@ jobs:
(diff -r docs-base docs-head && echo 'No differences!' || true) | tee ./result.diff
- name: Post comment with docs diff
- uses: actions/github-script@v5
+ uses: actions/github-script@v6
with:
script: |
var fs = require('fs');
diff --git a/.github/workflows/pnl-ci-docs.yml b/.github/workflows/pnl-ci-docs.yml
index f2396ef7a04..a37c9e7a250 100644
--- a/.github/workflows/pnl-ci-docs.yml
+++ b/.github/workflows/pnl-ci-docs.yml
@@ -65,7 +65,7 @@ jobs:
branch: master
- name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v3
+ uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
architecture: ${{ matrix.python-architecture }}
@@ -94,17 +94,21 @@ jobs:
- name: Add git tag
# The generated docs include PNL version,
# set it to a fixed value to prevent polluting the diff
+ # This needs to be done after installing PNL
+ # to not interfere with dependency resolution
+ id: add_zero_tag
if: github.event_name == 'pull_request'
- run: git tag --force 'v999.999.999.999'
+ run: git tag --force 'v0.0.0.0'
- name: Build Documentation
run: make -C docs/ html -e SPHINXOPTS="-aE -j auto"
- name: Remove git tag
# The generated docs include PNL version,
- # This was set to a fixed value to prevent polluting the diff
- if: github.event_name == 'pull_request' && always()
- run: git tag -d 'v999.999.999.999'
+ # A special tag was set to a fixed value
+ # to prevent polluting the diff
+ if: steps.add_zero_tag.outcome != 'skipped'
+ run: git tag -d 'v0.0.0.0'
- name: Upload Documentation
uses: actions/upload-artifact@v3
@@ -151,7 +155,7 @@ jobs:
ref: gh-pages
- name: Download branch docs
- uses: actions/download-artifact@v2
+ uses: actions/download-artifact@v3
with:
name: Documentation-head-${{ matrix.os }}-${{ matrix.python-version }}-x64
path: _built_docs/${{ github.ref }}
@@ -168,7 +172,7 @@ jobs:
if: github.ref == 'refs/heads/master' || github.ref == 'refs/heads/devel' || github.ref == 'refs/heads/docs'
- name: Download main docs
- uses: actions/download-artifact@v2
+ uses: actions/download-artifact@v3
with:
name: Documentation-head-${{ matrix.os }}-${{ matrix.python-version }}-x64
# This overwrites files in current directory
diff --git a/.github/workflows/pnl-ci.yml b/.github/workflows/pnl-ci.yml
index 25227973e1d..b97eaa55ecf 100644
--- a/.github/workflows/pnl-ci.yml
+++ b/.github/workflows/pnl-ci.yml
@@ -22,10 +22,6 @@ jobs:
extra-args: ['']
os: [ubuntu-latest, macos-latest, windows-latest]
include:
- # 3.7 is broken on macos-11, https://github.com/actions/virtual-environments/issues/4230
- - python-version: 3.7
- python-architecture: 'x64'
- os: macos-10.15
# add 32-bit build on windows
- python-version: 3.8
python-architecture: 'x86'
@@ -54,7 +50,7 @@ jobs:
run: git fetch --tags origin master
- name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v3
+ uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
architecture: ${{ matrix.python-architecture }}
diff --git a/.github/workflows/test-release.yml b/.github/workflows/test-release.yml
index 32b6467d85e..0b7887ea5ee 100644
--- a/.github/workflows/test-release.yml
+++ b/.github/workflows/test-release.yml
@@ -21,7 +21,7 @@ jobs:
uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v3
+ uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
@@ -78,13 +78,13 @@ jobs:
steps:
- name: Download dist files
- uses: actions/download-artifact@v2
+ uses: actions/download-artifact@v3
with:
name: Python-dist-files
path: dist/
- name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v3
+ uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
@@ -141,7 +141,7 @@ jobs:
steps:
- name: Download dist files
- uses: actions/download-artifact@v2
+ uses: actions/download-artifact@v3
with:
name: Python-dist-files
path: dist/
@@ -175,13 +175,13 @@ jobs:
steps:
- name: Download dist files
- uses: actions/download-artifact@v2
+ uses: actions/download-artifact@v3
with:
name: Python-dist-files
path: dist/
- name: Upload dist files to release
- uses: actions/github-script@v5
+ uses: actions/github-script@v6
with:
script: |
const fs = require('fs')
diff --git a/.gitignore b/.gitignore
index 0b0f973f543..84bfbe22d2a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,9 +1,9 @@
# Created by https://www.gitignore.io/api/osx,python,pycharm
-# Ignore JSON files created in tests/json/
+# Ignore JSON files created in tests/mdf/
# Maybe these should be generated in tmpdir instead
-tests/json/*.json
+tests/mdf/*.json
# Log files created by SLURM jobs in this directory
Scripts/Debug/predator_prey_opt/logs/
diff --git a/conftest.py b/conftest.py
index 8521ae6c014..94a4de81cc4 100644
--- a/conftest.py
+++ b/conftest.py
@@ -33,7 +33,22 @@
def pytest_addoption(parser):
parser.addoption('--{0}'.format(mark_stress_tests), action='store_true', default=False, help='Run {0} tests (long)'.format(mark_stress_tests))
+ parser.addoption('--fp-precision', action='store', default='fp64', choices=['fp32', 'fp64'],
+ help='Set default fp precision for the runtime compiler. Default: fp64')
+
+def pytest_sessionstart(session):
+ precision = session.config.getvalue("--fp-precision")
+ if precision == 'fp64':
+ pnlvm.LLVMBuilderContext.default_float_ty = pnlvm.ir.DoubleType()
+ elif precision == 'fp32':
+ pnlvm.LLVMBuilderContext.default_float_ty = pnlvm.ir.FloatType()
+ else:
+ assert False, "Unsupported precision parameter: {}".format(precision)
+
def pytest_runtest_setup(item):
+ # Check that all 'cuda' tests are also marked 'llvm'
+ assert 'llvm' in item.keywords or 'cuda' not in item.keywords
+
for m in marks_default_skip:
if m in item.keywords and not item.config.getvalue(m):
pytest.skip('{0} tests not requested'.format(m))
@@ -97,6 +112,16 @@ def comp_mode_no_llvm():
# dummy fixture to allow 'comp_mode' filtering
pass
+@pytest.helpers.register
+def llvm_current_fp_precision():
+ float_ty = pnlvm.LLVMBuilderContext.get_current().float_ty
+ if float_ty == pnlvm.ir.DoubleType():
+ return 'fp64'
+ elif float_ty == pnlvm.ir.FloatType():
+ return 'fp32'
+ else:
+ assert False, "Unknown floating point type: {}".format(float_ty)
+
@pytest.helpers.register
def get_comp_execution_modes():
return [pytest.param(pnlvm.ExecutionMode.Python),
diff --git a/cuda_requirements.txt b/cuda_requirements.txt
index 9a6d83d22c4..63e22850e71 100644
--- a/cuda_requirements.txt
+++ b/cuda_requirements.txt
@@ -1 +1 @@
-pycuda >2018, <2022
+pycuda >2018, <2023
diff --git a/dev_requirements.txt b/dev_requirements.txt
index 95b05810996..ad283dfc78d 100644
--- a/dev_requirements.txt
+++ b/dev_requirements.txt
@@ -1,5 +1,5 @@
jupyter<=1.0.0
-pytest<7.1.2
+pytest<7.1.3
pytest-benchmark<3.4.2
pytest-cov<3.0.1
pytest-helpers-namespace<2021.12.30
diff --git a/doc_requirements.txt b/doc_requirements.txt
index 043ea79e043..f4c95bd01e8 100644
--- a/doc_requirements.txt
+++ b/doc_requirements.txt
@@ -1,3 +1,3 @@
-psyneulink-sphinx-theme<1.2.3.1
+psyneulink-sphinx-theme<1.2.4.1
sphinx<4.2.1
sphinx_autodoc_typehints<1.16.0
diff --git a/docs/source/_static/Pathways_fig.svg b/docs/source/_static/Pathways_fig.svg
new file mode 100644
index 00000000000..a13eea7854f
--- /dev/null
+++ b/docs/source/_static/Pathways_fig.svg
@@ -0,0 +1,2489 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 25.3.1, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+ viewBox="0 0 807.1 712.5" style="enable-background:new 0 0 807.1 712.5;" xml:space="preserve">
+<style type="text/css">
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diff --git a/psyneulink/core/components/component.py b/psyneulink/core/components/component.py
index bbe4c760afe..e62540ece0f 100644
--- a/psyneulink/core/components/component.py
+++ b/psyneulink/core/components/component.py
@@ -513,7 +513,7 @@
from psyneulink.core import llvm as pnlvm
from psyneulink.core.globals.context import \
Context, ContextError, ContextFlags, INITIALIZATION_STATUS_FLAGS, _get_time, handle_external_context
-from psyneulink.core.globals.json import JSONDumpable
+from psyneulink.core.globals.mdf import MDFSerializable
from psyneulink.core.globals.keywords import \
CONTEXT, CONTROL_PROJECTION, DEFERRED_INITIALIZATION, EXECUTE_UNTIL_FINISHED, \
FUNCTION, FUNCTION_PARAMS, INIT_FULL_EXECUTE_METHOD, INPUT_PORTS, \
@@ -525,7 +525,7 @@
RESET_STATEFUL_FUNCTION_WHEN, VALUE, VARIABLE
from psyneulink.core.globals.log import LogCondition
from psyneulink.core.globals.parameters import \
- Defaults, SharedParameter, Parameter, ParameterAlias, ParameterError, ParametersBase, copy_parameter_value
+ Defaults, SharedParameter, Parameter, ParameterAlias, ParameterError, ParametersBase, check_user_specified, copy_parameter_value
from psyneulink.core.globals.preferences.basepreferenceset import BasePreferenceSet, VERBOSE_PREF
from psyneulink.core.globals.preferences.preferenceset import \
PreferenceLevel, PreferenceSet, _assign_prefs
@@ -724,7 +724,7 @@ def class_defaults(self):
return self.defaults
-class Component(JSONDumpable, metaclass=ComponentsMeta):
+class Component(MDFSerializable, metaclass=ComponentsMeta):
"""
Component( \
default_variable=None, \
@@ -909,7 +909,7 @@ class Component(JSONDumpable, metaclass=ComponentsMeta):
standard_constructor_args = [RESET_STATEFUL_FUNCTION_WHEN, EXECUTE_UNTIL_FINISHED, MAX_EXECUTIONS_BEFORE_FINISHED]
- # helper attributes for JSON model spec
+ # helper attributes for MDF model spec
_model_spec_id_parameters = 'parameters'
_model_spec_id_stateful_parameters = 'stateful_parameters'
@@ -1084,10 +1084,9 @@ def _parse_modulable(self, param_name, param_value):
# insuring that assignment by one instance will not affect the value of others.
name = None
- _deepcopy_shared_keys = frozenset([
- '_init_args',
- ])
+ _deepcopy_shared_keys = frozenset([])
+ @check_user_specified
def __init__(self,
default_variable,
param_defaults,
@@ -1303,6 +1302,9 @@ def __deepcopy__(self, memo):
newone.parameters._owner = newone
newone.defaults._owner = newone
+ for p in newone.parameters:
+ p._owner = newone.parameters
+
# by copying, this instance is no longer "inherent" to a single
# 'import psyneulink' call
newone._is_pnl_inherent = False
@@ -1331,6 +1333,10 @@ def _get_compilation_state(self):
if hasattr(self, 'nodes'):
whitelist.add("num_executions")
+ # Drop combination function params from RTM if not needed
+ if getattr(self.parameters, 'has_recurrent_input_port', False):
+ blacklist.update(['combination_function'])
+
def _is_compilation_state(p):
#FIXME: This should use defaults instead of 'p.get'
return p.name not in blacklist and \
@@ -1362,7 +1368,7 @@ def _convert(p):
state['buffer'], state['uinteger'], state['buffer_pos'],
state['has_uint32'], x.used_seed[0]))
elif isinstance(x, Time):
- val = tuple(getattr(x, graph_scheduler.time._time_scale_to_attr_str(t)) for t in TimeScale)
+ val = tuple(x._get_by_time_scale(t) for t in TimeScale)
elif isinstance(x, Component):
return x._get_state_initializer(context)
elif isinstance(x, ContentAddressableList):
@@ -1423,6 +1429,10 @@ def _get_compilation_params(self):
# "has_initializers" is only used by RTM
blacklist.update(["has_initializers"])
+ # Drop combination function params from RTM if not needed
+ if getattr(self.parameters, 'has_recurrent_input_port', False):
+ blacklist.update(['combination_function'])
+
def _is_compilation_param(p):
if p.name not in blacklist and not isinstance(p, (ParameterAlias, SharedParameter)):
#FIXME: this should use defaults
@@ -2015,32 +2025,30 @@ def _initialize_parameters(self, context=None, **param_defaults):
}
if param_defaults is not None:
- # Exclude any function_params from the items to set on this Component
- # because these should just be pointers to the parameters of the same
- # name on this Component's function
- # Exclude any pass parameters whose value is None (assume this means "use the normal default")
- d = {
- k: v for (k, v) in param_defaults.items()
- if (
- (
- k not in defaults
- and k not in alias_names
- )
- or v is not None
- )
- }
- for p in d:
+ for name, value in copy.copy(param_defaults).items():
try:
- parameter_obj = getattr(self.parameters, p)
+ parameter_obj = getattr(self.parameters, name)
except AttributeError:
- # p in param_defaults does not correspond to a Parameter
+ # name in param_defaults does not correspond to a Parameter
continue
- if d[p] is not None:
+ if (
+ name not in self._user_specified_args
+ and parameter_obj.constructor_argument not in self._user_specified_args
+ ):
+ continue
+
+ if (
+ (
+ name in self._user_specified_args
+ or parameter_obj.constructor_argument in self._user_specified_args
+ )
+ and (value is not None or parameter_obj.specify_none)
+ ):
parameter_obj._user_specified = True
if parameter_obj.structural:
- parameter_obj.spec = d[p]
+ parameter_obj.spec = value
if parameter_obj.modulable:
# later, validate this
@@ -2049,17 +2057,18 @@ def _initialize_parameters(self, context=None, **param_defaults):
parse=True,
modulable=True
)
- parsed = modulable_param_parser(p, d[p])
+ parsed = modulable_param_parser(name, value)
- if parsed is not d[p]:
+ if parsed is not value:
# we have a modulable param spec
- parameter_obj.spec = d[p]
- d[p] = parsed
- param_defaults[p] = parsed
+ parameter_obj.spec = value
+ value = parsed
+ param_defaults[name] = parsed
except AttributeError:
pass
- defaults.update(d)
+ if value is not None or parameter_obj.specify_none:
+ defaults[name] = value
for k in defaults:
defaults[k] = copy_parameter_value(
@@ -3712,7 +3721,9 @@ def parse_parameter_value(value, no_expand_components=False, functions_as_dill=F
else:
try:
value = value.as_mdf_model(simple_edge_format=False)
- except TypeError:
+ except TypeError as e:
+ if "got an unexpected keyword argument 'simple_edge_format'" not in str(e):
+ raise
value = value.as_mdf_model()
elif isinstance(value, ComponentsMeta):
value = value.__name__
diff --git a/psyneulink/core/components/functions/function.py b/psyneulink/core/components/functions/function.py
index cda41d037bf..968cd52a77c 100644
--- a/psyneulink/core/components/functions/function.py
+++ b/psyneulink/core/components/functions/function.py
@@ -159,13 +159,13 @@
IDENTITY_MATRIX, INVERSE_HOLLOW_MATRIX, NAME, PREFERENCE_SET_NAME, RANDOM_CONNECTIVITY_MATRIX, VALUE, VARIABLE,
MODEL_SPEC_ID_METADATA, MODEL_SPEC_ID_MDF_VARIABLE
)
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import REPORT_OUTPUT_PREF, is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.globals.registry import register_category
from psyneulink.core.globals.utilities import (
convert_to_np_array, get_global_seed, is_instance_or_subclass, object_has_single_value, parameter_spec, parse_valid_identifier, safe_len,
- SeededRandomState, contains_type
+ SeededRandomState, contains_type, is_numeric
)
__all__ = [
@@ -605,6 +605,7 @@ def _validate_changes_shape(self, param):
# Note: the following enforce encoding as 1D np.ndarrays (one array per variable)
variableEncodingDim = 1
+ @check_user_specified
@abc.abstractmethod
def __init__(
self,
@@ -897,7 +898,7 @@ def as_mdf_model(self):
if typ not in mdf_functions.mdf_functions:
warnings.warn(f'{typ} is not an MDF standard function, this is likely to produce an incompatible model.')
- model.function = {typ: parameters[self._model_spec_id_parameters]}
+ model.function = typ
return model
@@ -995,6 +996,7 @@ class Manner(Enum):
# These are used both to type-cast the params, and as defaults if none are assigned
# in the initialization call or later (using either _instantiate_defaults or during a function call)
+ @check_user_specified
def __init__(self,
default_variable=None,
propensity=10.0,
@@ -1145,6 +1147,7 @@ class Parameters(Function_Base.Parameters):
REPORT_OUTPUT_PREF: PreferenceEntry(False, PreferenceLevel.INSTANCE),
}
+ @check_user_specified
@tc.typecheck
def __init__(self,
function,
@@ -1185,7 +1188,14 @@ def get_matrix(specification, rows=1, cols=1, context=None):
# Matrix provided (and validated in _validate_params); convert to array
if isinstance(specification, (list, np.matrix)):
- return convert_to_np_array(specification)
+ # # MODIFIED 4/9/22 OLD:
+ # return convert_to_np_array(specification)
+ # MODIFIED 4/9/22 NEW:
+ if is_numeric(specification):
+ return convert_to_np_array(specification)
+ else:
+ return
+ # MODIFIED 4/9/22 END
if isinstance(specification, np.ndarray):
if specification.ndim == 2:
diff --git a/psyneulink/core/components/functions/nonstateful/combinationfunctions.py b/psyneulink/core/components/functions/nonstateful/combinationfunctions.py
index e91fd02a118..be28b1d62eb 100644
--- a/psyneulink/core/components/functions/nonstateful/combinationfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/combinationfunctions.py
@@ -45,7 +45,7 @@
PREFERENCE_SET_NAME, VARIABLE
from psyneulink.core.globals.utilities import convert_to_np_array, is_numeric, np_array_less_than_2d, parameter_spec
from psyneulink.core.globals.context import ContextFlags
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import \
REPORT_OUTPUT_PREF, is_pref_set, PreferenceEntry, PreferenceLevel
@@ -201,6 +201,7 @@ class Parameters(CombinationFunction.Parameters):
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
changes_shape = Parameter(True, stateful=False, loggable=False, pnl_internal=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -420,6 +421,7 @@ class Parameters(CombinationFunction.Parameters):
scale = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -723,6 +725,7 @@ class Parameters(CombinationFunction.Parameters):
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
changes_shape = Parameter(True, stateful=False, loggable=False, pnl_internal=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
# weights: tc.optional(parameter_spec)=None,
@@ -1165,6 +1168,7 @@ class Parameters(CombinationFunction.Parameters):
scale = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1689,6 +1693,7 @@ class Parameters(CombinationFunction.Parameters):
scale = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1948,6 +1953,7 @@ class Parameters(CombinationFunction.Parameters):
variable = Parameter(np.array([[1], [1]]), pnl_internal=True, constructor_argument='default_variable')
gamma = Parameter(1.0, modulable=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/nonstateful/distributionfunctions.py b/psyneulink/core/components/functions/nonstateful/distributionfunctions.py
index 91b255b14d4..b8a64bc1510 100644
--- a/psyneulink/core/components/functions/nonstateful/distributionfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/distributionfunctions.py
@@ -39,7 +39,7 @@
from psyneulink.core.globals.utilities import convert_to_np_array, parameter_spec
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
__all__ = [
'DistributionFunction', 'DRIFT_RATE', 'DRIFT_RATE_VARIABILITY', 'DriftDiffusionAnalytical', 'ExponentialDist',
@@ -159,6 +159,7 @@ class Parameters(DistributionFunction.Parameters):
random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -341,6 +342,7 @@ class Parameters(DistributionFunction.Parameters):
mean = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
standard_deviation = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -467,6 +469,7 @@ class Parameters(DistributionFunction.Parameters):
random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -593,6 +596,7 @@ class Parameters(DistributionFunction.Parameters):
random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -750,6 +754,7 @@ class Parameters(DistributionFunction.Parameters):
scale = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
dist_shape = Parameter(1.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -884,6 +889,7 @@ class Parameters(DistributionFunction.Parameters):
scale = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
mean = Parameter(1.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1120,6 +1126,7 @@ class Parameters(DistributionFunction.Parameters):
read_only=True
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/nonstateful/learningfunctions.py b/psyneulink/core/components/functions/nonstateful/learningfunctions.py
index c00959d8f6b..c4727f52628 100644
--- a/psyneulink/core/components/functions/nonstateful/learningfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/learningfunctions.py
@@ -39,7 +39,7 @@
CONTRASTIVE_HEBBIAN_FUNCTION, TDLEARNING_FUNCTION, LEARNING_FUNCTION_TYPE, LEARNING_RATE, \
KOHONEN_FUNCTION, GAUSSIAN, LINEAR, EXPONENTIAL, HEBBIAN_FUNCTION, RL_FUNCTION, BACKPROPAGATION_FUNCTION, MATRIX, \
MSE, SSE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import is_numeric, scalar_distance, convert_to_np_array
@@ -448,6 +448,7 @@ class Parameters(LearningFunction.Parameters):
gamma_size_n = 1
gamma_size_prior = 1
+ @check_user_specified
def __init__(self,
default_variable=None,
mu_0=None,
@@ -774,6 +775,7 @@ def _validate_distance_function(self, distance_function):
default_learning_rate = 0.05
+ @check_user_specified
def __init__(self,
default_variable=None,
# learning_rate: tc.optional(tc.optional(parameter_spec)) = None,
@@ -1045,6 +1047,7 @@ class Parameters(LearningFunction.Parameters):
modulable=True)
default_learning_rate = 0.05
+ @check_user_specified
def __init__(self,
default_variable=None,
learning_rate=None,
@@ -1278,6 +1281,7 @@ class Parameters(LearningFunction.Parameters):
default_learning_rate = 0.05
+ @check_user_specified
def __init__(self,
default_variable=None,
# learning_rate: tc.optional(tc.optional(parameter_spec)) = None,
@@ -1585,6 +1589,7 @@ class Parameters(LearningFunction.Parameters):
read_only=True
)
+ @check_user_specified
def __init__(self,
default_variable=None,
# learning_rate: tc.optional(tc.optional(parameter_spec)) = None,
@@ -1934,6 +1939,7 @@ class Parameters(LearningFunction.Parameters):
default_learning_rate = 1.0
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2175,6 +2181,7 @@ class TDLearning(Reinforcement):
"""
componentName = TDLEARNING_FUNCTION
+ @check_user_specified
def __init__(self,
default_variable=None,
learning_rate=None,
diff --git a/psyneulink/core/components/functions/nonstateful/objectivefunctions.py b/psyneulink/core/components/functions/nonstateful/objectivefunctions.py
index 286cf63a86e..1e8ac37f370 100644
--- a/psyneulink/core/components/functions/nonstateful/objectivefunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/objectivefunctions.py
@@ -33,7 +33,7 @@
DEFAULT_VARIABLE, DIFFERENCE, DISTANCE_FUNCTION, DISTANCE_METRICS, DistanceMetrics, \
ENERGY, ENTROPY, EUCLIDEAN, HOLLOW_MATRIX, MATRIX, MAX_ABS_DIFF, \
NORMED_L0_SIMILARITY, OBJECTIVE_FUNCTION_TYPE, SIZE, STABILITY_FUNCTION
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import is_distance_metric, safe_len, convert_to_np_array
from psyneulink.core.globals.utilities import is_iterable
@@ -206,6 +206,7 @@ class Parameters(ObjectiveFunction.Parameters):
transfer_fct = Parameter(None, stateful=False, loggable=False)
normalize = Parameter(False, stateful=False)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -558,6 +559,7 @@ class Energy(Stability):
specifies the `PreferenceSet` for the Function (see `prefs <Function_Base.prefs>` for details).
"""
+ @check_user_specified
def __init__(self,
default_variable=None,
size=None,
@@ -667,6 +669,7 @@ class Entropy(Stability):
specifies the `PreferenceSet` for the Function (see `prefs <Function_Base.prefs>` for details).
"""
+ @check_user_specified
def __init__(self,
default_variable=None,
normalize:bool=None,
@@ -779,6 +782,7 @@ class Parameters(ObjectiveFunction.Parameters):
variable = Parameter(np.array([[0], [0]]), read_only=True, pnl_internal=True, constructor_argument='default_variable')
metric = Parameter(DIFFERENCE, stateful=False)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py b/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py
index 1f70a337c64..df8d182577c 100644
--- a/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/optimizationfunctions.py
@@ -48,7 +48,7 @@
from psyneulink.core.globals.keywords import \
BOUNDS, GRADIENT_OPTIMIZATION_FUNCTION, GRID_SEARCH_FUNCTION, GAUSSIAN_PROCESS_FUNCTION, \
OPTIMIZATION_FUNCTION_TYPE, OWNER, VALUE, VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.sampleiterator import SampleIterator
from psyneulink.core.globals.utilities import call_with_pruned_args
@@ -404,6 +404,7 @@ class Parameters(Function_Base.Parameters):
saved_samples = Parameter([], read_only=True, pnl_internal=True)
saved_values = Parameter([], read_only=True, pnl_internal=True)
+ @check_user_specified
@tc.typecheck
def __init__(
self,
@@ -623,26 +624,26 @@ def _evaluate(self, variable=None, context=None, params=None):
assert all([not getattr(self.parameters, x)._user_specified for x in self._unspecified_args])
self._unspecified_args = []
- # Get initial sample in case it is needed by _search_space_evaluate (e.g., for gradient initialization)
- initial_sample = self._check_args(variable=variable, context=context, params=params)
- try:
- initial_value = self.owner.objective_mechanism.parameters.value._get(context)
- except AttributeError:
- initial_value = 0
-
# EVALUATE ALL SAMPLES IN SEARCH SPACE
# Evaluate all estimates of all samples in search_space
- # If execution mode is not Python and search_space is static, use parallelized evaluation:
- if (self.owner and self.owner.parameters.comp_execution_mode._get(context) != 'Python' and
- all(isinstance(sample_iterator.start, Number) and isinstance(sample_iterator.stop, Number)
- for sample_iterator in self.search_space)):
- # FIX: NEED TO FIX THIS ONCE _grid_evaluate RETURNS all_samples
- all_samples = []
+ # Run compiled mode if requested by parameter and everything is initialized
+ if self.owner and self.owner.parameters.comp_execution_mode._get(context) != 'Python' and \
+ ContextFlags.PROCESSING in context.flags:
+ all_samples = [s for s in itertools.product(*self.search_space)]
all_values, num_evals = self._grid_evaluate(self.owner, context)
+ assert len(all_values) == num_evals
+ assert len(all_samples) == num_evals
last_sample = last_value = None
# Otherwise, default sequential sampling
else:
+ # Get initial sample in case it is needed by _search_space_evaluate (e.g., for gradient initialization)
+ initial_sample = self._check_args(variable=variable, context=context, params=params)
+ try:
+ initial_value = self.owner.objective_mechanism.parameters.value._get(context)
+ except AttributeError:
+ initial_value = 0
+
last_sample, last_value, all_samples, all_values = self._sequential_evaluate(initial_sample,
initial_value,
context)
@@ -654,6 +655,11 @@ def _evaluate(self, variable=None, context=None, params=None):
self.parameters.randomization_dimension._get(context) and \
self.parameters.num_estimates._get(context) is not None:
+ # FIXME: This is easy to support in hybrid mode. We just need to convert ctype results
+ # returned from _grid_evaluate to numpy
+ assert not self.owner or self.owner.parameters.comp_execution_mode._get(context) == 'Python', \
+ "Aggregation function not supported in compiled mode!"
+
# Reshape all the values we encountered to group those that correspond to the same parameter values
# can be aggregated.
all_values = np.reshape(all_values, (-1, self.parameters.num_estimates._get(context)))
@@ -752,6 +758,17 @@ def _sequential_evaluate(self, initial_sample, initial_value, context):
def _grid_evaluate(self, ocm, context):
"""Helper method for evaluation of a grid of samples from search space via LLVM backends."""
+ # If execution mode is not Python, the search space has to be static
+ def _is_static(it:SampleIterator):
+ if isinstance(it.start, Number) and isinstance(it.stop, Number):
+ return True
+
+ if isinstance(it.generator, list):
+ return True
+
+ return False
+
+ assert all(_is_static(sample_iterator) for sample_iterator in self.search_space)
assert ocm is ocm.agent_rep.controller
# Compiled evaluate expects the same variable as mech function
variable = [input_port.parameters.value.get(context) for input_port in ocm.input_ports]
@@ -767,7 +784,6 @@ def _grid_evaluate(self, ocm, context):
else:
assert False, f"Unknown execution mode for {ocm.name}: {execution_mode}."
- # FIX: RETURN SHOULD BE: outcomes, all_samples (THEN FIX CALL IN _function)
return outcomes, num_evals
def _report_value(self, new_value):
@@ -1084,6 +1100,7 @@ def _parse_direction(self, direction):
else:
return -1
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1486,6 +1503,7 @@ class Parameters(OptimizationFunction.Parameters):
# TODO: should save_values be in the constructor if it's ignored?
# is False or True the correct value?
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1805,30 +1823,6 @@ def _gen_llvm_function_body(self, ctx, builder, params, state_features, arg_in,
builder.store(builder.load(min_value_ptr), out_value_ptr)
return builder
- def _run_grid(self, ocm, variable, context):
-
- # "ct" => c-type variables
- ct_values, num_evals = self._grid_evaluate(ocm, context)
-
- assert len(ct_values) == num_evals
- # Reduce array of values to min/max
- # select_min params are:
- # params, state, min_sample_ptr, sample_ptr, min_value_ptr, value_ptr, opt_count_ptr, count
- bin_func = pnlvm.LLVMBinaryFunction.from_obj(self, tags=frozenset({"select_min"}))
- ct_param = bin_func.byref_arg_types[0](*self._get_param_initializer(context))
- ct_state = bin_func.byref_arg_types[1](*self._get_state_initializer(context))
- ct_opt_sample = bin_func.byref_arg_types[2](float("NaN"))
- ct_alloc = None # NULL for samples
- ct_opt_value = bin_func.byref_arg_types[4]()
- ct_opt_count = bin_func.byref_arg_types[6](0)
- ct_start = bin_func.c_func.argtypes[7](0)
- ct_stop = bin_func.c_func.argtypes[8](len(ct_values))
-
- bin_func(ct_param, ct_state, ct_opt_sample, ct_alloc, ct_opt_value,
- ct_values, ct_opt_count, ct_start, ct_stop)
-
- return np.ctypeslib.as_array(ct_opt_sample), ct_opt_value.value, np.ctypeslib.as_array(ct_values)
-
def _function(self,
variable=None,
context=None,
@@ -1953,15 +1947,37 @@ def _function(self,
"PROGRAM ERROR: bad value for {} arg of {}: {}, {}". \
format(repr(DIRECTION), self.name, direction)
- ocm = self._get_optimized_controller()
+ # Evaluate objective_function for each sample
+ last_sample, last_value, all_samples, all_values = self._evaluate(
+ variable=variable,
+ context=context,
+ params=params,
+ )
# Compiled version
+ ocm = self._get_optimized_controller()
if ocm is not None and ocm.parameters.comp_execution_mode._get(context) in {"PTX", "LLVM"}:
- opt_sample, opt_value, all_values = self._run_grid(ocm, variable, context)
- # This should not be evaluated unless needed
- all_samples = [s for s in itertools.product(*self.search_space)]
- value_optimal = opt_value
- sample_optimal = opt_sample
+
+ # Reduce array of values to min/max
+ # select_min params are:
+ # params, state, min_sample_ptr, sample_ptr, min_value_ptr, value_ptr, opt_count_ptr, count
+ bin_func = pnlvm.LLVMBinaryFunction.from_obj(self, tags=frozenset({"select_min"}))
+ ct_param = bin_func.byref_arg_types[0](*self._get_param_initializer(context))
+ ct_state = bin_func.byref_arg_types[1](*self._get_state_initializer(context))
+ ct_opt_sample = bin_func.byref_arg_types[2](float("NaN"))
+ ct_alloc = None # NULL for samples
+ ct_values = all_values
+ ct_opt_value = bin_func.byref_arg_types[4]()
+ ct_opt_count = bin_func.byref_arg_types[6](0)
+ ct_start = bin_func.c_func.argtypes[7](0)
+ ct_stop = bin_func.c_func.argtypes[8](len(ct_values))
+
+ bin_func(ct_param, ct_state, ct_opt_sample, ct_alloc, ct_opt_value,
+ ct_values, ct_opt_count, ct_start, ct_stop)
+
+ value_optimal = ct_opt_value.value
+ sample_optimal = np.ctypeslib.as_array(ct_opt_sample)
+ all_values = np.ctypeslib.as_array(ct_values)
# These are normally stored in the parent function (OptimizationFunction).
# Since we didn't call super()._function like the python path,
@@ -1974,12 +1990,6 @@ def _function(self,
# Python version
else:
- # Evaluate objective_function for each sample
- last_sample, last_value, all_samples, all_values = self._evaluate(
- variable=variable,
- context=context,
- params=params,
- )
if all_values.size != all_samples.shape[-1]:
raise ValueError(f"OptimizationFunction Error: {self}._evaluate returned mismatched sizes for "
@@ -2198,6 +2208,7 @@ class Parameters(OptimizationFunction.Parameters):
# TODO: should save_values be in the constructor if it's ignored?
# is False or True the correct value?
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2452,12 +2463,13 @@ class Parameters(OptimizationFunction.Parameters):
:default value: True
:type: ``bool``
"""
- variable = Parameter([[0], [0]], read_only=True)
+ variable = Parameter([[0], [0]], read_only=True, constructor_argument='default_variable')
random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
save_samples = True
save_values = True
+ @check_user_specified
@tc.typecheck
def __init__(self,
priors,
diff --git a/psyneulink/core/components/functions/nonstateful/selectionfunctions.py b/psyneulink/core/components/functions/nonstateful/selectionfunctions.py
index aff4dc5764f..626f1ade454 100644
--- a/psyneulink/core/components/functions/nonstateful/selectionfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/selectionfunctions.py
@@ -36,7 +36,7 @@
from psyneulink.core.globals.keywords import \
MAX_VAL, MAX_ABS_VAL, MAX_INDICATOR, MAX_ABS_INDICATOR, MIN_VAL, MIN_ABS_VAL, MIN_INDICATOR, MIN_ABS_INDICATOR, \
MODE, ONE_HOT_FUNCTION, PROB, PROB_INDICATOR, SELECTION_FUNCTION_TYPE, PREFERENCE_SET_NAME
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import \
REPORT_OUTPUT_PREF, PreferenceEntry, PreferenceLevel, is_pref_set
@@ -201,6 +201,7 @@ def _validate_mode(self, mode):
# returns error message
return 'not one of {0}'.format(options)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/nonstateful/transferfunctions.py b/psyneulink/core/components/functions/nonstateful/transferfunctions.py
index 5bce6445bba..eef16ca3f36 100644
--- a/psyneulink/core/components/functions/nonstateful/transferfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/transferfunctions.py
@@ -70,7 +70,7 @@
RATE, RECEIVER, RELU_FUNCTION, SCALE, SLOPE, SOFTMAX_FUNCTION, STANDARD_DEVIATION, SUM, \
TRANSFER_FUNCTION_TYPE, TRANSFER_WITH_COSTS_FUNCTION, VARIANCE, VARIABLE, X_0, PREFERENCE_SET_NAME
from psyneulink.core.globals.parameters import \
- FunctionParameter, Parameter, get_validator_by_function
+ FunctionParameter, Parameter, get_validator_by_function, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import \
REPORT_OUTPUT_PREF, PreferenceEntry, PreferenceLevel, is_pref_set
from psyneulink.core.globals.utilities import parameter_spec, safe_len
@@ -197,6 +197,7 @@ class Identity(TransferFunction): # -------------------------------------------
REPORT_OUTPUT_PREF: PreferenceEntry(False, PreferenceLevel.INSTANCE),
}
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -364,6 +365,7 @@ class Parameters(TransferFunction.Parameters):
slope = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
intercept = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -625,6 +627,7 @@ class Parameters(TransferFunction.Parameters):
offset = Parameter(0.0, modulable=True)
bounds = (0, None)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -915,6 +918,7 @@ class Parameters(TransferFunction.Parameters):
scale = Parameter(1.0, modulable=True)
bounds = (0, 1)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1233,6 +1237,7 @@ class Parameters(TransferFunction.Parameters):
scale = Parameter(1.0, modulable=True)
bounds = (0, 1)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1497,6 +1502,7 @@ class Parameters(TransferFunction.Parameters):
leak = Parameter(0.0, modulable=True)
bounds = (None, None)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1705,6 +1711,7 @@ def _validate_variable(self, variable):
if variable.ndim != 1 or len(variable) < 2:
return f"must be list or 1d array of length 2 or greater."
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1970,6 +1977,7 @@ class Parameters(TransferFunction.Parameters):
offset = Parameter(0.0, modulable=True)
bounds = (None, None)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2243,6 +2251,7 @@ class Parameters(TransferFunction.Parameters):
seed = Parameter(DEFAULT_SEED, modulable=True, fallback_default=True, setter=_seed_setter)
bounds = (None, None)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2523,6 +2532,7 @@ def _validate_output(self, output):
else:
return 'not one of {0}'.format(options)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2578,7 +2588,7 @@ def _validate_variable(self, variable, context=None):
return np.asarray(variable)
- def __gen_llvm_exp_sum_max(self, builder, index, ctx, vi, gain, max_ptr, exp_sum_ptr, max_ind_ptr):
+ def __gen_llvm_exp_sum(self, builder, index, ctx, vi, gain, exp_sum_ptr):
ptri = builder.gep(vi, [ctx.int32_ty(0), index])
exp_f = ctx.get_builtin("exp", [ctx.float_ty])
@@ -2590,17 +2600,7 @@ def __gen_llvm_exp_sum_max(self, builder, index, ctx, vi, gain, max_ptr, exp_sum
new_exp_sum = builder.fadd(exp_sum, exp_val)
builder.store(new_exp_sum, exp_sum_ptr)
- old_max = builder.load(max_ptr)
- gt = builder.fcmp_ordered(">", exp_val, old_max)
- new_max = builder.select(gt, exp_val, old_max)
- builder.store(new_max, max_ptr)
-
- old_index = builder.load(max_ind_ptr)
- new_index = builder.select(gt, index, old_index)
- builder.store(new_index, max_ind_ptr)
-
def __gen_llvm_exp_div(self, builder, index, ctx, vi, vo, gain, exp_sum):
- assert self.output == ALL
ptro = builder.gep(vo, [ctx.int32_ty(0), index])
ptri = builder.gep(vi, [ctx.int32_ty(0), index])
exp_f = ctx.get_builtin("exp", [ctx.float_ty])
@@ -2611,65 +2611,70 @@ def __gen_llvm_exp_div(self, builder, index, ctx, vi, vo, gain, exp_sum):
builder.store(val, ptro)
- def __gen_llvm_apply(self, ctx, builder, params, _, arg_in, arg_out):
+ def __gen_llvm_apply(self, ctx, builder, params, state, arg_in, arg_out, tags:frozenset):
exp_sum_ptr = builder.alloca(ctx.float_ty)
builder.store(exp_sum_ptr.type.pointee(0), exp_sum_ptr)
- max_ptr = builder.alloca(ctx.float_ty)
- builder.store(max_ptr.type.pointee(float('-inf')), max_ptr)
-
- max_ind_ptr = builder.alloca(ctx.int32_ty)
- builder.store(max_ind_ptr.type.pointee(-1), max_ind_ptr)
-
gain_ptr = pnlvm.helpers.get_param_ptr(builder, self, params, GAIN)
gain = pnlvm.helpers.load_extract_scalar_array_one(builder, gain_ptr)
with pnlvm.helpers.array_ptr_loop(builder, arg_in, "exp_sum_max") as args:
- self.__gen_llvm_exp_sum_max(*args, ctx=ctx, vi=arg_in,
- max_ptr=max_ptr, gain=gain,
- max_ind_ptr=max_ind_ptr,
- exp_sum_ptr=exp_sum_ptr)
+ self.__gen_llvm_exp_sum(*args, ctx=ctx, vi=arg_in, gain=gain,
+ exp_sum_ptr=exp_sum_ptr)
- output_type = self.output
exp_sum = builder.load(exp_sum_ptr)
- index = builder.load(max_ind_ptr)
- ptro = builder.gep(arg_out, [ctx.int32_ty(0), index])
- if output_type == ALL:
+ if self.output == ALL:
with pnlvm.helpers.array_ptr_loop(builder, arg_in, "exp_div") as args:
self.__gen_llvm_exp_div(ctx=ctx, vi=arg_in, vo=arg_out,
gain=gain, exp_sum=exp_sum, *args)
- elif output_type == MAX_VAL:
- # zero out the output array
- with pnlvm.helpers.array_ptr_loop(builder, arg_in, "zero_output") as (b,i):
- b.store(ctx.float_ty(0), b.gep(arg_out, [ctx.int32_ty(0), i]))
-
- ptri = builder.gep(arg_in, [ctx.int32_ty(0), index])
- exp_f = ctx.get_builtin("exp", [ctx.float_ty])
- orig_val = builder.load(ptri)
- val = builder.fmul(orig_val, gain)
- val = builder.call(exp_f, [val])
- val = builder.fdiv(val, exp_sum)
- builder.store(val, ptro)
- elif output_type == MAX_INDICATOR:
- # zero out the output array
- with pnlvm.helpers.array_ptr_loop(builder, arg_in, "zero_output") as (b,i):
- b.store(ctx.float_ty(0), b.gep(arg_out, [ctx.int32_ty(0), i]))
- builder.store(ctx.float_ty(1), ptro)
+ return builder
+
+ one_hot_f = ctx.import_llvm_function(self.one_hot_function, tags=tags)
+ one_hot_p = pnlvm.helpers.get_param_ptr(builder, self, params, 'one_hot_function')
+ one_hot_s = pnlvm.helpers.get_state_ptr(builder, self, state, 'one_hot_function')
+
+ assert one_hot_f.args[3].type == arg_out.type
+ one_hot_out = arg_out
+ one_hot_in = builder.alloca(one_hot_f.args[2].type.pointee)
+
+ if self.output in {MAX_VAL, MAX_INDICATOR}:
+ with pnlvm.helpers.array_ptr_loop(builder, arg_in, "exp_div") as (b, i):
+ self.__gen_llvm_exp_div(ctx=ctx, vi=arg_in, vo=one_hot_in,
+ gain=gain, exp_sum=exp_sum, builder=b, index=i)
+
+ builder.call(one_hot_f, [one_hot_p, one_hot_s, one_hot_in, one_hot_out])
+
+ elif self.output == PROB:
+ one_hot_in_data = builder.gep(one_hot_in, [ctx.int32_ty(0), ctx.int32_ty(0)])
+ one_hot_in_dist = builder.gep(one_hot_in, [ctx.int32_ty(0), ctx.int32_ty(1)])
+
+ with pnlvm.helpers.array_ptr_loop(builder, arg_in, "exp_div") as (b, i):
+ self.__gen_llvm_exp_div(ctx=ctx, vi=arg_in, vo=one_hot_in_dist,
+ gain=gain, exp_sum=exp_sum, builder=b, index=i)
+
+ dist_in = b.gep(arg_in, [ctx.int32_ty(0), i])
+ dist_out = b.gep(one_hot_in_data, [ctx.int32_ty(0), i])
+ b.store(b.load(dist_in), dist_out)
+
+
+ builder.call(one_hot_f, [one_hot_p, one_hot_s, one_hot_in, one_hot_out])
+ else:
+ assert False, "Unsupported output in {}: {}".format(self, self.output)
return builder
- def _gen_llvm_function_body(self, ctx, builder, params, _, arg_in, arg_out, *, tags:frozenset):
+ def _gen_llvm_function_body(self, ctx, builder, params, state, arg_in, arg_out, *, tags:frozenset):
if self.parameters.per_item.get():
assert isinstance(arg_in.type.pointee.element, pnlvm.ir.ArrayType)
assert isinstance(arg_out.type.pointee.element, pnlvm.ir.ArrayType)
for i in range(arg_in.type.pointee.count):
inner_in = builder.gep(arg_in, [ctx.int32_ty(0), ctx.int32_ty(i)])
inner_out = builder.gep(arg_out, [ctx.int32_ty(0), ctx.int32_ty(i)])
- builder = self.__gen_llvm_apply(ctx, builder, params, _, inner_in, inner_out)
+ builder = self.__gen_llvm_apply(ctx, builder, params, state, inner_in, inner_out, tags=tags)
return builder
else:
- return self.__gen_llvm_apply(ctx, builder, params, _, arg_in, arg_out)
+ return self.__gen_llvm_apply(ctx, builder, params, state, arg_in, arg_out, tags=tags)
def apply_softmax(self, input_value, gain, output_type):
# Modulate input_value by gain
@@ -2925,6 +2930,7 @@ class Parameters(TransferFunction.Parameters):
# return True
# return False
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -3842,8 +3848,7 @@ class Parameters(TransferFunction.Parameters):
:default value: None
:type:
"""
- variable = Parameter(np.array([0]),
- history_min_length=1)
+ variable = Parameter(np.array([0]), history_min_length=1, constructor_argument='default_variable')
intensity = Parameter(np.zeros_like(variable.default_value),
history_min_length=1)
@@ -3927,6 +3932,7 @@ class Parameters(TransferFunction.Parameters):
function_parameter_name=ADDITIVE_PARAM,
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/stateful/integratorfunctions.py b/psyneulink/core/components/functions/stateful/integratorfunctions.py
index dd344d3b9f0..afca06fdb6e 100644
--- a/psyneulink/core/components/functions/stateful/integratorfunctions.py
+++ b/psyneulink/core/components/functions/stateful/integratorfunctions.py
@@ -48,7 +48,7 @@
INTERACTIVE_ACTIVATION_INTEGRATOR_FUNCTION, LEAKY_COMPETING_INTEGRATOR_FUNCTION, \
MULTIPLICATIVE_PARAM, NOISE, OFFSET, OPERATION, ORNSTEIN_UHLENBECK_INTEGRATOR_FUNCTION, OUTPUT_PORTS, PRODUCT, \
RATE, REST, SIMPLE_INTEGRATOR_FUNCTION, SUM, TIME_STEP_SIZE, THRESHOLD, VARIABLE, MODEL_SPEC_ID_MDF_VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import parameter_spec, all_within_range, \
convert_all_elements_to_np_array
@@ -220,6 +220,7 @@ class Parameters(StatefulFunction.Parameters):
previous_value = Parameter(np.array([0]), initializer='initializer')
initializer = Parameter(np.array([0]), pnl_internal=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -550,6 +551,7 @@ class Parameters(IntegratorFunction.Parameters):
rate = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM], function_arg=True)
increment = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM], function_arg=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -826,6 +828,7 @@ class Parameters(IntegratorFunction.Parameters):
rate = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM], function_arg=True)
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM], function_arg=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1061,6 +1064,7 @@ class Parameters(IntegratorFunction.Parameters):
rate = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM], function_arg=True)
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM], function_arg=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1573,6 +1577,7 @@ class Parameters(IntegratorFunction.Parameters):
long_term_logistic = None
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2014,6 +2019,7 @@ class Parameters(IntegratorFunction.Parameters):
max_val = Parameter(1.0, function_arg=True)
min_val = Parameter(-1.0, function_arg=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -2418,6 +2424,7 @@ def _parse_initializer(self, initializer):
else:
return initializer
+ @check_user_specified
@tc.typecheck
def __init__(
self,
@@ -2531,10 +2538,6 @@ def _gen_llvm_integrate(self, builder, index, ctx, vi, vo, params, state):
builder.call(rand_f, [random_state, rand_val_ptr])
rand_val = builder.load(rand_val_ptr)
- if isinstance(rate.type, pnlvm.ir.ArrayType):
- assert len(rate.type) == 1
- rate = builder.extract_value(rate, 0)
-
# Get state pointers
prev_ptr = pnlvm.helpers.get_state_ptr(builder, self, state, "previous_value")
prev_time_ptr = pnlvm.helpers.get_state_ptr(builder, self, state, "previous_time")
@@ -2543,10 +2546,8 @@ def _gen_llvm_integrate(self, builder, index, ctx, vi, vo, params, state):
# + np.sqrt(time_step_size * noise) * random_state.normal()
prev_val_ptr = builder.gep(prev_ptr, [ctx.int32_ty(0), index])
prev_val = builder.load(prev_val_ptr)
+
val = builder.load(builder.gep(vi, [ctx.int32_ty(0), index]))
- if isinstance(val.type, pnlvm.ir.ArrayType):
- assert len(val.type) == 1
- val = builder.extract_value(val, 0)
val = builder.fmul(val, rate)
val = builder.fmul(val, time_step_size)
val = builder.fadd(val, prev_val)
@@ -2894,7 +2895,7 @@ class Parameters(IntegratorFunction.Parameters):
# threshold = Parameter(100.0, modulable=True)
time_step_size = Parameter(1.0, modulable=True)
previous_time = Parameter(None, initializer='starting_point', pnl_internal=True)
- dimension = Parameter(2, stateful=False, read_only=True)
+ dimension = Parameter(3, stateful=False, read_only=True)
initializer = Parameter([0], initalizer='variable', stateful=True)
angle_function = Parameter(None, stateful=False, loggable=False)
random_state = Parameter(None, loggable=False, getter=_random_state_getter, dependencies='seed')
@@ -2933,6 +2934,7 @@ def _parse_noise(self, noise):
noise = np.array(noise)
return noise
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -3439,6 +3441,7 @@ class Parameters(IntegratorFunction.Parameters):
read_only=True
)
+ @check_user_specified
@tc.typecheck
def __init__(
self,
@@ -3733,6 +3736,7 @@ class Parameters(IntegratorFunction.Parameters):
offset = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM], function_arg=True)
time_step_size = Parameter(0.1, modulable=True, function_arg=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -4414,6 +4418,7 @@ class Parameters(IntegratorFunction.Parameters):
read_only=True
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/stateful/memoryfunctions.py b/psyneulink/core/components/functions/stateful/memoryfunctions.py
index abade02079c..c6fb7d67731 100644
--- a/psyneulink/core/components/functions/stateful/memoryfunctions.py
+++ b/psyneulink/core/components/functions/stateful/memoryfunctions.py
@@ -45,7 +45,7 @@
ADDITIVE_PARAM, BUFFER_FUNCTION, MEMORY_FUNCTION, COSINE, \
ContentAddressableMemory_FUNCTION, DictionaryMemory_FUNCTION, \
MIN_INDICATOR, MULTIPLICATIVE_PARAM, NEWEST, NOISE, OLDEST, OVERWRITE, RATE, RANDOM, VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import \
all_within_range, convert_to_np_array, convert_to_list, convert_all_elements_to_np_array
@@ -56,6 +56,16 @@
class MemoryFunction(StatefulFunction): # -----------------------------------------------------------------------------
componentType = MEMORY_FUNCTION
+ # TODO: refactor to avoid skip of direct super
+ def _update_default_variable(self, new_default_variable, context=None):
+ if not self.parameters.initializer._user_specified:
+ # use * 0 instead of zeros_like to deal with ragged arrays
+ self._initialize_previous_value([new_default_variable * 0], context)
+
+ # bypass the additional _initialize_previous_value call used by
+ # other stateful functions
+ super(StatefulFunction, self)._update_default_variable(new_default_variable, context=context)
+
class Buffer(MemoryFunction): # ------------------------------------------------------------------------------
"""
@@ -215,6 +225,7 @@ class Parameters(StatefulFunction.Parameters):
changes_shape = Parameter(True, stateful=False, loggable=False, pnl_internal=True)
+ @check_user_specified
@tc.typecheck
def __init__(self,
# FIX: 12/11/18 JDC - NOT SAFE TO SPECIFY A MUTABLE TYPE AS DEFAULT
@@ -259,16 +270,6 @@ def _initialize_previous_value(self, initializer, context=None):
return previous_value
- # TODO: Buffer variable fix: remove this or refactor to avoid skip
- # of direct super
- def _update_default_variable(self, new_default_variable, context=None):
- if not self.parameters.initializer._user_specified:
- self._initialize_previous_value([np.zeros_like(new_default_variable)], context)
-
- # bypass the additional _initialize_previous_value call used by
- # other stateful functions
- super(StatefulFunction, self)._update_default_variable(new_default_variable, context=context)
-
def _instantiate_attributes_before_function(self, function=None, context=None):
self.parameters.previous_value._set(
self._initialize_previous_value(
@@ -1152,6 +1153,7 @@ def _parse_initializer(self, initializer):
initializer = ContentAddressableMemory._enforce_memory_shape(initializer)
return initializer
+ @check_user_specified
@tc.typecheck
def __init__(self,
# FIX: REINSTATE WHEN 3.6 IS RETIRED:
@@ -2173,6 +2175,7 @@ class Parameters(StatefulFunction.Parameters):
selection_function = Parameter(OneHot(mode=MIN_INDICATOR), stateful=False, loggable=False)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/stateful/statefulfunction.py b/psyneulink/core/components/functions/stateful/statefulfunction.py
index 1a365aca476..5e22d460526 100644
--- a/psyneulink/core/components/functions/stateful/statefulfunction.py
+++ b/psyneulink/core/components/functions/stateful/statefulfunction.py
@@ -30,7 +30,7 @@
from psyneulink.core.components.functions.function import Function_Base, FunctionError, _noise_setter
from psyneulink.core.globals.context import handle_external_context
from psyneulink.core.globals.keywords import STATEFUL_FUNCTION_TYPE, STATEFUL_FUNCTION, NOISE, RATE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import iscompatible, convert_to_np_array, contains_type
@@ -213,6 +213,7 @@ def _validate_noise(self, noise):
return 'functions in a list must be instantiated and have the desired noise variable shape'
@handle_external_context()
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/functions/userdefinedfunction.py b/psyneulink/core/components/functions/userdefinedfunction.py
index 176eff725c7..0cb5db217f3 100644
--- a/psyneulink/core/components/functions/userdefinedfunction.py
+++ b/psyneulink/core/components/functions/userdefinedfunction.py
@@ -18,7 +18,7 @@
from psyneulink.core.globals.keywords import \
CONTEXT, CUSTOM_FUNCTION, OWNER, PARAMS, \
SELF, USER_DEFINED_FUNCTION, USER_DEFINED_FUNCTION_TYPE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences import is_pref_set
from psyneulink.core.globals.utilities import _is_module_class, iscompatible
@@ -450,6 +450,7 @@ class Parameters(Function_Base.Parameters):
pnl_internal=True,
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
custom_function=None,
diff --git a/psyneulink/core/components/mechanisms/mechanism.py b/psyneulink/core/components/mechanisms/mechanism.py
index e174f9004e2..567c2f3eeca 100644
--- a/psyneulink/core/components/mechanisms/mechanism.py
+++ b/psyneulink/core/components/mechanisms/mechanism.py
@@ -1098,7 +1098,7 @@
REMOVE_PORTS, PORT_SPEC, _parse_port_spec, PORT_SPECIFIC_PARAMS, PROJECTION_SPECIFIC_PARAMS
from psyneulink.core.components.shellclasses import Mechanism, Projection, Port
from psyneulink.core.globals.context import Context, ContextFlags, handle_external_context
-from psyneulink.core.globals.json import _get_variable_parameter_name, _substitute_expression_args
+from psyneulink.core.globals.mdf import _get_variable_parameter_name
# TODO: remove unused keywords
from psyneulink.core.globals.keywords import \
ADDITIVE_PARAM, EXECUTION_PHASE, EXPONENT, FUNCTION_PARAMS, \
@@ -1109,7 +1109,7 @@
NAME, OUTPUT, OUTPUT_LABELS_DICT, OUTPUT_PORT, OUTPUT_PORT_PARAMS, OUTPUT_PORTS, OWNER_EXECUTION_COUNT, OWNER_VALUE, \
PARAMETER_PORT, PARAMETER_PORT_PARAMS, PARAMETER_PORTS, PROJECTIONS, REFERENCE_VALUE, RESULT, \
TARGET_LABELS_DICT, VALUE, VARIABLE, WEIGHT, MODEL_SPEC_ID_MDF_VARIABLE, MODEL_SPEC_ID_INPUT_PORT_COMBINATION_FUNCTION
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.registry import register_category, remove_instance_from_registry
from psyneulink.core.globals.utilities import \
@@ -1680,6 +1680,7 @@ def _parse_output_ports(self, output_ports):
@tc.typecheck
@abc.abstractmethod
+ @check_user_specified
def __init__(self,
default_variable=None,
size=None,
@@ -2915,9 +2916,12 @@ def _gen_llvm_ports(self, ctx, builder, ports, group,
# the function result can result in 1d structure or scalar
# Casting the pointer is LLVM way of adding dimensions
array_1d = pnlvm.ir.ArrayType(p_input_data.type.pointee, 1)
- array_2d = pnlvm.ir.ArrayType(array_1d, 1)
- assert array_1d == p_function.args[2].type.pointee or array_2d == p_function.args[2].type.pointee, \
+ assert array_1d == p_function.args[2].type.pointee, \
"{} vs. {}".format(p_function.args[2].type.pointee, p_input_data.type.pointee)
+ # restrict shape matching to casting 1d values to 2d arrays
+ # for Control/Gating signals
+ assert len(p_function.args[2].type.pointee) == 1
+ assert str(port).startswith("(ControlSignal") or str(port).startswith("(GatingSignal")
p_input = builder.bitcast(p_input_data, p_function.args[2].type)
else:
@@ -3032,7 +3036,7 @@ def _gen_llvm_output_port_parse_variable(self, ctx, builder,
except TypeError as e:
# TypeError means we can't index.
# Convert this to assertion failure below
- pass
+ data = None
else:
#TODO: support more spec options
if name == OWNER_VALUE:
@@ -3042,18 +3046,19 @@ def _gen_llvm_output_port_parse_variable(self, ctx, builder,
else:
data = None
- if data is not None:
- parsed = builder.gep(data, [ctx.int32_ty(0), *(ctx.int32_ty(i) for i in ids)])
- # "num_executions" are kept as int64, we need to convert the value to float first
- if name == "num_executions":
- count = builder.load(parsed)
- count_fp = builder.uitofp(count, ctx.float_ty)
- parsed = builder.alloca(count_fp.type)
- builder.store(count_fp, parsed)
+ assert data is not None, "Unsupported OutputPort spec: {} ({})".format(port_spec, value.type)
- return parsed
+ parsed = builder.gep(data, [ctx.int32_ty(0), *(ctx.int32_ty(i) for i in ids)])
+ # "num_executions" are kept as int64, we need to convert the value to float first
+ # port inputs are also expected to be 1d arrays
+ if name == "num_executions":
+ count = builder.load(parsed)
+ count_fp = builder.uitofp(count, ctx.float_ty)
+ parsed = builder.alloca(pnlvm.ir.ArrayType(count_fp.type, 1))
+ ptr = builder.gep(parsed, [ctx.int32_ty(0), ctx.int32_ty(0)])
+ builder.store(count_fp, ptr)
- assert False, "Unsupported OutputPort spec: {} ({})".format(port_spec, value.type)
+ return parsed
def _gen_llvm_output_ports(self, ctx, builder, value,
mech_params, mech_state, mech_in, mech_out):
@@ -3071,29 +3076,34 @@ def _get_input_data_ptr(b, i):
mech_params, mech_state, mech_in)
return builder
- def _gen_llvm_invoke_function(self, ctx, builder, function, f_params, f_state, variable, *, tags:frozenset):
+ def _gen_llvm_invoke_function(self, ctx, builder, function, f_params, f_state,
+ variable, out, *, tags:frozenset):
+
fun = ctx.import_llvm_function(function, tags=tags)
- fun_out = builder.alloca(fun.args[3].type.pointee, name=function.name + "_output")
+ if out is None:
+ f_out = builder.alloca(fun.args[3].type.pointee, name=function.name + "_output")
+ else:
+ f_out = out
- builder.call(fun, [f_params, f_state, variable, fun_out])
+ builder.call(fun, [f_params, f_state, variable, f_out])
- return fun_out, builder
+ return f_out, builder
def _gen_llvm_is_finished_cond(self, ctx, builder, m_params, m_state):
return ctx.bool_ty(1)
- def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params, m_state, arg_in,
- ip_output, *, tags:frozenset):
+ def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params, m_state, m_in,
+ m_val, ip_output, *, tags:frozenset):
# Default mechanism runs only the main function
f_base_params = pnlvm.helpers.get_param_ptr(builder, self, m_base_params, "function")
f_params, builder = self._gen_llvm_param_ports_for_obj(
- self.function, f_base_params, ctx, builder, m_base_params, m_state, arg_in)
+ self.function, f_base_params, ctx, builder, m_base_params, m_state, m_in)
f_state = pnlvm.helpers.get_state_ptr(builder, self, m_state, "function")
return self._gen_llvm_invoke_function(ctx, builder, self.function,
f_params, f_state, ip_output,
- tags=tags)
+ m_val, tags=tags)
def _gen_llvm_function_internal(self, ctx, builder, m_params, m_state, arg_in,
arg_out, m_base_params, *, tags:frozenset):
@@ -3101,11 +3111,21 @@ def _gen_llvm_function_internal(self, ctx, builder, m_params, m_state, arg_in,
ip_output, builder = self._gen_llvm_input_ports(ctx, builder,
m_base_params, m_state, arg_in)
+ # This will move history items around to make space for a new entry
+ mech_val_ptr = pnlvm.helpers.get_state_space(builder, self, m_state, "value")
+
value, builder = self._gen_llvm_mechanism_functions(ctx, builder, m_base_params,
m_params, m_state, arg_in,
+ mech_val_ptr,
ip_output, tags=tags)
+ if mech_val_ptr.type.pointee == value.type.pointee:
+ assert value is mech_val_ptr
+ else:
+ # FIXME: Does this need some sort of parsing?
+ warnings.warn("Shape mismatch: function result does not match mechanism value param: {} vs. {}".format(value.type.pointee, mech_val_ptr.type.pointee))
+
# Update num_executions parameter
num_executions_ptr = pnlvm.helpers.get_state_ptr(builder, self, m_state, "num_executions")
for scale in TimeScale:
@@ -3117,13 +3137,6 @@ def _gen_llvm_function_internal(self, ctx, builder, m_params, m_state, arg_in,
new_val = builder.add(new_val, new_val.type(1))
builder.store(new_val, num_exec_time_ptr)
- val_ptr = pnlvm.helpers.get_state_ptr(builder, self, m_state, "value")
- if val_ptr.type.pointee == value.type.pointee:
- pnlvm.helpers.push_state_val(builder, self, m_state, "value", value)
- else:
- # FIXME: Does this need some sort of parsing?
- warnings.warn("Shape mismatch: function result does not match mechanism value param: {} vs. {}".format(value.type.pointee, val_ptr.type.pointee))
-
# Run output ports after updating the mech state (num_executions and value)
builder = self._gen_llvm_output_ports(ctx, builder, value, m_base_params, m_state, arg_in, arg_out)
@@ -4155,7 +4168,7 @@ def as_mdf_model(self):
model.functions.append(
mdf.Function(
id=combination_function_id,
- function={'onnx::ReduceSum': combination_function_args},
+ function='onnx::ReduceSum',
args=combination_function_args
)
)
@@ -4196,9 +4209,6 @@ def as_mdf_model(self):
)
model.functions.append(function_model)
- for func_model in model.functions:
- _substitute_expression_args(func_model)
-
return model
diff --git a/psyneulink/core/components/mechanisms/modulatory/control/controlmechanism.py b/psyneulink/core/components/mechanisms/modulatory/control/controlmechanism.py
index 3da410a7ae5..b4d82a6662e 100644
--- a/psyneulink/core/components/mechanisms/modulatory/control/controlmechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/control/controlmechanism.py
@@ -378,11 +378,12 @@
A ControlMechanism's `function <ControlMechanism.function>` uses its `outcome <ControlMechanism.outcome>`
attribute (the `value <InputPort.value>` of its *OUTCOME* `InputPort`) to generate a `control_allocation
<ControlMechanism.control_allocation>`. By default, its `function <ControlMechanism.function>` is assigned
-the `DefaultAllocationFunction`, which takes a single value as its input, and assigns that as the value of
-each item of `control_allocation <ControlMechanism.control_allocation>`. Each of these items is assigned as
-the allocation for the corresponding `ControlSignal` in `control_signals <ControlMechanism.control_signals>`. This
+the `Identity`, which takes a single value as its input, and copies it to the output, this assigns the value of
+each item of `control_allocation <ControlMechanism.control_allocation>`. This item is assigned as
+the allocation for the all `ControlSignal` in `control_signals <ControlMechanism.control_signals>`. This
distributes the ControlMechanism's input as the allocation to each of its `control_signals
-<ControlMechanism.control_signals>`. This same behavior also applies to any custom function assigned to a
+<ControlMechanism.control_signals>`.
+This same behavior also applies to any custom function assigned to a
ControlMechanism that returns a 2d array with a single item in its outer dimension (axis 0). If a function is
assigned that returns a 2d array with more than one item, and it has the same number of `control_signals
<ControlMechanism.control_signals>`, then each ControlSignal is assigned to the corresponding item of the function's
@@ -587,8 +588,8 @@
import numpy as np
import typecheck as tc
-from psyneulink.core import llvm as pnlvm
from psyneulink.core.components.functions.function import Function_Base, is_function_type
+from psyneulink.core.components.functions.nonstateful.transferfunctions import Identity
from psyneulink.core.components.functions.nonstateful.combinationfunctions import Concatenate
from psyneulink.core.components.functions.nonstateful.combinationfunctions import LinearCombination
from psyneulink.core.components.mechanisms.mechanism import Mechanism, Mechanism_Base
@@ -605,14 +606,13 @@
MECHANISM, MULTIPLICATIVE, MODULATORY_SIGNALS, MONITOR_FOR_CONTROL, MONITOR_FOR_MODULATION, \
OBJECTIVE_MECHANISM, OUTCOME, OWNER_VALUE, PARAMS, PORT_TYPE, PRODUCT, PROJECTION_TYPE, PROJECTIONS, \
SEPARATE, SIZE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import ContentAddressableList, convert_to_list, convert_to_np_array, is_iterable
__all__ = [
'CONTROL_ALLOCATION', 'GATING_ALLOCATION', 'ControlMechanism', 'ControlMechanismError', 'ControlMechanismRegistry',
- 'DefaultAllocationFunction'
]
CONTROL_ALLOCATION = 'control_allocation'
@@ -727,58 +727,6 @@ def _net_outcome_getter(owning_component=None, context=None):
return [0]
-class DefaultAllocationFunction(Function_Base):
- """Take a single 1d item and return a 2d array with n identical items
- Takes the default input (a single value in the *OUTCOME* InputPort of the ControlMechanism),
- and returns the same allocation for each of its `control_signals <ControlMechanism.control_signals>`.
- """
- componentName = 'Default Control Function'
- class Parameters(Function_Base.Parameters):
- """
- Attributes
- ----------
-
- num_control_signals
- see `num_control_signals <DefaultAllocationFunction.num_control_signals>`
-
- :default value: 1
- :type: ``int``
- """
- num_control_signals = Parameter(1, stateful=False)
-
- def __init__(self,
- default_variable=None,
- params=None,
- owner=None
- ):
-
- super().__init__(default_variable=default_variable,
- params=params,
- owner=owner,
- )
-
- def _function(self,
- variable=None,
- context=None,
- params=None,
- ):
- num_ctl_sigs = self._get_current_parameter_value('num_control_signals')
- result = np.array([variable[0]] * num_ctl_sigs)
- return self.convert_output_type(result)
-
- def reset(self, *args, force=False, context=None, **kwargs):
- # Override Component.reset which requires that the Component is stateful
- pass
-
- def _gen_llvm_function_body(self, ctx, builder, _1, _2, arg_in, arg_out, *, tags:frozenset):
- val_ptr = builder.gep(arg_in, [ctx.int32_ty(0), ctx.int32_ty(0)])
- val = builder.load(val_ptr)
- with pnlvm.helpers.array_ptr_loop(builder, arg_out, "alloc_loop") as (b, idx):
- out_ptr = builder.gep(arg_out, [ctx.int32_ty(0), idx])
- builder.store(val, out_ptr)
- return builder
-
-
class ControlMechanism(ModulatoryMechanism_Base):
"""
ControlMechanism( \
@@ -1201,7 +1149,7 @@ class Parameters(ModulatoryMechanism_Base.Parameters):
)
monitor_for_control = Parameter(
- [OUTCOME],
+ [],
stateful=False,
loggable=False,
read_only=True,
@@ -1218,6 +1166,7 @@ class Parameters(ModulatoryMechanism_Base.Parameters):
aliases=[CONTROL, CONTROL_SIGNALS],
constructor_argument=CONTROL
)
+ function = Parameter(Identity, stateful=False, loggable=False)
def _parse_output_ports(self, output_ports):
def is_2tuple(o):
@@ -1263,6 +1212,7 @@ def _validate_input_ports(self, input_ports):
# method?
# validate_monitored_port_spec(self._owner, input_ports)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1329,8 +1279,6 @@ def __init__(self,
f"creating unnecessary and/or duplicated Components.")
control = convert_to_list(args)
- function = function or DefaultAllocationFunction
-
super(ControlMechanism, self).__init__(
default_variable=default_variable,
size=size,
@@ -1727,42 +1675,33 @@ def _register_control_signal_type(self, context=None):
def _instantiate_control_signals(self, context):
"""Subclasses can override for class-specific implementation (see OptimizationControlMechanism for example)"""
- output_port_specs = list(enumerate(self.output_ports))
- for i, control_signal in output_port_specs:
+ for i, control_signal in enumerate(self.output_ports):
self.control[i] = self._instantiate_control_signal(control_signal, context=context)
- num_control_signals = i + 1
-
- # For DefaultAllocationFunction, set defaults.value to have number of items equal to num control_signals
- if isinstance(self.function, DefaultAllocationFunction):
- self.defaults.value = np.tile(self.function.value, (num_control_signals, 1))
- self.parameters.control_allocation._set(copy.deepcopy(self.defaults.value), context)
- self.function.num_control_signals = num_control_signals
- # For other functions, assume that if its value has:
+ # For functions, assume that if its value has:
# - one item, all control_signals should get it (i.e., the default: (OWNER_VALUE, 0));
# - same number of items as the number of control_signals;
# assign each control_signal to the corresponding item of the function's value
# - a different number of items than number of control_signals,
# leave things alone, and allow any errant indices for control_signals to be caught later.
- else:
- self.defaults.value = np.array(self.function.value)
- self.parameters.value._set(copy.deepcopy(self.defaults.value), context)
+ self.defaults.value = np.array(self.function.value)
+ self.parameters.value._set(copy.deepcopy(self.defaults.value), context)
- len_fct_value = len(self.function.value)
+ len_fct_value = len(self.function.value)
- # Assign each ControlSignal's variable_spec to index of ControlMechanism's value
- for i, control_signal in enumerate(self.control):
+ # Assign each ControlSignal's variable_spec to index of ControlMechanism's value
+ for i, control_signal in enumerate(self.control):
- # If number of control_signals is same as number of items in function's value,
- # assign each ControlSignal to the corresponding item of the function's value
- if len_fct_value == num_control_signals:
- control_signal._variable_spec = [(OWNER_VALUE, i)]
+ # If number of control_signals is same as number of items in function's value,
+ # assign each ControlSignal to the corresponding item of the function's value
+ if len_fct_value == len(self.control):
+ control_signal._variable_spec = (OWNER_VALUE, i)
- if not isinstance(control_signal.owner_value_index, int):
- assert False, \
- f"PROGRAM ERROR: The \'owner_value_index\' attribute for {control_signal.name} " \
- f"of {self.name} ({control_signal.owner_value_index})is not an int."
+ if not isinstance(control_signal.owner_value_index, int):
+ assert False, \
+ f"PROGRAM ERROR: The \'owner_value_index\' attribute for {control_signal.name} " \
+ f"of {self.name} ({control_signal.owner_value_index})is not an int."
def _instantiate_control_signal(self, control_signal, context=None):
"""Parse and instantiate ControlSignal (or subclass relevant to ControlMechanism subclass)
diff --git a/psyneulink/core/components/mechanisms/modulatory/control/defaultcontrolmechanism.py b/psyneulink/core/components/mechanisms/modulatory/control/defaultcontrolmechanism.py
index 0c92b09e3be..c82fff09f9c 100644
--- a/psyneulink/core/components/mechanisms/modulatory/control/defaultcontrolmechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/control/defaultcontrolmechanism.py
@@ -40,6 +40,7 @@
from psyneulink.core.components.mechanisms.processing.objectivemechanism import ObjectiveMechanism
from psyneulink.core.globals.defaults import defaultControlAllocation
from psyneulink.core.globals.keywords import CONTROL, INPUT_PORTS, NAME
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import ContentAddressableList
@@ -87,6 +88,7 @@ class DefaultControlMechanism(ControlMechanism):
# PREFERENCE_SET_NAME: 'DefaultControlMechanismCustomClassPreferences',
# PREFERENCE_KEYWORD<pref>: <setting>...}
+ @check_user_specified
@tc.typecheck
def __init__(self,
objective_mechanism:tc.optional(tc.any(ObjectiveMechanism, list))=None,
diff --git a/psyneulink/core/components/mechanisms/modulatory/control/gating/gatingmechanism.py b/psyneulink/core/components/mechanisms/modulatory/control/gating/gatingmechanism.py
index 5338d545fc4..8aa950f2b4a 100644
--- a/psyneulink/core/components/mechanisms/modulatory/control/gating/gatingmechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/control/gating/gatingmechanism.py
@@ -190,7 +190,7 @@
from psyneulink.core.globals.keywords import \
CONTROL, CONTROL_SIGNALS, GATE, GATING_PROJECTION, GATING_SIGNAL, GATING_SIGNALS, \
INIT_EXECUTE_METHOD_ONLY, MONITOR_FOR_CONTROL, PORT_TYPE, PROJECTIONS, PROJECTION_TYPE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import ContentAddressableList, convert_to_list
@@ -433,6 +433,7 @@ class Parameters(ControlMechanism.Parameters):
constructor_argument='gate'
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_gating_allocation=None,
diff --git a/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py b/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py
index 9b7517f0f60..67b665ce8cf 100644
--- a/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/control/optimizationcontrolmechanism.py
@@ -605,7 +605,7 @@
<ControlMechanism.net_outcome>` made for each `control_allocation <ControlMechanism.control_allocation>`).
COMMENT
- .. _OptimizationControlMechanism_State:
+.. _OptimizationControlMechanism_State:
*State*
~~~~~~~
@@ -748,23 +748,24 @@
If an OptimizationControlMechanism has an `objective_mechanism <ControlMechanism.objective_mechanism>`, it is
assigned a single outcome_input_port, named *OUTCOME*, that receives a Projection from the objective_mechanism's
`OUTCOME OutputPort <ObjectiveMechanism_Output>`. The OptimizationControlMechanism's `objective_mechanism
-<ControlMechanism>` is used to evaluate the outcome of executing its `agent_rep
+<ControlMechanism.objective_mechanism>` is used to evaluate the outcome of executing its `agent_rep
<OptimizationControlMechanism.agent_rep>` for a given `state <OptimizationControlMechanism_State>`. This passes
the result to the OptimizationControlMechanism's *OUTCOME* InputPort, that is placed in its `outcome
<ControlMechanism.outcome>` attribute.
.. note::
- An OptimizationControlMechanism's `objective_mechanism <ControlMechanism.objective_mechanism>` and its `function
- <ObjectiveMechanism.function>` are distinct from, and should not be confused with the `objective_function
- <OptimizationFunction.objective_function>` parameter of the OptimizationControlMechanism's `function
- <OptimizationControlMechanism.function>`. The `objective_mechanism <ControlMechanism.objective_mechanism>`\\'s
- `function <ObjectiveMechanism.function>` evaluates the `outcome <ControlMechanism.outcome>` of processing
- without taking into account the `costs <ControlMechanism.costs>` of the OptimizationControlMechanism's
- `control_signals <OptimizationControlMechanism.control_signals>`. In contrast, its `evaluate_agent_rep
- <OptimizationControlMechanism.evaluate_agent_rep>` method, which is assigned as the `objective_function`
- parameter of its `function <OptimizationControlMechanism.function>`, takes the `costs <ControlMechanism.costs>`
- of the OptimizationControlMechanism's `control_signals <OptimizationControlMechanism.control_signals>` into
- account when calculating the `net_outcome` that it returns as its result.
+ An OptimizationControlMechanism's `objective_mechanism <ControlMechanism.objective_mechanism>` and the `function
+ <ObjectiveMechanism.function>` of that Mechanism, are distinct from and should not be confused with the
+ `objective_function <OptimizationFunction.objective_function>` parameter of the OptimizationControlMechanism's
+ `function <OptimizationControlMechanism.function>`. The `objective_mechanism
+ <ControlMechanism.objective_mechanism>`\\'s `function <ObjectiveMechanism.function>` evaluates the `outcome
+ <ControlMechanism.outcome>` of processing without taking into account the `costs <ControlMechanism.costs>` of
+ the OptimizationControlMechanism's `control_signals <OptimizationControlMechanism.control_signals>`. In
+ contrast, its `evaluate_agent_rep <OptimizationControlMechanism.evaluate_agent_rep>` method, which is assigned
+ as the `objective_function` parameter of its `function <OptimizationControlMechanism.function>`, takes the
+ `costs <ControlMechanism.costs>` of the OptimizationControlMechanism's `control_signals
+ <OptimizationControlMechanism.control_signals>` into account when calculating the `net_outcome` that it
+ returns as its result.
COMMENT:
ADD HINT HERE RE: USE OF CONCATENATION
@@ -1098,9 +1099,9 @@
ALL, COMPOSITION, COMPOSITION_FUNCTION_APPROXIMATOR, CONCATENATE, DEFAULT_INPUT, DEFAULT_VARIABLE, EID_FROZEN, \
FUNCTION, INPUT_PORT, INTERNAL_ONLY, NAME, OPTIMIZATION_CONTROL_MECHANISM, NODE, OWNER_VALUE, PARAMS, PORT, \
PROJECTIONS, SHADOW_INPUTS, VALUE
-from psyneulink.core.globals.registry import rename_instance_in_registry
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
+from psyneulink.core.globals.registry import rename_instance_in_registry
from psyneulink.core.globals.sampleiterator import SampleIterator, SampleSpec
from psyneulink.core.globals.utilities import convert_to_list, ContentAddressableList, is_numeric
from psyneulink.core.llvm.debug import debug_env
@@ -1417,7 +1418,8 @@ class OptimizationControlMechanism(ControlMechanism):
its `monitor_for_control <ControlMechanism.monitor_for_control>` attribute, the values of which are used
to compute the `net_outcome <ControlMechanism.net_outcome>` of executing the `agent_rep
<OptimizationControlMechanism.agent_rep>` in a given `OptimizationControlMechanism_State`
- (see `Outcome <OptimizationControlMechanism_Outcome>` for additional details).
+ (see `objective_mechanism <OptimizationControlMechanism_ObjectiveMechanism>` and `outcome_input_ports
+ <OptimizationControlMechanism_Outcome>` for additional details).
state : ndarray
lists the values of the current state -- a concatenation of the `state_feature_values
@@ -1739,6 +1741,7 @@ def _validate_state_feature_default_spec(self, state_feature_default):
f"with a shape appropriate for all of the INPUT Nodes or InputPorts to which it will be applied."
@handle_external_context()
+ @check_user_specified
@tc.typecheck
def __init__(self,
agent_rep=None,
@@ -3496,9 +3499,16 @@ def _gen_llvm_function(self, *, ctx:pnlvm.LLVMBuilderContext, tags:frozenset):
return f
- def _gen_llvm_invoke_function(self, ctx, builder, function, params, context, variable, *, tags:frozenset):
+ def _gen_llvm_invoke_function(self, ctx, builder, function, params, context,
+ variable, out, *, tags:frozenset):
fun = ctx.import_llvm_function(function)
+
+ # The function returns (sample_optimal, value_optimal),
+ # but the value of mechanism is only 'sample_optimal'
+ # so we cannot reuse the space provided and need to explicitly copy
+ # the results later.
fun_out = builder.alloca(fun.args[3].type.pointee, name="func_out")
+ value = builder.gep(fun_out, [ctx.int32_ty(0), ctx.int32_ty(0)])
args = [params, context, variable, fun_out]
# If we're calling compiled version of Composition.evaluate,
@@ -3507,13 +3517,17 @@ def _gen_llvm_invoke_function(self, ctx, builder, function, params, context, var
args += builder.function.args[-3:]
builder.call(fun, args)
- return fun_out, builder
- def _gen_llvm_output_port_parse_variable(self, ctx, builder, params, state, value, port):
- # The function returns (sample_optimal, value_optimal),
- # but the value of mechanism is only 'sample_optimal'
- value = builder.gep(value, [ctx.int32_ty(0), ctx.int32_ty(0)])
- return super()._gen_llvm_output_port_parse_variable(ctx, builder, params, state, value, port)
+ # The mechanism also converts the value to array of arrays
+ # e.g. [3 x double] -> [3 x [1 x double]]
+ assert len(value.type.pointee) == len(out.type.pointee)
+ assert value.type.pointee.element == out.type.pointee.element.element
+ with pnlvm.helpers.array_ptr_loop(builder, out, id='mech_value_copy') as (b, idx):
+ src = b.gep(value, [ctx.int32_ty(0), idx])
+ dst = b.gep(out, [ctx.int32_ty(0), idx, ctx.int32_ty(0)])
+ b.store(b.load(src), dst)
+
+ return out, builder
@property
def agent_rep_type(self):
diff --git a/psyneulink/core/components/mechanisms/modulatory/learning/learningmechanism.py b/psyneulink/core/components/mechanisms/modulatory/learning/learningmechanism.py
index c61c02501f5..2ae1da4c11b 100644
--- a/psyneulink/core/components/mechanisms/modulatory/learning/learningmechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/learning/learningmechanism.py
@@ -545,7 +545,7 @@
ADDITIVE, AFTER, ASSERT, ENABLED, INPUT_PORTS, \
LEARNED_PARAM, LEARNING, LEARNING_MECHANISM, LEARNING_PROJECTION, LEARNING_SIGNAL, LEARNING_SIGNALS, \
MATRIX, NAME, ONLINE, OUTPUT_PORT, OWNER_VALUE, PARAMS, PROJECTIONS, SAMPLE, PORT_TYPE, VARIABLE
-from psyneulink.core.globals.parameters import FunctionParameter, Parameter
+from psyneulink.core.globals.parameters import FunctionParameter, Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import ContentAddressableList, convert_to_np_array, is_numeric, parameter_spec, \
@@ -999,6 +999,7 @@ class Parameters(ModulatoryMechanism_Base.Parameters):
structural=True,
)
+ @check_user_specified
@tc.typecheck
def __init__(self,
# default_variable:tc.any(list, np.ndarray),
diff --git a/psyneulink/core/components/mechanisms/modulatory/modulatorymechanism.py b/psyneulink/core/components/mechanisms/modulatory/modulatorymechanism.py
index df26cf1ded9..ebc92c25a03 100644
--- a/psyneulink/core/components/mechanisms/modulatory/modulatorymechanism.py
+++ b/psyneulink/core/components/mechanisms/modulatory/modulatorymechanism.py
@@ -140,6 +140,7 @@
from psyneulink.core.components.mechanisms.mechanism import Mechanism_Base
from psyneulink.core.globals.keywords import ADAPTIVE_MECHANISM
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
__all__ = [
@@ -191,6 +192,7 @@ class Parameters(Mechanism_Base.Parameters):
# PREFERENCE_SET_NAME: 'ModulatoryMechanismClassPreferences',
# PREFERENCE_KEYWORD<pref>: <setting>...}
+ @check_user_specified
def __init__(self,
default_variable,
size,
diff --git a/psyneulink/core/components/mechanisms/processing/compositioninterfacemechanism.py b/psyneulink/core/components/mechanisms/processing/compositioninterfacemechanism.py
index 80869f28701..94ce762873c 100644
--- a/psyneulink/core/components/mechanisms/processing/compositioninterfacemechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/compositioninterfacemechanism.py
@@ -122,7 +122,7 @@
from psyneulink.core.globals.context import ContextFlags, handle_external_context
from psyneulink.core.globals.keywords import COMPOSITION_INTERFACE_MECHANISM, INPUT_PORTS, OUTPUT_PORTS, \
PREFERENCE_SET_NAME
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
@@ -174,6 +174,7 @@ class Parameters(ProcessingMechanism_Base.Parameters):
"""
function = Parameter(Identity, stateful=False, loggable=False)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/mechanisms/processing/defaultprocessingmechanism.py b/psyneulink/core/components/mechanisms/processing/defaultprocessingmechanism.py
index 8bb14d9bd03..bf3770582bd 100644
--- a/psyneulink/core/components/mechanisms/processing/defaultprocessingmechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/defaultprocessingmechanism.py
@@ -18,6 +18,7 @@
from psyneulink.core.components.mechanisms.mechanism import Mechanism_Base
from psyneulink.core.globals.defaults import SystemDefaultInputValue
from psyneulink.core.globals.keywords import DEFAULT_PROCESSING_MECHANISM
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -50,8 +51,9 @@ class DefaultProcessingMechanism_Base(Mechanism_Base):
# PREFERENCE_KEYWORD<pref>: <setting>...}
class Parameters(Mechanism_Base.Parameters):
- variable = np.array([SystemDefaultInputValue])
+ variable = Parameter(np.array([SystemDefaultInputValue]), constructor_argument='default_variable')
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/mechanisms/processing/integratormechanism.py b/psyneulink/core/components/mechanisms/processing/integratormechanism.py
index 4da4319a3bc..e11dd8b47b4 100644
--- a/psyneulink/core/components/mechanisms/processing/integratormechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/integratormechanism.py
@@ -89,10 +89,9 @@
from psyneulink.core.components.functions.stateful.integratorfunctions import AdaptiveIntegrator
from psyneulink.core.components.mechanisms.processing.processingmechanism import ProcessingMechanism_Base
from psyneulink.core.components.mechanisms.mechanism import Mechanism
-from psyneulink.core.globals.json import _substitute_expression_args
from psyneulink.core.globals.keywords import \
DEFAULT_VARIABLE, INTEGRATOR_MECHANISM, VARIABLE, PREFERENCE_SET_NAME
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.globals.utilities import parse_valid_identifier
@@ -152,6 +151,7 @@ class Parameters(ProcessingMechanism_Base.Parameters):
function = Parameter(AdaptiveIntegrator(rate=0.5), stateful=False, loggable=False)
#
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -255,7 +255,4 @@ def as_mdf_model(self):
model.functions.extend(extra_noise_functions)
function_model.args['noise'] = main_noise_function.id
- for func_model in model.functions:
- _substitute_expression_args(func_model)
-
return model
diff --git a/psyneulink/core/components/mechanisms/processing/objectivemechanism.py b/psyneulink/core/components/mechanisms/processing/objectivemechanism.py
index 84b69156e63..2aaffee2c36 100644
--- a/psyneulink/core/components/mechanisms/processing/objectivemechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/objectivemechanism.py
@@ -378,7 +378,7 @@
from psyneulink.core.globals.keywords import \
CONTROL, EXPONENT, EXPONENTS, LEARNING, MATRIX, NAME, OBJECTIVE_MECHANISM, OUTCOME, OWNER_VALUE, \
PARAMS, PREFERENCE_SET_NAME, PROJECTION, PROJECTIONS, PORT_TYPE, VARIABLE, WEIGHT, WEIGHTS
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.globals.utilities import ContentAddressableList
@@ -562,6 +562,7 @@ class Parameters(ProcessingMechanism_Base.Parameters):
standard_output_port_names.extend([OUTCOME])
# FIX: TYPECHECK MONITOR TO LIST OR ZIP OBJECT
+ @check_user_specified
@tc.typecheck
def __init__(self,
monitor=None,
diff --git a/psyneulink/core/components/mechanisms/processing/processingmechanism.py b/psyneulink/core/components/mechanisms/processing/processingmechanism.py
index 8da4cbdfbe5..d6cbc63488c 100644
--- a/psyneulink/core/components/mechanisms/processing/processingmechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/processingmechanism.py
@@ -98,7 +98,8 @@
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.keywords import \
FUNCTION, MAX_ABS_INDICATOR, MAX_ABS_ONE_HOT, MAX_ABS_VAL, MAX_INDICATOR, MAX_ONE_HOT, MAX_VAL, MEAN, MEDIAN, \
- NAME, PROB, PROCESSING_MECHANISM, PREFERENCE_SET_NAME, STANDARD_DEVIATION, VARIANCE
+ NAME, PROB, PROCESSING_MECHANISM, PREFERENCE_SET_NAME, STANDARD_DEVIATION, VARIANCE, VARIABLE, OWNER_VALUE
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
@@ -165,9 +166,11 @@ class ProcessingMechanism_Base(Mechanism_Base):
{NAME: MAX_ABS_INDICATOR,
FUNCTION: OneHot(mode=MAX_ABS_INDICATOR)},
{NAME: PROB,
+ VARIABLE: OWNER_VALUE,
FUNCTION: SoftMax(output=PROB)}])
standard_output_port_names = [i['name'] for i in standard_output_ports]
+ @check_user_specified
def __init__(self,
default_variable=None,
size=None,
@@ -282,6 +285,7 @@ class ProcessingMechanism(ProcessingMechanism_Base):
PREFERENCE_SET_NAME: 'ProcessingMechanismCustomClassPreferences',
REPORT_OUTPUT_PREF: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/core/components/mechanisms/processing/transfermechanism.py b/psyneulink/core/components/mechanisms/processing/transfermechanism.py
index 44adbd44596..ca81117a2c5 100644
--- a/psyneulink/core/components/mechanisms/processing/transfermechanism.py
+++ b/psyneulink/core/components/mechanisms/processing/transfermechanism.py
@@ -842,13 +842,13 @@
from psyneulink.core.components.ports.inputport import InputPort
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.context import ContextFlags, handle_external_context
-from psyneulink.core.globals.json import _get_variable_parameter_name, _substitute_expression_args
+from psyneulink.core.globals.mdf import _get_variable_parameter_name
from psyneulink.core.globals.keywords import \
COMBINE, comparison_operators, EXECUTION_COUNT, FUNCTION, GREATER_THAN_OR_EQUAL, \
CURRENT_VALUE, LESS_THAN_OR_EQUAL, MAX_ABS_DIFF, \
NAME, NOISE, NUM_EXECUTIONS_BEFORE_FINISHED, OWNER_VALUE, RESET, RESULT, RESULTS, \
SELECTION_FUNCTION_TYPE, TRANSFER_FUNCTION_TYPE, TRANSFER_MECHANISM, VARIABLE
-from psyneulink.core.globals.parameters import Parameter, FunctionParameter
+from psyneulink.core.globals.parameters import Parameter, FunctionParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import \
@@ -1283,6 +1283,7 @@ def _validate_termination_comparison_op(self, termination_comparison_op):
return f"must be boolean comparison operator or one of the following strings:" \
f" {','.join(comparison_operators.keys())}."
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1543,13 +1544,11 @@ def _gen_llvm_is_finished_cond(self, ctx, builder, params, state):
return builder.fcmp_ordered("!=", is_finished_flag,
is_finished_flag.type(0))
- # If modulated, termination threshold is single element array
- if isinstance(threshold_ptr.type.pointee, pnlvm.ir.ArrayType):
- assert len(threshold_ptr.type.pointee) == 1
- threshold_ptr = builder.gep(threshold_ptr, [ctx.int32_ty(0),
- ctx.int32_ty(0)])
+ # If modulated, termination threshold is single element array.
+ # Otherwise, it is scalar
+ threshold = pnlvm.helpers.load_extract_scalar_array_one(builder,
+ threshold_ptr)
- threshold = builder.load(threshold_ptr)
cmp_val_ptr = builder.alloca(threshold.type, name="is_finished_value")
if self.termination_measure is max:
assert self._termination_measure_num_items_expected == 1
@@ -1605,7 +1604,7 @@ def _gen_llvm_is_finished_cond(self, ctx, builder, params, state):
return builder.fcmp_ordered(cmp_str, cmp_val, threshold)
def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params,
- m_state, arg_in, ip_out, *, tags:frozenset):
+ m_state, m_in, m_val, ip_out, *, tags:frozenset):
if self.integrator_mode:
if_state = pnlvm.helpers.get_state_ptr(builder, self, m_state,
@@ -1614,20 +1613,23 @@ def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params,
"integrator_function")
if_params, builder = self._gen_llvm_param_ports_for_obj(
self.integrator_function, if_base_params, ctx, builder,
- m_base_params, m_state, arg_in)
+ m_base_params, m_state, m_in)
mf_in, builder = self._gen_llvm_invoke_function(
- ctx, builder, self.integrator_function, if_params, if_state, ip_out, tags=tags)
+ ctx, builder, self.integrator_function, if_params,
+ if_state, ip_out, None, tags=tags)
else:
mf_in = ip_out
mf_state = pnlvm.helpers.get_state_ptr(builder, self, m_state, "function")
mf_base_params = pnlvm.helpers.get_param_ptr(builder, self, m_base_params, "function")
mf_params, builder = self._gen_llvm_param_ports_for_obj(
- self.function, mf_base_params, ctx, builder, m_base_params, m_state, arg_in)
+ self.function, mf_base_params, ctx, builder, m_base_params, m_state, m_in)
mf_out, builder = self._gen_llvm_invoke_function(ctx, builder,
- self.function, mf_params, mf_state, mf_in, tags=tags)
+ self.function, mf_params,
+ mf_state, mf_in, m_val,
+ tags=tags)
clip_ptr = pnlvm.helpers.get_param_ptr(builder, self, m_params, "clip")
if len(clip_ptr.type.pointee) != 0:
@@ -1852,7 +1854,4 @@ def as_mdf_model(self):
integrator_function_model, 'noise', main_noise_function.id
)
- for func_model in model.functions:
- _substitute_expression_args(func_model)
-
return model
diff --git a/psyneulink/core/components/ports/inputport.py b/psyneulink/core/components/ports/inputport.py
index 2b1ee1b637a..84fb891f715 100644
--- a/psyneulink/core/components/ports/inputport.py
+++ b/psyneulink/core/components/ports/inputport.py
@@ -589,7 +589,7 @@
LEARNING_SIGNAL, MAPPING_PROJECTION, MATRIX, NAME, OPERATION, OUTPUT_PORT, OUTPUT_PORTS, OWNER, \
PARAMS, PRODUCT, PROJECTIONS, REFERENCE_VALUE, \
SENDER, SHADOW_INPUTS, SHADOW_INPUT_NAME, SIZE, PORT_TYPE, SUM, VALUE, VARIABLE, WEIGHT
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import \
@@ -874,6 +874,7 @@ def _validate_default_input(self, default_input):
#endregion
@handle_external_context()
+ @check_user_specified
@tc.typecheck
def __init__(self,
owner=None,
diff --git a/psyneulink/core/components/ports/modulatorysignals/controlsignal.py b/psyneulink/core/components/ports/modulatorysignals/controlsignal.py
index a5e534579cc..5a9c22f9e6d 100644
--- a/psyneulink/core/components/ports/modulatorysignals/controlsignal.py
+++ b/psyneulink/core/components/ports/modulatorysignals/controlsignal.py
@@ -421,7 +421,8 @@
OUTPUT_PORT, OUTPUT_PORTS, OUTPUT_PORT_PARAMS, \
PARAMETER_PORT, PARAMETER_PORTS, PROJECTIONS, \
RECEIVER, FUNCTION
-from psyneulink.core.globals.parameters import FunctionParameter, Parameter, get_validator_by_function
+from psyneulink.core.globals.parameters import FunctionParameter, Parameter, get_validator_by_function, \
+ check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.sampleiterator import SampleSpec, SampleIterator
@@ -792,6 +793,7 @@ def _validate_allocation_samples(self, allocation_samples):
#endregion
+ @check_user_specified
@tc.typecheck
def __init__(self,
owner=None,
diff --git a/psyneulink/core/components/ports/modulatorysignals/gatingsignal.py b/psyneulink/core/components/ports/modulatorysignals/gatingsignal.py
index b24e5da5eb7..62d0476e1c3 100644
--- a/psyneulink/core/components/ports/modulatorysignals/gatingsignal.py
+++ b/psyneulink/core/components/ports/modulatorysignals/gatingsignal.py
@@ -252,7 +252,7 @@
from psyneulink.core.globals.keywords import \
GATE, GATING_PROJECTION, GATING_SIGNAL, INPUT_PORT, INPUT_PORTS, \
MODULATES, OUTPUT_PORT, OUTPUT_PORTS, OUTPUT_PORT_PARAMS, PROJECTIONS, RECEIVER
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -417,6 +417,7 @@ class Parameters(ControlSignal.Parameters):
#endregion
+ @check_user_specified
@tc.typecheck
def __init__(self,
owner=None,
diff --git a/psyneulink/core/components/ports/modulatorysignals/learningsignal.py b/psyneulink/core/components/ports/modulatorysignals/learningsignal.py
index 335896a8bc9..72500b84991 100644
--- a/psyneulink/core/components/ports/modulatorysignals/learningsignal.py
+++ b/psyneulink/core/components/ports/modulatorysignals/learningsignal.py
@@ -194,7 +194,7 @@
from psyneulink.core.components.ports.outputport import PRIMARY
from psyneulink.core.globals.keywords import \
LEARNING_PROJECTION, LEARNING_SIGNAL, OUTPUT_PORT_PARAMS, PARAMETER_PORT, PARAMETER_PORTS, RECEIVER
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import parameter_spec
@@ -333,6 +333,7 @@ class Parameters(ModulatorySignal.Parameters):
value = Parameter(np.array([0]), read_only=True, aliases=['learning_signal'], pnl_internal=True)
learning_rate = None
+ @check_user_specified
@tc.typecheck
def __init__(self,
owner=None,
diff --git a/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py b/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py
index deb1e474258..3e295b414cb 100644
--- a/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py
+++ b/psyneulink/core/components/ports/modulatorysignals/modulatorysignal.py
@@ -412,6 +412,7 @@
from psyneulink.core.globals.keywords import \
ADDITIVE_PARAM, CONTROL, DISABLE, MAYBE, MECHANISM, MODULATION, MODULATORY_SIGNAL, MULTIPLICATIVE_PARAM, \
OVERRIDE, PROJECTIONS, VARIABLE
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
__all__ = [
@@ -562,6 +563,7 @@ class Parameters(OutputPort.Parameters):
# PREFERENCE_SET_NAME: 'OutputPortCustomClassPreferences',
# PREFERENCE_KEYWORD<pref>: <setting>...}
+ @check_user_specified
def __init__(self,
owner=None,
size=None,
diff --git a/psyneulink/core/components/ports/outputport.py b/psyneulink/core/components/ports/outputport.py
index 5c2be3a09bc..5e1c2bc1eba 100644
--- a/psyneulink/core/components/ports/outputport.py
+++ b/psyneulink/core/components/ports/outputport.py
@@ -631,7 +631,7 @@
OWNER_VALUE, PARAMS, PARAMS_DICT, PROJECTION, PROJECTIONS, RECEIVER, REFERENCE_VALUE, STANDARD_OUTPUT_PORTS, PORT, \
VALUE, VARIABLE, \
output_port_spec_to_parameter_name, INPUT_PORT_VARIABLES
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import \
@@ -905,6 +905,7 @@ class Parameters(Port_Base.Parameters):
#endregion
+ @check_user_specified
@tc.typecheck
@handle_external_context()
def __init__(self,
diff --git a/psyneulink/core/components/ports/parameterport.py b/psyneulink/core/components/ports/parameterport.py
index c37514c3f58..cd05d489203 100644
--- a/psyneulink/core/components/ports/parameterport.py
+++ b/psyneulink/core/components/ports/parameterport.py
@@ -382,7 +382,7 @@
CONTEXT, CONTROL_PROJECTION, CONTROL_SIGNAL, CONTROL_SIGNALS, FUNCTION, FUNCTION_PARAMS, \
LEARNING_SIGNAL, LEARNING_SIGNALS, MECHANISM, NAME, PARAMETER_PORT, PARAMETER_PORT_PARAMS, PATHWAY_PROJECTION, \
PROJECTION, PROJECTIONS, PROJECTION_TYPE, REFERENCE_VALUE, SENDER, VALUE
-from psyneulink.core.globals.parameters import ParameterBase, ParameterAlias, SharedParameter
+from psyneulink.core.globals.parameters import ParameterBase, ParameterAlias, SharedParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities \
@@ -701,6 +701,7 @@ class ParameterPort(Port_Base):
#endregion
tc.typecheck
+ @check_user_specified
def __init__(self,
owner,
reference_value=None,
diff --git a/psyneulink/core/components/ports/port.py b/psyneulink/core/components/ports/port.py
index cdc89dc7b0b..bf17f401732 100644
--- a/psyneulink/core/components/ports/port.py
+++ b/psyneulink/core/components/ports/port.py
@@ -797,7 +797,7 @@ def test_multiple_modulatory_projections_with_mech_and_port_Name_specs(self):
RECEIVER, REFERENCE_VALUE, REFERENCE_VALUE_NAME, SENDER, STANDARD_OUTPUT_PORTS, \
PORT, PORT_COMPONENT_CATEGORY, PORT_CONTEXT, Port_Name, port_params, PORT_PREFS, PORT_TYPE, port_value, \
VALUE, VARIABLE, WEIGHT
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import VERBOSE_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.registry import register_category
@@ -1004,6 +1004,7 @@ class Parameters(Port.Parameters):
classPreferenceLevel = PreferenceLevel.CATEGORY
+ @check_user_specified
@tc.typecheck
@abc.abstractmethod
def __init__(self,
diff --git a/psyneulink/core/components/projections/modulatory/controlprojection.py b/psyneulink/core/components/projections/modulatory/controlprojection.py
index 624eb563a0d..72d17f635f6 100644
--- a/psyneulink/core/components/projections/modulatory/controlprojection.py
+++ b/psyneulink/core/components/projections/modulatory/controlprojection.py
@@ -120,7 +120,7 @@
from psyneulink.core.globals.context import ContextFlags
from psyneulink.core.globals.keywords import \
CONTROL, CONTROL_PROJECTION, CONTROL_SIGNAL, INPUT_PORT, OUTPUT_PORT, PARAMETER_PORT
-from psyneulink.core.globals.parameters import Parameter, SharedParameter
+from psyneulink.core.globals.parameters import Parameter, SharedParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -237,6 +237,7 @@ class Parameters(ModulatoryProjection_Base.Parameters):
projection_sender = ControlMechanism
+ @check_user_specified
@tc.typecheck
def __init__(self,
sender=None,
diff --git a/psyneulink/core/components/projections/modulatory/gatingprojection.py b/psyneulink/core/components/projections/modulatory/gatingprojection.py
index 1c852bbea2c..0bdcc4801e5 100644
--- a/psyneulink/core/components/projections/modulatory/gatingprojection.py
+++ b/psyneulink/core/components/projections/modulatory/gatingprojection.py
@@ -112,7 +112,7 @@
from psyneulink.core.globals.keywords import \
FUNCTION_OUTPUT_TYPE, GATE, GATING_MECHANISM, GATING_PROJECTION, GATING_SIGNAL, \
INPUT_PORT, OUTPUT_PORT
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -238,6 +238,7 @@ class Parameters(ModulatoryProjection_Base.Parameters):
projection_sender = GatingMechanism
+ @check_user_specified
@tc.typecheck
def __init__(self,
sender=None,
diff --git a/psyneulink/core/components/projections/modulatory/learningprojection.py b/psyneulink/core/components/projections/modulatory/learningprojection.py
index 4b1a4a8bb63..fe0d021db7a 100644
--- a/psyneulink/core/components/projections/modulatory/learningprojection.py
+++ b/psyneulink/core/components/projections/modulatory/learningprojection.py
@@ -202,7 +202,7 @@
from psyneulink.core.globals.keywords import \
LEARNING, LEARNING_PROJECTION, LEARNING_SIGNAL, \
MATRIX, PARAMETER_PORT, PROJECTION_SENDER, ONLINE, AFTER
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import iscompatible, parameter_spec
@@ -440,6 +440,7 @@ class Parameters(ModulatoryProjection_Base.Parameters):
projection_sender = LearningMechanism
+ @check_user_specified
@tc.typecheck
def __init__(self,
sender:tc.optional(tc.any(LearningSignal, LearningMechanism))=None,
diff --git a/psyneulink/core/components/projections/pathway/mappingprojection.py b/psyneulink/core/components/projections/pathway/mappingprojection.py
index ba6f37c23a8..557c1b3dbd4 100644
--- a/psyneulink/core/components/projections/pathway/mappingprojection.py
+++ b/psyneulink/core/components/projections/pathway/mappingprojection.py
@@ -299,7 +299,7 @@
MAPPING_PROJECTION, MATRIX, \
OUTPUT_PORT, VALUE
from psyneulink.core.globals.log import ContextFlags
-from psyneulink.core.globals.parameters import FunctionParameter, Parameter
+from psyneulink.core.globals.parameters import FunctionParameter, Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -442,6 +442,7 @@ class sockets:
projection_sender = OutputPort
+ @check_user_specified
def __init__(self,
sender=None,
receiver=None,
diff --git a/psyneulink/core/components/projections/pathway/pathwayprojection.py b/psyneulink/core/components/projections/pathway/pathwayprojection.py
index e777205f6c2..61952a9327b 100644
--- a/psyneulink/core/components/projections/pathway/pathwayprojection.py
+++ b/psyneulink/core/components/projections/pathway/pathwayprojection.py
@@ -16,8 +16,6 @@
* `PathwayProjection_Overview`
* `PathwayProjection_Creation`
* `PathwayProjection_Structure`
- - `PathwayProjection_Sender`
- - `PathwayProjection_Receiver`
* `PathwayProjection_Execution`
* `PathwayProjection_Class_Reference`
@@ -46,7 +44,6 @@
A PathwayProjection has the same structure as a `Projection <Projection_Structure>`.
-
.. _PathwayProjection_Execution:
Execution
@@ -63,10 +60,9 @@
"""
-from psyneulink.core.components.projections.projection import Projection_Base, ProjectionRegistry
+from psyneulink.core.components.projections.projection import Projection_Base
from psyneulink.core.globals.context import ContextFlags
from psyneulink.core.globals.keywords import NAME, PATHWAY_PROJECTION, RECEIVER, SENDER
-from psyneulink.core.globals.registry import remove_instance_from_registry
__all__ = []
diff --git a/psyneulink/core/components/projections/projection.py b/psyneulink/core/components/projections/projection.py
index 6999bca6702..d0f8c4c39b2 100644
--- a/psyneulink/core/components/projections/projection.py
+++ b/psyneulink/core/components/projections/projection.py
@@ -409,7 +409,7 @@
from psyneulink.core.components.ports.port import PortError
from psyneulink.core.components.shellclasses import Mechanism, Process_Base, Projection, Port
from psyneulink.core.globals.context import ContextFlags
-from psyneulink.core.globals.json import _get_variable_parameter_name
+from psyneulink.core.globals.mdf import _get_variable_parameter_name
from psyneulink.core.globals.keywords import \
CONTROL, CONTROL_PROJECTION, CONTROL_SIGNAL, EXPONENT, FUNCTION_PARAMS, GATE, GATING_PROJECTION, GATING_SIGNAL, \
INPUT_PORT, LEARNING, LEARNING_PROJECTION, LEARNING_SIGNAL, \
@@ -418,7 +418,7 @@
NAME, OUTPUT_PORT, OUTPUT_PORTS, PARAMS, PATHWAY, PROJECTION, PROJECTION_PARAMS, PROJECTION_SENDER, PROJECTION_TYPE, \
RECEIVER, SENDER, STANDARD_ARGS, PORT, PORTS, WEIGHT, ADD_INPUT_PORT, ADD_OUTPUT_PORT, \
PROJECTION_COMPONENT_CATEGORY
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.registry import register_category, remove_instance_from_registry
from psyneulink.core.globals.socket import ConnectionInfo
@@ -631,6 +631,7 @@ class Parameters(Projection.Parameters):
classPreferenceLevel = PreferenceLevel.CATEGORY
+ @check_user_specified
@abc.abstractmethod
def __init__(self,
receiver,
@@ -1066,8 +1067,8 @@ def as_mdf_model(self, simple_edge_format=True):
else:
sender_mech = parse_valid_identifier(self.sender.owner.name)
else:
- sender_name = None
- sender_mech = None
+ sender_name = ''
+ sender_mech = ''
if not isinstance(self.receiver, type):
try:
@@ -1086,8 +1087,8 @@ def as_mdf_model(self, simple_edge_format=True):
else:
receiver_mech = parse_valid_identifier(self.receiver.owner.name)
else:
- receiver_name = None
- receiver_mech = None
+ receiver_name = ''
+ receiver_mech = ''
socket_dict = {
MODEL_SPEC_ID_SENDER_PORT: f'{sender_mech}_{sender_name}',
@@ -1147,10 +1148,7 @@ def as_mdf_model(self, simple_edge_format=True):
else:
metadata = self._mdf_metadata
try:
- metadata[MODEL_SPEC_ID_METADATA]['functions'] = mdf.Function.to_dict_format(
- self.function.as_mdf_model(),
- ordered=False
- )
+ metadata[MODEL_SPEC_ID_METADATA]['functions'] = mdf.Function.to_dict(self.function.as_mdf_model())
except AttributeError:
# projection is in deferred init, special handling here?
pass
diff --git a/psyneulink/core/components/shellclasses.py b/psyneulink/core/components/shellclasses.py
index 7820abc7328..d1d2dc94f84 100644
--- a/psyneulink/core/components/shellclasses.py
+++ b/psyneulink/core/components/shellclasses.py
@@ -28,6 +28,7 @@
"""
from psyneulink.core.components.component import Component
+from psyneulink.core.globals.parameters import check_user_specified
__all__ = [
'Function', 'Mechanism', 'Process_Base', 'Projection', 'ShellClass', 'ShellClassError', 'Port', 'System_Base',
@@ -73,6 +74,7 @@ class Process_Base(ShellClass):
class Mechanism(ShellClass):
+ @check_user_specified
def __init__(self,
default_variable=None,
size=None,
diff --git a/psyneulink/core/compositions/composition.py b/psyneulink/core/compositions/composition.py
index 836454b9c0f..b720ef3921e 100644
--- a/psyneulink/core/compositions/composition.py
+++ b/psyneulink/core/compositions/composition.py
@@ -8,8 +8,8 @@
# ********************************************* Composition ************************************************************
-"""
+"""
Contents
--------
@@ -111,24 +111,36 @@
The following arguments of the Composition's constructor can be used to add Compnents when it is constructed:
+ .. _Composition_Pathways_Arg:
+
+ - **pathways**
+ adds one or more `Pathways <Composition_Pathways>` to the Composition; this is equivalent to constructing
+ the Composition and then calling its `add_pathways <Composition.add_pathways>` method, and can use the
+ same forms of specification as the **pathways** argument of that method (see `Pathway_Specification` for
+ additonal details). If any `learning Pathways <Composition_Learning_Pathway>` are included, then the
+ constructor's **disable_learning** argument can be used to disable learning on those by default (though it
+ will still allow learning to occur on any other Compositions, either nested within the current one,
+ or within which the current one is nested (see `Composition_Learning` for a full description).
+
+ .. _Composition_Nodes_Arg:
+
- **nodes**
adds the specified `Nodes <Composition_Nodes>` to the Composition; this is equivalent to constructing the
Composition and then calling its `add_nodes <Composition.add_nodes>` method, and takes the same values as the
- **nodes** argument of that method.
+ **nodes** argument of that method (note that this does *not* construct `Pathways <Pathway>` for the specified
+ nodes; the **pathways** arg or `add_pathways <Composition.add_pathways>` method must be used to do so).
+
+ .. _Composition_Projections_Arg:
- **projections**
adds the specified `Projections <Projection>` to the Composition; this is equivalent to constructing the
Composition and then calling its `add_projections <Composition.add_projections>` method, and takes the same
- values as the **projections** argument of that method.
+ values as the **projections** argument of that method. In general, this is not neded -- default Projections
+ are created for Pathways and/or Nodes added to the Composition using the methods described above; however
+ it can be useful for custom configurations, including the implementation of specific Projection `matrices
+ <MappingProjection.matrix>`.
- - **pathways**
- adds one or more `Pathways <Composition_Pathways>` to the Composition; this is equivalent to constructing the
- Composition and then calling its `add_pathways <Composition.add_pathways>` method, and can use the same forms
- of specification as the **pathways** argument of that method. If any `learning Pathways
- <Composition_Learning_Pathway>` are included, then the constructor's **disable_learning** argument can be
- used to disable learning on those by default (though it will still allow learning to occur on any other
- Compositions, either nested within the current one, or within which the current one is nested (see
- `Composition_Learning` for a full description).
+ .. _Composition_Controller_Arg:
- **controller**
adds the specified `ControlMechanism` (typically an `OptimizationControlMechanism`) as the `controller
@@ -179,10 +191,10 @@
- `add_linear_processing_pathway <Composition.add_linear_processing_pathway>`
- adds and a list of `Nodes <Composition_Nodes>` and `Projections <Projection>` to the Composition,
- inserting a default Projection between any adjacent pair of Nodes for which one is not otherwise specified
- (or possibly a set of Projections if either Node is a Composition -- see method documentation for details);
- returns the `Pathway` added to the Composition.
+ adds and a list of `Nodes <Composition_Nodes>` and `Projections <Projection>` to the Composition, inserting
+ a default Projection between any adjacent set(s) of Nodes for which a Projection is not otherwise specified
+ (see method documentation and `Pathway_Specification` for additonal details); returns the `Pathway` added to
+ the Composition.
COMMENT:
The following set of `learning methods <Composition_Learning_Methods>` can be used to add `Pathways
@@ -2730,7 +2742,8 @@ def input_function(env, result):
from psyneulink.core.components.functions.nonstateful.transferfunctions import Identity
from psyneulink.core.components.mechanisms.mechanism import Mechanism_Base, MechanismError, MechanismList
from psyneulink.core.components.mechanisms.modulatory.control.controlmechanism import ControlMechanism
-from psyneulink.core.components.mechanisms.modulatory.control.optimizationcontrolmechanism import AGENT_REP, OptimizationControlMechanism
+from psyneulink.core.components.mechanisms.modulatory.control.optimizationcontrolmechanism import AGENT_REP, \
+ OptimizationControlMechanism
from psyneulink.core.components.mechanisms.modulatory.learning.learningmechanism import \
LearningMechanism, ACTIVATION_INPUT_INDEX, ACTIVATION_OUTPUT_INDEX, ERROR_SIGNAL, ERROR_SIGNAL_INDEX
from psyneulink.core.components.mechanisms.modulatory.modulatorymechanism import ModulatoryMechanism_Base
@@ -2747,7 +2760,8 @@ def input_function(env, result):
from psyneulink.core.components.projections.modulatory.modulatoryprojection import ModulatoryProjection_Base
from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection, MappingError
from psyneulink.core.components.projections.pathway.pathwayprojection import PathwayProjection_Base
-from psyneulink.core.components.projections.projection import Projection_Base, ProjectionError, DuplicateProjectionError
+from psyneulink.core.components.projections.projection import \
+ Projection_Base, ProjectionError, DuplicateProjectionError
from psyneulink.core.components.shellclasses import Composition_Base
from psyneulink.core.components.shellclasses import Mechanism, Projection
from psyneulink.core.compositions.report import Report, \
@@ -2764,16 +2778,16 @@ def input_function(env, result):
MONITOR, MONITOR_FOR_CONTROL, NAME, NESTED, NO_CLAMP, NODE, OBJECTIVE_MECHANISM, ONLINE, OUTCOME, \
OUTPUT, OUTPUT_CIM_NAME, OUTPUT_MECHANISM, OUTPUT_PORTS, OWNER_VALUE, \
PARAMETER, PARAMETER_CIM_NAME, PORT, \
- PROCESSING_PATHWAY, PROJECTION, PROJECTION_TYPE, PROJECTION_PARAMS, PULSE_CLAMP, \
- SAMPLE, SHADOW_INPUTS, SOFT_CLAMP, SSE, \
+ PROCESSING_PATHWAY, PROJECTION, PROJECTION_TYPE, PROJECTION_PARAMS, PULSE_CLAMP, RECEIVER, \
+ SAMPLE, SENDER, SHADOW_INPUTS, SOFT_CLAMP, SSE, \
TARGET, TARGET_MECHANISM, TEXT, VARIABLE, WEIGHT, OWNER_MECH
from psyneulink.core.globals.log import CompositionLog, LogCondition
-from psyneulink.core.globals.parameters import Parameter, ParametersBase
+from psyneulink.core.globals.parameters import Parameter, ParametersBase, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import BasePreferenceSet
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel, _assign_prefs
from psyneulink.core.globals.registry import register_category
-from psyneulink.core.globals.utilities import \
- ContentAddressableList, call_with_pruned_args, convert_to_list, nesting_depth, convert_to_np_array, is_numeric, parse_valid_identifier
+from psyneulink.core.globals.utilities import ContentAddressableList, call_with_pruned_args, convert_to_list, \
+ nesting_depth, convert_to_np_array, is_numeric, is_matrix, parse_valid_identifier
from psyneulink.core.scheduling.condition import All, AllHaveRun, Always, Any, Condition, Never
from psyneulink.core.scheduling.scheduler import Scheduler, SchedulingMode
from psyneulink.core.scheduling.time import Time, TimeScale
@@ -3316,12 +3330,30 @@ class Composition(Composition_Base, metaclass=ComponentsMeta):
---------
pathways : Pathway specification or list[Pathway specification...]
- specifies one or more Pathways to add to the Compositions (see **pathways** argument of `add_pathways
- `Composition.add_pathways` for specification format).
+ specifies one or more Pathways to add to the Compositions. A list containing `Node <Composition_Nodes>`
+ and possible `Projection` specifications at its top level is treated as a single `Pathway`; a list containing
+ any nested lists or other forms of `Pathway specification <Pathway_Specification_Formats>` is treated as
+ `multiple pathways <Pathway_Specification_Multiple>` (see `pathways <Composition_Pathways_Arg>` as
+ well as `Pathway specification <Pathway_Specification>` for additional details).
+
+ .. technical_note::
+
+ The design pattern for use of sets and lists in specifying the **pathways** argument are:
+ - sets comprise Nodes that all occupy the same (parallel) position within a processing Pathway;
+ - lists comprise *sequences* of Nodes; embedded list are either ignored or a generate an error (see below)
+ (this is because lists of Nodes are interpreted as Pathways and Pathways cannot be nested, which would be
+ redundant since the same can be accomplished by simply including the items "inline" within a single list)
+ - if the Pathway specification contains (in its outer list):
+ - only a single item or set of items, each is treated as a SINGLETON <NodeRole.SINGLETON> in a Pathway;
+ - one or more lists, the items in each list are treated as separate (parallel) pathways;
+ - singly-nested lists ([[[A,B]],[[C,D]]]}), they are collapsed and treated as a Pathway;
+ - any list with more than one list nested within it ([[[A,B],[C,D]}), an error is generated;
+ - Pathway objects are treated as a list (if its pathway attribute is a set, it is wrapped in a list)
+ (see `tests <test_various_pathway_configurations_in_constructor>` for examples)
nodes : `Mechanism <Mechanism>`, `Composition` or list[`Mechanism <Mechanism>`, `Composition`] : default None
specifies one or more `Nodes <Composition_Nodes>` to add to the Composition; these are each treated as
- `SINGLETONs <NodeRole.SINGLETON>` unless they are explicitly assigned `Projections <Projection>`.
+ `SINGLETON <NodeRole.SINGLETON>`\\s unless they are explicitly assigned `Projections <Projection>`.
projections : `Projection <Projection>` or list[`Projection <Projection>`] : default None
specifies one or more `Projections <Projection>` to add to the Composition; these are not functional
@@ -3449,7 +3481,7 @@ class Composition(Composition_Base, metaclass=ComponentsMeta):
argument of the Composition's constructor and/or one of its `Pathway addition methods
<Composition_Pathway_Addition_Methods>`; each item is a list of `Nodes <Composition_Nodes>`
(`Mechanisms <Mechanism>` and/or Compositions) intercolated with the `Projection(s) <Projection>` between each
- pair of Nodes; both Nodes are Mechanism, then only a single Projection can be specified; if either is a
+ pair of Nodes; if both Nodes are Mechanisms, then only a single Projection can be specified; if either is a
Composition then, under some circumstances, there can be a set of Projections, specifying how the `INPUT
<NodeRole.INPUT>` Node(s) of the sender project to the `OUTPUT <NodeRole.OUTPUT>` Node(s) of the receiver
(see `add_linear_processing_pathway` for additional details).
@@ -3711,6 +3743,7 @@ class Parameters(ParametersBase):
class _CompilationData(ParametersBase):
execution = None
+ @check_user_specified
def __init__(
self,
pathways=None,
@@ -3954,9 +3987,6 @@ def _analyze_graph(self, context=None):
self._create_CIM_ports(context=context)
# Call after above so shadow_projections have relevant organization
self._update_shadow_projections(context=context)
- # # FIX: 12/29/21 / 3/30/22: MOVE TO _update_shadow_projections
- # # Call again to accommodate any changes from _update_shadow_projections
- # self._determine_node_roles(context=context)
self._check_for_projection_assignments(context=context)
self.needs_update_graph = False
@@ -4553,7 +4583,10 @@ def _determine_origin_and_terminal_nodes_from_consideration_queue(self):
# consideration set. Identifying these assumes that graph_processing has been called/updated,
# which identifies and "breaks" cycles, and assigns FEEDBACK_SENDER to the appropriate consideration set(s).
for node in self.nodes:
- if not any([efferent for efferent in node.efferents if efferent.receiver.owner is not self.output_CIM]):
+ if not any([
+ efferent.is_active_in_composition(self) for efferent in node.efferents
+ if efferent.receiver.owner is not self.output_CIM
+ ]):
self._add_node_role(node, NodeRole.TERMINAL)
def _add_node_aux_components(self, node, context=None):
@@ -4815,14 +4848,14 @@ def _determine_node_roles(self, context=None):
this is currently the case, but is inconsistent with the analog in Control,
where monitored Mechanisms *are* allowed to be OUTPUT;
therefore, might be worth allowing TARGET_MECHANISM to be assigned as OUTPUT
- - all Nodes for which OUTPUT has been assigned as a required_node_role, inculding by user
+ - all Nodes for which OUTPUT has been assigned as a required_node_role, inclUding by user
(i.e., in self.required_node_roles[NodeRole.OUTPUT]
TERMINAL:
- all Nodes that
- are not an ObjectiveMechanism assigned the role CONTROLLER_OBJECTIVE
- or have *no* efferent projections OR
- - or for for which any efferent projections are either:
+ - or for which any efferent projections are either:
- to output_CIM OR
- assigned as feedback (i.e., self.graph.comp_to_vertex[efferent].feedback == EdgeType.FEEDBACK
.. _note::
@@ -4917,9 +4950,9 @@ def _determine_node_roles(self, context=None):
# and doesn't project to any Nodes other than its `AutoassociativeLearningMechanism`
# (this is not picked up as a `TERMINAL` since it projects to the `AutoassociativeLearningMechanism`)
# but can (or already does) project to an output_CIM
- if all((p.receiver.owner is node
+ if all((p.receiver.owner is node # <- recurrence
or isinstance(p.receiver.owner, AutoAssociativeLearningMechanism)
- or p.receiver.owner is self.output_CIM)
+ or p.receiver.owner is self.output_CIM) # <- already projects to an output_CIM
for p in node.efferents):
self._add_node_role(node, NodeRole.OUTPUT)
continue
@@ -5746,13 +5779,9 @@ def add_projection(self,
return
else:
# Initialize Projection
- projection._init_args['sender'] = sender
- projection._init_args['receiver'] = receiver
- try:
- projection._deferred_init()
- except DuplicateProjectionError:
- # return projection
- return
+ projection._init_args[SENDER] = sender
+ projection._init_args[RECEIVER] = receiver
+ projection._deferred_init()
else:
existing_projections = self._check_for_existing_projections(projection, sender=sender, receiver=receiver)
@@ -5787,6 +5816,15 @@ def add_projection(self,
projection.is_processing = False
# KDM 5/24/19: removing below rename because it results in several existing_projections
# projection.name = f'{sender} to {receiver}'
+
+ # check for required role specification of feedback projections
+ for node, role in self.required_node_roles:
+ if (
+ (node == projection.sender.owner and role == NodeRole.FEEDBACK_SENDER)
+ or (node == projection.receiver.owner and role == NodeRole.FEEDBACK_RECEIVER)
+ ):
+ feedback = True
+
self.graph.add_component(projection, feedback=feedback)
try:
@@ -6339,6 +6377,46 @@ def _get_destination(self, projection):
# region ---------------------------------- PROCESSING -----------------------------------------------------------
+ def _parse_pathway(self, pathway, name, pathway_arg_str):
+ from psyneulink.core.compositions.pathway import Pathway, _is_pathway_entry_spec
+
+ # Deal with Pathway() or tuple specifications
+ if isinstance(pathway, Pathway):
+ # Give precedence to name specified in call to add_linear_processing_pathway
+ pathway_name = name or pathway.name
+ pathway = pathway.pathway
+ else:
+ pathway_name = name
+
+ if isinstance(pathway, tuple):
+ # If tuple is just a single Node specification for a pathway, return in list:
+ if _is_pathway_entry_spec(pathway, NODE):
+ pathway = [pathway]
+ # If tuple is used to specify a sequence of nodes, convert to list (even though not documented):
+ elif all(_is_pathway_entry_spec(n, ANY) for n in pathway):
+ pathway = list(pathway)
+ # If tuple is (pathway, LearningFunction), get pathway and ignore LearningFunction
+ elif isinstance(pathway[1],type) and issubclass(pathway[1], LearningFunction):
+ warnings.warn(f"{LearningFunction.__name__} found in specification of {pathway_arg_str}: {pathway[1]}; "
+ f"it will be ignored")
+ pathway = pathway[0]
+ else:
+ raise CompositionError(f"Unrecognized tuple specification in {pathway_arg_str}: {pathway}")
+ elif not isinstance(pathway, collections.abc.Iterable) or all(_is_pathway_entry_spec(n, ANY) for n in pathway):
+ pathway = convert_to_list(pathway)
+ else:
+ bad_entry_error_msg = f"The following entries in a pathway specified for '{self.name}' are not " \
+ f"a Node (Mechanism or Composition) or a Projection nor a set of either: "
+ bad_entries = [repr(entry) for entry in pathway if not _is_pathway_entry_spec(entry, ANY)]
+ raise CompositionError(f"{bad_entry_error_msg}{','.join(bad_entries)}")
+ # raise CompositionError(f"Unrecognized specification in {pathway_arg_str}: {pathway}")
+
+ lists = [entry for entry in pathway
+ if isinstance(entry, list) and all(_is_pathway_entry_spec(node, NODE) for node in entry)]
+ if lists:
+ raise CompositionError(f"Pathway specification for {pathway_arg_str} has embedded list(s): {lists}")
+ return pathway, pathway_name
+
# FIX: REFACTOR TO TAKE Pathway OBJECT AS ARGUMENT
def add_pathway(self, pathway):
"""Add an existing `Pathway <Composition_Pathways>` to the Composition
@@ -6370,51 +6448,194 @@ def add_pathway(self, pathway):
self._analyze_graph()
+ @handle_external_context()
+ def add_pathways(self, pathways, context=None):
+ """Add pathways to the Composition.
+
+ Arguments
+ ---------
+
+ pathways : Pathway or list[Pathway]
+ specifies one or more `Pathways <Pathway>` to add to the Composition. Any valid form of `Pathway
+ specification <Pathway_Specification>` can be used. A set can also be used, all elements of which are
+ `Nodes <Composition_Nodes>`, in which case a separate `Pathway` is constructed for each.
+
+ Returns
+ -------
+
+ list[Pathway] :
+ List of `Pathways <Pathway>` added to the Composition.
+
+ """
+
+ # Possible specifications for **pathways** arg:
+ # Node specs (single or set):
+ # 0 Single node: NODE
+ # 1 Set: {NODE...} -> generate a Pathway for each NODE
+ # Single pathway spec (list, tuple or dict):
+ # 2 single list: PWAY = [NODE] or [NODE...] in which *all* are NODES with optional intercolated Projections
+ # 2.5 single with sets: PWAY = [NODE or {NODE...}] or [NODE or {NODE...}, NODE or {NODE...}...]
+ # 3 single tuple: (PWAY, LearningFunction) = (NODE, LearningFunction) or
+ # ([NODE...], LearningFunction)
+ # 4 single dict: {NAME: PWAY} = {NAME: NODE} or
+ # {NAME: [NODE...]} or
+ # {NAME: ([NODE...], LearningFunction)}
+ # Multiple pathway specs (in outer list):
+ # 5 list with list(s): [PWAY] = [NODE, [NODE]] or [[NODE...]...]
+ # 6 list with tuple(s): [(PWAY, LearningFunction)...] = [(NODE..., LearningFunction)...] or
+ # [([NODE...], LearningFunction)...]
+ # 7 list with dict: [{NAME: PWAY}...] = [{NAME: NODE...}...] or
+ # [{NAME: [NODE...]}...] or
+ # [{NAME: (NODE, LearningFunction)}...] or
+ # [{NAME: ([NODE...], LearningFunction)}...]
+
+ from psyneulink.core.compositions.pathway import Pathway, _is_node_spec, _is_pathway_entry_spec
+
+ if context.source == ContextFlags.COMMAND_LINE:
+ pathways_arg_str = f"'pathways' arg for the add_pathways method of {self.name}"
+ elif context.source == ContextFlags.CONSTRUCTOR:
+ pathways_arg_str = f"'pathways' arg of the constructor for {self.name}"
+ else:
+ assert False, f"PROGRAM ERROR: unrecognized context passed to add_pathways of {self.name}."
+ context.string = pathways_arg_str
+
+ if not pathways:
+ return
+
+ # Possibilities 0, 3 or 4 (single NODE, set of NODESs tuple, dict or Pathway specified, so convert to list
+ if _is_node_spec(pathways) or isinstance(pathways, (tuple, dict, Pathway)):
+ pathways = convert_to_list(pathways)
+
+ # Possibility 1 (set of Nodes): create a Pathway for each Node (since set is in pathways arg)
+ elif isinstance(pathways, set):
+ pathways = [pathways]
+
+ # Possibility 2 (list is a single pathway spec) or 2.5 (includes one or more sets):
+ if (isinstance(pathways, list) and
+ # First item must be a node_spec or set of them
+ ((_is_node_spec(pathways[0])
+ or (isinstance(pathways[0], set) and all(_is_node_spec(item) for item in pathways[0])))
+ # All other items must be either Nodes, Projections or sets
+ and all(_is_pathway_entry_spec(p, ANY) for p in pathways))):
+ # Place in outter list (to conform to processing of multiple pathways below)
+ pathways = [pathways]
+ # assert False, f"GOT TO POSSIBILITY 2" # SHOULD HAVE BEEN DONE ABOVE
+
+ # If pathways is not now a list it must be illegitimate
+ if not isinstance(pathways, list):
+ raise CompositionError(f"The {pathways_arg_str} must be a "
+ f"Node, list, set, tuple, dict or Pathway object: {pathways}.")
+
+ # pathways should now be a list in which each entry should be *some* form of pathway specification
+ # (including original spec as possibilities 5, 6, or 7)
+
+ # If there are any lists of Nodes in pathway, or a Pathway or dict with such a list,
+ # then treat ALL entries as parallel pathways, and embed in lists"
+ if (isinstance(pathways, collections.abc.Iterable)
+ and any(isinstance(pathway, (list, dict, Pathway))) for pathway in pathways):
+ pathways = [pathway if isinstance(pathway, (list, dict, Pathway)) else [pathway] for pathway in pathways]
+ else:
+ # Put single pathway in outer list for consistency of handling below (with specified pathway as pathways[0])
+ pathways = np.atleast_2d(np.array(pathways, dtype=object)).tolist()
+
+ added_pathways = []
+
+ def identify_pway_type_and_parse_tuple_prn(pway, tuple_or_dict_str):
+ """
+ Determine whether pway is PROCESSING_PATHWAY or LEARNING_PATHWAY and, if it is the latter,
+ parse tuple into pathway specification and LearningFunction.
+ Return pathway type, pathway, and learning_function or None
+ """
+ learning_function = None
+
+ if isinstance(pway, Pathway):
+ pway = pway.pathway
+
+ if (_is_node_spec(pway) or isinstance(pway, (list, set)) or
+ # Forgive use of tuple to specify a pathway, and treat as if it was a list spec
+ (isinstance(pway, tuple) and all(_is_pathway_entry_spec(n, ANY) for n in pathway))):
+ pway_type = PROCESSING_PATHWAY
+ if isinstance(pway, set):
+ pway = [pway]
+ return pway_type, pway, None
+ elif isinstance(pway, tuple):
+ pway_type = LEARNING_PATHWAY
+ if len(pway)!=2:
+ raise CompositionError(f"A tuple specified in the {pathways_arg_str}"
+ f" has more than two items: {pway}")
+ pway, learning_function = pway
+ if not (_is_node_spec(pway) or isinstance(pway, (list, Pathway))):
+ raise CompositionError(f"The 1st item in {tuple_or_dict_str} specified in the "
+ f" {pathways_arg_str} must be a node or a list: {pway}")
+ if not (isinstance(learning_function, type) and issubclass(learning_function, LearningFunction)):
+ raise CompositionError(f"The 2nd item in {tuple_or_dict_str} specified in the "
+ f"{pathways_arg_str} must be a LearningFunction: {learning_function}")
+ return pway_type, pway, learning_function
+ else:
+ assert False, f"PROGRAM ERROR: arg to identify_pway_type_and_parse_tuple_prn in {self.name}" \
+ f"is not a Node, list or tuple: {pway}"
+
+ # Validate items in pathways list and add to Composition using relevant add_linear_<> method.
+ bad_entry_error_msg = f"Every item in the {pathways_arg_str} must be a " \
+ f"Node, list, set, tuple or dict; the following are not: "
+ for pathway in pathways:
+ pathway = pathway[0] if isinstance(pathway, list) and len(pathway) == 1 else pathway
+ pway_name = None
+ if isinstance(pathway, Pathway):
+ pway_name = pathway.name
+ pathway = pathway.pathway
+ if _is_node_spec(pathway) or isinstance(pathway, (list, set, tuple)):
+ if isinstance(pathway, set):
+ bad_entries = [repr(entry) for entry in pathway if not _is_node_spec(entry)]
+ if bad_entries:
+ raise CompositionError(f"{bad_entry_error_msg}{','.join(bad_entries)}")
+ pway_type, pway, pway_learning_fct = identify_pway_type_and_parse_tuple_prn(pathway, f"a tuple")
+ elif isinstance(pathway, dict):
+ if len(pathway)!=1:
+ raise CompositionError(f"A dict specified in the {pathways_arg_str} "
+ f"contains more than one entry: {pathway}.")
+ pway_name, pway = list(pathway.items())[0]
+ if not isinstance(pway_name, str):
+ raise CompositionError(f"The key in a dict specified in the {pathways_arg_str} must be a str "
+ f"(to be used as its name): {pway_name}.")
+ if _is_node_spec(pway) or isinstance(pway, (list, tuple, Pathway)):
+ pway_type, pway, pway_learning_fct = identify_pway_type_and_parse_tuple_prn(pway,
+ f"the value of a dict")
+ else:
+ raise CompositionError(f"The value in a dict specified in the {pathways_arg_str} must be "
+ f"a pathway specification (Node, list or tuple): {pway}.")
+ else:
+ raise CompositionError(f"{bad_entry_error_msg}{repr(pathway)}")
+
+ context.source = ContextFlags.METHOD
+ if pway_type == PROCESSING_PATHWAY:
+ new_pathway = self.add_linear_processing_pathway(pathway=pway,
+ name=pway_name,
+ context=context)
+ elif pway_type == LEARNING_PATHWAY:
+ new_pathway = self.add_linear_learning_pathway(pathway=pway,
+ learning_function=pway_learning_fct,
+ name=pway_name,
+ context=context)
+ else:
+ assert False, f"PROGRAM ERROR: failure to determine pathway_type in add_pathways for {self.name}."
+
+ added_pathways.append(new_pathway)
+
+ return added_pathways
+
@handle_external_context()
def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *args):
- """Add sequence of `Nodes <Composition_Nodes>` with intercolated Projections.
+ """Add sequence of `Nodes <Composition_Nodes>` with optionally intercolated `Projections <Projection>`.
.. _Composition_Add_Linear_Processing_Pathway:
- Each `Node <Composition_Nodes>` can be either a `Mechanism`, a `Composition`, or a tuple (Mechanism, `NodeRoles
- <NodeRole>`) that can be used to assign `required_roles` to Mechanisms (see `Composition_Nodes` for additional
- details).
-
- `Projections <Projection>` can be intercolated between any pair of `Nodes <Composition_Nodes>`. If both Nodes
- of a pair are Mechanisms, a single `MappingProjection` can be `specified <MappingProjection_Creation>`. The
- same applies if the first Node is a `Composition` with a single `OUTPUT <NodeRole.OUTPUT>` Node and/or the
- second is a `Composition` with a single `INPUT <NodeRole.INPUT>` Node. If either has more than one `INPUT
- <NodeRole.INPUT>` or `OUTPUT <NodeRole.OUTPUT>` Node, respectively, then a list or set of Projections can be
- specified for each pair of nested Nodes. If no `Projection` is specified between a pair of contiguous Nodes,
- then default Projection(s) are constructed between them, as follows:
-
- * *One to one* - if both Nodes are Mechanisms or, if either is a Composition, the first (sender) has
- only a single `OUTPUT <NodeRole.OUTPUT>` Node and the second (receiver) has only a single `INPUT
- <NodeRole.INPUT>` Node, then a default `MappingProjection` is created from the `primary OutputPort
- <OutputPort_Primary>` of the sender (or of its sole `OUTPUT <NodeRole.OUTPUT>` Node if the sener is a
- Composition) to the `primary InputPort <InputPort_Primary>` of the receiver (or of its sole of `INPUT
- <NodeRole.INPUT>` Node if the receiver is a Composition).
-
- * *One to many* - if the first Node (sender) is either a Mechanism or a Composition with a single
- `OUTPUT <NodeRole.OUTPUT>` Node, but the second (receiver) is a Composition with more than one
- `INPUT <NodeRole.INPUT>` Node, then a `MappingProjection` is created from the `primary OutputPort
- <OutputPort_Primary>` of the sender Mechanism (or of its sole `OUTPUT <NodeRole.OUTPUT>` Node if the
- sender is a Compostion) to each `INPUT <NodeRole.OUTPUT>` Node of the receiver, and a *set*
- containing the Projections is intercolated between the two Nodes in the `Pathway`.
-
- * *Many to one* - if the first Node (sender) is a Composition with more than one `OUTPUT <NodeRole.OUTPUT>`
- Node, and the second (receiver) is either a Mechanism or a Composition with a single `INPUT <NodeRole.INPUT>`
- Node, then a `MappingProjection` is created from each `OUPUT <NodeRole.OUTPUT>` Node of the sender to the
- `primary InputPort <InputPort_Primary>` of the receiver Mechanism (or of its sole `INPUT <NodeRole.INPUT>`
- Node if the receiver is a Composition), and a *set* containing the Projections is intercolated
- between the two Nodes in the `Pathway`.
-
- * *Many to many* - if both Nodes are Compositions in which the sender has more than one `INPUT <NodeRole.INPUT>`
- Node and the receiver has more than one `INPUT <NodeRole.INPUT>` Node, it is not possible to determine
- the correct configuration automatically, and an error is generated. In this case, a set of Projections
- must be explicitly specified.
-
- .. _note::
+ A Pathway is specified as a list, each element of which is either a `Node <Composition_Nodes>` or
+ set of Nodes, possibly intercolated with specifications of `Projections <Projection>` between them.
+ The Node(s) specified in each entry of the list project to the Node(s) specified in the next entry
+ (see `Pathway_Specification` for details).
+
+ .. note::
Any specifications of the **monitor_for_control** `argument <ControlMechanism_Monitor_for_Control_Argument>`
of a constructor for a `ControlMechanism` or the **monitor** argument in the constructor for an
`ObjectiveMechanism` in the **objective_mechanism** `argument <ControlMechanism_ObjectiveMechanism>` of a
@@ -6430,9 +6651,8 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
be used, however if a 2-item (Pathway, LearningFunction) tuple is used, the `LearningFunction` is ignored
(this should be used with `add_linear_learning_pathway` if a `learning Pathway
<Composition_Learning_Pathway>` is desired). A `Pathway` object can also be used; again, however, any
- learning-related specifications are ignored, as are its `name <Pathway.name>` if the **name**
- argument of add_linear_processing_pathway is specified.
- See `above <Composition_Add_Linear_Processing_Pathway>` for additional details.
+ learning-related specifications are ignored, as are its `name <Pathway.name>` if the **name** argument
+ of add_linear_processing_pathway is specified.
name : str
species the name used for `Pathway`; supercedes `name <Pathway.name>` of `Pathway` object if it is has one.
@@ -6442,12 +6662,15 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
`Pathway` :
`Pathway` added to Composition.
-
"""
from psyneulink.core.compositions.pathway import Pathway, _is_node_spec, _is_pathway_entry_spec
+ def _get_spec_if_tuple(spec):
+ return spec[0] if isinstance(spec, tuple) else spec
+
nodes = []
+ node_entries = []
# If called internally, use its pathway_arg_str in error messages (in context.string)
if context.source is not ContextFlags.COMMAND_LINE:
@@ -6459,48 +6682,37 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
context.source = ContextFlags.METHOD
context.string = pathway_arg_str
- # First, deal with Pathway() or tuple specifications
- if isinstance(pathway, Pathway):
- # Give precedence to name specified in call to add_linear_processing_pathway
- pathway_name = name or pathway.name
- pathway = pathway.pathway
- else:
- pathway_name = name
-
- if _is_pathway_entry_spec(pathway, ANY):
- pathway = convert_to_list(pathway)
- elif isinstance(pathway, tuple):
- # If tuple is used to specify a sequence of nodes, convert to list (even though not documented):
- if all(_is_pathway_entry_spec(n, ANY) for n in pathway):
- pathway = list(pathway)
- # If tuple is (pathway, LearningFunction), get pathway and ignore LearningFunction
- elif isinstance(pathway[1],type) and issubclass(pathway[1], LearningFunction):
- warnings.warn(f"{LearningFunction.__name__} found in specification of {pathway_arg_str}: {pathway[1]}; "
- f"it will be ignored")
- pathway = pathway[0]
- else:
- raise CompositionError(f"Unrecognized tuple specification in {pathway_arg_str}: {pathway}")
- else:
- raise CompositionError(f"Unrecognized specification in {pathway_arg_str}: {pathway}")
+ pathway, pathway_name = self._parse_pathway(pathway, name, pathway_arg_str)
- # Then, verify that the pathway begins with a node
+ # Verify that the pathway begins with a Node or set of Nodes
if _is_node_spec(pathway[0]):
# Use add_nodes so that node spec can also be a tuple with required_roles
- self.add_nodes(nodes=[pathway[0]],
- context=context)
+ self.add_nodes(nodes=[pathway[0]], context=context)
nodes.append(pathway[0])
+ node_entries.append(pathway[0])
+ # Or a set of Nodes
+ elif isinstance(pathway[0], set):
+ self.add_nodes(nodes=pathway[0], context=context)
+ nodes.extend(pathway[0])
+ node_entries.append(pathway[0])
else:
# 'MappingProjection has no attribute _name' error is thrown when pathway[0] is passed to the error msg
raise CompositionError(f"First item in {pathway_arg_str} must be "
f"a Node (Mechanism or Composition): {pathway}.")
- # Next, add all of the remaining nodes in the pathway
+ # Add all of the remaining nodes in the pathway
for c in range(1, len(pathway)):
- # if the current item is a Mechanism, Composition or (Mechanism, NodeRole(s)) tuple, add it
+ # if the entry is for a Node (Mechanism, Composition or (Mechanism, NodeRole(s)) tuple), add it
if _is_node_spec(pathway[c]):
self.add_nodes(nodes=[pathway[c]],
context=context)
nodes.append(pathway[c])
+ node_entries.append(pathway[c])
+ # If the entry is for a set of Nodes, add them
+ elif isinstance(pathway[c], set) and all(_is_node_spec(entry) for entry in pathway[c]):
+ self.add_nodes(nodes=pathway[c], context=context)
+ nodes.extend(pathway[c])
+ node_entries.append(pathway[c])
# Then, delete any ControlMechanism that has its monitor_for_control attribute assigned
# and any ObjectiveMechanism that projects to a ControlMechanism,
@@ -6532,146 +6744,271 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
projections = []
for c in range(1, len(pathway)):
- # if the current item is a Node
- if _is_node_spec(pathway[c]):
- if _is_node_spec(pathway[c - 1]):
- # if the previous item was also a node, add a MappingProjection between them
- if isinstance(pathway[c - 1], tuple):
- sender = pathway[c - 1][0]
- else:
- sender = pathway[c - 1]
- if isinstance(pathway[c], tuple):
- receiver = pathway[c][0]
- else:
- receiver = pathway[c]
-
- # If sender and/or receiver is a Composition with INPUT or OUTPUT Nodes,
- # replace it with those Nodes
- senders = self._get_nested_nodes_with_same_roles_at_all_levels(sender, NodeRole.OUTPUT)
- receivers = self._get_nested_nodes_with_same_roles_at_all_levels(receiver,
- NodeRole.INPUT, NodeRole.TARGET)
- if senders or receivers:
- senders = senders or convert_to_list(sender)
- receivers = receivers or convert_to_list(receiver)
- if len(senders) > 1 and len(receivers) > 1:
- raise CompositionError(f"Pathway specified with two contiguous Compositions, the first of "
- f"which ({sender.name}) has more than one OUTPUT Node, and second "
- f"of which ({receiver.name}) has more than one INPUT Node, making "
- f"the configuration of Projections between them ambiguous; please "
- f"specify those Projections explicitly.")
- proj = {self.add_projection(sender=s, receiver=r, allow_duplicates=False)
- for r in receivers for s in senders}
- else:
- proj = self.add_projection(sender=sender, receiver=receiver)
- if proj:
- projections.append(proj)
-
- # if the current item is a Projection specification
+ # NODE ENTRY ----------------------------------------------------------------------------------------
+ def _get_node_specs_for_entry(entry, include_roles=None, exclude_roles=None):
+ """Extract Nodes from any tuple specs and replace Compositions with their INPUT Nodes
+ """
+ nodes = []
+ for node in entry:
+ # Extract Nodes from any tuple specs
+ node = _get_spec_if_tuple(node)
+ # Replace any nested Compositions with their INPUT Nodes
+ node = (self._get_nested_nodes_with_same_roles_at_all_levels(node, include_roles, exclude_roles)
+ if isinstance(node, Composition) else [node])
+ nodes.extend(node)
+ return nodes
+
+ # The current entry is a Node or a set of them:
+ # - if it is a set, list or array, leave as is, else place in set for consistency of processing below
+ current_entry = pathway[c] if isinstance(pathway[c], (set, list, np.ndarray)) else {pathway[c]}
+ if all(_is_node_spec(entry) for entry in current_entry):
+ receivers = _get_node_specs_for_entry(current_entry, NodeRole.INPUT, NodeRole.TARGET)
+ # The preceding entry is a Node or set of them:
+ # - if it is a set, list or array, leave as is, else place in set for consistnecy of processin below
+ preceding_entry = (pathway[c - 1] if isinstance(pathway[c - 1], (set, list, np.ndarray))
+ else {pathway[c - 1]})
+ if all(_is_node_spec(sender) for sender in preceding_entry):
+ senders = _get_node_specs_for_entry(preceding_entry, NodeRole.OUTPUT)
+ projs = {self.add_projection(sender=s, receiver=r, allow_duplicates=False)
+ for r in receivers for s in senders}
+ if all(projs):
+ projs = projs.pop() if len(projs) == 1 else projs
+ projections.append(projs)
+
+ # PROJECTION ENTRY --------------------------------------------------------------------------
+ # Validate that it is between two nodes, then add the Projection;
+ # note: if Projection is already instantiated and valid, it is used as is; if it is a set or list:
+ # - those are implemented between the corresponding pairs of sender and receiver Nodes
+ # - the list or set has a default Projection or matrix specification,
+ # that is used between all pairs of Nodes for which a Projection has not been specified
+
+ # The current entry is a Projection specification or a list or set of them
elif _is_pathway_entry_spec(pathway[c], PROJECTION):
- # Convert pathway[c] to list (embedding in one if matrix) for consistency of handling below
- # try:
- # proj_specs = set(convert_to_list(pathway[c]))
- # except TypeError:
- # proj_specs = [pathway[c]]
- if is_numeric(pathway[c]):
- proj_specs = [pathway[c]]
+
+ # Validate that Projection specification is not last entry
+ if c == len(pathway) - 1:
+ raise CompositionError(f"The last item in the {pathway_arg_str} cannot be a Projection: "
+ f"{pathway[c]}.")
+
+ # Validate that entry is between two Nodes (or sets of Nodes)
+ # and get all pairings of sender and receiver nodes
+ prev_entry = pathway[c - 1]
+ next_entry = pathway[c + 1]
+ if ((_is_node_spec(prev_entry) or isinstance(prev_entry, set))
+ and (_is_node_spec(next_entry) or isinstance(next_entry, set))):
+ senders = [_get_spec_if_tuple(sender) for sender in convert_to_list(prev_entry)]
+ receivers = [_get_spec_if_tuple(receiver) for receiver in convert_to_list(next_entry)]
+ node_pairs = list(itertools.product(senders,receivers))
+ else:
+ raise CompositionError(f"A Projection specified in {pathway_arg_str} "
+ f"is not between two Nodes: {pathway[c]}")
+
+ # Convert specs in entry to list (embedding in one if matrix) for consistency of handling below
+ all_proj_specs = [pathway[c]] if is_numeric(pathway[c]) else convert_to_list(pathway[c])
+
+ # Get default Projection specification
+ # Must be a matrix spec, or a Projection with no sender or receiver specified
+ # If it is:
+ # - a single Projection, not in a set or list
+ # - appears only once in the pathways arg
+ # - it is preceded by only one sender Node and followed by only one receiver Node
+ # then treat as an individual Projection specification and not a default projection specification
+ possible_default_proj_spec = [proj_spec for proj_spec in all_proj_specs
+ if (is_matrix(proj_spec)
+ or (isinstance(proj_spec, Projection)
+ and proj_spec._initialization_status & ContextFlags.DEFERRED_INIT
+ and proj_spec._init_args[SENDER] is None
+ and proj_spec._init_args[RECEIVER] is None))]
+ # Validate that there is no more than one default Projection specification
+ if len(possible_default_proj_spec) > 1:
+ raise CompositionError(f"There is more than one matrix specification in the set of Projection "
+ f"specifications for entry {c} of the {pathway_arg_str}: "
+ f"{possible_default_proj_spec}.")
+ # Get spec from list:
+ spec = possible_default_proj_spec[0] if possible_default_proj_spec else None
+ # If it appears only once on its own in the pathways arg and there is only one sender and one receiver
+ # consider it an individual Projection specification rather than a specification of the default
+ if sum(isinstance(s, Projection) and s is spec for s in pathway) == len(senders) == len(receivers) == 1:
+ default_proj_spec = None
+ proj_specs = all_proj_specs
else:
- proj_specs = convert_to_list(pathway[c])
+ # Unpack if tuple spec, and assign feedback (with False as default)
+ default_proj_spec, feedback = (spec if isinstance(spec, tuple) else (spec, False))
+ # Get all specs other than default_proj_spec
+ # proj_specs = [proj_spec for proj_spec in all_proj_specs if proj_spec not in possible_default_proj_spec]
+ proj_specs = [proj_spec for proj_spec in all_proj_specs if proj_spec is not spec]
+
+ # Collect all Projection specifications (to add to Composition at end)
proj_set = []
- for proj_spec in proj_specs:
- if c == len(pathway) - 1:
- raise CompositionError(f"The last item in the {pathway_arg_str} cannot be a Projection: "
- f"{proj_spec}.")
- # confirm that it is between two nodes, then add the projection
- if isinstance(proj_spec, tuple):
- proj = proj_spec[0]
- feedback = proj_spec[1]
- else:
- proj = proj_spec
- feedback = False
- sender = pathway[c - 1]
- receiver = pathway[c + 1]
- if _is_node_spec(sender) and _is_node_spec(receiver):
- if isinstance(sender, tuple):
- sender = sender[0]
- if isinstance(receiver, tuple):
- receiver = receiver[0]
+
+ def handle_misc_errors(proj, error):
+ raise CompositionError(f"Bad Projection specification in {pathway_arg_str} ({proj}): "
+ f"{str(error.error_value)}")
+
+ def handle_duplicates(sender, receiver):
+ duplicate = [p for p in receiver.afferents if p in sender.efferents]
+ assert len(duplicate)==1, \
+ f"PROGRAM ERROR: Could not identify duplicate on DuplicateProjectionError " \
+ f"for {Projection.__name__} between {sender.name} and {receiver.name} " \
+ f"in call to {repr('add_linear_processing_pathway')} for {self.name}."
+ duplicate = duplicate[0]
+ warning_msg = f"Projection specified between {sender.name} and {receiver.name} " \
+ f"in {pathway_arg_str} is a duplicate of one"
+ # IMPLEMENTATION NOTE: Version that allows different Projections between same
+ # sender and receiver in different Compositions
+ # if duplicate in self.projections:
+ # warnings.warn(f"{warning_msg} already in the Composition ({duplicate.name}) "
+ # f"and so will be ignored.")
+ # proj=duplicate
+ # else:
+ # if self.prefs.verbosePref:
+ # warnings.warn(f" that already exists between those nodes ({duplicate.name}). The "
+ # f"new one will be used; delete it if you want to use the existing one")
+ # Version that forbids *any* duplicate Projections between same sender and receiver
+ warnings.warn(f"{warning_msg} that already exists between those nodes ({duplicate.name}) "
+ f"and so will be ignored.")
+ proj_set.append(self.add_projection(duplicate))
+
+ # PARSE PROJECTION SPECIFICATIONS AND INSTANTIATE PROJECTIONS
+ # IMPLEMENTATION NOTE:
+ # self.add_projection is called for each Projection
+ # to catch any duplicates with exceptions below
+
+ # FIX: 4/9/22 - REFACTOR TO DO ANY SPECIFIED ASSIGNMENTS FIRST, AND THEN DEFAULT ASSIGNMENTS (IF ANY)
+ if default_proj_spec is not None and not proj_specs:
+ # If there is a default specification and no other Projection specs,
+ # use default to construct Projections for all node_pairs
+ for sender, receiver in node_pairs:
try:
- if isinstance(proj, (np.ndarray, np.matrix, list)):
- # If proj is a matrix specification, use it as the matrix arg
- proj = MappingProjection(sender=sender,
- matrix=proj,
- receiver=receiver)
+ # Default is a Projection
+ if isinstance(default_proj_spec, Projection):
+ # Copy so that assignments made to instantiated Projection don't affect default
+ projection = self.add_projection(projection=deepcopy(default_proj_spec),
+ sender=sender,
+ receiver=receiver,
+ allow_duplicates=False,
+ feedback=feedback)
else:
- # Otherwise, if it is Port specification, implement default Projection
+ # Default is a matrix_spec
+ assert is_matrix(default_proj_spec), \
+ f"PROGRAM ERROR: Expected {default_proj_spec} to be " \
+ f"a matrix specification in {pathway_arg_str}."
+ projection = self.add_projection(projection=MappingProjection(sender=sender,
+ matrix=default_proj_spec,
+ receiver=receiver),
+ allow_duplicates=False,
+ feedback=feedback)
+ proj_set.append(projection)
+
+ except (InputPortError, ProjectionError, MappingError) as error:
+ handle_misc_errors(proj, error)
+ except DuplicateProjectionError:
+ handle_duplicates(sender, receiver)
+
+ else:
+ # FIX: 4/9/22 - PUT THIS FIRST (BEFORE BLOCK JUST ABOVE) AND THEN ASSIGN TO ANY LEFT IN node_pairs
+ # Projections have been specified
+ for proj_spec in proj_specs:
+ try:
+ proj = _get_spec_if_tuple(proj_spec)
+ feedback = proj_spec[1] if isinstance(proj_spec, tuple) else False
+
+ if isinstance(proj, Projection):
+ # FIX 4/9/22 - TEST FOR DEFERRED INIT HERE (THAT IS NOT A default_proj_spec)
+ # IF JUST SENDER OR RECEIVER, TREAT AS PER PORTS BELOW
+ # Validate that Projection is between a Node in senders and one in receivers
+ if proj._initialization_status & ContextFlags.DEFERRED_INIT:
+ sender_node = senders[0]
+ receiver_node = receivers[0]
+ else:
+ sender_node = proj.sender.owner
+ receiver_node = proj.receiver.owner
+ proj_set.append(self.add_projection(proj,
+ sender = sender_node,
+ receiver = receiver_node,
+ allow_duplicates=False, feedback=feedback))
+ if default_proj_spec:
+ # If there IS a default Projection specification, remove from node_pairs
+ # only the entry for the sender-receiver pair, so that the sender is assigned
+ # a default Projection to all other receivers (to which a Projection is not
+ # explicitly specified) and the receiver is assigned a default Projection from
+ # all other senders (from which a Projection is not explicitly specified).
+ node_pairs = [pair for pair in node_pairs
+ if not all(node in pair for node in {sender_node, receiver_node})]
+ else:
+ # If there is NOT a default Projection specification, remove from node_pairs
+ # all other entries with either the same sender OR receiver, so that neither
+ # the sender nor receiver are assigned any other default Projections.
+ node_pairs = [pair for pair in node_pairs
+ if not any(node in pair for node in {sender_node, receiver_node})]
+
+ # FIX: 4/9/22 - SHOULD INCLUDE MECH SPEC (AND USE PRIMARY PORT) HERE:
+ elif isinstance(proj, Port):
+ # Implement default Projection (using matrix if specified) for all remaining specs
try:
+ # FIX: 4/9/22 - INCLUDE TEST FOR DEFERRED_INIT WITH ONLY RECEIVER SPECIFIED
if isinstance(proj, InputPort):
- proj = MappingProjection(sender=sender,
- receiver=proj)
+ for sender in senders:
+ proj_set.append(self.add_projection(
+ projection=MappingProjection(sender=sender, receiver=proj),
+ allow_duplicates=False, feedback=feedback))
+ # FIX: 4/9/22 - INCLUDE TEST FOR DEFERRED_INIT WITH ONLY SENDER SPECIFIED
elif isinstance(proj, OutputPort):
- proj = MappingProjection(sender=proj,
- receiver=receiver)
+ for receiver in receivers:
+ proj_set.append(self.add_projection(
+ projection=MappingProjection(sender=proj, receiver=receiver),
+ allow_duplicates=False, feedback=feedback))
+ # Remove from node_pairs all pairs involving the owner of the Port
+ # (since all Projections to or from it have been implemented)
+ node_pairs = [pair for pair in node_pairs if (proj.owner not in pair)]
except (InputPortError, ProjectionError) as error:
raise ProjectionError(str(error.error_value))
except (InputPortError, ProjectionError, MappingError) as error:
- raise CompositionError(f"Bad Projection specification in {pathway_arg_str} ({proj}): "
- f"{str(error.error_value)}")
-
+ handle_misc_errors(proj, error)
except DuplicateProjectionError:
- # FIX: 7/22/19 ADD WARNING HERE??
- # FIX: 7/22/19 MAKE THIS A METHOD ON Projection??
- duplicate = [p for p in receiver.afferents if p in sender.efferents]
- assert len(duplicate)==1, \
- f"PROGRAM ERROR: Could not identify duplicate on DuplicateProjectionError " \
- f"for {Projection.__name__} between {sender.name} and {receiver.name} " \
- f"in call to {repr('add_linear_processing_pathway')} for {self.name}."
- duplicate = duplicate[0]
- warning_msg = f"Projection specified between {sender.name} and {receiver.name} " \
- f"in {pathway_arg_str} is a duplicate of one"
- # IMPLEMENTATION NOTE: Version that allows different Projections between same
- # sender and receiver in different Compositions
- # if duplicate in self.projections:
- # warnings.warn(f"{warning_msg} already in the Composition ({duplicate.name}) "
- # f"and so will be ignored.")
- # proj=duplicate
- # else:
- # if self.prefs.verbosePref:
- # warnings.warn(f" that already exists between those nodes ({duplicate.name}). The "
- # f"new one will be used; delete it if you want to use the existing one")
- # Version that forbids *any* duplicate Projections between same sender and receiver
- warnings.warn(f"{warning_msg} that already exists between those nodes ({duplicate.name}) "
- f"and so will be ignored.")
- proj=duplicate
-
- proj = self.add_projection(projection=proj,
- sender=sender,
- receiver=receiver,
- feedback=feedback,
- allow_duplicates=False)
- if proj:
- proj_set.append(proj)
- else:
- raise CompositionError(f"A Projection specified in {pathway_arg_str} "
- f"is not between two Nodes: {pathway[c]}")
+ handle_duplicates(sender, receiver)
+
+ # FIX: 4/9/22 - REPLACE BELOW WITH CALL TO _assign_default_proj_spec(sender, receiver)
+ # If a default Projection is specified and any sender-receiver pairs remain, assign default
+ if default_proj_spec and node_pairs:
+ for sender, receiver in node_pairs:
+ try:
+ p = self.add_projection(projection=deepcopy(default_proj_spec),
+ sender=sender,
+ receiver=receiver,
+ allow_duplicates=False,
+ feedback=feedback)
+ proj_set.append(p)
+ except (InputPortError, ProjectionError, MappingError) as error:
+ handle_misc_errors(proj, error)
+ except DuplicateProjectionError:
+ handle_duplicates(sender, receiver)
+
+ # If there is a single Projection, extract it from list and append as Projection
+ # IMPLEMENTATION NOTE:
+ # this is to support calls to add_learing_processing_pathway by add_learning_<> methods
+ # that do not yet support a list or set of Projection specifications
if len(proj_set) == 1:
projections.append(proj_set[0])
else:
projections.append(proj_set)
+ # BAD PATHWAY ENTRY: contains neither Node nor Projection specification(s)
else:
- raise CompositionError(f"An entry in {pathway_arg_str} is not a Node (Mechanism or Composition) "
- f"or a Projection: {repr(pathway[c])}.")
+ assert False, f"PROGRAM ERROR : An entry in {pathway_arg_str} is not a Node (Mechanism " \
+ f"or Composition) or a Projection nor a set of either: {repr(pathway[c])}."
# Finally, clean up any tuple specs
- for i, n in enumerate(nodes):
- if isinstance(n, tuple):
- nodes[i] = nodes[i][0]
- # interleave nodes and projections
- explicit_pathway = [nodes[0]]
+ for i, n_e in enumerate(node_entries):
+ for n in convert_to_list(n_e):
+ if isinstance(n, tuple):
+ nodes[i] = nodes[i][0]
+ # interleave (sets of) Nodes and (sets or lists of) Projections
+ explicit_pathway = [node_entries[0]]
for i in range(len(projections)):
explicit_pathway.append(projections[i])
- explicit_pathway.append(nodes[i + 1])
+ explicit_pathway.append(node_entries[i + 1])
# If pathway is an existing one, return that
existing_pathway = next((p for p in self.pathways if explicit_pathway==p.pathway), None)
@@ -6698,7 +7035,8 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
pass
else:
# Otherwise, something has gone wrong
- assert False, f"PROGRAM ERROR: Bad pathway specification for {self.name} in {pathway_arg_str}: {pathway}."
+ assert False, \
+ f"PROGRAM ERROR: Bad pathway specification for {self.name} in {pathway_arg_str}: {pathway}."
pathway = Pathway(pathway=explicit_pathway,
composition=self,
@@ -6710,150 +7048,6 @@ def add_linear_processing_pathway(self, pathway, name:str=None, context=None, *a
return pathway
- @handle_external_context()
- def add_pathways(self, pathways, context=None):
- """Add pathways to the Composition.
-
- Arguments
- ---------
-
- pathways : Pathway or list[Pathway]
- specifies one or more `Pathways <Pathway>` to add to the Composition (see `Pathway_Specification`).
-
- Returns
- -------
-
- list[Pathway] :
- List of `Pathways <Pathway>` added to the Composition.
-
- """
-
- # Possible specifications for **pathways** arg:
- # 1 Single node: NODE
- # Single pathway spec (list, tuple or dict):
- # 2 single list: PWAY = [NODE] or [NODE...] in which *all* are NODES with optional intercolated Projections
- # 3 single tuple: (PWAY, LearningFunction) = (NODE, LearningFunction) or
- # ([NODE...], LearningFunction)
- # 4 single dict: {NAME: PWAY} = {NAME: NODE} or
- # {NAME: [NODE...]} or
- # {NAME: ([NODE...], LearningFunction)}
- # Multiple pathway specs (outer list):
- # 5 list with list: [PWAY] = [NODE, [NODE]] or [[NODE...]...]
- # 6 list with tuple: [(PWAY, LearningFunction)...] = [(NODE..., LearningFunction)...] or
- # [([NODE...], LearningFunction)...]
- # 7 list with dict: [{NAME: PWAY}...] = [{NAME: NODE...}...] or
- # [{NAME: [NODE...]}...] or
- # [{NAME: (NODE, LearningFunction)}...] or
- # [{NAME: ([NODE...], LearningFunction)}...]
-
- from psyneulink.core.compositions.pathway import Pathway, _is_node_spec, _is_pathway_entry_spec
-
- if context.source == ContextFlags.COMMAND_LINE:
- pathways_arg_str = f"'pathways' arg for the add_pathways method of {self.name}"
- elif context.source == ContextFlags.CONSTRUCTOR:
- pathways_arg_str = f"'pathways' arg of the constructor for {self.name}"
- else:
- assert False, f"PROGRAM ERROR: unrecognized context pass to add_pathways of {self.name}."
- context.string = pathways_arg_str
-
- if not pathways:
- return
-
- # Possibilities 1, 3 or 4 (single NODE, tuple or dict specified, so convert to list
- elif _is_node_spec(pathways) or isinstance(pathways, (tuple, dict, Pathway)):
- pathways = convert_to_list(pathways)
-
- # Possibility 2 (list is a single pathway spec):
- if (isinstance(pathways, list)
- and _is_node_spec(pathways[0]) and all(_is_pathway_entry_spec(p, ANY) for p in pathways)):
- # Place in outter list (to conform to processing of multiple pathways below)
- pathways = [pathways]
- # If pathways is not now a list it must be illegitimate
- if not isinstance(pathways, list):
- raise CompositionError(f"The {pathways_arg_str} must be a "
- f"Node, list, tuple, dict or Pathway object: {pathways}.")
-
- # pathways should now be a list in which each entry should be *some* form of pathway specification
- # (including original spec as possibilities 5, 6, or 7)
-
- added_pathways = []
-
- def identify_pway_type_and_parse_tuple_prn(pway, tuple_or_dict_str):
- """
- Determine whether pway is PROCESSING_PATHWAY or LEARNING_PATHWAY and, if it is the latter,
- parse tuple into pathway specification and LearningFunction.
- Return pathway type, pathway, and learning_function or None
- """
- learning_function = None
-
- if isinstance(pway, Pathway):
- pway = pway.pathway
-
- if (_is_node_spec(pway) or isinstance(pway, list) or
- # Forgive use of tuple to specify a pathway, and treat as if it was a list spec
- (isinstance(pway, tuple) and all(_is_pathway_entry_spec(n, ANY) for n in pathway))):
- pway_type = PROCESSING_PATHWAY
- return pway_type, pway, None
- elif isinstance(pway, tuple):
- pway_type = LEARNING_PATHWAY
- if len(pway)!=2:
- raise CompositionError(f"A tuple specified in the {pathways_arg_str}"
- f" has more than two items: {pway}")
- pway, learning_function = pway
- if not (_is_node_spec(pway) or isinstance(pway, (list, Pathway))):
- raise CompositionError(f"The 1st item in {tuple_or_dict_str} specified in the "
- f" {pathways_arg_str} must be a node or a list: {pway}")
- if not (isinstance(learning_function, type) and issubclass(learning_function, LearningFunction)):
- raise CompositionError(f"The 2nd item in {tuple_or_dict_str} specified in the "
- f"{pathways_arg_str} must be a LearningFunction: {learning_function}")
- return pway_type, pway, learning_function
- else:
- assert False, f"PROGRAM ERROR: arg to identify_pway_type_and_parse_tuple_prn in {self.name}" \
- f"is not a Node, list or tuple: {pway}"
-
- # Validate items in pathways list and add to Composition using relevant add_linear_XXX method.
- for pathway in pathways:
- pway_name = None
- if isinstance(pathway, Pathway):
- pway_name = pathway.name
- pathway = pathway.pathway
- if _is_node_spec(pathway) or isinstance(pathway, (list, tuple)):
- pway_type, pway, pway_learning_fct = identify_pway_type_and_parse_tuple_prn(pathway, f"a tuple")
- elif isinstance(pathway, dict):
- if len(pathway)!=1:
- raise CompositionError(f"A dict specified in the {pathways_arg_str} "
- f"contains more than one entry: {pathway}.")
- pway_name, pway = list(pathway.items())[0]
- if not isinstance(pway_name, str):
- raise CompositionError(f"The key in a dict specified in the {pathways_arg_str} must be a str "
- f"(to be used as its name): {pway_name}.")
- if _is_node_spec(pway) or isinstance(pway, (list, tuple, Pathway)):
- pway_type, pway, pway_learning_fct = identify_pway_type_and_parse_tuple_prn(pway,
- f"the value of a dict")
- else:
- raise CompositionError(f"The value in a dict specified in the {pathways_arg_str} must be "
- f"a pathway specification (Node, list or tuple): {pway}.")
- else:
- raise CompositionError(f"Every item in the {pathways_arg_str} must be "
- f"a Node, list, tuple or dict: {repr(pathway)} is not.")
-
- context.source = ContextFlags.METHOD
- if pway_type == PROCESSING_PATHWAY:
- new_pathway = self.add_linear_processing_pathway(pathway=pway,
- name=pway_name,
- context=context)
- elif pway_type == LEARNING_PATHWAY:
- new_pathway = self.add_linear_learning_pathway(pathway=pway,
- learning_function=pway_learning_fct,
- name=pway_name,
- context=context)
- else:
- assert False, f"PROGRAM ERROR: failure to determine pathway_type in add_pathways for {self.name}."
-
- added_pathways.append(new_pathway)
-
- return added_pathways
-
# endregion PROCESSING PATHWAYS
# region ------------------------------------ LEARNING -------------------------------------------------------------
@@ -7446,7 +7640,7 @@ def _create_backpropagation_learning_pathway(self,
if path_length >= 3:
# get the "terminal_sequence" --
# the last 2 nodes in the back prop pathway and the projection between them
- # these components are are processed separately because
+ # these components are processed separately because
# they inform the construction of the Target and Comparator mechs
terminal_sequence = processing_pathway[path_length - 3: path_length]
else:
diff --git a/psyneulink/core/compositions/compositionfunctionapproximator.py b/psyneulink/core/compositions/compositionfunctionapproximator.py
index 0623bb72ddb..1b657ae102a 100644
--- a/psyneulink/core/compositions/compositionfunctionapproximator.py
+++ b/psyneulink/core/compositions/compositionfunctionapproximator.py
@@ -59,6 +59,8 @@
__all__ = ['CompositionFunctionApproximator']
+from psyneulink.core.globals.parameters import check_user_specified
+
class CompositionFunctionApproximatorError(Exception):
def __init__(self, error_value):
@@ -105,6 +107,7 @@ class CompositionFunctionApproximator(Composition):
componentCategory = COMPOSITION_FUNCTION_APPROXIMATOR
+ @check_user_specified
def __init__(self, name=None, **param_defaults):
# self.function = function
super().__init__(name=name, **param_defaults)
diff --git a/psyneulink/core/compositions/parameterestimationcomposition.py b/psyneulink/core/compositions/parameterestimationcomposition.py
index 0ab934d0fc2..3162eae360a 100644
--- a/psyneulink/core/compositions/parameterestimationcomposition.py
+++ b/psyneulink/core/compositions/parameterestimationcomposition.py
@@ -150,7 +150,7 @@
from psyneulink.core.compositions.composition import Composition
from psyneulink.core.globals.context import Context, ContextFlags, handle_external_context
from psyneulink.core.globals.keywords import BEFORE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
__all__ = ['ParameterEstimationComposition']
@@ -431,6 +431,7 @@ class Parameters(Composition.Parameters):
setter=_same_seed_for_all_parameter_combinations_setter)
@handle_external_context()
+ @check_user_specified
def __init__(self,
parameters, # OCM control_signals
outcome_variables, # OCM monitor_for_control
diff --git a/psyneulink/core/compositions/pathway.py b/psyneulink/core/compositions/pathway.py
index 951385a36bc..da18203bc84 100644
--- a/psyneulink/core/compositions/pathway.py
+++ b/psyneulink/core/compositions/pathway.py
@@ -26,6 +26,9 @@
- `Pathway_Assignment_to_Composition`
- `Pathway_Name`
- `Pathway_Specification`
+ - `Pathway_Specification_Formats`
+ - `Pathway_Specification_Projections`
+ - `Pathway_Specification_Multiple`
- `Composition_Add_Nested`
* `Pathway_Structure`
* `Pathway_Execution`
@@ -37,9 +40,9 @@
--------
A Pathway is a sequence of `Nodes <Composition_Nodes>` and `Projections <Projection>`. Generally, Pathways are assigned
-to `Compositions <Composition>`, but a Pathway object can be created on its and used as a template for specifying a
-Pathway for a Composition, as described below. See `Pathways <Composition_Pathways>` for additional information about
-Pathways in Compositions.
+to a `Compositions`, but a Pathway object can also be created on its and used as a template for specifying a Pathway for
+a Composition, as described below (see `Pathways <Composition_Pathways>` for additional information about Pathways in
+Compositions).
.. _Pathway_Creation:
@@ -54,7 +57,7 @@
*Pathway as a Template*
~~~~~~~~~~~~~~~~~~~~~~~
-A Pathway created on its own, using its constructor, is a **template**, that can be used to `specifiy a Pathway
+A Pathway created on its own, using its constructor, is a **template**, that can be used to `specify a Pathway
<Pathway_Specification>` for one or more Compositions, as described `below <Pathway_Assignment_to_Composition>`;
however, it cannot be executed on its own. When a Pathway object is used to assign a Pathway to a Composition,
its `pathway <Pathway.pathway>` attribute, and its `name <Pathway.name>` if that is not otherwise specified (see
@@ -82,7 +85,7 @@
If the **name** argument of the Pathway's constructor is used to assign it a name, this is used as the name of the
Pathway created when it is assigned to a Composition in its constructor, or using its `add_pathways
<Composition.add_pathways>` method. This is also the case if one of the Composition's other `Pathway addition methods
-<Compositiion_Pathway_Addition_Methods>` is used, as long as the **name** argument of those methods is not specified.
+<Composition_Pathway_Addition_Methods>` is used, as long as the **name** argument of those methods is not specified.
However, if the **name** argument is specified in those methods, or `Pathway specification dictionary
<Pathway_Specification_Dictionary>` is used to specify the Pathway's name, that takes precedence over, and replaces
one specified in the Pathway `template's <Pathway_Template>` `name <Pathway.name>` attribute.
@@ -93,27 +96,149 @@
*Pathway Specification*
~~~~~~~~~~~~~~~~~~~~~~~
-The following formats can be used to specify a Pathway in the **pathway** argument of the constructor for the
-Pathway, the **pathways** argument of a the constructor for a `Composition`, or the corresponding argument
+Pathway are specified as a list, each element of which is either a `Node <Composition_Nodes>` or set of Nodes,
+possibly intercolated with specifications of `Projections <Projection>` between them. `Nodes <Composition_Nodes>`
+can be either a `Mechanism`, a `Composition`, or a tuple (Mechanism or Composition, `NodeRoles <NodeRole>`) that can
+be used to assign `required_roles` to the Nodes in the Composition (see `Composition_Nodes` for additional details).
+The Node(s) specified in each entry of the list project to the Node(s) specified in the next entry.
+
+ .. _Pathway_Projection_List_Note:
+
+ .. note::
+ Only a *set* can be used to specify multiple Nodes for a given entry in a Pathway; a *list* can *not* be used
+ for this purpose, as a list containing Nodes is always interpreted as a Pathway. If a list *is* included in a
+ Pathway specification, then it and all other entries are considered as separate, parallel Pathways (see
+ example *vii* in the `figure <Pathway_Figure>` below).
+
+.. _Pathway_Specification_Projections:
+
+*Pathway Projection Specifications*
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Where no Projections are specified between entries in the list, default Projections (using a `FULL_CONNECTIVITY_MATRIX`;
+see `MappingProjection_Matrix_Specification`) are created from each Node in the first entry, as the sender(s),
+to each Node in the second, as receiver(s) (described further `below <Pathway_Projections>`). Projections between
+Nodes in the two entries can also be specified explicitly, by intercolating a Projection or set of Projections between
+the two entries in the list. If the sender and receiver are both a single Mechanism, then a single `MappingProjection`
+can be `specified<MappingProjection_Creation>` between them. The same applies if the sender is a `Composition` with
+a single `OUTPUT <NodeRole.OUTPUT>` Node and/or the receiver is a `Composition` with a single `INPUT <NodeRole.INPUT>`
+Node. If either is a set of Nodes, or is a `nested Composition <Composition_Nested>` with more than one `INPUT
+<NodeRole.INPUT>` or `OUTPUT <NodeRole.OUTPUT>` Node, respectively, then a collection of Projections can be specified
+between any or all pairs of the Nodes in the set(s) and/or nested Composition(s), using either a set or list of
+Projections (order of specification does not matter whether a set or a list is used). The collection can contain
+`MappingProjections <MappingProjection>` between a specific pairs of Nodes and/or a single default specification
+(either a `matrix <MappingProjection.matrix>` specification or a MappingProjection without any `sender
+<MappingProjection.sender>` or `receiver <MappingProjection.receiver>` specified).
+
+ .. _Pathway_Projection_Matrix_Note:
+
+ .. note::
+ If a collection of Projection specifications includes a default matrix specification, then a list must be used
+ to specify the collection and *not* a set (since a matrix is unhashable and thus cannot be included in a set).
+
+If a default Projection specification is included in the set, it is used to implement a Projection between any pair
+of Nodes for which no MappingProjection is otherwise specified, whether within the collection or on its own; if no
+Projections are specified for any individual pairs, a default Projection is created for every pairing of senders and
+receivers. If a collection contains Projections for one or more pairs of Nodes, but does not include a default
+projection specification, then no Projection is created between any of the other pairings.
+
+If a pair of entries in a pathway has multiple sender and/or receiver Nodes specified (either in a set and/or belonging
+to `nested Composition <Composition_Nested>`, and either no Projection(s) or only a default Projection is intercollated
+between them, then a default set of Projections is constructed (using the default Projection specification, if provided)
+between each pair of sender and receiver Nodes in the set(s) or nested Composition(s), as follows:
+
+.. _Pathway_Projections:
+
+* *One to one* - if both the sender and receiver entries are Mechanisms, or if either is a Composition and the
+ sender has a single `OUTPUT <NodeRole.OUTPUT>` Node and the receiver has a single `INPUT <NodeRole.INPUT>`
+ Node, then a default `MappingProjection` is created from the `primary OutputPort <OutputPort_Primary>` of the
+ sender (or of its sole `OUTPUT <NodeRole.OUTPUT>` Node, if the sender is a Composition) to the `primary InputPort
+ <InputPort_Primary>` of the receiver (or of its sole of `INPUT <NodeRole.INPUT>` Node, if the receiver is
+ a Composition), and the Projection specification is intercolated between the two entries in the `Pathway`.
+
+* *One to many* - if the sender is either a Mechanism or a Composition with a single `OUTPUT <NodeRole.OUTPUT>` Node,
+ but the receiver is either a Composition with more than one `INPUT <NodeRole.INPUT>` Node or a set of Nodes, then
+ a `MappingProjection` is created from the `primary OutputPort <OutputPort_Primary>` of the sender Mechanism (or of
+ its sole `OUTPUT <NodeRole.OUTPUT>` Node if the sender is a Composition) to the `primary InputPort
+ <InputPort_Primary>` of each `INPUT <NodeRole.OUTPUT>` Node of the receiver Composition and/or Mechanism in the
+ receiver set, and a set containing the Projections is intercolated between the two entries in the `Pathway`.
+
+* *Many to one* - if the sender is a Composition with more than one `OUTPUT <NodeRole.OUTPUT>` Node or a set of
+ Nodes, and the receiver is either a Mechanism or a Composition with a single `INPUT <NodeRole.INPUT>` `OUTPUT
+ <NodeRole.OUTPUT>` Node in the Composition or Mechanism in the set of sender(s), to the `primary InputPort
+ <InputPort_Primary>` of the receiver Mechanism (or of its sole `INPUT <NodeRole.INPUT>` Node if the receiver is
+ a Composition), and a set containing the Projections is intercolated between the two entries in the `Pathway`.
+
+* *Many to many* - if both the sender and receiver entries contain multiple Nodes (i.e., are sets, and/or the
+ the sender is a Composition that has more than one `INPUT <NodeRole.INPUT>` Node and/or the receiver has more
+ than one `OUTPUT <NodeRole.OutPUT>` Node), then a Projection is constructed for every pairing of Nodes in the
+ sender and receiver entries, using the `primary OutputPort <OutputPort_Primary>` of each sender Node and the
+ `primary InputPort <InputPort_Primary>` of each receiver node.
+
+|
+
+ .. _Pathway_Figure:
+
+ .. figure:: _static/Pathways_fig.svg
+ :scale: 50%
+
+ **Examples of Pathway specifications** (including in the **pathways** argument of a `Composition`. *i)* Set
+ of `Nodes <Composition_Nodes>`: each is treated as a `SINGLETON <NodeRole.SINGLETON>` within a single Pathway.
+ *ii)* List of Nodes: forms a sequential Pathway. *iii)* Single Node followed by a set: one to many mapping.
+ *iv)* Set followed by a single Node: many to one mapping. *v)* Set followed by a set: many to many mapping.
+ *vi)* Set followed by a list: because there is a list in the specification (``[C,D]``) all other entries are
+ also treated as parallel Pathways (see `note <Pathway_Projection_List_Note>` above), so ``A`` and ``B`` in the
+ set are `SINGLETON <NodeRole.SINGLETON>`\\s. *vii)* Set of Projections intercolated between two sets of Nodes:
+ since the set of Projections does not include any involving ``B`` or ``E`` nor a default Projection specification,
+ they are treated as `SINGLETON <NodeRole.SINGLETON>`\\s (compare with *x*). *viii)* Set followed by a Node and
+ then a set: many to one to many mapping. *ix)* Node followed by one that is a `nested Composition
+ <Composition_Nested>` then another Node: one to many to one mapping. *x)* Set followed by a list of Projections
+ then another set: since the list of Projections contains a default Projection specification (``matrix``)
+ Projections are created between all pairings of nodes in the sets that precede and follow the list (compare with
+ *vii*); note that the Projections must be specified in a list because the matrix is a list (or array), which
+ cannot be included in a set (see `note <Pathway_Projection_Matrix_Note>` above).
+
+ .. technical_note::
+ The full code for the examples above can be found in `test_pathways_examples`,
+ although some have been graphically rearranged for illustrative purposes.
+
+
+
+.. _Pathway_Specification_Formats:
+
+*Pathway Specification Formats*
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The following formats can be used to specify a Pathway in the **pathway** argument of the constructor for
+the Pathway, the **pathways** argument of the constructor for a `Composition`, or the corresponding argument
of any of a Composition's `Pathway addition methods <Composition_Pathway_Addition_Methods>`:
- * `Node <Composition_Nodes>`: -- assigns the Node to a `SINGLETON` Pathway.
+ * `Node <Composition_Nodes>`: -- assigns the Node as `SINGLETON <NodeRole.SINGLETON>` in a Pathway.
..
.. _Pathway_Specification_List:
* **list**: [`Node <Composition_Nodes>`, <`Projection(s) <Projection>`,> `Node <Composition_Nodes>`...] --
- each item of the list must be a `Node <Composition_Nodes>` -- i.e., Mechanism or Composition, or a
- (`Mechanism <Mechanism>`, `NodeRoles <NodeRole>`) tuple -- or, optionally, a `Projection specification
- <Projection_Specification>`, a (`Projection specification <Projection_Specification>`, `feedback specification
- <Composition_Feedback_Designation>`) tuple, or a set of either interposed between a pair of nodes (see
+ each item of the list must be a `Node <Composition_Nodes>` (i.e., Mechanism or Composition, or a
+ (`Mechanism <Mechanism>`, `NodeRoles <NodeRole>`) tuple) or set of Nodes, optionally with a `Projection
+ specification <Projection_Specification>`, a (`Projection specification <Projection_Specification>`,
+ `feedback specification <Composition_Feedback_Designation>`) tuple, or a set of either interposed between
+ a pair of (sets of) Nodes (see `add_linear_processing_pathway <Composition.add_linear_processing_pathway>`
+ for additional details). The list must begin and end with a (set of) Node(s).
+ ..
+ * **set**: {`Node <Composition_Nodes>`, `Node <Composition_Nodes>`...} --
+ each item of the set must be a `Node <Composition_Nodes>` (i.e., Mechanism or Composition, or a
+ (`Mechanism <Mechanism>`, `NodeRoles <NodeRole>`) tuple); each Node is treated as a `SINGLETON
+ <NodeRole.SINGLETON>`. Sets can also be used in a list specification (see above; and see
`add_linear_processing_pathway <Composition.add_linear_processing_pathway>` for additional details).
- The list must begin and end with a node.
..
* **2-item tuple**: (Pathway, `LearningFunction`) -- used to specify a `learning Pathway
- <Composition_Learning_Pathway>`; the 1st item must be a `Node <Composition_Nodes>` or list, as
- described above, and the 2nd item be a subclass of `LearningFunction`.
+ <Composition_Learning_Pathway>`; the 1st item must be one of the forms of Pathway specification
+ described above, and the 2nd item must be a subclass of `LearningFunction`.
-.. _Multiple_Pathway_Specification:
+.. _Pathway_Specification_Multiple:
+
+*Multiple Pathway Specifications*
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition to the forms of single Pathway specification `above <Pathway_Specification>`, where multiple Pathways
can be specified (e.g., the **pathways** argument of the constructor for a `Composition` or its `add_pathways
@@ -130,15 +255,15 @@
If any of the following is used to specify the **pathways** argument:
* a **standalone** `Node <Composition_Nodes>` (i.e., not in a list), \n
* a **single Node** alone in a list, \n
+ * a **set** of Nodes, \n
* one or more Nodes with any other form of `Pathway specification <Pathway_Specification>` in the list \n
- then each such Node in the list is treated as its own `SINGLETON` pathway (i.e., one containing a single
- Node that is both the `ORIGIN` and the`TERMINAL` of the Pathway). However, if the list contains only
- Nodes, then it is treated as a single Pathway (i.e., the list form of `Pathway specification
- <Pathway_Specification>`. Thus:
+ then each such Node in the list is assigned as a `SINGLETON <NodeRole.SINGLETON>` Node in its own Pathway.
+ However, if the list contains only Nodes, then it is treated as a single Pathway (i.e., the list form of
+ `Pathway specification <Pathway_Specification>` described above. Thus:
**pathway**: NODE -> single pathway \n
**pathway**: [NODE] -> single pathway \n
**pathway**: [NODE, NODE...] -> single pathway \n
- **pathway**: [NODE, NODE, () or {} or `Pathway`...] -> three or more pathways
+ **pathway**: [NODE, () or {} or `Pathway`...] -> individual Pathways for each specification.
.. _Pathway_Structure:
@@ -210,13 +335,13 @@
def _is_pathway_entry_spec(entry, desired_type:tc.enum(NODE, PROJECTION, ANY)):
"""Test whether pathway entry is specified type (NODE or PROJECTION)"""
from psyneulink.core.components.projections.projection import _is_projection_spec
- node_specs = (Mechanism, Composition)
- is_node = is_proj = False
+ node_types = (Mechanism, Composition)
+ is_node = is_proj = is_set = False
if desired_type in {NODE, ANY}:
- is_node = (isinstance(entry, node_specs)
+ is_node = (isinstance(entry, node_types)
or (isinstance(entry, tuple)
- and isinstance(entry[0], node_specs)
+ and isinstance(entry[0], node_types)
and (isinstance(entry[1], NodeRole) or
(isinstance(entry[1], list) and all(isinstance(nr, NodeRole) for nr in entry[1])))))
@@ -226,9 +351,13 @@ def _is_pathway_entry_spec(entry, desired_type:tc.enum(NODE, PROJECTION, ANY)):
and _is_projection_spec(entry[0])
and entry[1] in {True, FEEDBACK, False, MAYBE})
or (isinstance(entry, (set,list))
+ # or (isinstance(entry, set)
and all(_is_projection_spec(item) for item in entry)))
- if is_node or is_proj:
+ if desired_type in {ANY}:
+ is_set = (isinstance(entry, set) and all(_is_node_spec(item) for item in entry))
+
+ if is_node or is_proj or is_set:
return True
else:
return False
diff --git a/psyneulink/core/globals/__init__.py b/psyneulink/core/globals/__init__.py
index 2119b48aeb9..45cdf94c8fa 100644
--- a/psyneulink/core/globals/__init__.py
+++ b/psyneulink/core/globals/__init__.py
@@ -1,6 +1,6 @@
from . import context
from . import defaults
-from . import json
+from . import mdf
from . import keywords
from . import kvo
from . import log
@@ -12,10 +12,10 @@
from .context import *
from .defaults import *
-from .json import *
from .keywords import *
from .kvo import *
from .log import *
+from .mdf import *
from .parameters import *
from .preferences import *
from .registry import *
@@ -24,10 +24,10 @@
__all__ = list(context.__all__)
__all__.extend(defaults.__all__)
-__all__.extend(json.__all__)
__all__.extend(keywords.__all__)
__all__.extend(kvo.__all__)
__all__.extend(log.__all__)
+__all__.extend(mdf.__all__)
__all__.extend(parameters.__all__)
__all__.extend(preferences.__all__)
__all__.extend(registry.__all__)
diff --git a/psyneulink/core/globals/keywords.py b/psyneulink/core/globals/keywords.py
index c65e6a5c965..713e9e16dfb 100644
--- a/psyneulink/core/globals/keywords.py
+++ b/psyneulink/core/globals/keywords.py
@@ -997,10 +997,6 @@ def _is_metric(metric):
MODEL_SPEC_ID_PARAMETER_VALUE = 'value'
MODEL_SPEC_ID_PARAMETER_INITIAL_VALUE = 'default_initial_value'
-MODEL_SPEC_ID_NODES = 'nodes'
-MODEL_SPEC_ID_PROJECTIONS = 'edges'
-MODEL_SPEC_ID_COMPOSITION = 'graphs'
-
MODEL_SPEC_ID_MDF_VARIABLE = 'variable0'
MODEL_SPEC_ID_SHAPE = 'shape'
diff --git a/psyneulink/core/globals/json.py b/psyneulink/core/globals/mdf.py
similarity index 54%
rename from psyneulink/core/globals/json.py
rename to psyneulink/core/globals/mdf.py
index 4c598cc8c4d..d898bb8394a 100644
--- a/psyneulink/core/globals/json.py
+++ b/psyneulink/core/globals/mdf.py
@@ -3,36 +3,36 @@
Contents
--------
- * `JSON_Overview`
- * `JSON_Examples`
- * `JSON_Model_Specification`
+ * `MDF_Overview`
+ * `MDF_Examples`
+ * `MDF_Model_Specification`
-.. _JSON_Overview:
+.. _MDF_Overview:
Overview
--------
The developers of PsyNeuLink are collaborating with the scientific community, as part of the `OpenNeuro effort
-<https://openneuro.org>`_, to create a standard, JSON-based format for the description and exchange of computational
+<https://openneuro.org>`_, to create a standard, serialzied format for the description and exchange of computational
models of brain and psychological function across different simulation environments. As part of this effort,
PsyNeuLink supports the `ModECI Model Description Format <https://github.com/ModECI/MDF/includes>`_ (MDF) by
including the ability to produce an MDF-compatible model from a PsyNeuLink model and to construct valid Python
scripts that express a PsyNeuLink model from an MDF model.
Any PsyNeuLink `Composition` or `Component` can be exported to MDF format using its `as_mdf_model` method or
-to JSON format using its `json_summary` method. `json_summary` generates a string that, passed into the
-`generate_script_from_json` function, produces a valid Python script replicating the original PsyNeuLink model.
-`write_json_file` can be used to write the json_summary for one or more Compositions into a specified file (though
-see `note <JSON_Write_Multiple_Compositions_Note>`). `generate_script_from_json` can accept either the string returned
-by `generate_script_from_json` or the name of a file containing one.
-Calling ``exec(generate_script_from_json(<input>))`` will load into the current namespace all of the PsyNeuLink
+to serialized format using its `json_summary` or `yaml_summary` methods. These methods generate strings that, passed into the
+`generate_script_from_mdf` function, produce a valid Python script replicating the original PsyNeuLink model.
+`write_mdf_file` can be used to write the serialization for one or more Compositions into a specified file (though
+see `note <MDF_Write_Multiple_Compositions_Note>`). `generate_script_from_mdf` can accept either the string returned
+by `get_mdf_serialized` or the name of a file containing one.
+Calling ``exec(generate_script_from_mdf(<input>))`` will load into the current namespace all of the PsyNeuLink
objects specified in the ``input``; and `get_compositions` can be used to retrieve a list of all of the Compositions
-in that namespace, including any generated by execution of `generate_script_from_json`. `generate_script_from_mdf`
+in that namespace, including any generated by execution of `generate_script_from_mdf`. `generate_script_from_mdf`
may similarly be used to create a PsyNeuLink Python script from a ModECI MDF Model object, such as that created
by `as_mdf_model <Composition.as_mdf_model>`.
-.. _JSON_Security_Warning:
+.. _MDF_Security_Warning:
.. warning::
Use of `generate_script_from_json` or `generate_script_from_mdf` to generate a Python script from a file without taking proper precautions can
@@ -40,7 +40,7 @@
exec, which has the potential to execute non-PsyNeuLink-related code embedded in the file. Therefore,
`generate_script_from_json` or `generate_script_from_mdf` should be used to read only files of known and secure origin.
-.. _JSON_Examples:
+.. _MDF_Examples:
Model Examples
--------------
@@ -50,14 +50,14 @@
that will give the same results when run on the same input as the original.
:download:`Download stroop_conflict_monitoring.py
-<../../tests/json/stroop_conflict_monitoring.py>`
+<../../tests/mdf/stroop_conflict_monitoring.py>`
:download:`Download stroop_conflict_monitoring.json
<../../docs/source/_static/stroop_conflict_monitoring.json>`
-.. _JSON_Model_Specification:
+.. _MDF_Model_Specification:
-JSON/MDF Model Specification
+MDF Model Specification
------------------------
.. note::
@@ -66,12 +66,22 @@
See https://github.com/ModECI/MDF/blob/main/docs/README.md#model
+.. _MDF_Simple_Edge_Format:
+
+MDF Simple Edge Format
+----------------------
+
+Models may be output as they are in PsyNeuLink or in "simple edge"
+format. In simple edge format, PsyNeuLink Projections are written as a
+combination of two Edges and an intermediate Node, because the generic
+MDF execution engine does not support using Functions on Edges.
+PsyNeuLink is capable of re-importing models exported by PsyNeuLink in
+either form.
"""
import ast
import base64
import binascii
-import copy
import dill
import enum
import graph_scheduler
@@ -80,38 +90,53 @@
import math
import numbers
import numpy
+import os
import pickle
import pint
import psyneulink
import re
+import tempfile
import types
+import time
import warnings
from psyneulink.core.globals.keywords import \
- MODEL_SPEC_ID_COMPOSITION, MODEL_SPEC_ID_GENERIC, MODEL_SPEC_ID_NODES, MODEL_SPEC_ID_PARAMETER_SOURCE, \
- MODEL_SPEC_ID_PARAMETER_INITIAL_VALUE, MODEL_SPEC_ID_PARAMETER_VALUE, MODEL_SPEC_ID_PROJECTIONS, MODEL_SPEC_ID_PSYNEULINK, MODEL_SPEC_ID_RECEIVER_MECH, MODEL_SPEC_ID_RECEIVER_PORT, \
- MODEL_SPEC_ID_SENDER_MECH, MODEL_SPEC_ID_SENDER_PORT, MODEL_SPEC_ID_TYPE, MODEL_SPEC_ID_OUTPUT_PORTS, MODEL_SPEC_ID_MDF_VARIABLE, MODEL_SPEC_ID_INPUT_PORTS, MODEL_SPEC_ID_SHAPE, MODEL_SPEC_ID_METADATA, MODEL_SPEC_ID_INPUT_PORT_COMBINATION_FUNCTION
+ MODEL_SPEC_ID_GENERIC, MODEL_SPEC_ID_PARAMETER_SOURCE, \
+ MODEL_SPEC_ID_PARAMETER_INITIAL_VALUE, MODEL_SPEC_ID_PARAMETER_VALUE, MODEL_SPEC_ID_PSYNEULINK, \
+ MODEL_SPEC_ID_TYPE, MODEL_SPEC_ID_MDF_VARIABLE, MODEL_SPEC_ID_SHAPE, MODEL_SPEC_ID_METADATA, MODEL_SPEC_ID_INPUT_PORT_COMBINATION_FUNCTION
from psyneulink.core.globals.parameters import ParameterAlias
from psyneulink.core.globals.sampleiterator import SampleIterator
from psyneulink.core.globals.utilities import convert_to_list, gen_friendly_comma_str, get_all_explicit_arguments, \
parse_string_to_psyneulink_object_string, parse_valid_identifier, safe_equals, convert_to_np_array
__all__ = [
- 'PNLJSONError', 'JSONDumpable', 'PNLJSONEncoder',
+ 'MDFError', 'MDFSerializable', 'PNLJSONEncoder',
'generate_json', 'generate_script_from_json', 'generate_script_from_mdf',
- 'write_json_file'
+ 'write_json_file', 'get_mdf_model', 'get_mdf_serialized', 'write_mdf_file'
]
-class PNLJSONError(Exception):
+# file extension to mdf common name
+supported_formats = {
+ 'json': 'json',
+ 'yml': 'yaml',
+ 'yaml': 'yaml',
+}
+
+
+class MDFError(Exception):
pass
-class JSONDumpable:
+class MDFSerializable:
@property
def json_summary(self):
return self.as_mdf_model().to_json()
+ @property
+ def yaml_summary(self):
+ return self.as_mdf_model().to_yaml()
+
# leaving this due to instructions in test_documentation_models
# (useful for exporting Composition results to JSON)
@@ -173,14 +198,36 @@ def _get_variable_parameter_name(obj):
return MODEL_SPEC_ID_MDF_VARIABLE
-def _substitute_expression_args(model):
- # currently cannot use args with value expressions
- if model.value is not None:
- for arg, val in model.args.items():
- model.value = model.value.replace(arg, str(val))
+def _mdf_obj_from_dict(d):
+ import modeci_mdf.mdf as mdf
+
+ def _get_mdf_object(obj, cls_):
+ try:
+ model_id = obj['id']
+ except KeyError:
+ try:
+ model_id = obj['metadata']['name']
+ except KeyError:
+ model_id = f'{cls_.__name__}_{time.perf_counter_ns()}'
+
+ return cls_.from_dict({model_id: obj})
+
+ for cls_name in mdf.__all__:
+ cls_ = getattr(mdf, cls_name)
+ if all([attr.name in d or attr.name in {'id', 'parameters'} for attr in cls_.__attrs_attrs__]):
+ return _get_mdf_object(d, cls_)
+
+ if 'function' in d and 'args' in d:
+ return _get_mdf_object(d, mdf.Function)
+ # nothing else seems to fit, try Function (unreliable)
+ if 'value' in d:
+ return _get_mdf_object(d, mdf.Function)
-def _parse_component_type(component_dict):
+ return None
+
+
+def _parse_component_type(model_obj):
def get_pnl_component_type(s):
from psyneulink.core.components.component import ComponentsMeta
@@ -196,14 +243,15 @@ def get_pnl_component_type(s):
raise
type_str = None
- if MODEL_SPEC_ID_TYPE in component_dict:
- type_dict = component_dict[MODEL_SPEC_ID_TYPE]
- else:
+ try:
try:
- type_dict = component_dict[MODEL_SPEC_ID_METADATA][MODEL_SPEC_ID_TYPE]
- except KeyError:
- # specifically for functions the keyword is not 'type'
- type_str = component_dict['function']
+ type_dict = model_obj.metadata[MODEL_SPEC_ID_TYPE]
+ except AttributeError:
+ # could be a dict specification
+ type_str = model_obj[MODEL_SPEC_ID_METADATA][MODEL_SPEC_ID_TYPE]
+ except (KeyError, TypeError):
+ # specifically for functions the keyword is not 'type'
+ type_str = model_obj.function
if type_str is None:
try:
@@ -216,12 +264,12 @@ def get_pnl_component_type(s):
type_str = type_dict
elif isinstance(type_str, dict):
if len(type_str) != 1:
- raise PNLJSONError
+ raise MDFError
else:
elem = list(type_str.keys())[0]
# not a function_type: args dict
if MODEL_SPEC_ID_METADATA in type_str[elem]:
- raise PNLJSONError
+ raise MDFError
else:
type_str = elem
@@ -256,21 +304,19 @@ def get_pnl_component_type(s):
else:
return type_str
- raise PNLJSONError(
- 'Invalid type specified for JSON object: {0}'.format(
- component_dict
- )
- )
+ raise MDFError(f'Invalid type specified for MDF object: {model_obj}')
def _parse_parameter_value(value, component_identifiers=None, name=None, parent_parameters=None):
+ import modeci_mdf.mdf as mdf
+
if component_identifiers is None:
component_identifiers = {}
exec('import numpy')
try:
pnl_type = _parse_component_type(value)
- except (KeyError, TypeError, PNLJSONError):
+ except (AttributeError, TypeError, MDFError):
# ignore parameters that aren't components
pnl_type = None
@@ -296,8 +342,8 @@ def _parse_parameter_value(value, component_identifiers=None, name=None, parent_
try:
value_type = eval(value[MODEL_SPEC_ID_TYPE])
except Exception as e:
- raise PNLJSONError(
- 'Invalid python type specified in JSON object: {0}'.format(
+ raise MDFError(
+ 'Invalid python type specified in MDF object: {0}'.format(
value[MODEL_SPEC_ID_TYPE]
)
) from e
@@ -334,57 +380,51 @@ def _parse_parameter_value(value, component_identifiers=None, name=None, parent_
parent_parameters
)
else:
- # it is either a Component spec or just a plain dict
- try:
- # try handling as a Component spec
+ if len(value) == 1:
try:
- comp_name = value['name']
+ identifier = list(value.keys())[0]
except KeyError:
- comp_name = name
-
- if comp_name is not None:
- identifier = parse_valid_identifier(comp_name)
- if len(value) == 1:
- try:
- value = value[comp_name]
- except KeyError:
- pass
- else:
- if len(value) == 1:
- comp_name = list(value.keys())[0]
- identifier = parse_valid_identifier(comp_name)
- if isinstance(value[comp_name], dict):
- value = value[comp_name]
- else:
- raise PNLJSONError(
- f'Component without name could reference multiple objects: {value}',
- )
+ identifier = name
- if (
- identifier in component_identifiers
- and component_identifiers[identifier]
- ):
- # if this spec is already created as a node elsewhere,
- # then just use a reference
- value = identifier
- else:
+ mdf_object = value[identifier]
+ else:
+ try:
+ identifier = value['id']
+ except KeyError:
+ identifier = name
+
+ mdf_object = value
+
+ # it is either a Component spec or just a plain dict
+ if (
+ identifier in component_identifiers
+ and component_identifiers[identifier]
+ ):
+ # if this spec is already created as a node elsewhere,
+ # then just use a reference
+ value = identifier
+ else:
+ if not isinstance(mdf_object, mdf.Base):
+ mdf_object = _mdf_obj_from_dict(mdf_object)
+
+ try:
value = _generate_component_string(
- value,
+ mdf_object,
component_identifiers,
- component_name=comp_name,
+ component_name=identifier,
parent_parameters=parent_parameters
)
- except (PNLJSONError, KeyError, TypeError):
- # standard dict handling
- value = '{{{0}}}'.format(
- ', '.join([
- '{0}: {1}'.format(
- str(_parse_parameter_value(k, component_identifiers, name)),
- str(_parse_parameter_value(v, component_identifiers, name))
- )
- for k, v in value.items()
- ])
- )
+ except (AttributeError, MDFError, KeyError, TypeError):
+ # standard dict handling
+ value = '{{{0}}}'.format(
+ ', '.join([
+ '{0}: {1}'.format(
+ str(_parse_parameter_value(k, component_identifiers, name)),
+ str(_parse_parameter_value(v, component_identifiers, name))
+ )
+ for k, v in value.items()
+ ])
+ )
elif isinstance(value, str):
# handle pointer to parent's parameter value
@@ -458,11 +498,19 @@ def _parse_parameter_value(value, component_identifiers=None, name=None, parent_
):
value = f"'{value}'"
+ elif isinstance(value, mdf.Base):
+ value = _generate_component_string(
+ value,
+ component_identifiers,
+ component_name=value.id,
+ parent_parameters=parent_parameters
+ )
+
return value
def _generate_component_string(
- component_dict,
+ component_model,
component_identifiers,
component_name=None,
parent_parameters=None,
@@ -471,63 +519,63 @@ def _generate_component_string(
):
from psyneulink.core.components.functions.function import Function_Base
from psyneulink.core.components.functions.userdefinedfunction import UserDefinedFunction
- from psyneulink.core.components.projections.projection import Projection_Base
try:
- component_type = _parse_component_type(component_dict)
- except KeyError as e:
+ component_type = _parse_component_type(component_model)
+ except AttributeError as e:
# acceptable to exclude type currently
if default_type is not None:
component_type = default_type
else:
raise type(e)(
- f'{component_dict} has no PNL or generic type and no '
+ f'{component_model} has no PNL or generic type and no '
'default_type is specified'
) from e
if component_name is None:
- name = component_dict['name']
+ name = component_model.id
else:
name = component_name
try:
- assert component_name == component_dict['name']
+ assert component_name == component_model.id
except KeyError:
pass
is_user_defined_function = False
try:
- parameters = dict(component_dict[component_type._model_spec_id_parameters])
+ parameters = dict(getattr(component_model, component_type._model_spec_id_parameters))
except AttributeError:
is_user_defined_function = True
- except KeyError:
+ except TypeError:
parameters = {}
if is_user_defined_function or component_type is UserDefinedFunction:
custom_func = component_type
component_type = UserDefinedFunction
try:
- parameters = dict(component_dict[component_type._model_spec_id_parameters])
- except KeyError:
+ parameters = dict(getattr(component_model, component_type._model_spec_id_parameters))
+ except TypeError:
parameters = {}
parameters['custom_function'] = f'{custom_func}'
try:
- del component_dict[MODEL_SPEC_ID_METADATA]['custom_function']
+ del component_model.metadata['custom_function']
except KeyError:
pass
try:
- parameters.update(component_dict[component_type._model_spec_id_stateful_parameters])
- except KeyError:
+ parameters.update(getattr(component_model, component_type._model_spec_id_parameters))
+ except TypeError:
pass
try:
# args in function dict
- parameters.update(component_dict['function'][list(component_dict['function'].keys())[0]])
+ parameters.update(component_model.function[list(component_model.function.keys())[0]])
except (AttributeError, KeyError):
pass
parameter_names = {}
+ # TODO: remove this?
# If there is a parameter that is the psyneulink identifier string
# (as of this comment, 'pnl'), then expand these parameters as
# normal ones. We don't check and expand for other
@@ -540,20 +588,20 @@ def _generate_component_string(
pass
try:
- metadata = component_dict[MODEL_SPEC_ID_METADATA]
- except KeyError:
- metadata = {}
-
- if issubclass(component_type, Projection_Base):
+ functions = component_model.functions
+ except AttributeError:
try:
- component_dict['functions'] = metadata['functions']
+ functions = [_mdf_obj_from_dict(v) for k, v in component_model.metadata['functions'].items()]
except KeyError:
- pass
+ functions = None
+ except AttributeError:
+ functions = component_model.metadata['functions']
# pnl objects only have one function unless specified in another way
# than just "function"
- if 'functions' in component_dict:
- dup_function_names = set([name for name in component_dict['functions'] if name in component_identifiers])
+
+ if functions is not None:
+ dup_function_names = set([f.id for f in functions if f.id in component_identifiers])
if len(dup_function_names) > 0:
warnings.warn(
f'Functions ({gen_friendly_comma_str(dup_function_names)}) of'
@@ -564,8 +612,8 @@ def _generate_component_string(
function_determined_by_output_port = False
try:
- output_ports = component_dict[MODEL_SPEC_ID_OUTPUT_PORTS]
- except KeyError:
+ output_ports = component_model.output_ports
+ except AttributeError:
pass
else:
if len(output_ports) == 1 or isinstance(output_ports, list):
@@ -577,7 +625,7 @@ def _generate_component_string(
else:
try:
# 'out_port' appears to be the general primary output_port term
- # should ideally have a marker in json to define it as primary
+ # should ideally have a marker in mdf to define it as primary
primary_output_port = output_ports['out_port']
except KeyError:
pass
@@ -585,17 +633,15 @@ def _generate_component_string(
function_determined_by_output_port = True
# neuroml-style mdf has MODEL_SPEC_ID_PARAMETER_VALUE in output port definitions
- if function_determined_by_output_port and MODEL_SPEC_ID_PARAMETER_VALUE in primary_output_port:
- parameter_names['function'] = re.sub(r'(.*)\[\d+\]', '\\1', primary_output_port[MODEL_SPEC_ID_PARAMETER_VALUE])
+ if function_determined_by_output_port and hasattr(primary_output_port, MODEL_SPEC_ID_PARAMETER_VALUE):
+ parameter_names['function'] = re.sub(r'(.*)\[\d+\]', '\\1', getattr(primary_output_port, MODEL_SPEC_ID_PARAMETER_VALUE))
else:
parameter_names['function'] = [
- f for f in component_dict['functions']
- if not f.endswith(MODEL_SPEC_ID_INPUT_PORT_COMBINATION_FUNCTION)
+ f.id for f in functions
+ if not f.id.endswith(MODEL_SPEC_ID_INPUT_PORT_COMBINATION_FUNCTION)
][0]
- parameters['function'] = {
- parameter_names['function']: component_dict['functions'][parameter_names['function']]
- }
+ parameters['function'] = [f for f in functions if f.id == parameter_names['function']][0]
assignment_str = f'{parse_valid_identifier(name)} = ' if assignment else ''
@@ -617,7 +663,7 @@ def _generate_component_string(
parameters = {
**{k: v for k, v in parent_parameters.items() if isinstance(v, dict) and MODEL_SPEC_ID_PARAMETER_INITIAL_VALUE in v},
**parameters,
- **metadata
+ **(component_model.metadata if component_model.metadata is not None else {})
}
# MDF input ports do not have functions, so their shape is
@@ -626,13 +672,9 @@ def _generate_component_string(
# the input port shape if input_ports parameter is specified
if 'variable' not in parameters and 'input_ports' not in parameters:
try:
- ip = parameters['function'][Function_Base._model_spec_id_parameters][MODEL_SPEC_ID_MDF_VARIABLE]
+ ip = getattr(parameters['function'], Function_Base._model_spec_id_parameters)[MODEL_SPEC_ID_MDF_VARIABLE]
var = convert_to_np_array(
- numpy.zeros(
- ast.literal_eval(
- component_dict[MODEL_SPEC_ID_INPUT_PORTS][ip][MODEL_SPEC_ID_SHAPE]
- )
- ),
+ numpy.zeros(ast.literal_eval(component_model.input_ports[ip][MODEL_SPEC_ID_SHAPE])),
dimension=2
).tolist()
parameters['variable'] = var
@@ -766,57 +808,46 @@ def parameter_value_matches_default(component_type, param, value):
def _generate_scheduler_string(
scheduler_id,
- scheduler_dict,
+ scheduler_model,
component_identifiers,
blacklist=[]
):
output = []
- try:
- node_specific_conds = scheduler_dict['node_specific']
- except KeyError:
- pass
- else:
- for node, condition in node_specific_conds.items():
- if node not in blacklist:
- output.append(
- '{0}.add_condition({1}, {2})'.format(
- scheduler_id,
- parse_valid_identifier(node),
- _generate_condition_string(
- condition,
- component_identifiers
- )
+
+ for node, condition in scheduler_model.node_specific.items():
+ if node not in blacklist:
+ output.append(
+ '{0}.add_condition({1}, {2})'.format(
+ scheduler_id,
+ parse_valid_identifier(node),
+ _generate_condition_string(
+ condition,
+ component_identifiers
)
)
-
- output.append('')
+ )
termination_str = []
- try:
- termination_conds = scheduler_dict['termination']
- except KeyError:
- pass
- else:
- for scale, cond in termination_conds.items():
- termination_str.insert(
- 1,
- 'psyneulink.{0}: {1}'.format(
- f'TimeScale.{str.upper(scale)}',
- _generate_condition_string(cond, component_identifiers)
- )
+ for scale, cond in scheduler_model.termination.items():
+ termination_str.insert(
+ 1,
+ 'psyneulink.{0}: {1}'.format(
+ f'TimeScale.{str.upper(scale)}',
+ _generate_condition_string(cond, component_identifiers)
)
+ )
- output.append(
- '{0}.termination_conds = {{{1}}}'.format(
- scheduler_id,
- ', '.join(termination_str)
- )
+ output.append(
+ '{0}.termination_conds = {{{1}}}'.format(
+ scheduler_id,
+ ', '.join(termination_str)
)
+ )
return '\n'.join(output)
-def _generate_condition_string(condition_dict, component_identifiers):
+def _generate_condition_string(condition_model, component_identifiers):
def _parse_condition_arg_value(value):
try:
identifier = parse_valid_identifier(value)
@@ -827,7 +858,7 @@ def _parse_condition_arg_value(value):
return str(identifier)
try:
- getattr(psyneulink.core.scheduling.condition, value['type'])
+ getattr(psyneulink.core.scheduling.condition, value.type)
except (AttributeError, KeyError, TypeError):
pass
else:
@@ -853,7 +884,7 @@ def _parse_graph_scheduler_type(typ):
return typ
args_str = ''
- cond_type = _parse_graph_scheduler_type(condition_dict[MODEL_SPEC_ID_TYPE])
+ cond_type = _parse_graph_scheduler_type(condition_model.type)
sig = inspect.signature(getattr(psyneulink, cond_type).__init__)
var_positional_arg_name = None
@@ -863,7 +894,7 @@ def _parse_graph_scheduler_type(typ):
var_positional_arg_name = name
break
- args_dict = condition_dict['args']
+ args_dict = condition_model.kwargs
try:
pos_args = args_dict[var_positional_arg_name]
@@ -906,30 +937,8 @@ def _parse_graph_scheduler_type(typ):
return f'psyneulink.{cond_type}({arguments_str})'
-def _generate_composition_string(graphs_dict, component_identifiers):
- def _replace_function_node_with_mech_node(function_dict, name, typ=None):
- if typ is None:
- typ = _parse_component_type(function_dict)
- else:
- typ = typ.__name__
-
- mech_func_dict = {
- 'functions': {
- name: {
- MODEL_SPEC_ID_TYPE: {MODEL_SPEC_ID_PSYNEULINK: typ},
- psyneulink.Function_Base._model_spec_id_parameters: function_dict[psyneulink.Component._model_spec_id_parameters]
- },
- }
- }
-
- try:
- del function_dict[MODEL_SPEC_ID_TYPE]
- except KeyError:
- pass
-
- function_dict['name'] = f"{name}_wrapped_mech"
-
- return {**function_dict, **mech_func_dict}
+def _generate_composition_string(graph, component_identifiers):
+ import modeci_mdf.mdf as mdf
# used if no generic types are specified
default_composition_type = psyneulink.Composition
@@ -947,410 +956,321 @@ def _replace_function_node_with_mech_node(function_dict, name, typ=None):
)
output = []
- # may be given multiple compositions
- for comp_name, composition_dict in graphs_dict.items():
- try:
- assert comp_name == composition_dict['name']
- except KeyError:
- pass
-
- comp_identifer = parse_valid_identifier(comp_name)
+ comp_identifer = parse_valid_identifier(graph.id)
- def alphabetical_order(items):
- alphabetical = enumerate(
- sorted(items)
- )
- return {
- parse_valid_identifier(item[1]): item[0]
- for item in alphabetical
- }
-
- # get order in which nodes were added
- # may be node names or dictionaries
- try:
- node_order = composition_dict[MODEL_SPEC_ID_METADATA]['node_ordering']
- node_order = {
- parse_valid_identifier(list(node.keys())[0]) if isinstance(node, dict)
- else parse_valid_identifier(node): node_order.index(node)
- for node in node_order
- }
-
- unspecified_node_order = {
- node: position + len(node_order)
- for node, position in alphabetical_order([
- n for n in composition_dict[MODEL_SPEC_ID_NODES] if n not in node_order
- ]).items()
- }
-
- node_order.update(unspecified_node_order)
-
- assert all([
- (parse_valid_identifier(node) in node_order)
- for node in composition_dict[MODEL_SPEC_ID_NODES]
- ])
- except (KeyError, TypeError, AssertionError):
- # if no node_ordering attribute exists, fall back to
- # alphabetical order
- node_order = alphabetical_order(composition_dict[MODEL_SPEC_ID_NODES])
-
- # clean up pnl-specific and other software-specific items
- pnl_specific_items = {}
- keys_to_delete = []
-
- for name, node in composition_dict[MODEL_SPEC_ID_NODES].items():
- try:
- component_type = _parse_component_type(node)
- except KeyError:
- # will use a default type
- pass
- except PNLJSONError:
- # node isn't a node dictionary, but a dict of dicts,
- # indicating a software-specific set of nodes or
- # a composition
- if name == MODEL_SPEC_ID_PSYNEULINK:
- pnl_specific_items = node
-
- if MODEL_SPEC_ID_COMPOSITION not in node:
- keys_to_delete.append(name)
- else:
- # projection was written out as a node for simple_edge_format
- if issubclass(component_type, psyneulink.Projection_Base):
- assert len(node[MODEL_SPEC_ID_INPUT_PORTS]) == 1
- assert len(node[MODEL_SPEC_ID_OUTPUT_PORTS]) == 1
-
- extra_projs_to_delete = set()
-
- sender = None
- sender_port = None
- receiver = None
- receiver_port = None
-
- for proj_name, proj in composition_dict[MODEL_SPEC_ID_PROJECTIONS].items():
- if proj[MODEL_SPEC_ID_RECEIVER_MECH] == name:
- assert 'dummy' in proj_name
- sender = proj[MODEL_SPEC_ID_SENDER_MECH]
- sender_port = proj[MODEL_SPEC_ID_SENDER_PORT]
- extra_projs_to_delete.add(proj_name)
-
- if proj[MODEL_SPEC_ID_SENDER_MECH] == name:
- assert 'dummy' in proj_name
- receiver = proj[MODEL_SPEC_ID_RECEIVER_MECH]
- receiver_port = proj[MODEL_SPEC_ID_RECEIVER_PORT]
- # if for some reason the projection has node as both sender and receiver
- # this is a bug, let the deletion fail
- extra_projs_to_delete.add(proj_name)
-
- if sender is None:
- raise PNLJSONError(f'Dummy node {name} for projection has no sender in projections list')
-
- if receiver is None:
- raise PNLJSONError(f'Dummy node {name} for projection has no receiver in projections list')
-
- proj_dict = {
- **{
- MODEL_SPEC_ID_SENDER_PORT: sender_port,
- MODEL_SPEC_ID_RECEIVER_PORT: receiver_port,
- MODEL_SPEC_ID_SENDER_MECH: sender,
- MODEL_SPEC_ID_RECEIVER_MECH: receiver
- },
- **{
- MODEL_SPEC_ID_METADATA: {
- # variable isn't specified for projections
- **{k: v for k, v in node[MODEL_SPEC_ID_METADATA].items() if k != 'variable'},
- 'functions': node['functions']
- }
- },
- }
- try:
- proj_dict[component_type._model_spec_id_parameters] = node[psyneulink.Component._model_spec_id_parameters]
- except KeyError:
- pass
-
- composition_dict[MODEL_SPEC_ID_PROJECTIONS][name.rstrip('_dummy_node')] = proj_dict
-
- keys_to_delete.append(name)
- for p in extra_projs_to_delete:
- del composition_dict[MODEL_SPEC_ID_PROJECTIONS][p]
+ def alphabetical_order(items):
+ alphabetical = enumerate(
+ sorted(items)
+ )
+ return {
+ parse_valid_identifier(item[1]): item[0]
+ for item in alphabetical
+ }
- for nr_item in ['required_node_roles', 'excluded_node_roles']:
- nr_removal_indices = []
+ # get order in which nodes were added
+ # may be node names or dictionaries
+ try:
+ node_order = graph.metadata['node_ordering']
+ node_order = {
+ parse_valid_identifier(list(node.keys())[0]) if isinstance(node, dict)
+ else parse_valid_identifier(node): node_order.index(node)
+ for node in node_order
+ }
- for i, (nr_name, nr_role) in enumerate(
- composition_dict[MODEL_SPEC_ID_METADATA][nr_item]
- ):
- if nr_name == name:
- nr_removal_indices.append(i)
+ unspecified_node_order = {
+ node: position + len(node_order)
+ for node, position in alphabetical_order([
+ parse_valid_identifier(n.id) for n in graph.nodes if n.id not in node_order
+ ]).items()
+ }
- for i in nr_removal_indices:
- del composition_dict[MODEL_SPEC_ID_METADATA][nr_item][i]
+ node_order.update(unspecified_node_order)
- for nodes_dict in pnl_specific_items:
- for name, node in nodes_dict.items():
- composition_dict[MODEL_SPEC_ID_NODES][name] = node
+ assert all([
+ (parse_valid_identifier(node.id) in node_order)
+ for node in graph.nodes
+ ])
+ except (KeyError, TypeError, AssertionError):
+ # if no node_ordering attribute exists, fall back to
+ # alphabetical order
+ node_order = alphabetical_order([parse_valid_identifier(n.id) for n in graph.nodes])
- for name_to_delete in keys_to_delete:
- del composition_dict[MODEL_SPEC_ID_NODES][name_to_delete]
+ keys_to_delete = []
+ for node in graph.nodes:
try:
- edges_dict = composition_dict[MODEL_SPEC_ID_PROJECTIONS]
- pnl_specific_items = {}
- keys_to_delete = []
- except KeyError:
+ component_type = _parse_component_type(node)
+ except (AttributeError, KeyError):
+ # will use a default type
pass
else:
- for name, edge in edges_dict.items():
- try:
- _parse_component_type(edge)
- except KeyError:
- # will use a default type
- pass
- except PNLJSONError:
- if name == MODEL_SPEC_ID_PSYNEULINK:
- pnl_specific_items = edge
-
- keys_to_delete.append(name)
-
- for name, edge in pnl_specific_items.items():
- # exclude CIM projections because they are automatically
- # generated
- if (
- edge[MODEL_SPEC_ID_SENDER_MECH] != comp_name
- and edge[MODEL_SPEC_ID_RECEIVER_MECH] != comp_name
- ):
- composition_dict[MODEL_SPEC_ID_PROJECTIONS][name] = edge
-
- for name_to_delete in keys_to_delete:
- del composition_dict[MODEL_SPEC_ID_PROJECTIONS][name_to_delete]
-
- # generate string for Composition itself
- output.append(
- "{0} = {1}\n".format(
- comp_identifer,
- _generate_component_string(
- composition_dict,
- component_identifiers,
- component_name=comp_name,
- default_type=default_composition_type
+ # projection was written out as a node for simple_edge_format
+ if issubclass(component_type, psyneulink.Projection_Base):
+ assert len(node.input_ports) == 1
+ assert len(node.output_ports) == 1
+
+ extra_projs_to_delete = set()
+
+ sender = None
+ sender_port = None
+ receiver = None
+ receiver_port = None
+
+ for proj in graph.edges:
+ if proj.receiver == node.id:
+ assert 'dummy' in proj.id
+ sender = proj.sender
+ sender_port = proj.sender_port
+ extra_projs_to_delete.add(proj.id)
+
+ if proj.sender == node.id:
+ assert 'dummy' in proj.id
+ receiver = proj.receiver
+ receiver_port = proj.receiver_port
+ # if for some reason the projection has node as both sender and receiver
+ # this is a bug, let the deletion fail
+ extra_projs_to_delete.add(proj.id)
+
+ if sender is None:
+ raise MDFError(f'Dummy node {node.id} for projection has no sender in projections list')
+
+ if receiver is None:
+ raise MDFError(f'Dummy node {node.id} for projection has no receiver in projections list')
+
+ main_proj = mdf.Edge(
+ id=node.id.rstrip('_dummy_node'),
+ sender=sender,
+ receiver=receiver,
+ sender_port=sender_port,
+ receiver_port=receiver_port,
+ metadata={
+ # variable isn't specified for projections
+ **{k: v for k, v in node.metadata.items() if k != 'variable'},
+ 'functions': node.functions
+ }
)
+ proj.parameters = {p.id: p for p in node.parameters}
+ graph.edges.append(main_proj)
+
+ keys_to_delete.append(node.id)
+ for p in extra_projs_to_delete:
+ del graph.edges[graph.edges.index([e for e in graph.edges if e.id == p][0])]
+
+ for nr_item in ['required_node_roles', 'excluded_node_roles']:
+ nr_removal_indices = []
+
+ for i, (nr_name, nr_role) in enumerate(
+ graph.metadata[nr_item]
+ ):
+ if nr_name == node.id:
+ nr_removal_indices.append(i)
+
+ for i in nr_removal_indices:
+ del graph.metadata[nr_item][i]
+
+ for name_to_delete in keys_to_delete:
+ del graph.nodes[graph.nodes.index([n for n in graph.nodes if n.id == name_to_delete][0])]
+
+ # generate string for Composition itself
+ output.append(
+ "{0} = {1}\n".format(
+ comp_identifer,
+ _generate_component_string(
+ graph,
+ component_identifiers,
+ component_name=graph.id,
+ default_type=default_composition_type
)
)
- component_identifiers[comp_identifer] = True
-
- mechanisms = {}
- compositions = {}
- control_mechanisms = {}
- implicit_mechanisms = {}
-
- # add nested compositions and mechanisms in order they were added
- # to this composition
- for name, node in sorted(
- composition_dict[MODEL_SPEC_ID_NODES].items(),
- key=lambda item: node_order[parse_valid_identifier(item[0])]
- ):
- if MODEL_SPEC_ID_COMPOSITION in node:
- compositions[name] = node[MODEL_SPEC_ID_COMPOSITION]
- else:
- try:
- component_type = _parse_component_type(node)
- except KeyError:
- component_type = default_node_type
- identifier = parse_valid_identifier(name)
- if issubclass(component_type, control_mechanism_types):
- control_mechanisms[name] = node
- component_identifiers[identifier] = True
- elif issubclass(component_type, implicit_types):
- implicit_mechanisms[name] = node
- else:
- mechanisms[name] = node
- component_identifiers[identifier] = True
-
- implicit_names = [
- x
- for x in [*implicit_mechanisms.keys(), *control_mechanisms.keys()]
- ]
-
- for name, mech in copy.copy(mechanisms).items():
+ )
+ component_identifiers[comp_identifer] = True
+
+ mechanisms = []
+ compositions = []
+ control_mechanisms = []
+ implicit_mechanisms = []
+
+ # add nested compositions and mechanisms in order they were added
+ # to this composition
+ for node in sorted(
+ graph.nodes,
+ key=lambda item: node_order[parse_valid_identifier(item.id)]
+ ):
+ if isinstance(node, mdf.Graph):
+ compositions.append(node)
+ else:
try:
- mech_type = _parse_component_type(mech)
- except KeyError:
- mech_type = None
-
- if (
- isinstance(mech_type, type)
- and issubclass(mech_type, psyneulink.Function)
- ):
- mech = _replace_function_node_with_mech_node(mech, name, mech_type)
-
- component_identifiers[mech['name']] = component_identifiers[name]
- del component_identifiers[name]
-
- node_order[mech['name']] = node_order[name]
- del node_order[name]
+ component_type = _parse_component_type(node)
+ except (AttributeError, KeyError):
+ component_type = default_node_type
+ identifier = parse_valid_identifier(node.id)
+ if issubclass(component_type, control_mechanism_types):
+ control_mechanisms.append(node)
+ component_identifiers[identifier] = True
+ elif issubclass(component_type, implicit_types):
+ implicit_mechanisms.append(node)
+ else:
+ mechanisms.append(node)
+ component_identifiers[identifier] = True
- mechanisms[mech['name']] = mechanisms[name]
- del mechanisms[name]
+ implicit_names = [node.id for node in implicit_mechanisms + control_mechanisms]
- composition_dict['nodes'][mech['name']] = composition_dict['nodes'][name]
- del composition_dict['nodes'][name]
+ for mech in mechanisms:
+ try:
+ mech_type = _parse_component_type(mech)
+ except (AttributeError, KeyError):
+ mech_type = None
- name = mech['name']
+ if (
+ isinstance(mech_type, type)
+ and issubclass(mech_type, psyneulink.Function)
+ ):
+ # removed branch converting functions defined as nodes
+ # should no longer happen with recent MDF versions
+ assert False
- output.append(
- _generate_component_string(
- mech,
- component_identifiers,
- component_name=name,
- assignment=True,
- default_type=default_node_type
- )
+ output.append(
+ _generate_component_string(
+ mech,
+ component_identifiers,
+ component_name=parse_valid_identifier(mech.id),
+ assignment=True,
+ default_type=default_node_type
)
- if len(mechanisms) > 0:
- output.append('')
+ )
+ if len(mechanisms) > 0:
+ output.append('')
- for name, mech in control_mechanisms.items():
- output.append(
- _generate_component_string(
- mech,
- component_identifiers,
- component_name=name,
- assignment=True,
- default_type=default_node_type
- )
+ for mech in control_mechanisms:
+ output.append(
+ _generate_component_string(
+ mech,
+ component_identifiers,
+ component_name=parse_valid_identifier(mech.id),
+ assignment=True,
+ default_type=default_node_type
)
+ )
- if len(control_mechanisms) > 0:
- output.append('')
+ if len(control_mechanisms) > 0:
+ output.append('')
- # recursively generate string for inner Compositions
- for name, comp in compositions.items():
- output.append(
- _generate_composition_string(
- comp,
- component_identifiers
- )
+ # recursively generate string for inner Compositions
+ for comp in compositions:
+ output.append(
+ _generate_composition_string(
+ comp,
+ component_identifiers
)
- if len(compositions) > 0:
- output.append('')
-
- # generate string to add the nodes to this Composition
- try:
- node_roles = {
- parse_valid_identifier(node): role for (node, role) in
- composition_dict[MODEL_SPEC_ID_METADATA]['required_node_roles']
- }
- except KeyError:
- node_roles = []
+ )
+ if len(compositions) > 0:
+ output.append('')
- try:
- excluded_node_roles = {
- parse_valid_identifier(node): role for (node, role) in
- composition_dict[MODEL_SPEC_ID_METADATA]['excluded_node_roles']
- }
- except KeyError:
- excluded_node_roles = []
+ # generate string to add the nodes to this Composition
+ try:
+ node_roles = {
+ parse_valid_identifier(node): role for (node, role) in
+ graph.metadata['required_node_roles']
+ }
+ except KeyError:
+ node_roles = []
- # do not add the controller as a normal node
- try:
- controller_name = list(composition_dict[MODEL_SPEC_ID_METADATA]['controller'].keys())[0]
- except (AttributeError, KeyError, TypeError):
- controller_name = None
+ try:
+ excluded_node_roles = {
+ parse_valid_identifier(node): role for (node, role) in
+ graph.metadata['excluded_node_roles']
+ }
+ except KeyError:
+ excluded_node_roles = []
- for name in sorted(
- composition_dict[MODEL_SPEC_ID_NODES],
- key=lambda item: node_order[parse_valid_identifier(item)]
+ # do not add the controller as a normal node
+ try:
+ controller_name = graph.metadata['controller']['id']
+ except (AttributeError, KeyError, TypeError):
+ controller_name = None
+
+ for node in sorted(
+ graph.nodes,
+ key=lambda item: node_order[parse_valid_identifier(item.id)]
+ ):
+ name = node.id
+ if (
+ name not in implicit_names
+ and name != controller_name
):
- if (
- name not in implicit_names
- and name != controller_name
- ):
- name = parse_valid_identifier(name)
+ name = parse_valid_identifier(name)
+ output.append(
+ '{0}.add_node({1}{2})'.format(
+ comp_identifer,
+ name,
+ ', {0}'.format(
+ _parse_parameter_value(
+ node_roles[name],
+ component_identifiers
+ )
+ ) if name in node_roles else ''
+ )
+ )
+ if len(graph.nodes) > 0:
+ output.append('')
+
+ if len(excluded_node_roles) > 0:
+ for node, roles in excluded_node_roles.items():
+ if name not in implicit_names and name != controller_name:
output.append(
- '{0}.add_node({1}{2})'.format(
- comp_identifer,
- name,
- ', {0}'.format(
- _parse_parameter_value(
- node_roles[name],
- component_identifiers
- )
- ) if name in node_roles else ''
- )
+ f'{comp_identifer}.exclude_node_roles({node}, {_parse_parameter_value(roles, component_identifiers)})'
)
- if len(composition_dict[MODEL_SPEC_ID_NODES]) > 0:
- output.append('')
-
- if len(excluded_node_roles) > 0:
- for node, roles in excluded_node_roles.items():
- if name not in implicit_names and name != controller_name:
- output.append(
- f'{comp_identifer}.exclude_node_roles({node}, {_parse_parameter_value(roles, component_identifiers)})'
- )
- output.append('')
+ output.append('')
+ # generate string to add the projections
+ for proj in graph.edges:
try:
- edges_dict = composition_dict[MODEL_SPEC_ID_PROJECTIONS]
- except KeyError:
- pass
- else:
- # generate string to add the projections
- for name, projection_dict in edges_dict.items():
- try:
- projection_type = _parse_component_type(projection_dict)
- except KeyError:
- projection_type = default_edge_type
-
- if (
- not issubclass(projection_type, implicit_types)
- and projection_dict[MODEL_SPEC_ID_SENDER_MECH] not in implicit_names
- and projection_dict[MODEL_SPEC_ID_RECEIVER_MECH] not in implicit_names
- ):
- output.append(
- '{0}.add_projection(projection={1}, sender={2}, receiver={3})'.format(
- comp_identifer,
- _generate_component_string(
- projection_dict,
- component_identifiers,
- component_name=name,
- default_type=default_edge_type
- ),
- parse_valid_identifier(
- projection_dict[MODEL_SPEC_ID_SENDER_MECH]
- ),
- parse_valid_identifier(
- projection_dict[MODEL_SPEC_ID_RECEIVER_MECH]
- ),
- )
- )
+ projection_type = _parse_component_type(proj)
+ except (AttributeError, KeyError):
+ projection_type = default_edge_type
- # add controller if it exists (must happen after projections)
- if controller_name is not None:
+ if (
+ not issubclass(projection_type, implicit_types)
+ and proj.sender not in implicit_names
+ and proj.receiver not in implicit_names
+ ):
output.append(
- '{0}.add_controller({1})'.format(
+ '{0}.add_projection(projection={1}, sender={2}, receiver={3})'.format(
comp_identifer,
- parse_valid_identifier(controller_name)
+ _generate_component_string(
+ proj,
+ component_identifiers,
+ default_type=default_edge_type
+ ),
+ parse_valid_identifier(proj.sender),
+ parse_valid_identifier(proj.receiver),
)
)
- # add schedulers
- # blacklist automatically generated nodes because they will
- # not exist in the script namespace
- try:
- conditions = composition_dict['conditions']
- except KeyError:
- conditions = {}
-
- output.append('')
+ # add controller if it exists (must happen after projections)
+ if controller_name is not None:
output.append(
- _generate_scheduler_string(
- f'{comp_identifer}.scheduler',
- conditions,
- component_identifiers,
- blacklist=implicit_names
+ '{0}.add_controller({1})'.format(
+ comp_identifer,
+ parse_valid_identifier(controller_name)
)
)
- return '\n'.join(output)
+ # add schedulers
+ # blacklist automatically generated nodes because they will
+ # not exist in the script namespace
+ output.append('')
+ output.append(
+ _generate_scheduler_string(
+ f'{comp_identifer}.scheduler',
+ graph.conditions,
+ component_identifiers,
+ blacklist=implicit_names
+ )
+ )
+
+ return output
def generate_script_from_json(model_input, outfile=None):
@@ -1379,67 +1299,76 @@ def generate_script_from_json(model_input, outfile=None):
"""
+ warnings.warn(
+ 'generate_script_from_json is replaced by generate_script_from_mdf and will be removed in a future version',
+ FutureWarning
+ )
+ return generate_script_from_mdf(model_input, outfile)
+
+
+def generate_script_from_mdf(model_input, outfile=None):
+ """
+ Generate a Python script from MDF model **model_input**
- def get_declared_identifiers(graphs_dict):
- names = set()
+ .. warning::
+ Use of `generate_script_from_mdf` to generate a Python script from a model without taking proper precautions
+ can introduce a security risk to the system on which the Python interpreter is running. This is because it
+ calls exec, which has the potential to execute non-PsyNeuLink-related code embedded in the file. Therefore,
+ `generate_script_from_mdf` should be used to read only model of known and secure origin.
- for comp_name, composition_dict in graphs_dict.items():
- try:
- assert comp_name == composition_dict['name']
- except KeyError:
- pass
+ Arguments
+ ---------
- names.add(parse_valid_identifier(comp_name))
- for name, node in composition_dict[MODEL_SPEC_ID_NODES].items():
- if MODEL_SPEC_ID_COMPOSITION in node:
- names.update(
- get_declared_identifiers(
- node[MODEL_SPEC_ID_COMPOSITION]
- )
- )
+ model_input : modeci_mdf.Model
- names.add(parse_valid_identifier(name))
+ Returns
+ -------
+
+ Text of Python script : str
+ """
+ import modeci_mdf.mdf as mdf
+ from modeci_mdf.utils import load_mdf
+
+ def get_declared_identifiers(model):
+ names = set()
+
+ for graph in model.graphs:
+ names.add(parse_valid_identifier(graph.id))
+ for node in graph.nodes:
+ if isinstance(node, mdf.Graph):
+ names.update(get_declared_identifiers(graph))
+
+ names.add(parse_valid_identifier(node.id))
return names
# accept either json string or filename
try:
- model_input = open(model_input, 'r').read()
- except (FileNotFoundError, OSError):
- pass
-
- try:
- model_input = json.loads(model_input)
- except json.decoder.JSONDecodeError:
- raise ValueError(
- f'{model_input} is neither valid JSON nor a file containing JSON'
- )
-
- assert len(model_input.keys()) == 1
- model_input = model_input[list(model_input.keys())[0]]
+ model = load_mdf(model_input)
+ except (FileNotFoundError, OSError, ValueError):
+ try:
+ model = mdf.Model.from_json(model_input)
+ except json.decoder.JSONDecodeError:
+ # assume yaml
+ # delete=False because of problems with reading file on windows
+ with tempfile.NamedTemporaryFile(mode='w', suffix='.yml', delete=False) as f:
+ f.write(model_input)
+ model = load_mdf(f.name)
imports_str = ''
- if MODEL_SPEC_ID_COMPOSITION in model_input:
- # maps declared names to whether they are accessible in the script
- # locals. that is, each of these will be names specified in the
- # composition and subcomposition nodes, and their value in this dict
- # will correspond to True if they can be referenced by this name in the
- # script
- component_identifiers = {
- i: False
- for i in get_declared_identifiers(model_input[MODEL_SPEC_ID_COMPOSITION])
- }
+ comp_strs = []
+ # maps declared names to whether they are accessible in the script
+ # locals. that is, each of these will be names specified in the
+ # composition and subcomposition nodes, and their value in this dict
+ # will correspond to True if they can be referenced by this name in the
+ # script
+ component_identifiers = {
+ i: False
+ for i in get_declared_identifiers(model)
+ }
- comp_str = _generate_composition_string(
- model_input[MODEL_SPEC_ID_COMPOSITION],
- component_identifiers
- )
- else:
- comp_str = _generate_component_string(
- model_input,
- component_identifiers={},
- assignment=True
- )
+ for graph in model.graphs:
+ comp_strs.append(_generate_composition_string(graph, component_identifiers))
module_friendly_name_mapping = {
'psyneulink': 'pnl',
@@ -1447,92 +1376,98 @@ def get_declared_identifiers(graphs_dict):
'numpy': 'np'
}
+ potential_module_names = set()
module_names = set()
+ model_output = []
+
+ for i in range(len(comp_strs)):
+ # greedy and non-greedy
+ for cs in comp_strs[i]:
+ potential_module_names = set([
+ *re.findall(r'([A-Za-z_\.]+)\.', cs),
+ *re.findall(r'([A-Za-z_\.]+?)\.', cs)
+ ])
- # greedy and non-greedy
- potential_module_names = set([
- *re.findall(r'([A-Za-z_\.]+)\.', comp_str),
- *re.findall(r'([A-Za-z_\.]+?)\.', comp_str)
- ])
- for module in potential_module_names:
- if module not in component_identifiers:
- try:
- exec(f'import {module}')
- module_names.add(module)
- except (ImportError, ModuleNotFoundError, SyntaxError):
- pass
+ for module in potential_module_names:
+ if module not in component_identifiers:
+ try:
+ exec(f'import {module}')
+ module_names.add(module)
+ except (ImportError, ModuleNotFoundError, SyntaxError):
+ pass
- for module in module_names.copy():
- try:
- friendly_name = module_friendly_name_mapping[module]
- comp_str = re.sub(f'{module}\\.', f'{friendly_name}.', comp_str)
- except KeyError:
- friendly_name = module
+ for j in range(len(comp_strs[i])):
+ for module in module_names.copy():
+ try:
+ friendly_name = module_friendly_name_mapping[module]
+ comp_strs[i][j] = re.sub(f'{module}\\.', f'{friendly_name}.', comp_strs[i][j])
+ except KeyError:
+ pass
- if not re.findall(rf'[^\.]{friendly_name}\.', comp_str):
- module_names.remove(module)
+ for m in module_names.copy():
+ for n in module_names.copy():
+ # remove potential modules that are substrings of another
+ if m is not n and m in n:
+ module_names.remove(m)
- for m in module_names.copy():
- for n in module_names.copy():
- # remove potential modules that are substrings of another
- if m is not n and m in n:
- module_names.remove(m)
+ for module in sorted(module_names):
+ try:
+ friendly_name = module_friendly_name_mapping[module]
+ except KeyError:
+ friendly_name = module
- for module in sorted(module_names):
- try:
- friendly_name = module_friendly_name_mapping[module]
- except KeyError:
- friendly_name = module
+ imports_str += 'import {0}{1}\n'.format(
+ module,
+ f' as {friendly_name}' if friendly_name != module else ''
+ )
- imports_str += 'import {0}{1}\n'.format(
- module,
- f' as {friendly_name}' if friendly_name != module else ''
- )
+ comp_strs[i] = '\n'.join(comp_strs[i])
model_output = '{0}{1}{2}'.format(
imports_str,
'\n' if len(imports_str) > 0 else '',
- comp_str
+ '\n'.join(comp_strs)
)
if outfile is not None:
# pass through any file exceptions
with open(outfile, 'w') as outfile:
outfile.write(model_output)
- print(f'Wrote JSON to {outfile.name}')
+ print(f'Wrote script to {outfile.name}')
else:
return model_output
-def generate_script_from_mdf(model_input, outfile=None):
+def generate_json(*compositions, simple_edge_format=True):
"""
- Generate a Python script from MDF model **model_input**
-
- .. warning::
- Use of `generate_script_from_mdf` to generate a Python script from a model without taking proper precautions
- can introduce a security risk to the system on which the Python interpreter is running. This is because it
- calls exec, which has the potential to execute non-PsyNeuLink-related code embedded in the file. Therefore,
- `generate_script_from_mdf` should be used to read only model of known and secure origin.
-
- Arguments
- ---------
-
- model_input : modeci_mdf.Model
+ Generate the `general JSON format <JSON_Model_Specification>`
+ for one or more `Compositions <Composition>` and associated
+ objects.
+ .. _MDF_Write_Multiple_Compositions_Note:
- Returns
- -------
+ .. note::
+ At present, if more than one Composition is specified, all
+ must be fully disjoint; that is, they must not share any
+ `Components <Component>` (e.g., `Mechanism`, `Projections`
+ etc.). This limitation will be addressed in a future update.
- Text of Python script : str
+ Arguments:
+ *compositions : Composition
+ specifies `Composition` or iterable of ones to be output
+ in JSON
"""
- return generate_script_from_json(model_input.to_json(), outfile)
+ warnings.warn(
+ 'generate_json is replaced by get_mdf_serialized and will be removed in a future version',
+ FutureWarning
+ )
+ return get_mdf_serialized(*compositions, fmt='json', simple_edge_format=simple_edge_format)
-def generate_json(*compositions, simple_edge_format=True):
+def get_mdf_serialized(*compositions, fmt='json', simple_edge_format=True):
"""
- Generate the `general JSON format <JSON_Model_Specification>`
+ Generate the `general MDF serialized format <JSON_Model_Specification>`
for one or more `Compositions <Composition>` and associated
objects.
- .. _JSON_Write_Multiple_Compositions_Note:
.. note::
At present, if more than one Composition is specified, all
@@ -1543,28 +1478,23 @@ def generate_json(*compositions, simple_edge_format=True):
Arguments:
*compositions : Composition
specifies `Composition` or iterable of ones to be output
- in JSON
- """
- import modeci_mdf
- import modeci_mdf.mdf as mdf
- from psyneulink.core.compositions.composition import Composition
+ in **fmt**
- model_name = "_".join([c.name for c in compositions])
+ fmt : str
+ specifies file format of output. Current options ('json', 'yml'/'yaml')
- model = mdf.Model(
- id=model_name,
- format=f'ModECI MDF v{modeci_mdf.__version__}',
- generating_application=f'PsyNeuLink v{psyneulink.__version__}',
- )
-
- for c in compositions:
- if not isinstance(c, Composition):
- raise PNLJSONError(
- f'Item in compositions arg of {__name__}() is not a Composition: {c}.'
- )
- model.graphs.append(c.as_mdf_model(simple_edge_format=simple_edge_format))
+ simple_edge_format : bool
+ specifies use of
+ `simple edge format <MDF_Simple_Edge_Format>` or not
+ """
+ model = get_mdf_model(*compositions, simple_edge_format=simple_edge_format)
- return model.to_json()
+ try:
+ return getattr(model, f'to_{supported_formats[fmt]}')()
+ except AttributeError as e:
+ raise ValueError(
+ f'Unsupported MDF output format "{fmt}". Supported formats: {gen_friendly_comma_str(supported_formats.keys())}'
+ ) from e
def write_json_file(compositions, filename:str, path:str=None, simple_edge_format=True):
@@ -1572,7 +1502,7 @@ def write_json_file(compositions, filename:str, path:str=None, simple_edge_forma
Write one or more `Compositions <Composition>` and associated objects to file in the `general JSON format
<JSON_Model_Specification>`
- .. _JSON_Write_Multiple_Compositions_Note:
+ .. _MDF_Write_Multiple_Compositions_Note:
.. note::
At present, if more than one Composition is specified, all must be fully disjoint; that is, they must not
@@ -1592,8 +1522,103 @@ def write_json_file(compositions, filename:str, path:str=None, simple_edge_forma
specifies path of file for JSON specification; if it is not specified then the current directory is used.
"""
+ warnings.warn(
+ 'write_json_file is replaced by write_mdf_file and will be removed in a future version',
+ FutureWarning
+ )
+ write_mdf_file(compositions, filename, path, 'json', simple_edge_format)
+
+
+def write_mdf_file(compositions, filename: str, path: str = None, fmt: str = None, simple_edge_format: bool = True):
+ """
+ Write the `general MDF serialized format <MDF_Model_Specification>`
+ for one or more `Compositions <Composition>` and associated
+ objects to file.
+
+ .. note::
+ At present, if more than one Composition is specified, all
+ must be fully disjoint; that is, they must not share any
+ `Components <Component>` (e.g., `Mechanism`, `Projections`
+ etc.). This limitation will be addressed in a future update.
+
+ Arguments:
+ compositions : Composition or list
+ specifies `Composition` or list of ones to be written to
+ **filename**
+
+ filename : str
+ specifies name of file in which to write MDF
+ specification of `Composition(s) <Composition>` and
+ associated objects.
+
+ path : str : default None
+ specifies path of file for MDF specification; if it is
+ not specified then the current directory is used.
+ fmt : str
+ specifies file format of output. Current options ('json', 'yml'/'yaml')
+
+ simple_edge_format : bool
+ specifies use of
+ `simple edge format <MDF_Simple_Edge_Format>` or not
+ """
compositions = convert_to_list(compositions)
+ model = get_mdf_model(*compositions, simple_edge_format=simple_edge_format)
+
+ if fmt is None:
+ try:
+ fmt = re.match(r'(.*)\.(.*)$', filename).groups(1)
+ except AttributeError:
+ fmt = 'json'
+
+ if path is not None:
+ filename = os.path.join(path, filename)
+
+ try:
+ return getattr(model, f'to_{supported_formats[fmt]}_file')(filename)
+ except AttributeError as e:
+ raise ValueError(
+ f'Unsupported MDF output format "{fmt}". Supported formats: {gen_friendly_comma_str(supported_formats.keys())}'
+ ) from e
+
+
+def get_mdf_model(*compositions, simple_edge_format=True):
+ """
+ Generate the MDF Model object for one or more
+ `Compositions <Composition>` and associated objects.
+
+ .. note::
+ At present, if more than one Composition is specified, all
+ must be fully disjoint; that is, they must not share any
+ `Components <Component>` (e.g., `Mechanism`, `Projections`
+ etc.). This limitation will be addressed in a future update.
+
+ Arguments:
+ *compositions : Composition
+ specifies `Composition` or iterable of ones to be output
+ in the Model
+
+ simple_edge_format : bool
+ specifies use of
+ `simple edge format <MDF_Simple_Edge_Format>` or not
+ """
+ import modeci_mdf
+ import modeci_mdf.mdf as mdf
+ from psyneulink.core.compositions.composition import Composition
+
+ model_name = "_".join([c.name for c in compositions])
+
+ model = mdf.Model(
+ id=model_name,
+ format=f'ModECI MDF v{modeci_mdf.__version__}',
+ generating_application=f'PsyNeuLink v{psyneulink.__version__}',
+ )
+
+ for c in compositions:
+ if not isinstance(c, Composition):
+ raise MDFError(
+ f'Item in compositions arg of {__name__}() is not a Composition: {c}.'
+ )
+ model.graphs.append(c.as_mdf_model(simple_edge_format=simple_edge_format))
- with open(filename, 'w') as json_file:
- json_file.write(generate_json(*compositions, simple_edge_format=simple_edge_format))
+ return model
diff --git a/psyneulink/core/globals/parameters.py b/psyneulink/core/globals/parameters.py
index d7cc7233a38..819db375349 100644
--- a/psyneulink/core/globals/parameters.py
+++ b/psyneulink/core/globals/parameters.py
@@ -99,7 +99,10 @@
class B(A):
class Parameters(A.Parameters):
p = 1.0
- q = Parameter(1.0, modulable=True)
+ q = Parameter()
+
+ def __init__(p=None, q=1.0):
+ super(p=p, q=q)
- create an inner class Parameters on the Component, inheriting from the parent Component's Parameters class
@@ -108,6 +111,8 @@ class Parameters(A.Parameters):
- as with *p*, specifying only a value uses default values for the attributes of the Parameter
- as with *q*, specifying an explicit instance of the Parameter class allows you to modify the
`Parameter attributes <Parameter_Attributes_Table>`
+- default values for the parameters can be specified in the Parameters class body, or in the
+ arguments for *B*.__init__. If both are specified and the values differ, an exception will be raised
- if you want assignments to parameter *p* to be validated, add a method _validate_p(value),
that returns None if value is a valid assignment, or an error string if value is not a valid assignment
- if you want all values set to *p* to be parsed beforehand, add a method _parse_p(value) that returns the parsed value
@@ -295,6 +300,8 @@ def _recurrent_transfer_mechanism_matrix_setter(value, owning_component=None, co
import collections
import copy
+import functools
+import inspect
import itertools
import logging
import types
@@ -307,7 +314,7 @@ def _recurrent_transfer_mechanism_matrix_setter(value, owning_component=None, co
from psyneulink.core.globals.context import time as time_object
from psyneulink.core.globals.log import LogCondition, LogEntry, LogError
from psyneulink.core.globals.utilities import call_with_pruned_args, copy_iterable_with_shared, \
- get_alias_property_getter, get_alias_property_setter, get_deepcopy_with_shared, unproxy_weakproxy, create_union_set
+ get_alias_property_getter, get_alias_property_setter, get_deepcopy_with_shared, unproxy_weakproxy, create_union_set, safe_equals, get_function_sig_default_value
from psyneulink.core.rpc.graph_pb2 import Entry, ndArray
__all__ = [
@@ -392,6 +399,92 @@ def copy_parameter_value(value, shared_types=None, memo=None):
return value
+def get_init_signature_default_value(obj, parameter):
+ """
+ Returns:
+ the default value of the **parameter** argument of
+ the __init__ method of **obj** if it exists, or inspect._empty
+ """
+ # only use the signature if it's on the owner class, not a parent
+ if '__init__' in obj.__dict__:
+ return get_function_sig_default_value(obj.__init__, parameter)
+ else:
+ return inspect._empty
+
+
+def check_user_specified(func):
+ @functools.wraps(func)
+ def check_user_specified_wrapper(self, *args, **kwargs):
+ if 'params' in kwargs and kwargs['params'] is not None:
+ orig_kwargs = copy.copy(kwargs)
+ kwargs = {**kwargs, **kwargs['params']}
+ del kwargs['params']
+ else:
+ orig_kwargs = kwargs
+
+ # find the corresponding constructor in chained wrappers
+ constructor = func
+ while '__init__' not in constructor.__qualname__:
+ constructor = constructor.__wrapped__
+
+ for k, v in kwargs.items():
+ try:
+ p = getattr(self.parameters, k)
+ except AttributeError:
+ pass
+ else:
+ if k == p.constructor_argument:
+ kwargs[p.name] = v
+
+ try:
+ self._user_specified_args
+ except AttributeError:
+ self._prev_constructor = constructor if '__init__' in type(self).__dict__ else None
+ self._user_specified_args = copy.copy(kwargs)
+ else:
+ # add args determined in constructor to user_specifed.
+ # since some args are set by the values of other
+ # user_specified args in a constructor, we label these as
+ # user_specified also (ex. LCAMechanism hetero/competition)
+ for k, v in kwargs.items():
+ # we only know changes in passed parameter values after
+ # calling the next __init__ in the hierarchy, so can
+ # only check _prev_constructor
+ if k not in self._user_specified_args and self._prev_constructor is not None:
+ prev_constructor_default = get_function_sig_default_value(
+ self._prev_constructor, k
+ )
+ if (
+ # arg value passed through constructor is
+ # different than default arg in signature
+ (
+ type(prev_constructor_default) != type(v)
+ or not safe_equals(prev_constructor_default, v)
+ )
+ # arg value is different than the value given
+ # from the previous constructor in the class
+ # hierarchy
+ and (
+ k not in self._prev_kwargs
+ or (
+ type(self._prev_kwargs[k]) != type(v)
+ or not safe_equals(self._prev_kwargs[k], v)
+ )
+ )
+ ):
+ # NOTE: this is a good place to identify
+ # potentially unnecessary/inconsistent default
+ # parameter settings in body of constructors
+ self._user_specified_args[k] = v
+
+ self._prev_constructor = constructor
+
+ self._prev_kwargs = kwargs
+ return func(self, *args, **orig_kwargs)
+
+ return check_user_specified_wrapper
+
+
class ParametersTemplate:
_deepcopy_shared_keys = ['_parent', '_params', '_owner_ref', '_children']
_values_default_excluded_attrs = {'user': False}
@@ -572,7 +665,6 @@ def __getattr__(self, attr):
def __setattr__(self, attr, value):
if (attr[:1] != '_'):
param = getattr(self._owner.parameters, attr)
- param._inherited = False
param.default_value = value
else:
super().__setattr__(attr, value)
@@ -796,6 +888,12 @@ class Parameter(ParameterBase):
:default: None
+ specify_none
+ if True, a user-specified value of None for this Parameter
+ will set the _user_specified flag to True
+
+ :default: False
+
"""
# The values of these attributes will never be inherited from parent Parameters
# KDM 7/12/18: consider inheriting ONLY default_value?
@@ -823,7 +921,6 @@ class Parameter(ParameterBase):
'default_value',
'history_max_length',
'log_condition',
- 'delivery_condition',
'spec',
}
@@ -861,6 +958,7 @@ def __init__(
initializer=None,
port=None,
mdf_name=None,
+ specify_none=False,
_owner=None,
_inherited=False,
# this stores a reference to the Parameter object that is the
@@ -925,9 +1023,11 @@ def __init__(
initializer=initializer,
port=port,
mdf_name=mdf_name,
+ specify_none=specify_none,
_inherited=_inherited,
_inherited_source=_inherited_source,
_user_specified=_user_specified,
+ _temp_uninherited=set(),
**kwargs
)
@@ -1012,10 +1112,15 @@ def __getattr__(self, attr):
def __setattr__(self, attr, value):
if attr in self._additional_param_attr_properties:
+ self._temp_uninherited.add(attr)
+ self._inherited = False
+
try:
getattr(self, '_set_{0}'.format(attr))(value)
except AttributeError:
super().__setattr__(attr, value)
+
+ self._temp_uninherited.remove(attr)
else:
super().__setattr__(attr, value)
@@ -1024,20 +1129,34 @@ def reset(self):
Resets *default_value* to the value specified in its `Parameters` class declaration, or
inherits from parent `Parameters` classes if it is not explicitly specified.
"""
- try:
- self.default_value = self._owner.__class__.__dict__[self.name].default_value
- except (AttributeError, KeyError):
+ # check for default in Parameters class
+ cls_param_value = inspect._empty
+ if self._owner._param_is_specified_in_class(self.name):
try:
- self.default_value = self._owner.__class__.__dict__[self.name]
+ cls_param_value = self._owner.__class__.__dict__[self.name]
except KeyError:
- if self._parent is not None:
- self._inherited = True
- else:
- raise ParameterError(
- 'Parameter {0} cannot be reset, as it does not have a default specification '
- 'or a parent. This may occur if it was added dynamically rather than in an'
- 'explict Parameters inner class on a Component'
- )
+ pass
+ else:
+ try:
+ cls_param_value = cls_param_value.default_value
+ except AttributeError:
+ pass
+
+ # check for default in __init__ signature
+ value = self._owner._reconcile_value_with_init_default(self.name, cls_param_value)
+ if value is not inspect._empty:
+ self.default_value = value
+ return
+
+ # no default specified, must be inherited or invalid
+ if self._parent is not None:
+ self._inherited = True
+ else:
+ raise ParameterError(
+ 'Parameter {0} cannot be reset, as it does not have a default specification '
+ 'or a parent. This may occur if it was added dynamically rather than in an'
+ 'explict Parameters inner class on a Component'
+ )
def _register_alias(self, name):
if self.aliases is None:
@@ -1054,6 +1173,7 @@ def _inherited(self, value):
if value is not self._inherited:
# invalid if set to inherited
self._is_invalid_source = value
+ self.__inherited = value
if value:
self._cache_inherited_attrs()
@@ -1079,14 +1199,14 @@ def _inherited(self, value):
self._restore_inherited_attrs()
- self.__inherited = value
-
def _inherit_from(self, parent):
self._inherited_source = weakref.ref(parent)
def _cache_inherited_attrs(self, exclusions=None):
if exclusions is None:
- exclusions = self._uninherited_attrs
+ exclusions = set()
+
+ exclusions = self._uninherited_attrs.union(self._temp_uninherited).union(exclusions)
for attr in self._param_attrs:
if attr not in exclusions:
@@ -1095,7 +1215,9 @@ def _cache_inherited_attrs(self, exclusions=None):
def _restore_inherited_attrs(self, exclusions=None):
if exclusions is None:
- exclusions = self._uninherited_attrs
+ exclusions = set()
+
+ exclusions = self._uninherited_attrs.union(self._temp_uninherited).union(exclusions)
for attr in self._param_attrs:
if (
@@ -1787,12 +1909,12 @@ def __setattr__(self, attr, value):
def _cache_inherited_attrs(self):
super()._cache_inherited_attrs(
- exclusions=self._uninherited_attrs.union(self._sourced_attrs)
+ exclusions=self._sourced_attrs
)
def _restore_inherited_attrs(self):
super()._restore_inherited_attrs(
- exclusions=self._uninherited_attrs.union(self._sourced_attrs)
+ exclusions=self._sourced_attrs
)
def _set_name(self, name):
@@ -1944,16 +2066,20 @@ class ParametersBase(ParametersTemplate):
_validation_method_prefix = '_validate_'
def __init__(self, owner, parent=None):
+ self._initializing = True
+
super().__init__(owner=owner, parent=parent)
aliases_to_create = set()
for param_name, param_value in self.values(show_all=True).items():
+ constructor_default = get_init_signature_default_value(self._owner, param_name)
+
if (
- param_name in self.__class__.__dict__
- and (
- param_name not in self._parent.__class__.__dict__
- or self._parent.__class__.__dict__[param_name] is not self.__class__.__dict__[param_name]
+ (
+ constructor_default is not None
+ and constructor_default is not inspect._empty
)
+ or self._param_is_specified_in_class(param_name)
):
# KDM 6/25/18: NOTE: this may need special handling if you're creating a ParameterAlias directly
# in a class's Parameters class
@@ -1979,6 +2105,8 @@ def __init__(self, owner, parent=None):
for param, value in self.values(show_all=True).items():
self._validate(param, value.default_value)
+ self._initializing = False
+
def __getattr__(self, attr):
def throw_error():
try:
@@ -2017,10 +2145,20 @@ def __setattr__(self, attr, value):
super().__setattr__(attr, value)
else:
if isinstance(value, Parameter):
+ if value._owner is None:
+ value._owner = self
+ elif value._owner is not self and self._initializing:
+ # case where no Parameters class defined on subclass
+ # but default value overridden in __init__
+ value = copy.deepcopy(value)
+ value._owner = self
+
if value.name is None:
value.name = attr
- value._owner = self
+ if self._initializing and not value._inherited:
+ value.default_value = self._reconcile_value_with_init_default(attr, value.default_value)
+
super().__setattr__(attr, value)
if value.aliases is not None:
@@ -2072,6 +2210,9 @@ def __setattr__(self, attr, value):
except AttributeError:
current_value = None
+ if self._initializing:
+ value = self._reconcile_value_with_init_default(attr, value)
+
# assign value to default_value
if isinstance(current_value, (Parameter, ParameterAlias)):
# construct a copy because the original may be used as a base for reset()
@@ -2092,6 +2233,39 @@ def __setattr__(self, attr, value):
self._validate(attr, getattr(self, attr).default_value)
self._register_parameter(attr)
+ def _reconcile_value_with_init_default(self, attr, value):
+ constructor_default = get_init_signature_default_value(self._owner, attr)
+ if constructor_default is not None and constructor_default is not inspect._empty:
+ if (
+ value is None
+ or not self._param_is_specified_in_class(attr)
+ or (
+ type(constructor_default) == type(value)
+ and safe_equals(constructor_default, value)
+ )
+ ):
+ # TODO: consider placing a developer-focused warning here?
+ return constructor_default
+ else:
+ assert False, (
+ 'PROGRAM ERROR: '
+ f'Conflicting default parameter values assigned for Parameter {attr} of {self._owner} in:'
+ f'\n\t{self._owner}.Parameters: {value}'
+ f'\n\t{self._owner}.__init__: {constructor_default}'
+ f'\nRemove one of these assignments. Prefer removing the default_value of {attr} in {self._owner}.Parameters'
+ )
+
+ return value
+
+ def _param_is_specified_in_class(self, param_name):
+ return (
+ param_name in self.__class__.__dict__
+ and (
+ param_name not in self._parent.__class__.__dict__
+ or self._parent.__class__.__dict__[param_name] is not self.__class__.__dict__[param_name]
+ )
+ )
+
def _get_prefixed_method(
self,
parse=False,
diff --git a/psyneulink/core/globals/utilities.py b/psyneulink/core/globals/utilities.py
index a77f319061c..84fd6a73f93 100644
--- a/psyneulink/core/globals/utilities.py
+++ b/psyneulink/core/globals/utilities.py
@@ -144,6 +144,7 @@
]
logger = logging.getLogger(__name__)
+_signature_cache = weakref.WeakKeyDictionary()
class UtilitiesError(Exception):
@@ -1672,9 +1673,6 @@ def _get_arg_from_stack(arg_name:str):
return arg_val
-_unused_args_sig_cache = weakref.WeakKeyDictionary()
-
-
def prune_unused_args(func, args=None, kwargs=None):
"""
Arguments
@@ -1695,10 +1693,10 @@ def prune_unused_args(func, args=None, kwargs=None):
"""
# use the func signature to filter out arguments that aren't compatible
try:
- sig = _unused_args_sig_cache[func]
+ sig = _signature_cache[func]
except KeyError:
sig = inspect.signature(func)
- _unused_args_sig_cache[func] = sig
+ _signature_cache[func] = sig
has_args_param = False
has_kwargs_param = False
@@ -1943,3 +1941,24 @@ def _is_module_class(class_: type, module: types.ModuleType) -> bool:
pass
return False
+
+
+def get_function_sig_default_value(
+ function: typing.Union[types.FunctionType, types.MethodType],
+ parameter: str
+):
+ """
+ Returns:
+ the default value of the **parameter** argument of
+ **function** if it exists, or inspect._empty
+ """
+ try:
+ sig = _signature_cache[function]
+ except KeyError:
+ sig = inspect.signature(function)
+ _signature_cache[function] = sig
+
+ try:
+ return sig.parameters[parameter].default
+ except KeyError:
+ return inspect._empty
diff --git a/psyneulink/core/llvm/__init__.py b/psyneulink/core/llvm/__init__.py
index f59a46e4dde..a62f8c875e3 100644
--- a/psyneulink/core/llvm/__init__.py
+++ b/psyneulink/core/llvm/__init__.py
@@ -158,12 +158,15 @@ def cuda_max_block_size(self, override):
def cuda_call(self, *args, threads=1, block_size=None):
block_size = self.cuda_max_block_size(block_size)
grid = ((threads + block_size - 1) // block_size, 1)
- self._cuda_kernel(*args, np.int32(threads),
- block=(block_size, 1, 1), grid=grid)
+ ktime = self._cuda_kernel(*args, np.int32(threads), time_kernel="time_stat" in debug_env,
+ block=(block_size, 1, 1), grid=grid)
+ if "time_stat" in debug_env:
+ print("Time to run kernel '{}' using {} threads: {}".format(
+ self.name, threads, ktime))
- def cuda_wrap_call(self, *args, threads=1, block_size=None):
+ def cuda_wrap_call(self, *args, **kwargs):
wrap_args = (jit_engine.pycuda.driver.InOut(a) if isinstance(a, np.ndarray) else a for a in args)
- self.cuda_call(*wrap_args, threads=threads, block_size=block_size)
+ self.cuda_call(*wrap_args, **kwargs)
@staticmethod
@functools.lru_cache(maxsize=32)
diff --git a/psyneulink/core/llvm/builder_context.py b/psyneulink/core/llvm/builder_context.py
index 6fa5af54287..8695d1e5347 100644
--- a/psyneulink/core/llvm/builder_context.py
+++ b/psyneulink/core/llvm/builder_context.py
@@ -55,8 +55,9 @@ def module_count():
_BUILTIN_PREFIX = "__pnl_builtin_"
-_builtin_intrinsics = frozenset(('pow', 'log', 'exp', 'tanh', 'coth', 'csch', 'is_close', 'mt_rand_init',
- 'philox_rand_init'))
+_builtin_intrinsics = frozenset(('pow', 'log', 'exp', 'tanh', 'coth', 'csch',
+ 'is_close_float', 'is_close_double',
+ 'mt_rand_init', 'philox_rand_init'))
class _node_wrapper():
@@ -198,7 +199,7 @@ def get_builtin(self, name: str, args=[], function_type=None):
if name in _builtin_intrinsics:
return self.import_llvm_function(_BUILTIN_PREFIX + name)
if name in ('maxnum'):
- function_type = pnlvm.ir.FunctionType(args[0], [args[0], args[0]])
+ function_type = ir.FunctionType(args[0], [args[0], args[0]])
return self.module.declare_intrinsic("llvm." + name, args, function_type)
def create_llvm_function(self, args, component, name=None, *, return_type=ir.VoidType(), tags:frozenset=frozenset()):
@@ -206,21 +207,20 @@ def create_llvm_function(self, args, component, name=None, *, return_type=ir.Voi
# Builtins are already unique and need to keep their special name
func_name = name if name.startswith(_BUILTIN_PREFIX) else self.get_unique_name(name)
- func_ty = pnlvm.ir.FunctionType(return_type, args)
- llvm_func = pnlvm.ir.Function(self.module, func_ty, name=func_name)
+ func_ty = ir.FunctionType(return_type, args)
+ llvm_func = ir.Function(self.module, func_ty, name=func_name)
llvm_func.attributes.add('argmemonly')
for a in llvm_func.args:
if isinstance(a.type, ir.PointerType):
a.attributes.add('nonnull')
metadata = self.get_debug_location(llvm_func, component)
- if metadata is not None:
- scope = dict(metadata.operands)["scope"]
- llvm_func.set_metadata("dbg", scope)
+ scope = dict(metadata.operands)["scope"]
+ llvm_func.set_metadata("dbg", scope)
# Create entry block
block = llvm_func.append_basic_block(name="entry")
- builder = pnlvm.ir.IRBuilder(block)
+ builder = ir.IRBuilder(block)
builder.debug_metadata = metadata
return builder
@@ -262,12 +262,9 @@ def get_random_state_ptr(self, builder, component, state, params):
used_seed = builder.load(used_seed_ptr)
seed_ptr = helpers.get_param_ptr(builder, component, params, "seed")
- if isinstance(seed_ptr.type.pointee, ir.ArrayType):
- # Modulated params are usually single element arrays
- seed_ptr = builder.gep(seed_ptr, [self.int32_ty(0), self.int32_ty(0)])
- new_seed = builder.load(seed_ptr)
+ new_seed = pnlvm.helpers.load_extract_scalar_array_one(builder, seed_ptr)
# FIXME: The seed should ideally be integer already.
- # However, it can be modulated and we don't support,
+ # However, it can be modulated and we don't support
# passing integer values as computed results.
new_seed = builder.fptoui(new_seed, used_seed.type)
@@ -286,9 +283,6 @@ def get_random_state_ptr(self, builder, component, state, params):
@staticmethod
def get_debug_location(func: ir.Function, component):
- if "debug_info" not in debug_env:
- return
-
mod = func.module
path = inspect.getfile(component.__class__) if component is not None else "<pnl_builtin>"
d_version = mod.add_metadata([ir.IntType(32)(2), "Dwarf Version", ir.IntType(32)(4)])
@@ -327,6 +321,14 @@ def get_debug_location(func: ir.Function, component):
})
return di_loc
+ @staticmethod
+ def update_debug_loc_position(di_loc: ir.DIValue, line:int, column:int):
+ di_func = dict(di_loc.operands)["scope"]
+
+ return di_loc.parent.add_debug_info("DILocation", {
+ "line": line, "column": column, "scope": di_func,
+ })
+
@_comp_cached
def get_input_struct_type(self, component):
self._stats["input_structs_generated"] += 1
@@ -615,6 +617,10 @@ def _convert_llvm_ir_to_ctype(t: ir.Type):
return ctypes.c_double
elif type_t is ir.FloatType:
return ctypes.c_float
+ elif type_t is ir.HalfType:
+ # There's no half type in ctypes. Use uint16 instead.
+ # User will need to do the necessary casting.
+ return ctypes.c_uint16
elif type_t is ir.PointerType:
pointee = _convert_llvm_ir_to_ctype(t.pointee)
ret_t = ctypes.POINTER(pointee)
diff --git a/psyneulink/core/llvm/builtins.py b/psyneulink/core/llvm/builtins.py
index a64be3d4c6a..30973992713 100644
--- a/psyneulink/core/llvm/builtins.py
+++ b/psyneulink/core/llvm/builtins.py
@@ -11,14 +11,10 @@
from llvmlite import ir
-from . import debug
from . import helpers
from .builder_context import LLVMBuilderContext, _BUILTIN_PREFIX
-debug_env = debug.debug_env
-
-
def _setup_builtin_func_builder(ctx, name, args, *, return_type=ir.VoidType()):
builder = ctx.create_llvm_function(args, None, _BUILTIN_PREFIX + name,
return_type=return_type)
@@ -389,24 +385,27 @@ def setup_mat_add(ctx):
def setup_is_close(ctx):
- builder = _setup_builtin_func_builder(ctx, "is_close", [ctx.float_ty,
- ctx.float_ty,
- ctx.float_ty,
- ctx.float_ty],
- return_type=ctx.bool_ty)
- val1, val2, rtol, atol = builder.function.args
+ # Make sure we always have fp64 variant
+ for float_ty in {ctx.float_ty, ir.DoubleType()}:
+ name = "is_close_{}".format(float_ty)
+ builder = _setup_builtin_func_builder(ctx, name, [float_ty,
+ float_ty,
+ float_ty,
+ float_ty],
+ return_type=ctx.bool_ty)
+ val1, val2, rtol, atol = builder.function.args
- fabs_f = ctx.get_builtin("fabs", [val2.type])
+ fabs_f = ctx.get_builtin("fabs", [val2.type])
- diff = builder.fsub(val1, val2, "is_close_diff")
- abs_diff = builder.call(fabs_f, [diff], "is_close_abs")
+ diff = builder.fsub(val1, val2, "is_close_diff")
+ abs_diff = builder.call(fabs_f, [diff], "is_close_abs")
- abs2 = builder.call(fabs_f, [val2], "abs_val2")
+ abs2 = builder.call(fabs_f, [val2], "abs_val2")
- rtol = builder.fmul(rtol, abs2, "is_close_rtol")
- tol = builder.fadd(rtol, atol, "is_close_atol")
- res = builder.fcmp_ordered("<=", abs_diff, tol, "is_close_cmp")
- builder.ret(res)
+ rtol = builder.fmul(rtol, abs2, "is_close_rtol")
+ tol = builder.fadd(rtol, atol, "is_close_atol")
+ res = builder.fcmp_ordered("<=", abs_diff, tol, "is_close_cmp")
+ builder.ret(res)
def setup_csch(ctx):
@@ -415,11 +414,14 @@ def setup_csch(ctx):
x = builder.function.args[0]
exp_f = ctx.get_builtin("exp", [x.type])
# (2e**x)/(e**2x - 1)
+ # 2/(e**x - e**-x)
ex = builder.call(exp_f, [x])
- num = builder.fmul(ex.type(2), ex)
- _2x = builder.fmul(x.type(2), x)
- e2x = builder.call(exp_f, [_2x])
- den = builder.fsub(e2x, e2x.type(1))
+
+ nx = helpers.fneg(builder, x)
+ enx = builder.call(exp_f, [nx])
+ den = builder.fsub(ex, enx)
+ num = den.type(2)
+
res = builder.fdiv(num, den)
builder.ret(res)
@@ -429,12 +431,13 @@ def setup_tanh(ctx):
return_type=ctx.float_ty)
x = builder.function.args[0]
exp_f = ctx.get_builtin("exp", [x.type])
- # (e**2x - 1)/(e**2x + 1)
+ # (e**2x - 1)/(e**2x + 1) is faster but doesn't handle large inputs (exp -> Inf) well (Inf/Inf = NaN)
+ # (1 - (2/(exp(2*x) + 1))) is a bit slower but handles large inputs better
_2x = builder.fmul(x.type(2), x)
e2x = builder.call(exp_f, [_2x])
- num = builder.fsub(e2x, e2x.type(1))
den = builder.fadd(e2x, e2x.type(1))
- res = builder.fdiv(num, den)
+ res = builder.fdiv(den.type(2), den)
+ res = builder.fsub(res.type(1), res)
builder.ret(res)
@@ -443,12 +446,14 @@ def setup_coth(ctx):
return_type=ctx.float_ty)
x = builder.function.args[0]
exp_f = ctx.get_builtin("exp", [x.type])
+ # (e**2x + 1)/(e**2x - 1) is faster but doesn't handle large inputs (exp -> Inf) well (Inf/Inf = NaN)
+ # (1 + (2/(exp(2*x) - 1))) is a bit slower but handles large inputs better
# (e**2x + 1)/(e**2x - 1)
_2x = builder.fmul(x.type(2), x)
e2x = builder.call(exp_f, [_2x])
- num = builder.fadd(e2x, e2x.type(1))
den = builder.fsub(e2x, e2x.type(1))
- res = builder.fdiv(num, den)
+ res = builder.fdiv(den.type(2), den)
+ res = builder.fadd(res.type(1), res)
builder.ret(res)
diff --git a/psyneulink/core/llvm/codegen.py b/psyneulink/core/llvm/codegen.py
index 7b8258e077b..76f29f8bbfb 100644
--- a/psyneulink/core/llvm/codegen.py
+++ b/psyneulink/core/llvm/codegen.py
@@ -75,6 +75,10 @@ def np_cmp(builder, x, y):
self.name_constants = name_constants
super().__init__()
+ def _update_debug_metadata(self, builder: ir.IRBuilder, node:ast.AST):
+ builder.debug_metadata = self.ctx.update_debug_loc_position(builder.debug_metadata,
+ node.lineno,
+ node.col_offset)
def get_rval(self, val):
if helpers.is_pointer(val):
return self.builder.load(val)
@@ -99,18 +103,22 @@ def visit_arguments(self, node):
else:
self.register[param.arg] = self.func_params[param.arg]
- def visit_FunctionDef(self, node):
+ def visit_FunctionDef(self, node:ast.AST):
# the current position will be used to create temp space
# for local variables. This block dominates all others
# generated by this visitor.
self.var_builder = self.builder
+ self._update_debug_metadata(self.var_builder, node)
# Create a new basic block to house the generated code
udf_block = self.builder.append_basic_block(name="udf_body")
self.builder = ir.IRBuilder(udf_block)
+ self.builder.debug_metadata = self.var_builder.debug_metadata
super().generic_visit(node)
+ self._update_debug_metadata(self.builder, node)
+
if not self.builder.block.is_terminated:
# the function didn't use return as the last statement
# e.g. only includes 'return' statements in if blocks
@@ -120,10 +128,11 @@ def visit_FunctionDef(self, node):
self.var_builder.branch(udf_block)
return self.builder
- def visit_Lambda(self, node):
+ def visit_Lambda(self, node:ast.AST):
self.visit(node.args)
expr = self.visit(node.body)
+ self._update_debug_metadata(self.builder, node)
# store the lambda expression in the result and terminate
self.builder.store(expr, self.arg_out)
self.builder.ret_void()
@@ -197,9 +206,10 @@ def _not(builder, x):
def visit_Name(self, node):
return self.register.get(node.id, None)
- def visit_Attribute(self, node):
+ def visit_Attribute(self, node:ast.AST):
val = self.visit(node.value)
+ self._update_debug_metadata(self.builder, node)
# special case numpy attributes
if node.attr == "shape":
shape = helpers.get_array_shape(val)
@@ -233,11 +243,17 @@ def visit_Num(self, node):
return self.ctx.float_ty(node.n)
def visit_Assign(self, node):
- value = self.get_rval(self.visit(node.value))
+ value = self.visit(node.value)
+
+ self._update_debug_metadata(self.builder, node)
+ value = self.get_rval(value)
for t in node.targets:
target = self.visit(t)
+ # Visiting 't' might have changed code location metadata
+ self._update_debug_metadata(self.builder, node)
if target is None: # Allocate space for new variable
+ self._update_debug_metadata(self.var_builder, node)
target = self.var_builder.alloca(value.type, name=str(t.id) + '_local_variable')
self.register[t.id] = target
assert self.is_lval(target)
@@ -248,10 +264,13 @@ def visit_NameConstant(self, node):
assert val, f"Failed to convert NameConstant {node.value}"
return val
- def visit_Tuple(self, node):
+ def visit_Tuple(self, node:ast.AST):
elements = (self.visit(element) for element in node.elts)
+
+ self._update_debug_metadata(self.builder, node)
element_values = [self.builder.load(element) if helpers.is_pointer(element) else element for element in elements]
element_types = [element.type for element in element_values]
+
if len(element_types) > 0 and all(x == element_types[0] for x in element_types):
result = ir.ArrayType(element_types[0], len(element_types))(ir.Undefined)
else:
@@ -277,9 +296,12 @@ def _do_unary_op(self, builder, x, scalar_op):
return result
- def visit_UnaryOp(self, node):
+ def visit_UnaryOp(self, node:ast.AST):
operator = self.visit(node.op)
- operand = self.get_rval(self.visit(node.operand))
+
+ operand = self.visit(node.operand)
+ self._update_debug_metadata(self.builder, node)
+ operand = self.get_rval(operand)
return self._do_unary_op(self.builder, operand, operator)
def _do_bin_op(self, builder, x, y, scalar_op):
@@ -308,15 +330,22 @@ def _do_bin_op(self, builder, x, y, scalar_op):
return res
- def visit_BinOp(self, node):
+ def visit_BinOp(self, node:ast.AST):
operator = self.visit(node.op)
- lhs = self.get_rval(self.visit(node.left))
- rhs = self.get_rval(self.visit(node.right))
+ lhs = self.visit(node.left)
+ rhs = self.visit(node.right)
+
+ self._update_debug_metadata(self.builder, node)
+ lhs = self.get_rval(lhs)
+ rhs = self.get_rval(rhs)
return self._do_bin_op(self.builder, lhs, rhs, operator)
- def visit_BoolOp(self, node):
+ def visit_BoolOp(self, node:ast.AST):
operator = self.visit(node.op)
- values = (self.get_rval(self.visit(value)) for value in node.values)
+ values = list(self.visit(value) for value in node.values)
+
+ self._update_debug_metadata(self.builder, node)
+ values = (self.get_rval(v) for v in values)
ret_val = next(values)
for value in values:
assert ret_val.type == value.type, "Don't know how to mix types in boolean expressions!"
@@ -342,7 +371,11 @@ def _or(builder, x, y):
return _or
def visit_List(self, node):
- element_values = [self.get_rval(self.visit(element)) for element in node.elts]
+ elements = list(self.visit(element) for element in node.elts)
+
+ self._update_debug_metadata(self.builder, node)
+ element_values = [self.get_rval(e) for e in elements]
+
element_types = [element.type for element in element_values]
assert all(e_type == element_types[0] for e_type in element_types), f"Unable to convert {node} into a list! (Elements differ in type!)"
result = ir.ArrayType(element_types[0], len(element_types))(ir.Undefined)
@@ -382,17 +415,22 @@ def visit_GtE(self, node):
return self._generate_fcmp_handler(self.ctx, self.builder, ">=")
def visit_Compare(self, node):
- result = self.get_rval(self.visit(node.left))
+ res = self.visit(node.left)
+ comparators = list(self.visit(comparator) for comparator in node.comparators)
+ ops = list(self.visit(op) for op in node.ops)
- comparators = (self.visit(comparator) for comparator in node.comparators)
+ self._update_debug_metadata(self.builder, node)
+ result = self.get_rval(res)
values = (self.builder.load(val) if helpers.is_pointer(val) else val for val in comparators)
- ops = (self.visit(op) for op in node.ops)
for val, op in zip(values, ops):
result = self._do_bin_op(self.builder, result, val, op)
return result
- def visit_If(self, node):
- cond_val = self.get_rval(self.visit(node.test))
+ def visit_If(self, node:ast.AST):
+ cond = self.visit(node.test)
+
+ self._update_debug_metadata(self.builder, node)
+ cond_val = self.get_rval(cond)
predicate = helpers.convert_type(self.builder, cond_val, self.ctx.bool_ty)
with self.builder.if_else(predicate) as (then, otherwise):
@@ -403,10 +441,11 @@ def visit_If(self, node):
for child in node.orelse:
self.visit(child)
- def visit_Return(self, node):
+ def visit_Return(self, node:ast.AST):
ret_val = self.visit(node.value)
arg_out = self.arg_out
+ self._update_debug_metadata(self.builder, node)
# dereference pointer
if helpers.is_pointer(ret_val):
ret_val = self.builder.load(ret_val)
@@ -423,9 +462,11 @@ def visit_Return(self, node):
self.builder.store(ret_val, arg_out)
self.builder.ret_void()
- def visit_Subscript(self, node):
+ def visit_Subscript(self, node:ast.AST):
node_val = self.visit(node.value)
index = self.visit(node.slice)
+
+ self._update_debug_metadata(self.builder, node)
node_slice_val = helpers.convert_type(self.builder, index, self.ctx.int32_ty)
if not self.is_lval(node_val):
temp_node_val = self.builder.alloca(node_val.type)
@@ -434,7 +475,7 @@ def visit_Subscript(self, node):
return self.builder.gep(node_val, [self.ctx.int32_ty(0), node_slice_val])
- def visit_Index(self, node):
+ def visit_Index(self, node:ast.AST):
"""
Returns the wrapped value.
@@ -442,12 +483,13 @@ def visit_Index(self, node):
"""
return self.visit(node.value)
- def visit_Call(self, node):
+ def visit_Call(self, node:ast.AST):
node_args = [self.visit(arg) for arg in node.args]
call_func = self.visit(node.func)
assert callable(call_func), f"Uncallable function {node.func}!"
+ self._update_debug_metadata(self.builder, node)
return call_func(self.builder, *node_args)
# Python builtins
@@ -513,7 +555,7 @@ def call_builtin_np_max(self, builder, x):
x = self.get_rval(x)
if helpers.is_scalar(x):
return x
- res = self.ctx.float_ty("-Inf")
+ res = self.ctx.float_ty(float("-Inf"))
def find_max(builder, x):
nonlocal res
# to propagate NaNs we use unordered >,
diff --git a/psyneulink/core/llvm/debug.py b/psyneulink/core/llvm/debug.py
index 0a6788f838d..02038133f0a 100644
--- a/psyneulink/core/llvm/debug.py
+++ b/psyneulink/core/llvm/debug.py
@@ -23,7 +23,6 @@
* "print_values" -- Enabled printfs in llvm code (from ctx printf helper)
Compilation modifiers:
- * "debug_info" -- emit line debugging information when generating LLVM IR
* "const_data" -- hardcode initial output values into generated code,
instead of loading them from the data argument
* "const_input" -- hardcode input values for composition runs
diff --git a/psyneulink/core/llvm/execution.py b/psyneulink/core/llvm/execution.py
index ff7a2defdd6..ab96adfafd4 100644
--- a/psyneulink/core/llvm/execution.py
+++ b/psyneulink/core/llvm/execution.py
@@ -48,10 +48,23 @@ def _tupleize(x):
return x if x is not None else tuple()
def _element_dtype(x):
+ """
+ Extract base builtin type from aggregate type.
+
+ Throws assertion failure if the aggregate type includes more than one base type.
+ The assumption is that array of builtin type has the same binary layout as
+ the original aggregate and it's easier to construct
+ """
dt = np.dtype(x)
while dt.subdtype is not None:
dt = dt.subdtype[0]
+ if not dt.isbuiltin:
+ fdts = (_element_dtype(f[0]) for f in dt.fields.values())
+ dt = next(fdts)
+ assert all(dt == fdt for fdt in fdts)
+
+ assert dt.isbuiltin, "Element type is not builtin: {} from {}".format(dt, np.dtype(x))
return dt
def _pretty_size(size):
@@ -683,7 +696,7 @@ def _prepare_evaluate(self, variable, num_evaluations):
# Construct input variable
var_dty = _element_dtype(bin_func.byref_arg_types[5])
- converted_variable = np.asfarray(np.concatenate(variable), dtype=var_dty)
+ converted_variable = np.concatenate(variable, dtype=var_dty)
# Output ctype
out_ty = bin_func.byref_arg_types[4] * num_evaluations
@@ -715,17 +728,27 @@ def thread_evaluate(self, variable, num_evaluations):
ct_results = out_ty()
ct_variable = converted_variale.ctypes.data_as(self.__bin_func.c_func.argtypes[5])
- # There are 7 arguments to evaluate_alloc_range:
- # comp_param, comp_state, from, to, results, input, comp_data
jobs = min(os.cpu_count(), num_evaluations)
evals_per_job = (num_evaluations + jobs - 1) // jobs
- executor = concurrent.futures.ThreadPoolExecutor(max_workers=jobs)
- for i in range(jobs):
- start = i * evals_per_job
- stop = min((i + 1) * evals_per_job, num_evaluations)
- executor.submit(self.__bin_func, ct_param, ct_state, int(start),
- int(stop), ct_results, ct_variable, ct_data)
-
- executor.shutdown()
+
+ parallel_start = time.time()
+ with concurrent.futures.ThreadPoolExecutor(max_workers=jobs) as ex:
+ # There are 7 arguments to evaluate_alloc_range:
+ # comp_param, comp_state, from, to, results, input, comp_data
+ results = [ex.submit(self.__bin_func, ct_param, ct_state,
+ int(i * evals_per_job),
+ min((i + 1) * evals_per_job, num_evaluations),
+ ct_results, ct_variable, ct_data)
+ for i in range(jobs)]
+
+ parallel_stop = time.time()
+ if "time_stat" in self._debug_env:
+ print("Time to run {} executions of '{}' in {} threads: {}".format(
+ num_evaluations, self.__bin_func.name, jobs,
+ parallel_stop - parallel_start))
+
+
+ exceptions = [r.exception() for r in results]
+ assert all(e is None for e in exceptions), "Not all jobs finished sucessfully: {}".format(exceptions)
return ct_results
diff --git a/psyneulink/core/llvm/helpers.py b/psyneulink/core/llvm/helpers.py
index 6b54aaf4bde..bdb887e7ddc 100644
--- a/psyneulink/core/llvm/helpers.py
+++ b/psyneulink/core/llvm/helpers.py
@@ -144,15 +144,14 @@ def get_state_ptr(builder, component, state_ptr, stateful_name, hist_idx=0):
return ptr
-def push_state_val(builder, component, state_ptr, name, new_val):
+def get_state_space(builder, component, state_ptr, name):
val_ptr = get_state_ptr(builder, component, state_ptr, name, None)
for i in range(len(val_ptr.type.pointee) - 1, 0, -1):
dest_ptr = get_state_ptr(builder, component, state_ptr, name, i)
src_ptr = get_state_ptr(builder, component, state_ptr, name, i - 1)
builder.store(builder.load(src_ptr), dest_ptr)
- dest_ptr = get_state_ptr(builder, component, state_ptr, name)
- builder.store(builder.load(new_val), dest_ptr)
+ return get_state_ptr(builder, component, state_ptr, name)
def unwrap_2d_array(builder, element):
@@ -219,7 +218,8 @@ def csch(ctx, builder, x):
def is_close(ctx, builder, val1, val2, rtol=1e-05, atol=1e-08):
- is_close_f = ctx.get_builtin("is_close")
+ assert val1.type == val2.type
+ is_close_f = ctx.get_builtin("is_close_{}".format(val1.type))
rtol_val = val1.type(rtol)
atol_val = val1.type(atol)
return builder.call(is_close_f, [val1, val2, rtol_val, atol_val])
@@ -304,6 +304,24 @@ def convert_type(builder, val, t):
# Python integers are signed
return builder.fptosi(val, t)
+ if is_floating_point(val) and is_floating_point(t):
+ if isinstance(val.type, ir.HalfType) or isinstance(t, ir.DoubleType):
+ return builder.fpext(val, t)
+ elif isinstance(val.type, ir.DoubleType) or isinstance(t, ir.HalfType):
+ # FIXME: Direct conversion from double to half needs a runtime
+ # function (__truncdfhf2). llvmlite MCJIT fails to provide
+ # it and instead generates invocation of a NULL pointer.
+ # Use double conversion (double->float->half) instead.
+ # Both steps can be done in one CPU instruction,
+ # but the result can be slightly different
+ # see: https://github.com/numba/llvmlite/issues/834
+ if isinstance(val.type, ir.DoubleType) and isinstance(t, ir.HalfType):
+ val = builder.fptrunc(val, ir.FloatType())
+ return builder.fptrunc(val, t)
+ else:
+ assert val.type == t
+ return val
+
assert False, "Unknown type conversion: {} -> {}".format(val.type, t)
@@ -463,18 +481,24 @@ def get_private_condition_initializer(self, composition):
return ((0, 0, 0),
tuple((0, (-1, -1, -1)) for _ in composition.nodes))
- def get_condition_struct_type(self, composition=None):
- composition = self.composition if composition is None else composition
- structs = [self.get_private_condition_struct_type(composition)]
- for node in composition.nodes:
- structs.append(self.get_condition_struct_type(node) if isinstance(node, type(self.composition)) else ir.LiteralStructType([]))
+ def get_condition_struct_type(self, node=None):
+ node = self.composition if node is None else node
+
+ subnodes = getattr(node, 'nodes', [])
+ structs = [self.get_condition_struct_type(n) for n in subnodes]
+ if len(structs) != 0:
+ structs.insert(0, self.get_private_condition_struct_type(node))
+
return ir.LiteralStructType(structs)
- def get_condition_initializer(self, composition=None):
- composition = self.composition if composition is None else composition
- data = [self.get_private_condition_initializer(composition)]
- for node in composition.nodes:
- data.append(self.get_condition_initializer(node) if isinstance(node, type(self.composition)) else tuple())
+ def get_condition_initializer(self, node=None):
+ node = self.composition if node is None else node
+
+ subnodes = getattr(node, 'nodes', [])
+ data = [self.get_condition_initializer(n) for n in subnodes]
+ if len(data) != 0:
+ data.insert(0, self.get_private_condition_initializer(node))
+
return tuple(data)
def bump_ts(self, builder, cond_ptr, count=(0, 0, 1)):
diff --git a/psyneulink/core/scheduling/condition.py b/psyneulink/core/scheduling/condition.py
index aba519892b7..2d0e1fbfdf3 100644
--- a/psyneulink/core/scheduling/condition.py
+++ b/psyneulink/core/scheduling/condition.py
@@ -20,7 +20,7 @@
import numpy as np
from psyneulink.core.globals.context import handle_external_context
-from psyneulink.core.globals.json import JSONDumpable
+from psyneulink.core.globals.mdf import MDFSerializable
from psyneulink.core.globals.keywords import MODEL_SPEC_ID_TYPE, comparison_operators
from psyneulink.core.globals.parameters import parse_context
from psyneulink.core.globals.utilities import parse_valid_identifier
@@ -58,7 +58,7 @@ def _create_as_pnl_condition(condition):
return res
-class Condition(graph_scheduler.Condition, JSONDumpable):
+class Condition(graph_scheduler.Condition, MDFSerializable):
@handle_external_context()
def is_satisfied(self, *args, context=None, execution_id=None, **kwargs):
if execution_id is None:
@@ -293,6 +293,6 @@ def as_mdf_model(self):
m = super().as_mdf_model()
if self.parameter == 'value':
- m.args['parameter'] = f'{self.dependency.name}_OutputPort_0'
+ m.kwargs['parameter'] = f'{self.dependency.name}_OutputPort_0'
return m
diff --git a/psyneulink/core/scheduling/scheduler.py b/psyneulink/core/scheduling/scheduler.py
index f163dc19900..3db80e0551a 100644
--- a/psyneulink/core/scheduling/scheduler.py
+++ b/psyneulink/core/scheduling/scheduler.py
@@ -8,6 +8,7 @@
# ********************************************* Scheduler **************************************************************
import copy
+import logging
import typing
import graph_scheduler
@@ -15,7 +16,7 @@
from psyneulink import _unit_registry
from psyneulink.core.globals.context import Context, handle_external_context
-from psyneulink.core.globals.json import JSONDumpable
+from psyneulink.core.globals.mdf import MDFSerializable
from psyneulink.core.globals.utilities import parse_valid_identifier
from psyneulink.core.scheduling.condition import _create_as_pnl_condition
@@ -24,10 +25,11 @@
]
+logger = logging.getLogger(__name__)
SchedulingMode = graph_scheduler.scheduler.SchedulingMode
-class Scheduler(graph_scheduler.Scheduler, JSONDumpable):
+class Scheduler(graph_scheduler.Scheduler, MDFSerializable):
def __init__(
self,
composition=None,
@@ -50,7 +52,7 @@ def __init__(
default_execution_id = composition.default_execution_id
# TODO: consider integrating something like this into graph-scheduler?
- self._user_specified_conds = copy.copy(conditions)
+ self._user_specified_conds = copy.copy(conditions) if conditions is not None else {}
super().__init__(
graph=graph,
@@ -70,19 +72,51 @@ def replace_term_conds(term_conds):
self.default_termination_conds = replace_term_conds(self.default_termination_conds)
self.termination_conds = replace_term_conds(self.termination_conds)
+ def _validate_conditions(self):
+ unspecified_nodes = []
+ for node in self.nodes:
+ if node not in self.conditions:
+ dependencies = list(self.dependency_dict[node])
+ if len(dependencies) == 0:
+ cond = graph_scheduler.Always()
+ elif len(dependencies) == 1:
+ cond = graph_scheduler.EveryNCalls(dependencies[0], 1)
+ else:
+ cond = graph_scheduler.All(*[graph_scheduler.EveryNCalls(x, 1) for x in dependencies])
+
+ # TODO: replace this call in graph-scheduler if adding _user_specified_conds
+ self._add_condition(node, cond)
+ unspecified_nodes.append(node)
+ if len(unspecified_nodes) > 0:
+ logger.info(
+ 'These nodes have no Conditions specified, and will be scheduled with conditions: {0}'.format(
+ {node: self.conditions[node] for node in unspecified_nodes}
+ )
+ )
+
def add_condition(self, owner, condition):
- super().add_condition(owner, _create_as_pnl_condition(condition))
+ self._user_specified_conds[owner] = condition
+ self._add_condition(owner, condition)
+
+ def _add_condition(self, owner, condition):
+ condition = _create_as_pnl_condition(condition)
+ super().add_condition(owner, condition)
def add_condition_set(self, conditions):
+ self._user_specified_conds.update(conditions)
+ self._add_condition_set(conditions)
+
+ def _add_condition_set(self, conditions):
try:
conditions = conditions.conditions
except AttributeError:
pass
- super().add_condition_set({
+ conditions = {
node: _create_as_pnl_condition(cond)
for node, cond in conditions.items()
- })
+ }
+ super().add_condition_set(conditions)
@handle_external_context(fallback_default=True)
def run(
diff --git a/psyneulink/library/components/mechanisms/modulatory/control/agt/agtcontrolmechanism.py b/psyneulink/library/components/mechanisms/modulatory/control/agt/agtcontrolmechanism.py
index f97e9a80003..92c245d3275 100644
--- a/psyneulink/library/components/mechanisms/modulatory/control/agt/agtcontrolmechanism.py
+++ b/psyneulink/library/components/mechanisms/modulatory/control/agt/agtcontrolmechanism.py
@@ -169,6 +169,7 @@
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.keywords import \
INIT_EXECUTE_METHOD_ONLY, MECHANISM, OBJECTIVE_MECHANISM
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -244,6 +245,7 @@ class AGTControlMechanism(ControlMechanism):
# PREFERENCE_SET_NAME: 'ControlMechanismClassPreferences',
# PREFERENCE_KEYWORD<pref>: <setting>...}
+ @check_user_specified
@tc.typecheck
def __init__(self,
monitored_output_ports=None,
diff --git a/psyneulink/library/components/mechanisms/modulatory/control/agt/lccontrolmechanism.py b/psyneulink/library/components/mechanisms/modulatory/control/agt/lccontrolmechanism.py
index bcee443a12e..ae6b0af7a9d 100644
--- a/psyneulink/library/components/mechanisms/modulatory/control/agt/lccontrolmechanism.py
+++ b/psyneulink/library/components/mechanisms/modulatory/control/agt/lccontrolmechanism.py
@@ -307,7 +307,7 @@
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.keywords import \
INIT_EXECUTE_METHOD_ONLY, MULTIPLICATIVE_PARAM, PROJECTIONS
-from psyneulink.core.globals.parameters import Parameter, ParameterAlias
+from psyneulink.core.globals.parameters import Parameter, ParameterAlias, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import is_iterable, convert_to_list
@@ -662,6 +662,7 @@ class Parameters(ControlMechanism.Parameters):
modulated_mechanisms = Parameter(None, stateful=False, loggable=False)
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -821,8 +822,7 @@ def _execute(
):
"""Updates LCControlMechanism's ControlSignal based on input and mode parameter value
"""
- # IMPLEMENTATION NOTE: skip ControlMechanism._execute since it is a stub method that returns input_values
- output_values = super(ControlMechanism, self)._execute(
+ output_values = super()._execute(
variable=variable,
context=context,
runtime_params=runtime_params,
@@ -834,58 +834,54 @@ def _execute(
return gain_t, output_values[0], output_values[1], output_values[2]
- def _gen_llvm_invoke_function(self, ctx, builder, function, params, state, variable, *, tags:frozenset):
- assert function is self.function
- mf_out, builder = super()._gen_llvm_invoke_function(ctx, builder, function, params, state, variable, tags=tags)
+ def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params, m_state, m_in,
+ m_val, ip_output, *, tags:frozenset):
+ mf_out, builder = super()._gen_llvm_mechanism_functions(ctx, builder, m_base_params,
+ m_params, m_state, m_in,
+ None, ip_output, tags=tags)
# prepend gain type (matches output[1] type)
gain_ty = mf_out.type.pointee.elements[1]
- elements = gain_ty, *mf_out.type.pointee.elements
- elements_ty = pnlvm.ir.LiteralStructType(elements)
- # allocate new output type
- new_out = builder.alloca(elements_ty, name="function_out")
+ assert all(e == gain_ty for e in mf_out.type.pointee.elements)
+ mech_out_ty = pnlvm.ir.ArrayType(gain_ty, len(mf_out.type.pointee.elements) + 1)
+
+ # allocate a new output location if the type doesn't match the one
+ # provided by the caller.
+ if mech_out_ty != m_val.type.pointee:
+ m_val = builder.alloca(mech_out_ty, name="mechanism_out")
# Load mechanism parameters
- params = builder.function.args[0]
- scaling_factor_ptr = pnlvm.helpers.get_param_ptr(builder, self, params,
+ scaling_factor_ptr = pnlvm.helpers.get_param_ptr(builder, self, m_params,
"scaling_factor_gain")
- base_factor_ptr = pnlvm.helpers.get_param_ptr(builder, self, params,
+ base_factor_ptr = pnlvm.helpers.get_param_ptr(builder, self, m_params,
"base_level_gain")
- # If modulated, scaling factor is a single element array
- if isinstance(scaling_factor_ptr.type.pointee, pnlvm.ir.ArrayType):
- assert len(scaling_factor_ptr.type.pointee) == 1
- scaling_factor_ptr = builder.gep(scaling_factor_ptr,
- [ctx.int32_ty(0), ctx.int32_ty(0)])
- # If modulated, base factor is a single element array
- if isinstance(base_factor_ptr.type.pointee, pnlvm.ir.ArrayType):
- assert len(base_factor_ptr.type.pointee) == 1
- base_factor_ptr = builder.gep(base_factor_ptr,
- [ctx.int32_ty(0), ctx.int32_ty(0)])
- scaling_factor = builder.load(scaling_factor_ptr)
- base_factor = builder.load(base_factor_ptr)
-
- # Apply to the entire vector
+ # If modulated, parameters are single element array
+ scaling_factor = pnlvm.helpers.load_extract_scalar_array_one(builder, scaling_factor_ptr)
+ base_factor = pnlvm.helpers.load_extract_scalar_array_one(builder, base_factor_ptr)
+
+ # Apply to the entire first subvector
vi = builder.gep(mf_out, [ctx.int32_ty(0), ctx.int32_ty(1)])
- vo = builder.gep(new_out, [ctx.int32_ty(0), ctx.int32_ty(0)])
+ vo = builder.gep(m_val, [ctx.int32_ty(0), ctx.int32_ty(0)])
with pnlvm.helpers.array_ptr_loop(builder, vi, "LC_gain") as (b1, index):
in_ptr = b1.gep(vi, [ctx.int32_ty(0), index])
+ out_ptr = b1.gep(vo, [ctx.int32_ty(0), index])
+
val = b1.load(in_ptr)
val = b1.fmul(val, scaling_factor)
val = b1.fadd(val, base_factor)
- out_ptr = b1.gep(vo, [ctx.int32_ty(0), index])
b1.store(val, out_ptr)
# copy the main function return value
for i, _ in enumerate(mf_out.type.pointee.elements):
ptr = builder.gep(mf_out, [ctx.int32_ty(0), ctx.int32_ty(i)])
- out_ptr = builder.gep(new_out, [ctx.int32_ty(0), ctx.int32_ty(i + 1)])
+ out_ptr = builder.gep(m_val, [ctx.int32_ty(0), ctx.int32_ty(i + 1)])
val = builder.load(ptr)
builder.store(val, out_ptr)
- return new_out, builder
+ return m_val, builder
# 5/8/20: ELIMINATE SYSTEM
# SEEMS TO STILL BE USED BY SOME MODELS; DELETE WHEN THOSE ARE UPDATED
diff --git a/psyneulink/library/components/mechanisms/modulatory/learning/autoassociativelearningmechanism.py b/psyneulink/library/components/mechanisms/modulatory/learning/autoassociativelearningmechanism.py
index ec540c8764e..80e0e5fb43a 100644
--- a/psyneulink/library/components/mechanisms/modulatory/learning/autoassociativelearningmechanism.py
+++ b/psyneulink/library/components/mechanisms/modulatory/learning/autoassociativelearningmechanism.py
@@ -104,7 +104,7 @@
from psyneulink.core.globals.context import ContextFlags
from psyneulink.core.globals.keywords import \
ADDITIVE, AUTOASSOCIATIVE_LEARNING_MECHANISM, LEARNING, LEARNING_PROJECTION, LEARNING_SIGNAL, NAME, OWNER_VALUE, VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import is_numeric, parameter_spec
@@ -319,6 +319,7 @@ class Parameters(LearningMechanism.Parameters):
classPreferenceLevel = PreferenceLevel.TYPE
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable:tc.any(list, np.ndarray),
diff --git a/psyneulink/library/components/mechanisms/modulatory/learning/kohonenlearningmechanism.py b/psyneulink/library/components/mechanisms/modulatory/learning/kohonenlearningmechanism.py
index d6717abd1d9..9122b97282b 100644
--- a/psyneulink/library/components/mechanisms/modulatory/learning/kohonenlearningmechanism.py
+++ b/psyneulink/library/components/mechanisms/modulatory/learning/kohonenlearningmechanism.py
@@ -108,7 +108,7 @@
from psyneulink.core.globals.keywords import \
ADDITIVE, KOHONEN_LEARNING_MECHANISM, \
LEARNING, LEARNING_PROJECTION, LEARNING_SIGNAL
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
from psyneulink.core.globals.utilities import is_numeric, parameter_spec
@@ -320,6 +320,7 @@ class Parameters(LearningMechanism.Parameters):
learning_timing = LearningTiming.EXECUTION_PHASE
modulation = ADDITIVE
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable:tc.any(list, np.ndarray),
diff --git a/psyneulink/library/components/mechanisms/processing/integrator/ddm.py b/psyneulink/library/components/mechanisms/processing/integrator/ddm.py
index c3bac361a1d..5f790fc9200 100644
--- a/psyneulink/library/components/mechanisms/processing/integrator/ddm.py
+++ b/psyneulink/library/components/mechanisms/processing/integrator/ddm.py
@@ -380,7 +380,7 @@
from psyneulink.core.globals.keywords import \
ALLOCATION_SAMPLES, FUNCTION, FUNCTION_PARAMS, INPUT_PORT_VARIABLES, NAME, OWNER_VALUE, \
THRESHOLD, VARIABLE, PREFERENCE_SET_NAME
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.globals.utilities import convert_all_elements_to_np_array, is_numeric, is_same_function_spec, object_has_single_value, get_global_seed
@@ -753,6 +753,7 @@ class Parameters(ProcessingMechanism.Parameters):
]
standard_output_port_names = [i['name'] for i in standard_output_ports]
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1099,25 +1100,20 @@ def _execute(
return_value[self.DECISION_VARIABLE_INDEX] = threshold
return return_value
- def _gen_llvm_invoke_function(self, ctx, builder, function, params, state, variable, *, tags:frozenset):
-
- mf_out, builder = super()._gen_llvm_invoke_function(ctx, builder, function, params, state, variable, tags=tags)
-
- mech_out_ty = ctx.convert_python_struct_to_llvm_ir(self.defaults.value)
- mech_out = builder.alloca(mech_out_ty, name="mech_out")
+ def _gen_llvm_invoke_function(self, ctx, builder, function, params, state,
+ variable, m_val, *, tags:frozenset):
if isinstance(self.function, IntegratorFunction):
- # Integrator version of the DDM mechanism converts the
- # second element to a 2d array
- builder.store(builder.load(builder.gep(mf_out, [ctx.int32_ty(0),
- ctx.int32_ty(0)])),
- builder.gep(mech_out, [ctx.int32_ty(0),
- ctx.int32_ty(0)]))
- builder.store(builder.load(builder.gep(mf_out, [ctx.int32_ty(0),
- ctx.int32_ty(1)])),
- builder.gep(mech_out, [ctx.int32_ty(0),
- ctx.int32_ty(1)]))
+ # Integrator based DDM works like other mechanisms
+ return super()._gen_llvm_invoke_function(ctx, builder, function,
+ params, state, variable,
+ m_val, tags=tags)
+
elif isinstance(self.function, DriftDiffusionAnalytical):
+ mf_out, builder = super()._gen_llvm_invoke_function(ctx, builder, function,
+ params, state, variable,
+ None, tags=tags)
+ # The order and number of returned values is different for DDA
for res_idx, idx in enumerate((self.RESPONSE_TIME_INDEX,
self.PROBABILITY_LOWER_THRESHOLD_INDEX,
self.RT_CORRECT_MEAN_INDEX,
@@ -1127,47 +1123,68 @@ def _gen_llvm_invoke_function(self, ctx, builder, function, params, state, varia
self.RT_INCORRECT_VARIANCE_INDEX,
self.RT_INCORRECT_SKEW_INDEX)):
src = builder.gep(mf_out, [ctx.int32_ty(0), ctx.int32_ty(res_idx)])
- dst = builder.gep(mech_out, [ctx.int32_ty(0), ctx.int32_ty(idx)])
+ dst = builder.gep(m_val, [ctx.int32_ty(0), ctx.int32_ty(idx)])
builder.store(builder.load(src), dst)
- # Handle upper threshold probability
- src = builder.gep(mf_out, [ctx.int32_ty(0), ctx.int32_ty(1),
- ctx.int32_ty(0)])
- dst = builder.gep(mech_out, [ctx.int32_ty(0),
- ctx.int32_ty(self.PROBABILITY_UPPER_THRESHOLD_INDEX),
- ctx.int32_ty(0)])
+ # Handle upper threshold probability (1 - Lower Threshold)
+ src = builder.gep(m_val, [ctx.int32_ty(0),
+ ctx.int32_ty(self.PROBABILITY_LOWER_THRESHOLD_INDEX),
+ ctx.int32_ty(0)])
+ dst = builder.gep(m_val, [ctx.int32_ty(0),
+ ctx.int32_ty(self.PROBABILITY_UPPER_THRESHOLD_INDEX),
+ ctx.int32_ty(0)])
prob_lower_thr = builder.load(src)
- prob_upper_thr = builder.fsub(prob_lower_thr.type(1),
- prob_lower_thr)
+ prob_upper_thr = builder.fsub(prob_lower_thr.type(1), prob_lower_thr)
builder.store(prob_upper_thr, dst)
- # Load function threshold
+ # Store threshold as decision variable output
+ # this will be used by the mechanism to return the right decision
threshold_ptr = pnlvm.helpers.get_param_ptr(builder, self.function,
params, THRESHOLD)
- threshold = pnlvm.helpers.load_extract_scalar_array_one(builder,
- threshold_ptr)
- # Load mechanism state to generate random numbers
- mech_params = builder.function.args[0]
- mech_state = builder.function.args[1]
- random_state = ctx.get_random_state_ptr(builder, self, mech_state, mech_params)
+ threshold = pnlvm.helpers.load_extract_scalar_array_one(builder, threshold_ptr)
+ decision_ptr = builder.gep(m_val, [ctx.int32_ty(0),
+ ctx.int32_ty(self.DECISION_VARIABLE_INDEX),
+ ctx.int32_ty(0)])
+ builder.store(threshold, decision_ptr)
+ else:
+ assert False, "Unknown mode in compiled DDM!"
+
+ return m_val, builder
+
+ def _gen_llvm_mechanism_functions(self, ctx, builder, m_base_params, m_params, m_state, m_in,
+ m_val, ip_output, *, tags:frozenset):
+
+ mf_out, builder = super()._gen_llvm_mechanism_functions(ctx, builder, m_base_params,
+ m_params, m_state, m_in, m_val,
+ ip_output, tags=tags)
+ assert mf_out is m_val
+
+ if isinstance(self.function, DriftDiffusionAnalytical):
+ random_state = ctx.get_random_state_ptr(builder, self, m_state, m_params)
random_f = ctx.get_uniform_dist_function_by_state(random_state)
random_val_ptr = builder.alloca(random_f.args[1].type.pointee, name="random_out")
builder.call(random_f, [random_state, random_val_ptr])
random_val = builder.load(random_val_ptr)
# Convert ER to decision variable:
- dst = builder.gep(mech_out, [ctx.int32_ty(0),
- ctx.int32_ty(self.DECISION_VARIABLE_INDEX),
- ctx.int32_ty(0)])
+ prob_lthr_ptr = builder.gep(m_val, [ctx.int32_ty(0),
+ ctx.int32_ty(self.PROBABILITY_LOWER_THRESHOLD_INDEX),
+ ctx.int32_ty(0)])
+ prob_lower_thr = builder.load(prob_lthr_ptr)
thr_cmp = builder.fcmp_ordered("<", random_val, prob_lower_thr)
+
+ # The correct (modulated) threshold value is passed as
+ # decision variable output
+ decision_ptr = builder.gep(m_val, [ctx.int32_ty(0),
+ ctx.int32_ty(self.DECISION_VARIABLE_INDEX),
+ ctx.int32_ty(0)])
+ threshold = builder.load(decision_ptr)
neg_threshold = pnlvm.helpers.fneg(builder, threshold)
res = builder.select(thr_cmp, neg_threshold, threshold)
- builder.store(res, dst)
- else:
- assert False, "Unknown mode in compiled DDM!"
+ builder.store(res, decision_ptr)
- return mech_out, builder
+ return m_val, builder
@handle_external_context(fallback_most_recent=True)
def reset(self, *args, force=False, context=None, **kwargs):
diff --git a/psyneulink/library/components/mechanisms/processing/integrator/episodicmemorymechanism.py b/psyneulink/library/components/mechanisms/processing/integrator/episodicmemorymechanism.py
index 268ba986a5b..3286eff87c8 100644
--- a/psyneulink/library/components/mechanisms/processing/integrator/episodicmemorymechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/integrator/episodicmemorymechanism.py
@@ -415,7 +415,7 @@
from psyneulink.core.components.mechanisms.processing.processingmechanism import ProcessingMechanism_Base
from psyneulink.core.components.ports.inputport import InputPort
from psyneulink.core.globals.keywords import EPISODIC_MEMORY_MECHANISM, INITIALIZER, NAME, OWNER_VALUE, VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import deprecation_warning, convert_to_np_array, convert_all_elements_to_np_array
@@ -512,6 +512,7 @@ class Parameters(ProcessingMechanism_Base.Parameters):
variable = Parameter([[0,0]], pnl_internal=True, constructor_argument='default_variable')
function = Parameter(ContentAddressableMemory, stateful=False, loggable=False)
+ @check_user_specified
def __init__(self,
default_variable:Union[int, list, np.ndarray]=None,
size:Optional[Union[int, list, np.ndarray]]=None,
diff --git a/psyneulink/library/components/mechanisms/processing/leabramechanism.py b/psyneulink/library/components/mechanisms/processing/leabramechanism.py
index 16cbc400030..ff78c9f3f39 100644
--- a/psyneulink/library/components/mechanisms/processing/leabramechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/leabramechanism.py
@@ -106,7 +106,7 @@
from psyneulink.core.components.functions.function import Function_Base
from psyneulink.core.components.mechanisms.processing.processingmechanism import ProcessingMechanism_Base
from psyneulink.core.globals.keywords import LEABRA_FUNCTION, LEABRA_FUNCTION_TYPE, LEABRA_MECHANISM, NETWORK, PREFERENCE_SET_NAME
-from psyneulink.core.globals.parameters import FunctionParameter, Parameter
+from psyneulink.core.globals.parameters import FunctionParameter, Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.scheduling.time import TimeScale
@@ -212,6 +212,7 @@ class Parameters(Function_Base.Parameters):
variable = Parameter(np.array([[0], [0]]), read_only=True, pnl_internal=True, constructor_argument='default_variable')
network = None
+ @check_user_specified
def __init__(self,
default_variable=None,
network=None,
@@ -471,6 +472,7 @@ class Parameters(ProcessingMechanism_Base.Parameters):
network = FunctionParameter(None)
training_flag = Parameter(False, setter=_training_flag_setter, dependencies='network')
+ @check_user_specified
def __init__(self,
network=None,
input_size=None,
diff --git a/psyneulink/library/components/mechanisms/processing/objective/comparatormechanism.py b/psyneulink/library/components/mechanisms/processing/objective/comparatormechanism.py
index 50381e88984..3a8e169b380 100644
--- a/psyneulink/library/components/mechanisms/processing/objective/comparatormechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/objective/comparatormechanism.py
@@ -153,7 +153,7 @@
from psyneulink.core.components.ports.port import _parse_port_spec
from psyneulink.core.globals.keywords import \
COMPARATOR_MECHANISM, FUNCTION, INPUT_PORTS, NAME, OUTCOME, SAMPLE, TARGET, VARIABLE, PREFERENCE_SET_NAME, MSE, SSE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
from psyneulink.core.globals.utilities import \
@@ -323,6 +323,7 @@ class Parameters(ObjectiveMechanism.Parameters):
standard_output_port_names = ObjectiveMechanism.standard_output_port_names.copy()
standard_output_port_names.extend([SSE, MSE])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/library/components/mechanisms/processing/objective/predictionerrormechanism.py b/psyneulink/library/components/mechanisms/processing/objective/predictionerrormechanism.py
index c106444d7f6..548d0a40d3a 100644
--- a/psyneulink/library/components/mechanisms/processing/objective/predictionerrormechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/objective/predictionerrormechanism.py
@@ -172,7 +172,7 @@
from psyneulink.core.components.mechanisms.mechanism import Mechanism_Base
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.keywords import PREDICTION_ERROR_MECHANISM, SAMPLE, TARGET
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set, REPORT_OUTPUT_PREF
from psyneulink.core.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel, PREFERENCE_SET_NAME
from psyneulink.core.globals.utilities import is_numeric
@@ -283,6 +283,7 @@ class Parameters(ComparatorMechanism.Parameters):
sample = None
target = None
+ @check_user_specified
@tc.typecheck
def __init__(self,
sample: tc.optional(tc.any(OutputPort, Mechanism_Base, dict,
diff --git a/psyneulink/library/components/mechanisms/processing/transfer/contrastivehebbianmechanism.py b/psyneulink/library/components/mechanisms/processing/transfer/contrastivehebbianmechanism.py
index 69a48ef6fc5..d4e8482ebbc 100644
--- a/psyneulink/library/components/mechanisms/processing/transfer/contrastivehebbianmechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/transfer/contrastivehebbianmechanism.py
@@ -342,7 +342,7 @@
from psyneulink.core.globals.keywords import \
CONTRASTIVE_HEBBIAN_MECHANISM, COUNT, FUNCTION, HARD_CLAMP, HOLLOW_MATRIX, MAX_ABS_DIFF, NAME, \
SIZE, SOFT_CLAMP, TARGET, VARIABLE
-from psyneulink.core.globals.parameters import Parameter, SharedParameter
+from psyneulink.core.globals.parameters import Parameter, SharedParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import is_numeric_or_none, parameter_spec
from psyneulink.library.components.mechanisms.processing.transfer.recurrenttransfermechanism import \
@@ -977,6 +977,7 @@ class Parameters(RecurrentTransferMechanism.Parameters):
standard_output_port_names = RecurrentTransferMechanism.standard_output_port_names.copy()
standard_output_port_names = [i['name'] for i in standard_output_ports]
+ @check_user_specified
@tc.typecheck
def __init__(self,
input_size:int,
diff --git a/psyneulink/library/components/mechanisms/processing/transfer/kohonenmechanism.py b/psyneulink/library/components/mechanisms/processing/transfer/kohonenmechanism.py
index 9339c89b1d5..ba0d3840a41 100644
--- a/psyneulink/library/components/mechanisms/processing/transfer/kohonenmechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/transfer/kohonenmechanism.py
@@ -90,7 +90,7 @@
from psyneulink.core.globals.keywords import \
DEFAULT_MATRIX, FUNCTION, GAUSSIAN, IDENTITY_MATRIX, KOHONEN_MECHANISM, \
LEARNING_SIGNAL, MATRIX, MAX_INDICATOR, NAME, OWNER_VALUE, OWNER_VARIABLE, RESULT, VARIABLE
-from psyneulink.core.globals.parameters import Parameter, SharedParameter
+from psyneulink.core.globals.parameters import Parameter, SharedParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import is_numeric_or_none, parameter_spec
from psyneulink.library.components.mechanisms.modulatory.learning.kohonenlearningmechanism import KohonenLearningMechanism
@@ -274,6 +274,7 @@ class Parameters(TransferMechanism.Parameters):
FUNCTION: OneHot(mode=MAX_INDICATOR)}
])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/library/components/mechanisms/processing/transfer/kwtamechanism.py b/psyneulink/library/components/mechanisms/processing/transfer/kwtamechanism.py
index 12c1369996e..2ffe285dfae 100644
--- a/psyneulink/library/components/mechanisms/processing/transfer/kwtamechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/transfer/kwtamechanism.py
@@ -187,10 +187,11 @@
from psyneulink.core.components.functions.nonstateful.transferfunctions import Logistic
from psyneulink.core.globals.keywords import KWTA_MECHANISM, K_VALUE, RATIO, RESULT, THRESHOLD
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.utilities import is_numeric_or_none
from psyneulink.library.components.mechanisms.processing.transfer.recurrenttransfermechanism import RecurrentTransferMechanism
+from psyneulink.library.components.projections.pathway.autoassociativeprojection import get_auto_matrix, get_hetero_matrix
__all__ = [
'KWTAMechanism', 'KWTAError',
@@ -342,6 +343,7 @@ class Parameters(RecurrentTransferMechanism.Parameters):
average_based = False
inhibition_only = True
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -414,6 +416,17 @@ def _instantiate_attributes_before_function(self, function=None, context=None):
# so it shouldn't be a problem)
self.indexOfInhibitionInputPort = len(self.input_ports) - 1
+ # NOTE: this behavior matches what kwta tests assert. Values for
+ # auto and hetero were ALWAYS "user_specified" due to using
+ # values set in KWTAMechanism.__init__. To change this and use
+ # default RecurrentTransferMechanism behavior, the test values
+ # must be changed
+ matrix = (
+ get_auto_matrix(self.defaults.auto, self.recurrent_size)
+ + get_hetero_matrix(self.defaults.hetero, self.recurrent_size)
+ )
+ self.parameters.matrix._set(matrix, context)
+
def _kwta_scale(self, current_input, context=None):
k_value = self._get_current_parameter_value(self.parameters.k_value, context)
threshold = self._get_current_parameter_value(self.parameters.threshold, context)
diff --git a/psyneulink/library/components/mechanisms/processing/transfer/lcamechanism.py b/psyneulink/library/components/mechanisms/processing/transfer/lcamechanism.py
index 9402929ec4c..31dd42b52d4 100644
--- a/psyneulink/library/components/mechanisms/processing/transfer/lcamechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/transfer/lcamechanism.py
@@ -199,7 +199,7 @@
from psyneulink.core.globals.keywords import \
CONVERGENCE, FUNCTION, GREATER_THAN_OR_EQUAL, LCA_MECHANISM, LESS_THAN_OR_EQUAL, MATRIX, NAME, \
RESULT, TERMINATION_THRESHOLD, TERMINATION_MEASURE, TERMINATION_COMPARISION_OP, VALUE, INVERSE_HOLLOW_MATRIX, AUTO
-from psyneulink.core.globals.parameters import FunctionParameter, Parameter
+from psyneulink.core.globals.parameters import FunctionParameter, Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.library.components.mechanisms.processing.transfer.recurrenttransfermechanism import \
RecurrentTransferMechanism, _recurrent_transfer_mechanism_matrix_getter, _recurrent_transfer_mechanism_matrix_setter
@@ -437,6 +437,7 @@ def _validate_integration_rate(self, integration_rate):
{NAME:MAX_VS_AVG,
FUNCTION:max_vs_avg}])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
diff --git a/psyneulink/library/components/mechanisms/processing/transfer/recurrenttransfermechanism.py b/psyneulink/library/components/mechanisms/processing/transfer/recurrenttransfermechanism.py
index bd300b0c98c..3724b53f732 100644
--- a/psyneulink/library/components/mechanisms/processing/transfer/recurrenttransfermechanism.py
+++ b/psyneulink/library/components/mechanisms/processing/transfer/recurrenttransfermechanism.py
@@ -210,7 +210,7 @@
from psyneulink.core.globals.context import handle_external_context
from psyneulink.core.globals.keywords import \
AUTO, ENERGY, ENTROPY, HETERO, HOLLOW_MATRIX, INPUT_PORT, MATRIX, NAME, RECURRENT_TRANSFER_MECHANISM, RESULT
-from psyneulink.core.globals.parameters import Parameter, SharedParameter
+from psyneulink.core.globals.parameters import Parameter, SharedParameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.registry import register_instance, remove_instance_from_registry
from psyneulink.core.globals.socket import ConnectionInfo
@@ -644,6 +644,7 @@ class Parameters(TransferMechanism.Parameters):
standard_output_port_names = TransferMechanism.standard_output_port_names.copy()
standard_output_port_names.extend([ENERGY_OUTPUT_PORT_NAME, ENTROPY_OUTPUT_PORT_NAME])
+ @check_user_specified
@tc.typecheck
def __init__(self,
default_variable=None,
@@ -1340,6 +1341,8 @@ def _gen_llvm_input_ports(self, ctx, builder, params, state, arg_in):
# input
builder.call(recurrent_f, [recurrent_params, recurrent_state, recurrent_in, recurrent_out])
+ assert not self.has_recurrent_input_port, "Configuration using combination function is not supported!"
+
return super()._gen_llvm_input_ports(ctx, builder, params, state, arg_in)
def _gen_llvm_output_ports(self, ctx, builder, value,
diff --git a/psyneulink/library/components/projections/pathway/autoassociativeprojection.py b/psyneulink/library/components/projections/pathway/autoassociativeprojection.py
index 011106e512e..98c9948ca5d 100644
--- a/psyneulink/library/components/projections/pathway/autoassociativeprojection.py
+++ b/psyneulink/library/components/projections/pathway/autoassociativeprojection.py
@@ -112,7 +112,7 @@
from psyneulink.core.components.shellclasses import Mechanism
from psyneulink.core.components.ports.outputport import OutputPort
from psyneulink.core.globals.keywords import AUTO_ASSOCIATIVE_PROJECTION, DEFAULT_MATRIX, HOLLOW_MATRIX, FUNCTION, OWNER_MECH
-from psyneulink.core.globals.parameters import SharedParameter, Parameter
+from psyneulink.core.globals.parameters import SharedParameter, Parameter, check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -236,6 +236,7 @@ class Parameters(MappingProjection.Parameters):
classPreferenceLevel = PreferenceLevel.TYPE
+ @check_user_specified
@tc.typecheck
def __init__(self,
owner=None,
diff --git a/psyneulink/library/components/projections/pathway/maskedmappingprojection.py b/psyneulink/library/components/projections/pathway/maskedmappingprojection.py
index c521d123319..7fd93defa26 100644
--- a/psyneulink/library/components/projections/pathway/maskedmappingprojection.py
+++ b/psyneulink/library/components/projections/pathway/maskedmappingprojection.py
@@ -73,6 +73,7 @@
from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection
from psyneulink.core.components.projections.projection import projection_keywords
from psyneulink.core.globals.keywords import MASKED_MAPPING_PROJECTION, MATRIX
+from psyneulink.core.globals.parameters import check_user_specified
from psyneulink.core.globals.preferences.basepreferenceset import is_pref_set
from psyneulink.core.globals.preferences.preferenceset import PreferenceLevel
@@ -170,6 +171,7 @@ def _validate_mask_operation(self, mode):
classPreferenceLevel = PreferenceLevel.TYPE
+ @check_user_specified
@tc.typecheck
def __init__(self,
sender=None,
diff --git a/psyneulink/library/compositions/autodiffcomposition.py b/psyneulink/library/compositions/autodiffcomposition.py
index 81142b6f4f2..28b4e6cf81d 100644
--- a/psyneulink/library/compositions/autodiffcomposition.py
+++ b/psyneulink/library/compositions/autodiffcomposition.py
@@ -150,7 +150,7 @@
from psyneulink.core.globals.context import Context, ContextFlags, handle_external_context
from psyneulink.core.globals.keywords import AUTODIFF_COMPOSITION, SOFT_CLAMP
from psyneulink.core.scheduling.scheduler import Scheduler
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.scheduling.time import TimeScale
from psyneulink.core import llvm as pnlvm
@@ -222,6 +222,7 @@ class Parameters(Composition.Parameters):
pytorch_representation = None
# TODO (CW 9/28/18): add compositions to registry so default arg for name is no longer needed
+ @check_user_specified
def __init__(self,
learning_rate=None,
optimizer_type='sgd',
diff --git a/psyneulink/library/compositions/gymforagercfa.py b/psyneulink/library/compositions/gymforagercfa.py
index 64250e035f6..e8b0e3f535b 100644
--- a/psyneulink/library/compositions/gymforagercfa.py
+++ b/psyneulink/library/compositions/gymforagercfa.py
@@ -81,7 +81,7 @@
from psyneulink.library.compositions.regressioncfa import RegressionCFA
from psyneulink.core.components.functions.nonstateful.learningfunctions import BayesGLM
from psyneulink.core.globals.keywords import DEFAULT_VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
__all__ = ['GymForagerCFA']
@@ -108,6 +108,7 @@ class GymForagerCFA(RegressionCFA):
class Parameters(RegressionCFA.Parameters):
update_weights = Parameter(BayesGLM, stateful=False, loggable=False)
+ @check_user_specified
def __init__(self,
name=None,
update_weights=BayesGLM,
diff --git a/psyneulink/library/compositions/pytorchcomponents.py b/psyneulink/library/compositions/pytorchcomponents.py
index 27f72292951..43122730437 100644
--- a/psyneulink/library/compositions/pytorchcomponents.py
+++ b/psyneulink/library/compositions/pytorchcomponents.py
@@ -131,7 +131,9 @@ def _gen_llvm_execute_derivative_func(self, ctx, builder, state, params, arg_in)
self._mechanism.function, f_params_ptr, ctx, builder, mech_params, mech_state, mech_input)
f_state = pnlvm.helpers.get_state_ptr(builder, self._mechanism, mech_state, "function")
- output, _ = self._mechanism._gen_llvm_invoke_function(ctx, builder, self._mechanism.function, f_params, f_state, mech_input, tags=frozenset({"derivative"}))
+ output, _ = self._mechanism._gen_llvm_invoke_function(ctx, builder, self._mechanism.function,
+ f_params, f_state, mech_input, None,
+ tags=frozenset({"derivative"}))
return builder.gep(output, [ctx.int32_ty(0),
ctx.int32_ty(0)])
diff --git a/psyneulink/library/compositions/regressioncfa.py b/psyneulink/library/compositions/regressioncfa.py
index 7682d9ecbba..5d1f3eef154 100644
--- a/psyneulink/library/compositions/regressioncfa.py
+++ b/psyneulink/library/compositions/regressioncfa.py
@@ -85,7 +85,7 @@
from psyneulink.core.components.ports.port import _parse_port_spec
from psyneulink.core.compositions.compositionfunctionapproximator import CompositionFunctionApproximator
from psyneulink.core.globals.keywords import ALL, CONTROL_SIGNALS, DEFAULT_VARIABLE, VARIABLE
-from psyneulink.core.globals.parameters import Parameter
+from psyneulink.core.globals.parameters import Parameter, check_user_specified
from psyneulink.core.globals.utilities import get_deepcopy_with_shared, powerset, tensor_power
__all__ = ['PREDICTION_TERMS', 'PV', 'RegressionCFA']
@@ -246,6 +246,7 @@ class Parameters(CompositionFunctionApproximator.Parameters):
previous_state = None
regression_weights = None
+ @check_user_specified
def __init__(self,
name=None,
update_weights=None,
diff --git a/requirements.txt b/requirements.txt
index 02d2dd607e5..302ecce50e9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,22 +1,22 @@
-autograd<=1.3
-graph-scheduler>=0.2.0, <1.1.1
+autograd<1.5
+graph-scheduler>=0.2.0, <1.1.2
dill<=0.32
-elfi<0.8.4
-graphviz<0.20.0
+elfi<0.8.5
+graphviz<0.21.0
grpcio<1.43.0
grpcio-tools<1.43.0
-llvmlite<0.39
-matplotlib<3.5.2
-modeci_mdf>=0.3.2, <0.3.4
-modelspec<0.2.0
-networkx<2.8
-numpy<1.21.4, >=1.17.0
-pillow<9.1.0
-pint<0.18
+llvmlite<0.40
+matplotlib<3.5.3
+modeci_mdf>=0.3.4, <0.4.2
+modelspec<0.2.6
+networkx<2.9
+numpy<1.21.7, >=1.17.0
+pillow<9.3.0
+pint<0.20.0
toposort<1.8
-torch>=1.8.0, <2.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64') and platform_python_implementation == 'CPython' and implementation_name == 'cpython'
+torch>=1.8.0, <1.12.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64') and platform_python_implementation == 'CPython' and implementation_name == 'cpython'
typecheck-decorator<=1.2
leabra-psyneulink<=0.3.2
rich>=10.1, <10.13
-pandas<=1.4.1
-fastkde==1.0.19
\ No newline at end of file
+pandas<1.4.4
+fastkde==1.0.19
diff --git a/setup.cfg b/setup.cfg
index 9bc862ce6b8..fbd3d06c3bb 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -34,6 +34,7 @@ markers =
cuda: Tests using LLVM runtime compiler and CUDA GPGPU backend
control: Tests including control mechanism and/or control projection
state_features: Tests for OptimizationControlMechanism state_features specifications
+ pathways: Tests for pathway arg of Composition constructor and node Roles
projection
nested: Tests including nested compositions
function: Tests of Function classes
diff --git a/tests/components/test_general.py b/tests/components/test_general.py
index 762bf894a07..dd4c8f23de7 100644
--- a/tests/components/test_general.py
+++ b/tests/components/test_general.py
@@ -55,33 +55,12 @@ def test_function_parameters_stateless(class_):
pass
-@pytest.mark.parametrize(
- 'class_',
- component_classes
-)
-def test_parameters_user_specified(class_):
- violators = set()
- constructor_parameters = inspect.signature(class_.__init__).parameters
- for name, param in constructor_parameters.items():
- if (
- param.kind in {
- inspect.Parameter.POSITIONAL_OR_KEYWORD,
- inspect.Parameter.KEYWORD_ONLY
- }
- and name in class_.parameters.names()
- and param.default is not inspect.Parameter.empty
- and param.default is not None
- ):
- violators.add(name)
-
- message = (
- "If a value other than None is used as the default value in a class's"
- + ' constructor/__init__, for an argument corresponding to a Parameter,'
- + ' _user_specified will always be True. The default value should be'
- + " specified in the class's Parameters inner class. Violators for"
- + f' {class_.__name__}: {violators}'
+@pytest.mark.parametrize("class_", component_classes)
+def test_constructors_have_check_user_specified(class_):
+ assert "check_user_specified" in inspect.getsource(class_.__init__), (
+ f"The __init__ method of Component {class_.__name__} must be wrapped by"
+ f" check_user_specified in {pnl.core.globals.parameters.check_user_specified.__module__}"
)
- assert violators == set(), message
@pytest.fixture(scope='module')
diff --git a/tests/composition/test_autodiffcomposition.py b/tests/composition/test_autodiffcomposition.py
index 6eab099d8fc..2bc81653862 100644
--- a/tests/composition/test_autodiffcomposition.py
+++ b/tests/composition/test_autodiffcomposition.py
@@ -333,8 +333,14 @@ def test_optimizer_specs(self, learning_rate, weight_decay, optimizer_type, expe
"targets": {xor_out:xor_targets},
"epochs": 10}, execution_mode=autodiff_mode)
+ # fp32 results are different due to rounding
+ if pytest.helpers.llvm_current_fp_precision() == 'fp32' and \
+ autodiff_mode != pnl.ExecutionMode.Python and \
+ optimizer_type == 'sgd' and \
+ learning_rate == 10:
+ expected = [[[0.9918830394744873]], [[0.9982172846794128]], [[0.9978305697441101]], [[0.9994590878486633]]]
# FIXME: LLVM version is broken with learning rate == 1.5
- if learning_rate != 1.5 or autodiff_mode is pnl.ExecutionMode.Python:
+ if learning_rate != 1.5 or autodiff_mode == pnl.ExecutionMode.Python:
assert np.allclose(results_before_proc, expected)
if benchmark.enabled:
diff --git a/tests/composition/test_composition.py b/tests/composition/test_composition.py
index 1f06cce8a50..230af9f4c34 100644
--- a/tests/composition/test_composition.py
+++ b/tests/composition/test_composition.py
@@ -575,6 +575,7 @@ def test_unused_projections_warning(self):
assert repr(warning[1].message.args[0]) == '"\\nThe following Projections were specified but are not being used by Nodes in \'COMP_2\':\\n\\tMappingProjection from A[OutputPort-0] to C[InputPort-0] (to \'C\' from \'A\')."'
+@pytest.mark.pathways
class TestPathway:
def test_pathway_standalone_object(self):
@@ -616,7 +617,47 @@ def test_pathway_illegal_arg_error(self):
assert "Illegal argument(s) used in constructor for Pathway: foo." in str(error_text.value)
-class TestCompositionPathwayAdditionMethods:
+@pytest.mark.pathways
+class TestCompositionPathwayArgsAndAdditionMethods:
+
+ def test_add_pathways_with_all_types(self):
+ A = ProcessingMechanism(name='A')
+ B = ProcessingMechanism(name='B')
+ C = ProcessingMechanism(name='C')
+ D = ProcessingMechanism(name='D')
+ E = ProcessingMechanism(name='E')
+ X = ProcessingMechanism(name='X')
+ Y = ProcessingMechanism(name='Y')
+ F = ProcessingMechanism(name='F')
+ G = ProcessingMechanism(name='G')
+ H = ProcessingMechanism(name='H')
+ J = ProcessingMechanism(name='J')
+ K = ProcessingMechanism(name='K')
+ L = ProcessingMechanism(name='L')
+ M = ProcessingMechanism(name='M')
+
+ # FIX: 4/9/22 - ADD SET SPEC
+ p = Pathway(pathway=[L,M], name='P')
+ c = Composition()
+ c.add_pathways(pathways=[A,
+ [B,C],
+ (D,E),
+ {X,Y},
+ {'DICT PATHWAY': F},
+ ([G, H], BackPropagation),
+ {'LEARNING PATHWAY': ([J,K], Reinforcement)},
+ p])
+ assert len(c.pathways) == 8
+ assert isinstance(c.pathways[0].pathway, list) and len(c.pathways[0].pathway) == 1
+ assert isinstance(c.pathways[1].pathway, list) and len(c.pathways[1].pathway) == 3
+ assert isinstance(c.pathways[2].pathway, list) and len(c.pathways[2].pathway) == 3
+ assert isinstance(c.pathways[3].pathway[0], set) and len(c.pathways[3].pathway) == 1
+ assert c.pathways['P'].input == L
+ assert c.pathways['DICT PATHWAY'].input == F
+ assert c.pathways['DICT PATHWAY'].output == F
+ assert c.pathways['LEARNING PATHWAY'].output == K
+ assert [p for p in c.pathways if p.input == G][0].learning_function == BackPropagation
+ assert c.pathways['LEARNING PATHWAY'].learning_function == Reinforcement
def test_pathway_attributes(self):
c = Composition()
@@ -837,44 +878,148 @@ def test_add_td_learning_pathway_arg_pathway(self):
PathwayRole.LEARNING,
PathwayRole.OUTPUT}
- def test_add_pathways_with_all_types(self):
+ config = [
+ ('[A,{B,C}]', 's1'), # SEQUENTIAL A->{B,C})
+ ('[A,[B,C]]', 'p1'), # PARALLEL: A, B->C
+ ('[{A},{B,C}]', 's1'), # SEQUENTIAL: A->{B,C}
+ ('[[A],{B,C}]', 'p2'), # PARALLEL: A, B, C
+ ('[[A,B],{C,D}]', 'p3'), # PARALLEL: A->B, C, D
+ ('[[A,B],C,D ]', 'p3'), # PARALLEL: A->B, C, D
+ ('[[A,B],[C,D]]', 'p5'), # PARALLEL: A->B, C->D
+ ('[{A,B}, MapProj(B,D), C, D]', 's2'), # SEQUENTIAL: A, B->D, C->D
+ ('[{A,B}, [MapProj(B,D)], C, D]', 's2'), # SEQUENTIAL: A, B->D, C->D
+ ('[{A,B}, {MapProj(B,D)}, C, D]', 's2'), # SEQUENTIAL: A, B->D, C->D
+ ('[{A,B}, [[C,D]]]', 'p4'), # PARALLEL: A, B, C->D (FORGIVES EMBEDDED LIST OF [C,D])
+ ('[[A,B], [[C,D]]]', 'p5'), # PARALLEL: A->B, C->D (FORGIVES EMBEDDED LIST OF [C,D])
+ ('[[[A,B]], [[C,D]]]','p5'), # PARALLEL: A->B, C->D (FORGIVES EMBEDDED LISTS OF [A, B] and [C,D])
+ ('[A, "B"]','e1'), # BAD ITEM ERROR
+ ('[[A,B, [C,D]],[E,F]]','e2'), # EMBEDDED LIST ERROR
+ ('[{A,B}, [MapProj(B,D)], [C,D]]', 'e3') # BAD ITEM ERROR, FIX: SHOULD ALLOW EMBEDDED PER ABOVE
+ ]
+ @pytest.mark.parametrize('config', config, ids=[x[0] for x in config])
+ def test_various_pathway_configurations_in_constructor(self, config):
+ """Test combinations of sets and lists in pathways specification of Composition constructor
+ Principles:
+ if outer list (pathways spec) contains:
+ - single item or only sets, treat single (sequential) pathway
+ - one or more lists within it, treat all items as a separate (parallel) pathways
+ - one or more lists each with a single list within it ([[[A,B]],[[C,D]]]}), each is treated as a pathway
+ - any list with more than a single list within it ([[[A,B],[C,D]}), an error is generated
+ - any bad items (strings, misplaced items), an error is generated
+ """
+
A = ProcessingMechanism(name='A')
B = ProcessingMechanism(name='B')
+ # B_comparator = ComparatorMechanism(name='B COMPARATOR')
C = ProcessingMechanism(name='C')
D = ProcessingMechanism(name='D')
E = ProcessingMechanism(name='E')
F = ProcessingMechanism(name='F')
- G = ProcessingMechanism(name='G')
- H = ProcessingMechanism(name='H')
- J = ProcessingMechanism(name='J')
- K = ProcessingMechanism(name='K')
- L = ProcessingMechanism(name='L')
- M = ProcessingMechanism(name='M')
- p = Pathway(pathway=[L,M], name='P')
- c = Composition()
- c.add_pathways(pathways=[A,
- [B,C],
- (D,E),
- {'DICT PATHWAY': F},
- ([G, H], BackPropagation),
- {'LEARNING PATHWAY': ([J,K], Reinforcement)},
- p])
- assert len(c.pathways) == 7
- assert c.pathways['P'].input == L
- assert c.pathways['DICT PATHWAY'].input == F
- assert c.pathways['DICT PATHWAY'].output == F
- assert c.pathways['LEARNING PATHWAY'].output == K
- [p for p in c.pathways if p.input == G][0].learning_function == BackPropagation
- assert c.pathways['LEARNING PATHWAY'].learning_function == Reinforcement
+ # LEGAL:
+ if config[0] == '[A,{B,C}]': # SEQUENTIAL A->{B,C}) (s1)
+ comp = Composition([A,{B,C}])
+ elif config[0] == '[A,[B,C]]': # PARALLEL: A, B->C (p1)
+ comp = Composition([A,[B,C]])
+ elif config[0] == '[{A},{B,C}]': # SEQUENTIAL: A->{B,C} (s1)
+ comp = Composition([{A},{B,C}])
+ elif config[0] == '[[A],{B,C}]': # PARALLEL: A, B, C (p2)
+ comp = Composition([[A],{B,C}])
+ elif config[0] == '[[A,B],{C,D}]': # PARALLEL: A->B, C, D (p3)
+ comp = Composition([[A,B],{C,D}])
+ elif config[0] == '[[A,B],C,D ]': # PARALLEL: A->B, C, D (p3)
+ comp = Composition([[A,B],C,D ])
+ elif config[0] == '[[A,B],[C,D]]': # PARALLEL: A->B, C->D {p5)
+ comp = Composition([[A,B],[C,D]])
+ elif config[0] == '[{A,B}, MapProj(B,D), C, D]': # SEQUENTIAL: A, B->D, C->D (s2)
+ comp = Composition([{A,B}, MappingProjection(B,D), C, D])
+ elif config[0] == '[{A,B}, [MapProj(B,D)], C, D]': # SEQUENTIAL: A, B->D, C->D (s2)
+ comp = Composition([{A,B}, [MappingProjection(B,D)], C, D])
+ elif config[0] == '[{A,B}, {MapProj(B,D)}, C, D]': # SEQUENTIAL: A, B->D, C->D (s2)
+ comp = Composition([{A,B}, {MappingProjection(B,D)}, C, D])
+ elif config[0] == '[{A,B}, [[C,D]]]': # PARALLEL: A, B, C->D (FORGIVES EMBEDDED LIST [C,D]) (p4)
+ comp = Composition([{A,B}, [[C,D]]])
+ elif config[0] == '[[A,B], [[C,D]]]': # PARALLEL: A->B, C->D (SINGLE EMBEDDED LIST OK [C,D]) (p5)
+ comp = Composition([[A,B], [[C,D]]])
+ elif config[0] == '[[[A,B]], [[C,D]]]': # PARALLEL: A->B, C->D (FORGIVES EMBEDDED LISTS [A,B] & [C,D]) (p5)
+ comp = Composition([[[A,B]], [[C,D]]])
+
+ # ERRORS:
+ elif config[0] == '[A, "B"]': # BAD ITEM ERROR (e1)
+ with pytest.raises(CompositionError) as error_text:
+ comp = Composition([A, "B"])
+ assert f"Every item in the 'pathways' arg of the constructor for Composition-0 must be " \
+ f"a Node, list, set, tuple or dict; the following are not: 'B'" in str(error_text.value)
+ elif config[0] == '[[A,B, [C,D]],[E,F]]': # EMBEDDED LIST ERROR (e2)
+ with pytest.raises(CompositionError) as error_text:
+ comp = Composition([[A,B, [C,D]],[E,F]])
+ assert f"The following entries in a pathway specified for \'Composition-0\' are not " \
+ f"a Node (Mechanism or Composition) or a Projection nor a set of either: " \
+ f"[(ProcessingMechanism C), (ProcessingMechanism D)]" in str(error_text.value)
+ elif config[0] == '[{A,B}, [MapProj(B,D)], [C,D]]': # BAD ITEM ERROR (e3)
+ with pytest.raises(CompositionError) as error_text:
+ comp = Composition([{A,B}, [MappingProjection(B,D)], [C,D]])
+ assert f"Every item in the 'pathways' arg of the constructor for Composition-0 must be " \
+ f"a Node, list, set, tuple or dict; the following are not: " \
+ f"(MappingProjection MappingProjection from B[OutputPort-0] to D[InputPort-0])" \
+ in str(error_text.value)
+
+ else:
+ assert False, f"BAD CONFIG ARG: {config}"
+
+ # Tests:
+ if config[1] == 's1':
+ assert len(A.efferents) == 2
+ assert all(len(receiver.path_afferents) == 1 for receiver in {B,C})
+ assert all(receiver in [p.receiver.owner for p in A.efferents] for receiver in {B,C})
+ assert [A] == comp.get_nodes_by_role(NodeRole.INPUT)
+ assert all(node in comp.get_nodes_by_role(NodeRole.OUTPUT) for node in {B,C})
+ if config[1] == 's2':
+ assert all(len(sender.efferents) == 1 for sender in {B,C})
+ assert len(D.path_afferents) == 2
+ assert all(D in [p.receiver.owner for p in receiver.efferents] for receiver in {B,C})
+ assert [A] == comp.get_nodes_by_role(NodeRole.SINGLETON)
+ assert all(node in comp.get_nodes_by_role(NodeRole.INPUT) for node in {B,C})
+ assert all(node in comp.get_nodes_by_role(NodeRole.OUTPUT) for node in {A,D})
+ if config[1] == 'p1':
+ assert len(B.efferents) == 1
+ assert len(C.path_afferents) == 1
+ assert B.efferents[0].receiver.owner == C
+ assert [A] == comp.get_nodes_by_role(NodeRole.SINGLETON)
+ assert all(node in comp.get_nodes_by_role(NodeRole.INPUT) for node in {A,B})
+ assert all(node in comp.get_nodes_by_role(NodeRole.OUTPUT) for node in {A,C})
+ if config[1] == 'p2':
+ assert all(node in comp.get_nodes_by_role(NodeRole.SINGLETON) for node in {A,B,C})
+ if config[1] == 'p3':
+ assert len(A.efferents) == 1
+ assert len(B.path_afferents) == 1
+ assert A.efferents[0].receiver.owner == B
+ assert all(node in comp.get_nodes_by_role(NodeRole.SINGLETON) for node in {C,D})
+ if config[1] == 'p4':
+ assert len(C.efferents) == 1
+ assert len(D.path_afferents) == 1
+ assert C.efferents[0].receiver.owner == D
+ assert all(node in comp.get_nodes_by_role(NodeRole.SINGLETON) for node in {A,B})
+ assert all(node in comp.get_nodes_by_role(NodeRole.INPUT) for node in {A,B,C})
+ assert all(node in comp.get_nodes_by_role(NodeRole.OUTPUT) for node in {A,D})
+ if config[1] == 'p5':
+ assert len(A.efferents) == 1
+ assert len(B.path_afferents) == 1
+ assert A.efferents[0].receiver.owner == B
+ assert len(C.efferents) == 1
+ assert len(D.path_afferents) == 1
+ assert C.efferents[0].receiver.owner == D
+ assert all(node in comp.get_nodes_by_role(NodeRole.INPUT) for node in {A,C})
+ assert all(node in comp.get_nodes_by_role(NodeRole.OUTPUT) for node in {B,D})
def test_add_pathways_bad_arg_error(self):
I = InputPort(name='I')
c = Composition()
with pytest.raises(pnl.CompositionError) as error_text:
c.add_pathways(pathways=I)
- assert ("The \'pathways\' arg for the add_pathways method" in str(error_text.value)
- and "must be a Node, list, tuple, dict or Pathway object" in str(error_text.value))
+ assert f"The 'pathways' arg for the add_pathways method of Composition-0 must be a " \
+ f"Node, list, set, tuple, dict or Pathway object: (InputPort I [Deferred Init])." \
+ in str(error_text.value)
def test_add_pathways_arg_pathways_list_and_item_not_list_or_dict_or_node_error(self):
A = ProcessingMechanism(name='A')
@@ -882,8 +1027,8 @@ def test_add_pathways_arg_pathways_list_and_item_not_list_or_dict_or_node_error(
c = Composition()
with pytest.raises(pnl.CompositionError) as error_text:
c.add_pathways(pathways=[[A,B], 'C'])
- assert ("Every item in the \'pathways\' arg for the add_pathways method" in str(error_text.value)
- and "must be a Node, list, tuple or dict:" in str(error_text.value))
+ assert f"Every item in the 'pathways' arg for the add_pathways method of Composition-0 must be a " \
+ f"Node, list, set, tuple or dict; the following are not: 'C'" in str(error_text.value)
def test_for_add_processing_pathway_recursion_error(self):
A = TransferMechanism()
@@ -902,6 +1047,7 @@ def test_for_add_learning_pathway_recursion_error(self):
f"add_backpropagation_learning_pathway method of {C.name}." in str(error_text.value)
+@pytest.mark.pathways
class TestDuplicatePathwayWarnings:
def test_add_processing_pathway_exact_duplicate_warning(self):
@@ -936,9 +1082,10 @@ def test_add_processing_pathway_subset_duplicate_warning(self):
comp.add_linear_processing_pathway(pathway=[A,B,C])
regexp = "Pathway specified in 'pathway' arg for add_linear_procesing_pathway method .*"\
- f"has same Nodes in same order as one already in {comp.name}"
+ f"has same Nodes in same order as one already in {comp.name}"
with pytest.warns(UserWarning, match=regexp):
comp.add_linear_processing_pathway(pathway=[A,B])
+ assert True
def test_add_backpropagation_pathway_exact_duplicate_warning(self):
A = TransferMechanism()
@@ -996,6 +1143,7 @@ def test_add_processing_pathway_same_nodes_but_reversed_order_is_OK(self):
len(comp.pathways)==2
+@pytest.mark.pathways
class TestCompositionPathwaysArg:
def test_composition_pathways_arg_pathway_object(self):
@@ -1042,6 +1190,202 @@ def test_composition_pathways_arg_mech(self):
PathwayRole.OUTPUT,
PathwayRole.TERMINAL}
+ def test_composition_pathways_arg_set(self):
+ A = ProcessingMechanism(name='A')
+ B = ProcessingMechanism(name='B')
+ C = ProcessingMechanism(name='C')
+ c = Composition({A,B,C})
+ # # MODIFIED 4/11/22 OLD:
+ # # assert all(name in c.pathways.names for name in {'Pathway-0', 'Pathway-1', 'Pathway-2'})
+ # MODIFIED 4/11/22 NEW:
+ assert all(name in c.pathways.names for name in {'Pathway-0'})
+ # MODIFIED 4/11/22 END
+ assert all(set(c.get_roles_by_node(node)) == {NodeRole.INPUT,
+ NodeRole.ORIGIN,
+ NodeRole.SINGLETON,
+ NodeRole.OUTPUT,
+ NodeRole.TERMINAL}
+ for node in {A,B,C})
+ # assert all(set(c.pathways[i].roles) == {PathwayRole.INPUT,
+ # PathwayRole.ORIGIN,
+ # PathwayRole.SINGLETON,
+ # PathwayRole.OUTPUT,
+ # PathwayRole.TERMINAL}
+ # for i in range(0,1))
+
+ with pytest.raises(CompositionError) as err:
+ d = Composition({A,B,C.input_port})
+ assert f"Every item in the \'pathways\' arg of the constructor for Composition-1 must be " \
+ f"a Node, list, set, tuple or dict; the following are not: (InputPort InputPort-0)" in str(err.value)
+
+ @pytest.mark.parametrize("nodes_config", [
+ "many_many",
+ "many_one_many",
+ ])
+ @pytest.mark.parametrize("projs", [
+ "none",
+ "default_proj",
+ "matrix_spec",
+ "some_projs_no_default",
+ "some_projs_and_matrix_spec",
+ "some_projs_and_default_proj"
+ ])
+ @pytest.mark.parametrize("set_or_list", [
+ "set",
+ "list"
+ ])
+ def test_composition_pathways_arg_with_various_set_or_list_configurations(self, nodes_config, projs, set_or_list):
+ import itertools
+
+ A = ProcessingMechanism(name='A')
+ B = ProcessingMechanism(name='B')
+ # FIX: 4/9/22 - INCLUDE TWO PORT MECHANISM:
+ # B_comparator = ComparatorMechanism(name='B COMPARATOR')
+ C = ProcessingMechanism(name='C')
+ D = ProcessingMechanism(name='D')
+ E = ProcessingMechanism(name='E')
+ F = ProcessingMechanism(name='F')
+ M = ProcessingMechanism(name='M')
+ # C = A.input_port
+ # proj = MappingProjection(sender=A, receiver=B)
+
+ default_proj = MappingProjection(matrix=[2])
+ default_matrix = [1]
+ # For many_many:
+ A_D = MappingProjection(sender=A, receiver=D, matrix=[2])
+ B_D = MappingProjection(sender=B, receiver=D, matrix=[3])
+ B_E = MappingProjection(sender=B, receiver=E, matrix=[4])
+ C_E = MappingProjection(sender=C, receiver=E, matrix=[5])
+ # For many_one_many:
+ A_M = MappingProjection(sender=A, receiver=M, matrix=[6])
+ C_M = MappingProjection(sender=C, receiver=M, matrix=[7])
+ M_D = MappingProjection(sender=M, receiver=D, matrix=[8])
+ M_F = MappingProjection(sender=M, receiver=F, matrix=[9])
+ B_M = MappingProjection(sender=B, receiver=M, matrix=[100])
+
+ nodes_1 = {A,B,C}
+ nodes_2 = {D,E,F}
+ # FIX: 4/9/22 - MODIFY TO INCLUDE many to first (set->list) and last to many(list->set)
+ # FIX: 4/9/22 - INCLUDE PORT SPECS:
+ # nodes_1 = {A.output_port,B,C} if set_or_list == 'set' else [A.output_port,B,C]
+ # nodes_2 = {D,E,F.input_port} if set_or_list == 'set' else [D,E,F.input_port]
+
+ if projs != "none":
+ if nodes_config == "many_many":
+ projections = {
+ "default_proj": default_proj,
+ "matrix_spec": [10],
+ "some_projs_no_default": {A_D, B_E} if set_or_list == 'set' else [A_D, B_E],
+ "some_projs_and_matrix_spec": [A_D, C_E, default_matrix], # matrix spec requires list
+ "some_projs_and_default_proj":
+ {B_D, B_E, default_proj} if set_or_list == 'set' else [B_D, B_E, default_proj]
+ }
+ elif nodes_config == "many_one_many":
+ # Tuples with first item for nodes_1 -> M and second item M -> nodes_2
+ projections = {
+ "default_proj": (default_proj, default_proj),
+ "matrix_spec": ([11], [12]),
+ "some_projs_no_default":
+ ({A_M, C_M}, {M_D, M_F}) if set_or_list == 'set' else ([A_M, C_M], [M_D, M_F]),
+ "some_projs_and_matrix_spec": ([A_M, C_M, default_matrix],
+ [M_D, M_F, default_matrix]), # matrix spec requires list
+ "some_projs_and_default_proj":
+ ({A_M, C_M, default_proj}, {M_D, M_F, default_proj})
+ if set_or_list == 'set' else ([A_M, C_M, default_proj], [M_D, M_F, default_proj])
+ }
+ else:
+ assert False, f"TEST ERROR: No handling for '{nodes_config}' condition."
+
+ if projs in {'default_proj', 'some_projs_and_default_proj'}:
+ matrix_val = default_proj._init_args['matrix']
+ elif projs == 'matrix_spec':
+ matrix_val = projections[projs]
+ elif projs == "some_projs_and_matrix_spec":
+ matrix_val = default_matrix
+
+ if nodes_config == "many_many":
+
+ if projs == 'none':
+ comp = Composition([nodes_1, nodes_2])
+ matrix_val = default_matrix
+ else:
+ comp = Composition([nodes_1, projections[projs], nodes_2])
+
+ if projs == "some_projs_no_default":
+ assert A_D in comp.projections
+ assert B_E in comp.projections
+ # Pre-specified Projections that were not included in pathways should not be in Composition:
+ assert B_D not in comp.projections
+ assert C_E not in comp.projections
+ assert C in comp.get_nodes_by_role(NodeRole.SINGLETON)
+ assert F in comp.get_nodes_by_role(NodeRole.SINGLETON)
+
+ else:
+ # If there is no Projection specification or a default one, then there should be all-to-all Projections
+ # Each sender projects to all three 3 receivers
+ assert all(len([p for p in node.efferents if p in comp.projections])==3 for node in {A,B,C})
+ # Each receiver gets Projections from all 3 senders
+ assert all(len([p for p in node.path_afferents if p in comp.projections])==3 for node in {D,E,F})
+ for sender,receiver in itertools.product([A,B,C],[D,E,F]):
+ # Each sender projects to each of the receivers
+ assert sender in {p.sender.owner for p in receiver.path_afferents if p in comp.projections}
+ # Each receiver receives a Projection from each of the senders
+ assert receiver in {p.receiver.owner for p in sender.efferents if p in comp.projections}
+
+ # Matrices for pre-specified Projections should preserve their specified value:
+ A_D.parameters.matrix.get() == [2]
+ B_D.parameters.matrix.get() == [3]
+ B_E.parameters.matrix.get() == [4]
+ C_E.parameters.matrix.get() == [5]
+ # Matrices for pairs without pre-specified Projections should be assigned value of default
+ assert [p.parameters.matrix.get() for p in A.efferents if p.receiver.owner.name == 'E'] == matrix_val
+ assert [p.parameters.matrix.get() for p in A.efferents if p.receiver.owner.name == 'F'] == matrix_val
+ assert [p.parameters.matrix.get() for p in B.efferents if p.receiver.owner.name == 'F'] == matrix_val
+ assert [p.parameters.matrix.get() for p in C.efferents if p.receiver.owner.name == 'D'] == matrix_val
+ assert [p.parameters.matrix.get() for p in C.efferents if p.receiver.owner.name == 'F'] == matrix_val
+
+ elif nodes_config == 'many_one_many':
+ if projs == 'none':
+ comp = Composition([nodes_1, M, nodes_2])
+ matrix_val = default_matrix
+
+ else:
+ comp = Composition([nodes_1, projections[projs][0], M, projections[projs][1], nodes_2])
+ if projs == 'matrix_spec':
+ matrix_val = projections[projs][1]
+
+ if projs == "some_projs_no_default":
+ assert all(p in comp.projections for p in {A_M, C_M, M_D, M_F})
+ # Pre-specified Projections that were not included in pathways should not be in Composition:
+ assert B_M not in comp.projections
+ assert B in comp.get_nodes_by_role(NodeRole.SINGLETON)
+ assert E in comp.get_nodes_by_role(NodeRole.SINGLETON)
+
+ else:
+ # Each sender projects to just one receiver
+ assert all(len([p for p in node.efferents if p in comp.projections])==1 for node in {A,B,C})
+ # Each receiver receives from just one sender
+ assert all(len([p for p in node.path_afferents if p in comp.projections])==1 for node in {D,E,F})
+ for sender,receiver in itertools.product([A,B,C],[M]):
+ # Each sender projects to M:
+ assert sender in {p.sender.owner for p in receiver.path_afferents if p in comp.projections}
+ # Each receiver receives from M:
+ assert receiver in {p.receiver.owner for p in sender.efferents if p in comp.projections}
+ # Matrices for pre-specified Projections should preserve their specified value:
+ A_M.parameters.matrix.get() == [6]
+ C_M.parameters.matrix.get() == [7]
+ M_D.parameters.matrix.get() == [8]
+ M_F.parameters.matrix.get() == [9]
+ # Matrices for pairs without pre-specified Projections should be assigned value of default
+ assert [p.parameters.matrix.get() for p in B.efferents if p.receiver.owner.name == 'M'] == [100]
+ assert [p.parameters.matrix.get() for p in M.efferents if p.receiver.owner.name == 'E'] == matrix_val
+
+ else:
+ assert False, f"TEST ERROR: No handling for '{nodes_config}' condition."
+
+ def test_pathways_examples(self):
+ pass
+
def test_composition_pathways_arg_dict_and_list_and_pathway_roles(self):
A = ProcessingMechanism(name='A')
B = ProcessingMechanism(name='B')
@@ -1107,8 +1451,8 @@ def test_composition_pathways_arg_pathways_list_and_item_not_list_or_dict_or_nod
B = ProcessingMechanism(name='B')
with pytest.raises(pnl.CompositionError) as error_text:
c = Composition(pathways=[[A,B], 'C'])
- assert ("Every item in the \'pathways\' arg of the constructor" in str(error_text.value) and
- "must be a Node, list, tuple or dict:" in str(error_text.value))
+ assert ("Every item in the 'pathways' arg of the constructor for Composition-0 must be "
+ "a Node, list, set, tuple or dict; the following are not: 'C'" in str(error_text.value))
def test_composition_pathways_arg_pathways_dict_and_item_not_list_dict_or_node_error(self):
A = ProcessingMechanism(name='A')
@@ -1117,8 +1461,8 @@ def test_composition_pathways_arg_pathways_dict_and_item_not_list_dict_or_node_e
D = ProcessingMechanism(name='D')
with pytest.raises(pnl.CompositionError) as error_text:
c = Composition(pathways=[{'P1':[A,B]}, 'C'])
- assert ("Every item in the \'pathways\' arg of the constructor" in str(error_text.value) and
- "must be a Node, list, tuple or dict:" in str(error_text.value))
+ assert ("Every item in the 'pathways' arg of the constructor for Composition-0 must be "
+ "a Node, list, set, tuple or dict; the following are not: 'C'" in str(error_text.value))
def test_composition_pathways_arg_dict_with_more_than_one_entry_error(self):
A = ProcessingMechanism(name='A')
@@ -1212,16 +1556,17 @@ def test_composition_pathways_bad_arg_error(self):
I = InputPort(name='I')
with pytest.raises(pnl.CompositionError) as error_text:
c = Composition(pathways=I)
- assert ("The \'pathways\' arg of the constructor" in str(error_text.value) and
- "must be a Node, list, tuple, dict or Pathway object" in str(error_text.value))
+ assert ("The 'pathways' arg of the constructor for Composition-0 must be a Node, list, "
+ "set, tuple, dict or Pathway object: (InputPort I [Deferred Init])."
+ in str(error_text.value))
def test_composition_arg_pathways_list_and_item_not_list_or_dict_or_node_error(self):
A = ProcessingMechanism(name='A')
B = ProcessingMechanism(name='B')
with pytest.raises(pnl.CompositionError) as error_text:
c = Composition(pathways=[[A,B], 'C'])
- assert ("Every item in the \'pathways\' arg of the constructor" in str(error_text.value) and
- "must be a Node, list, tuple or dict:" in str(error_text.value))
+ assert ("Every item in the 'pathways' arg of the constructor for Composition-0 must be a "
+ "Node, list, set, tuple or dict; the following are not: 'C'" in str(error_text.value))
def test_composition_learning_pathway_dict_and_list_error(self):
A = ProcessingMechanism(name='A')
@@ -1695,7 +2040,7 @@ def test_recurrent_transfer_mechanisms(self):
output = comp.run(inputs={R1: [1.0]}, num_trials=3)
assert np.allclose(output, [[np.array([22.])]])
-
+@pytest.mark.pathways
class TestExecutionOrder:
def test_2_node_loop(self):
A = ProcessingMechanism(name="A")
@@ -2406,7 +2751,7 @@ def test_exact_time(self):
assert comp.scheduler.execution_list[comp.default_execution_id] == [{A, B}]
assert comp.scheduler.execution_timestamps[comp.default_execution_id][0].absolute == 1 * pnl._unit_registry.ms
-
+@pytest.mark.pathways
class TestGetMechanismsByRole:
def test_multiple_roles(self):
@@ -3283,8 +3628,9 @@ def test_lpp_invalid_matrix_keyword(self):
with pytest.raises(CompositionError) as error_text:
# Typo in IdentityMatrix
comp.add_linear_processing_pathway([A, "IdntityMatrix", B])
- assert ("An entry in \'pathway\' arg for add_linear_procesing_pathway method" in str(error_text.value) and
- "is not a Node (Mechanism or Composition) or a Projection: \'IdntityMatrix\'." in str(error_text.value))
+ assert (f"The following entries in a pathway specified for 'Composition-0' are not a Node "
+ f"(Mechanism or Composition) or a Projection nor a set of either: 'IdntityMatrix'"
+ in str(error_text.value))
@pytest.mark.composition
def test_LPP_two_origins_one_terminal(self, comp_mode):
@@ -4585,153 +4931,7 @@ def test_combine_two_overlapping_trees(self):
assert len(terminals) == 1
assert myMech5 in terminals
- # MODIFIED 5/8/20 OLD: ELIMINATE SYSTEM:
- # FIX SHOULD THESE BE RE-WRITTEN WITH STANDARD NESTED COMPOSITIONS AND PATHWAYS?
- # def test_one_pathway_inside_one_system(self):
- # # create a PathwayComposition | blank slate for composition
- # myPath = PathwayComposition()
- #
- # # create mechanisms to add to myPath
- # myMech1 = TransferMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech2 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myMech3 = TransferMechanism(function=Linear(slope=2.0)) # 4 x 2 = 8
- #
- # # add mechanisms to myPath with default MappingProjections between them
- # myPath.add_linear_processing_pathway([myMech1, myMech2, myMech3])
- #
- # # assign input to origin mech
- # stimulus = {myMech1: [[1]]}
- #
- # # execute path (just for comparison)
- # myPath.run(inputs=stimulus)
- #
- # # create a SystemComposition | blank slate for composition
- # sys = SystemComposition()
- #
- # # add a PathwayComposition [myPath] to the SystemComposition [sys]
- # sys.add_pathway(myPath)
- #
- # # execute the SystemComposition
- # output = sys.run(inputs=stimulus)
- # assert np.allclose([8], output)
- #
- # def test_two_paths_converge_one_system(self):
- #
- # # mech1 ---> mech2 --
- # # --> mech3
- # # mech4 ---> mech5 --
- #
- # # 1x2=2 ---> 2x2=4 --
- # # --> (4+4)x2=16
- # # 1x2=2 ---> 2x2=4 --
- #
- # # create a PathwayComposition | blank slate for composition
- # myPath = PathwayComposition()
- #
- # # create mechanisms to add to myPath
- # myMech1 = TransferMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech2 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myMech3 = TransferMechanism(function=Linear(slope=2.0)) # 4 x 2 = 8
- #
- # # add mechanisms to myPath with default MappingProjections between them
- # myPath.add_linear_processing_pathway([myMech1, myMech2, myMech3])
- #
- # myPath2 = PathwayComposition()
- # myMech4 = TransferMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech5 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myPath2.add_linear_processing_pathway([myMech4, myMech5, myMech3])
- #
- # sys = SystemComposition()
- # sys.add_pathway(myPath)
- # sys.add_pathway(myPath2)
- # # assign input to origin mechs
- # stimulus = {myMech1: [[1]], myMech4: [[1]]}
- #
- # # schedule = Scheduler(composition=sys)
- # output = sys.run(inputs=stimulus)
- # assert np.allclose(16, output)
- #
- # def test_two_paths_in_series_one_system(self):
- #
- # # [ mech1 --> mech2 --> mech3 ] --> [ mech4 --> mech5 --> mech6 ]
- # # 1x2=2 --> 2x2=4 --> 4x2=8 --> (8+1)x2=18 --> 18x2=36 --> 36*2=64
- # # X
- # # |
- # # 1
- # # (if mech4 were recognized as an origin mech, and used SOFT_CLAMP, we would expect the final result to be 72)
- # # create a PathwayComposition | blank slate for composition
- # myPath = PathwayComposition()
- #
- # # create mechanisms to add to myPath
- # myMech1 = TransferMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech2 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myMech3 = TransferMechanism(function=Linear(slope=2.0)) # 4 x 2 = 8
- #
- # # add mechanisms to myPath with default MappingProjections between them
- # myPath.add_linear_processing_pathway([myMech1, myMech2, myMech3])
- #
- # myPath2 = PathwayComposition()
- # myMech4 = TransferMechanism(function=Linear(slope=2.0))
- # myMech5 = TransferMechanism(function=Linear(slope=2.0))
- # myMech6 = TransferMechanism(function=Linear(slope=2.0))
- # myPath2.add_linear_processing_pathway([myMech4, myMech5, myMech6])
- #
- # sys = SystemComposition()
- # sys.add_pathway(myPath)
- # sys.add_pathway(myPath2)
- # sys.add_projection(projection=MappingProjection(sender=myMech3,
- # receiver=myMech4), sender=myMech3, receiver=myMech4)
- #
- # # assign input to origin mechs
- # # myMech4 ignores its input from the outside world because it is no longer considered an origin!
- # stimulus = {myMech1: [[1]]}
- #
- # # schedule = Scheduler(composition=sys)
- # output = sys.run(inputs=stimulus)
- #
- # assert np.allclose([64], output)
- #
- # def test_two_paths_converge_one_system_scheduling_matters(self):
- #
- # # mech1 ---> mech2 --
- # # --> mech3
- # # mech4 ---> mech5 --
- #
- # # 1x2=2 ---> 2x2=4 --
- # # --> (4+4)x2=16
- # # 1x2=2 ---> 2x2=4 --
- #
- # # create a PathwayComposition | blank slate for composition
- # myPath = PathwayComposition()
- #
- # # create mechanisms to add to myPath
- # myMech1 = IntegratorMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech2 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myMech3 = TransferMechanism(function=Linear(slope=2.0)) # 4 x 2 = 8
- #
- # # add mechanisms to myPath with default MappingProjections between them
- # myPath.add_linear_processing_pathway([myMech1, myMech2, myMech3])
- #
- # myPathScheduler = Scheduler(composition=myPath)
- # myPathScheduler.add_condition(myMech2, AfterNCalls(myMech1, 2))
- #
- # myPath.run(inputs={myMech1: [[1]]}, scheduler=myPathScheduler)
- # myPath.run(inputs={myMech1: [[1]]}, scheduler=myPathScheduler)
- # myPath2 = PathwayComposition()
- # myMech4 = TransferMechanism(function=Linear(slope=2.0)) # 1 x 2 = 2
- # myMech5 = TransferMechanism(function=Linear(slope=2.0)) # 2 x 2 = 4
- # myPath2.add_linear_processing_pathway([myMech4, myMech5, myMech3])
- #
- # sys = SystemComposition()
- # sys.add_pathway(myPath)
- # sys.add_pathway(myPath2)
- # # assign input to origin mechs
- # stimulus = {myMech1: [[1]], myMech4: [[1]]}
- #
- # # schedule = Scheduler(composition=sys)
- # output = sys.run(inputs=stimulus)
- # assert np.allclose(16, output)
- # MODIFIED 5/8/20 END
+ @pytest.mark.pathways
def test_three_level_deep_pathway_routing_single_mech(self):
p2 = ProcessingMechanism(name='p2')
p0 = ProcessingMechanism(name='p0')
@@ -4744,6 +4944,7 @@ def test_three_level_deep_pathway_routing_single_mech(self):
result = c0.run([5])
assert result == [5]
+ @pytest.mark.pathways
def test_three_level_deep_pathway_routing_two_mech(self):
p3a = ProcessingMechanism(name='p3a')
p3b = ProcessingMechanism(name='p3b')
@@ -4759,6 +4960,7 @@ def test_three_level_deep_pathway_routing_two_mech(self):
result = c1.run([5])
assert result == [5, 5]
+ @pytest.mark.pathways
def test_three_level_deep_modulation_routing_single_mech(self):
p3 = ProcessingMechanism(name='p3')
ctrl1 = ControlMechanism(name='ctrl1',
@@ -4771,6 +4973,7 @@ def test_three_level_deep_modulation_routing_single_mech(self):
result = c1.run({c2: 2, ctrl1: 5})
assert result == [10]
+ @pytest.mark.pathways
def test_three_level_deep_modulation_routing_two_mech(self):
p3a = ProcessingMechanism(name='p3a')
p3b = ProcessingMechanism(name='p3b')
@@ -4787,6 +4990,7 @@ def test_three_level_deep_modulation_routing_two_mech(self):
result = c1.run({c2: [[2], [2]], ctrl1: [5]})
assert result == [10, 10]
+ @pytest.mark.pathways
@pytest.mark.state_features
def test_four_level_nested_transfer_mechanism_composition_parallel(self):
# mechanisms
@@ -7147,6 +7351,31 @@ def test_controller_role(self):
assert comp.get_nodes_by_role(NodeRole.CONTROLLER) == [comp.controller]
assert comp.nodes_to_roles[comp.controller] == {NodeRole.CONTROLLER}
+ def test_inactive_terminal_projection(self):
+ A = pnl.ProcessingMechanism(name='A')
+ B = pnl.ProcessingMechanism(name='B')
+ C = pnl.ProcessingMechanism(name='C')
+ D = pnl.ProcessingMechanism(name='D')
+
+ pnl.MappingProjection(sender=A, receiver=D)
+ comp = pnl.Composition([[A],[B,C]])
+
+ assert comp.nodes_to_roles[A] == {NodeRole.INPUT, NodeRole.OUTPUT, NodeRole.SINGLETON, NodeRole.ORIGIN, NodeRole.TERMINAL}
+
+ def test_feedback_projection_added_by_pathway(self):
+ A = pnl.ProcessingMechanism(name='A')
+ B = pnl.ProcessingMechanism(name='B')
+ C = pnl.ProcessingMechanism(name='C')
+
+ icomp = pnl.Composition(pathways=[C])
+ ocomp = pnl.Composition(pathways=[A, icomp, (B, pnl.NodeRole.FEEDBACK_SENDER), A])
+
+ assert ocomp.nodes_to_roles == {
+ A: {NodeRole.ORIGIN, NodeRole.INPUT, NodeRole.FEEDBACK_RECEIVER},
+ icomp: {NodeRole.INTERNAL},
+ B: {NodeRole.TERMINAL, NodeRole.OUTPUT, NodeRole.FEEDBACK_SENDER},
+ }
+
class TestMisc:
@@ -7386,6 +7615,52 @@ def test_remove_node_learning(self):
comp.remove_node(D)
comp.learn(inputs={n: [0] for n in comp.get_nodes_by_role(pnl.NodeRole.INPUT)})
+ def test_rebuild_scheduler_after_add_node(self):
+ A = ProcessingMechanism(name='A')
+ B = ProcessingMechanism(name='B')
+ C = ProcessingMechanism(name='C')
+
+ comp = Composition(pathways=[A, C])
+
+ comp.scheduler.add_condition(C, pnl.EveryNCalls(A, 2))
+ comp.add_node(B)
+ comp.scheduler.add_condition(B, pnl.EveryNCalls(A, 2))
+
+ comp.run(inputs={A: [0], B: [0]})
+
+ assert type(comp.scheduler.conditions[A]) is pnl.Always
+ assert(
+ type(comp.scheduler.conditions[B]) is pnl.EveryNCalls
+ and comp.scheduler.conditions[B].args == (A, 2)
+ )
+ assert(
+ type(comp.scheduler.conditions[C]) is pnl.EveryNCalls
+ and comp.scheduler.conditions[C].args == (A, 2)
+ )
+ assert comp.scheduler.execution_list[comp.default_execution_id] == [{A}, {A, B}, {C}]
+ assert set(comp.scheduler._user_specified_conds.keys()) == {B, C}
+
+ def test_rebuild_scheduler_after_remove_node(self):
+ A = ProcessingMechanism(name='A')
+ B = ProcessingMechanism(name='B')
+ C = ProcessingMechanism(name='C')
+
+ comp = Composition(pathways=[[A, C], [B, C]])
+
+ comp.scheduler.add_condition(C, pnl.EveryNCalls(A, 2))
+ comp.remove_node(B)
+
+ comp.run(inputs={A: [0]})
+
+ assert type(comp.scheduler.conditions[A]) is pnl.Always
+ assert B not in comp.scheduler.conditions
+ assert(
+ type(comp.scheduler.conditions[C]) is pnl.EveryNCalls
+ and comp.scheduler.conditions[C].args == (A, 2)
+ )
+ assert comp.scheduler.execution_list[comp.default_execution_id] == [{A}, {A}, {C}]
+ assert set(comp.scheduler._user_specified_conds.keys()) == {C}
+
class TestInputSpecsDocumentationExamples:
diff --git a/tests/composition/test_control.py b/tests/composition/test_control.py
index 26169e34515..706dc08ef19 100644
--- a/tests/composition/test_control.py
+++ b/tests/composition/test_control.py
@@ -2464,10 +2464,15 @@ def test_modulation_of_random_state_direct(self, comp_mode, benchmark, prng):
if prng == 'Default':
prngs = {s:np.random.RandomState([s]) for s in seeds}
+ def get_val(s, dty):
+ return prngs[s].uniform()
elif prng == 'Philox':
prngs = {s:_SeededPhilox([s]) for s in seeds}
+ def get_val(s, dty):
+ return prngs[s].random(dtype=dty)
- expected = [prngs[s].uniform() for s in seeds] * 2
+ dty = np.float32 if pytest.helpers.llvm_current_fp_precision() == 'fp32' else np.float64
+ expected = [get_val(s, dty) for s in seeds] * 2
assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), expected)
@pytest.mark.benchmark
@@ -2496,10 +2501,15 @@ def test_modulation_of_random_state_DDM(self, comp_mode, benchmark, prng):
# cycle over the seeds twice setting and resetting the random state
benchmark(comp.run, inputs={ctl_mech:seeds, mech:5.0}, num_trials=len(seeds) * 2, execution_mode=comp_mode)
+ precision = pytest.helpers.llvm_current_fp_precision()
if prng == 'Default':
assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[100, 21], [100, 23], [100, 20]] * 2)
- elif prng == 'Philox':
+ elif prng == 'Philox' and precision == 'fp64':
assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[100, 19], [100, 21], [100, 21]] * 2)
+ elif prng == 'Philox' and precision == 'fp32':
+ assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[100, 17], [100, 22], [100, 20]] * 2)
+ else:
+ assert False, "Unknown PRNG!"
@pytest.mark.benchmark
@pytest.mark.control
@@ -2525,10 +2535,15 @@ def test_modulation_of_random_state_DDM_Analytical(self, comp_mode, benchmark, p
# cycle over the seeds twice setting and resetting the random state
benchmark(comp.run, inputs={ctl_mech:seeds, mech:0.1}, num_trials=len(seeds) * 2, execution_mode=comp_mode)
+ precision = pytest.helpers.llvm_current_fp_precision()
if prng == 'Default':
assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[-1, 3.99948962], [1, 3.99948962], [-1, 3.99948962]] * 2)
- elif prng == 'Philox':
+ elif prng == 'Philox' and precision == 'fp64':
assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[-1, 3.99948962], [-1, 3.99948962], [1, 3.99948962]] * 2)
+ elif prng == 'Philox' and precision == 'fp32':
+ assert np.allclose(np.squeeze(comp.results[:len(seeds) * 2]), [[1, 3.99948978], [-1, 3.99948978], [1, 3.99948978]] * 2)
+ else:
+ assert False, "Unknown PRNG!"
@pytest.mark.control
@pytest.mark.composition
@@ -2608,41 +2623,27 @@ def test_ocm_default_function(self):
assert type(comp.controller.function) == pnl.GridSearch
assert comp.run([1]) == [10]
- def test_ocm_searchspace_arg(self):
- a = pnl.ProcessingMechanism()
- comp = pnl.Composition(
- controller_mode=pnl.BEFORE,
- nodes=[a],
- controller=pnl.OptimizationControlMechanism(
- control=pnl.ControlSignal(
- modulates=(pnl.SLOPE, a),
- intensity_cost_function=lambda x: 0,
- adjustment_cost_function=lambda x: 0,
- ),
- state_features=[a.input_port],
- objective_mechanism=pnl.ObjectiveMechanism(
- monitor=[a.output_port]
- ),
- search_space=[pnl.SampleIterator([1, 10])]
- )
- )
- assert type(comp.controller.function) == pnl.GridSearch
- assert comp.run([1]) == [10]
+ @pytest.mark.parametrize("nested", [True, False])
+ @pytest.mark.parametrize("format", ["list", "tuple", "SampleIterator", "SampleIteratorArray", "SampleSpec", "ndArray"])
+ @pytest.mark.parametrize("mode", pytest.helpers.get_comp_execution_modes() +
+ [pytest.helpers.cuda_param('Python-PTX'),
+ pytest.param('Python-LLVM', marks=pytest.mark.llvm)])
+ def test_ocm_searchspace_format_equivalence(self, format, nested, mode):
+ if str(mode).startswith('Python-'):
+ ocm_mode = mode.split('-')[1]
+ mode = pnl.ExecutionMode.Python
+ else:
+ # OCM default mode is Python
+ ocm_mode = 'Python'
- @pytest.mark.parametrize("format,nested",
- [("list", True), ("list", False),
- ("tuple", True), ("tuple", False),
- ("SampleIterator", True), ("SampleIterator", False),
- ("SampleSpec", True), ("SampleSpec", False),
- ("ndArray", True), ("ndArray", False),
- ],)
- def test_ocm_searchspace_format_equivalence(self, format, nested):
if format == "list":
search_space = [1, 10]
elif format == "tuple":
search_space = (1, 10)
elif format == "SampleIterator":
- search_space = SampleIterator((1,10))
+ search_space = SampleIterator((1, 10))
+ elif format == "SampleIteratorArray":
+ search_space = SampleIterator([1, 10])
elif format == "SampleSpec":
search_space = SampleSpec(1, 10, 9)
elif format == "ndArray":
@@ -2658,8 +2659,7 @@ def test_ocm_searchspace_format_equivalence(self, format, nested):
controller=pnl.OptimizationControlMechanism(
control=pnl.ControlSignal(
modulates=(pnl.SLOPE, a),
- intensity_cost_function=lambda x: 0,
- adjustment_cost_function=lambda x: 0,
+ cost_options=None
),
state_features=[a.input_port],
objective_mechanism=pnl.ObjectiveMechanism(
@@ -2668,8 +2668,10 @@ def test_ocm_searchspace_format_equivalence(self, format, nested):
search_space=search_space
)
)
+ comp.controller.comp_execution_mode = ocm_mode
+
assert type(comp.controller.function) == pnl.GridSearch
- assert comp.run([1]) == [10]
+ assert comp.run([1], execution_mode=mode) == [[10]]
def test_evc(self):
# Mechanisms
diff --git a/tests/composition/test_learning.py b/tests/composition/test_learning.py
index cdf3c289165..cbba3e2d0c8 100644
--- a/tests/composition/test_learning.py
+++ b/tests/composition/test_learning.py
@@ -2291,9 +2291,9 @@ def test_backprop_with_various_intersecting_pathway_configurations(self, configu
@pytest.mark.parametrize('order', [
- 'color_full',
- 'word_partial',
- 'word_full',
+ # 'color_full',
+ # 'word_partial',
+ # 'word_full',
'full_overlap'
])
def test_stroop_model_learning(self, order):
diff --git a/tests/functions/test_default_allocation.py b/tests/functions/test_default_allocation.py
deleted file mode 100644
index 4486e7d7042..00000000000
--- a/tests/functions/test_default_allocation.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import numpy as np
-import pytest
-
-import psyneulink.core.llvm as pnlvm
-from psyneulink.core.components.mechanisms.modulatory.control.controlmechanism import DefaultAllocationFunction
-
-@pytest.mark.function
-@pytest.mark.identity_function
-@pytest.mark.benchmark(group="IdentityFunction")
-def test_basic(benchmark, func_mode):
- variable = np.random.rand(1)
- f = DefaultAllocationFunction()
- EX = pytest.helpers.get_func_execution(f, func_mode)
-
- res = benchmark(EX, variable)
- assert np.allclose(res, variable)
diff --git a/tests/functions/test_distribution.py b/tests/functions/test_distribution.py
index dcdf066e092..2b0d111d2c3 100644
--- a/tests/functions/test_distribution.py
+++ b/tests/functions/test_distribution.py
@@ -1,5 +1,6 @@
import numpy as np
import pytest
+import sys
import psyneulink.core.llvm as pnlvm
import psyneulink.core.components.functions.nonstateful.distributionfunctions as Functions
@@ -14,61 +15,119 @@
RAND4 = np.random.rand()
RAND5 = np.random.rand()
+dda_expected_default = (1.9774974807292212, 0.012242689689501842,
+ 1.9774974807292207, 1.3147677945132479, 1.7929299891370192,
+ 1.9774974807292207, 1.3147677945132479, 1.7929299891370192)
+dda_expected_random = (0.4236547993389047, -2.7755575615628914e-17,
+ 0.5173675420165031, 0.06942854144616283, 6.302631815990666,
+ 1.4934079600147951, 0.4288991185241868, 1.7740760781361433)
+dda_expected_negative = (0.42365479933890504, 0.0,
+ 0.5173675420165031, 0.06942854144616283, 6.302631815990666,
+ 1.4934079600147951, 0.4288991185241868, 1.7740760781361433)
+dda_expected_small = (0.5828813465336954, 0.04801236718458773,
+ 0.532471083815943, 0.09633801362499317, 6.111833139205608,
+ 1.5821207676710864, 0.5392724012504414, 1.8065252817609618)
+# Different libm implementations produce slightly different results
+if sys.platform.startswith("win") or sys.platform.startswith("darwin"):
+ dda_expected_small = (0.5828813465336954, 0.04801236718458773,
+ 0.5324710838150166, 0.09633802135385469, 6.119380538293901,
+ 1.58212076767016, 0.5392724012504414, 1.8065252817609618)
+
+normal_expected_mt = (1.0890232855122397)
+uniform_expected_mt = (0.6879771504250405)
+normal_expected_philox = (0.5910357654927911)
+uniform_expected_philox = (0.6043448764869507)
+
+llvm_expected = {}
+llvm_expected = {'fp64': {}, 'fp32': {}}
+llvm_expected['fp64'][dda_expected_small] = (0.5828813465336954, 0.04801236718458773,
+ 0.5324710838085324, 0.09633787836991654, 6.0158766570416775,
+ 1.5821207675877176, 0.5392731045768397, 1.8434859117411773)
+
+# add fp32 results
+llvm_expected['fp32'][dda_expected_random] = (0.42365485429763794, 0.0,
+ 0.5173675417900085, 0.06942801177501678, 6.302331447601318,
+ 1.4934077262878418, 0.428894966840744, 1.7738982439041138)
+llvm_expected['fp32'][dda_expected_negative] = (0.4236549735069275, 5.960464477539063e-08,
+ 0.5173678398132324, 0.06942889094352722, 6.303247451782227,
+ 1.4934080839157104, 0.42889583110809326, 1.7739603519439697)
+llvm_expected['fp32'][dda_expected_small] = None
+llvm_expected['fp32'][normal_expected_philox] = (0.5655658841133118)
+llvm_expected['fp32'][uniform_expected_philox] = (0.6180108785629272)
+
test_data = [
- (Functions.DriftDiffusionAnalytical, test_var, {}, None,
- (1.9774974807292212, 0.012242689689501842, 1.9774974807292207, 1.3147677945132479, 1.7929299891370192, 1.9774974807292207, 1.3147677945132479, 1.7929299891370192)),
- (Functions.DriftDiffusionAnalytical, test_var, {"drift_rate": RAND1, "threshold": RAND2, "starting_value": RAND3, "non_decision_time":RAND4, "noise": RAND5}, None,
- (0.4236547993389047, -2.7755575615628914e-17, 0.5173675420165031, 0.06942854144616283, 6.302631815990666, 1.4934079600147951, 0.4288991185241868, 1.7740760781361433)),
- (Functions.DriftDiffusionAnalytical, -test_var, {"drift_rate": RAND1, "threshold": RAND2, "starting_value": RAND3, "non_decision_time":RAND4, "noise": RAND5}, None,
- (0.42365479933890504, 0.0, 0.5173675420165031, 0.06942854144616283, 6.302631815990666, 1.4934079600147951, 0.4288991185241868, 1.7740760781361433)),
-# FIXME: Rounding errors result in different behaviour on different platforms
-# (Functions.DriftDiffusionAnalytical, 1e-4, {"drift_rate": 1e-5, "threshold": RAND2, "starting_value": RAND3, "non_decision_time":RAND4, "noise": RAND5}, "Rounding errors",
-# (0.5828813465336954, 0.04801236718458773, 0.532471083815943, 0.09633801362499317, 6.111833139205608, 1.5821207676710864, 0.5392724012504414, 1.8065252817609618)),
+ pytest.param(Functions.DriftDiffusionAnalytical, test_var, {}, None, None,
+ dda_expected_default, id="DriftDiffusionAnalytical-DefaultParameters"),
+ pytest.param(Functions.DriftDiffusionAnalytical, test_var,
+ {"drift_rate": RAND1, "threshold": RAND2, "starting_value": RAND3,
+ "non_decision_time":RAND4, "noise": RAND5}, None, None,
+ dda_expected_random, id="DriftDiffusionAnalytical-RandomParameters"),
+ pytest.param(Functions.DriftDiffusionAnalytical, -test_var,
+ {"drift_rate": RAND1, "threshold": RAND2, "starting_value": RAND3,
+ "non_decision_time":RAND4, "noise": RAND5}, None, None,
+ dda_expected_negative, id="DriftDiffusionAnalytical-NegInput"),
+ pytest.param(Functions.DriftDiffusionAnalytical, 1e-4,
+ {"drift_rate": 1e-5, "threshold": RAND2, "starting_value": RAND3,
+ "non_decision_time":RAND4, "noise": RAND5}, None, "Rounding Errors",
+ dda_expected_small, id="DriftDiffusionAnalytical-SmallDriftRate"),
+ pytest.param(Functions.DriftDiffusionAnalytical, -1e-4,
+ {"drift_rate": 1e-5, "threshold": RAND2, "starting_value": RAND3,
+ "non_decision_time":RAND4, "noise": RAND5}, None, "Rounding Errors",
+ dda_expected_small, id="DriftDiffusionAnalytical-SmallDriftRate-NegInput"),
+ pytest.param(Functions.DriftDiffusionAnalytical, 1e-4,
+ {"drift_rate": -1e-5, "threshold": RAND2, "starting_value": RAND3,
+ "non_decision_time":RAND4, "noise": RAND5}, None, "Rounding Errors",
+ dda_expected_small, id="DriftDiffusionAnalytical-SmallNegDriftRate"),
# Two tests with different inputs to show that input is ignored.
- (Functions.NormalDist, 1e14, {"mean": RAND1, "standard_deviation": RAND2}, None, (1.0890232855122397)),
- (Functions.NormalDist, 1e-4, {"mean": RAND1, "standard_deviation": RAND2}, None, (1.0890232855122397)),
- (Functions.UniformDist, 1e14, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)}, None, (0.6879771504250405)),
- (Functions.UniformDist, 1e-4, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)}, None, (0.6879771504250405)),
+ pytest.param(Functions.NormalDist, 1e14, {"mean": RAND1, "standard_deviation": RAND2},
+ None, None, normal_expected_mt, id="NormalDist"),
+ pytest.param(Functions.NormalDist, 1e-4, {"mean": RAND1, "standard_deviation": RAND2},
+ None, None, normal_expected_mt, id="NormalDist Small Input"),
+ pytest.param(Functions.UniformDist, 1e14, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)},
+ None, None, uniform_expected_mt, id="UniformDist"),
+ pytest.param(Functions.UniformDist, 1e-4, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)},
+ None, None, uniform_expected_mt, id="UniformDist"),
# Inf inputs select Philox PRNG, test_var should never be inf
- (Functions.NormalDist, np.inf, {"mean": RAND1, "standard_deviation": RAND2}, None, (0.5910357654927911)),
- (Functions.NormalDist, -np.inf, {"mean": RAND1, "standard_deviation": RAND2}, None, (0.5910357654927911)),
- (Functions.UniformDist, np.inf, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)}, None, (0.6043448764869507)),
- (Functions.UniformDist, -np.inf, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)}, None, (0.6043448764869507)),
+ pytest.param(Functions.NormalDist, 1e14, {"mean": RAND1, "standard_deviation": RAND2},
+ _SeededPhilox, None, normal_expected_philox, id="NormalDist Philox"),
+ pytest.param(Functions.NormalDist, 1e-4, {"mean": RAND1, "standard_deviation": RAND2},
+ _SeededPhilox, None, normal_expected_philox, id="NormalDist Philox"),
+ pytest.param(Functions.UniformDist, 1e14, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)},
+ _SeededPhilox, None, uniform_expected_philox, id="UniformDist Philox"),
+ pytest.param(Functions.UniformDist, 1e-4, {"low": min(RAND1, RAND2), "high": max(RAND1, RAND2)},
+ _SeededPhilox, None, uniform_expected_philox, id="UniformDist Philox"),
]
-# use list, naming function produces ugly names
-names = [
- "DriftDiffusionAnalytical-DefaultParameters",
- "DriftDiffusionAnalytical-RandomParameters",
- "DriftDiffusionAnalytical-NegInput",
-# "DriftDiffusionAnalytical-SmallDriftRate",
- "NormalDist1",
- "NormalDist2",
- "UniformDist1",
- "UniformDist2",
- "NormalDist1 Philox",
- "NormalDist2 Philox",
- "UniformDist1 Philox",
- "UniformDist2 Philox",
-]
-
-
@pytest.mark.function
@pytest.mark.transfer_function
@pytest.mark.benchmark
-@pytest.mark.parametrize("func, variable, params, llvm_skip, expected", test_data, ids=names)
-def test_execute(func, variable, params, llvm_skip, expected, benchmark, func_mode):
+@pytest.mark.parametrize("func, variable, params, prng, llvm_skip, expected", test_data)
+def test_execute(func, variable, params, prng, llvm_skip, expected, benchmark, func_mode):
benchmark.group = "TransferFunction " + func.componentName
- if func_mode != 'Python' and llvm_skip:
+ if func_mode != 'Python':
+ precision = pytest.helpers.llvm_current_fp_precision()
+ # PTX needs only one special case, this is not worth adding
+ # it to the mechanism above
+ if func_mode == "PTX" and precision == 'fp32' and expected is dda_expected_negative:
+ expected = (0.4236549735069275, 5.960464477539063e-08,
+ 0.5173678398132324, 0.06942889094352722, 6.303247451782227,
+ 1.4934064149856567, 0.42889145016670227, 1.7737685441970825)
+ expected = llvm_expected.get(precision, {}).get(expected, expected)
+
+ if expected is None:
pytest.skip(llvm_skip)
f = func(default_variable=variable, **params)
- if np.isinf(variable):
- f.parameters.random_state.set(_SeededPhilox([0]))
+ if prng is not None:
+ f.parameters.random_state.set(prng([0]))
ex = pytest.helpers.get_func_execution(f, func_mode)
res = ex(variable)
- assert np.allclose(res, expected)
+ if pytest.helpers.llvm_current_fp_precision() == 'fp32':
+ assert np.allclose(res, expected)
+ else:
+ np.testing.assert_allclose(res, expected)
+
if benchmark.enabled:
benchmark(ex, variable)
diff --git a/tests/functions/test_memory.py b/tests/functions/test_memory.py
index fe712bc49bb..92d736fda8a 100644
--- a/tests/functions/test_memory.py
+++ b/tests/functions/test_memory.py
@@ -18,16 +18,13 @@
np.random.seed(0)
SIZE=10
test_var = np.random.rand(2, SIZE)
-#TODO: Initializer should use different values to test recall
-test_initializer = np.array([[test_var[0], test_var[1]]])
+test_initializer = np.array([[test_var[0] * 5, test_var[1] * 4]])
test_noise_arr = np.random.rand(SIZE)
RAND1 = np.random.random(1)
RAND2 = np.random.random()
philox_var = np.random.rand(2, SIZE)
-#TODO: Initializer should use different values to test recall
-philox_initializer = np.array([[philox_var[0], philox_var[1]]])
test_data = [
# Default initializer does not work
@@ -87,25 +84,25 @@
pytest.param(Functions.DictionaryMemory, philox_var, {'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="DictionaryMemory (Philox)"),
+ id="DictionaryMemory Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="DictionaryMemory Rate (Philox)"),
+ id="DictionaryMemory Rate Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="DictionaryMemory Initializer (Philox)"),
- pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.1, 'seed': module_seed},
+ id="DictionaryMemory Initializer Philox"),
+ pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
- id="DictionaryMemory Low Retrieval (Philox)"),
+ id="DictionaryMemory Low Retrieval Philox"),
pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'storage_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
- id="DictionaryMemory Low Storage (Philox)"),
- pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.9, 'storage_prob':0.9, 'seed': module_seed},
+ id="DictionaryMemory Low Storage Philox"),
+ pytest.param(Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.95, 'storage_prob':0.95, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="DictionaryMemory High Storage/Retrieve (Philox)"),
+ id="DictionaryMemory High Storage/Retrieve Philox"),
# Disable noise tests for now as they trigger failure in DictionaryMemory lookup
# (Functions.DictionaryMemory, philox_var, {'rate':RAND1, 'noise':RAND2}, [[
# 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606, 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215 ],[
@@ -121,18 +118,18 @@
#]]),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.1, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
- id="ContentAddressableMemory Low Retrieval (Philox)"),
+ id="ContentAddressableMemory Low Retrieval Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'storage_prob':0.01, 'seed': module_seed},
[[ 0. for i in range(SIZE) ],[ 0. for i in range(SIZE) ]],
- id="ContentAddressableMemory Low Storage (Philox)"),
+ id="ContentAddressableMemory Low Storage Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'rate':RAND1, 'retrieval_prob':0.9, 'storage_prob':0.9, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="ContentAddressableMemory High Storage/Retrieval (Philox)"),
+ id="ContentAddressableMemory High Storage/Retrieval Philox"),
pytest.param(Functions.ContentAddressableMemory, philox_var, {'initializer':test_initializer, 'rate':RAND1, 'seed': module_seed},
[[0.45615033221654855, 0.5684339488686485, 0.018789800436355142, 0.6176354970758771, 0.6120957227224214, 0.6169339968747569, 0.9437480785146242, 0.6818202991034834, 0.359507900573786, 0.43703195379934145],
[0.6976311959272649, 0.06022547162926983, 0.6667667154456677, 0.6706378696181594, 0.2103825610738409, 0.1289262976548533, 0.31542835092418386, 0.3637107709426226, 0.5701967704178796, 0.43860151346232035]],
- id="ContentAddressableMemory Initializer (Philox)"),
+ id="ContentAddressableMemory Initializer Philox"),
]
@pytest.mark.function
diff --git a/tests/functions/test_selection.py b/tests/functions/test_selection.py
index 3ca5059706a..8fe21b1c5b2 100644
--- a/tests/functions/test_selection.py
+++ b/tests/functions/test_selection.py
@@ -16,20 +16,27 @@
test_philox = np.random.rand(SIZE)
test_philox /= sum(test_philox)
+expected_philox_prob = (0., 0.43037873274483895, 0., 0., 0., 0., 0., 0., 0., 0.)
+expected_philox_ind = (0., 1., 0., 0., 0., 0., 0., 0., 0., 0.)
+
+llvm_res = {'fp32': {}, 'fp64': {}}
+llvm_res['fp32'][expected_philox_prob] = (0.09762700647115707, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
+llvm_res['fp32'][expected_philox_ind] = (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
+
test_data = [
- (Functions.OneHot, test_var, {'mode':kw.MAX_VAL}, [0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.]),
- (Functions.OneHot, test_var, {'mode':kw.MAX_ABS_VAL}, [0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.]),
- (Functions.OneHot, -test_var, {'mode':kw.MAX_ABS_VAL}, [0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.]),
- (Functions.OneHot, test_var, {'mode':kw.MAX_INDICATOR}, [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]),
- (Functions.OneHot, test_var, {'mode':kw.MAX_ABS_INDICATOR}, [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]),
- (Functions.OneHot, test_var, {'mode':kw.MIN_VAL}, [0., 0., 0., 0., 0., 0., 0., 0., 0., -0.23311696]),
- (Functions.OneHot, test_var, {'mode':kw.MIN_ABS_VAL}, [0., 0., 0., 0.08976637, 0., 0., 0., 0., 0., 0.]),
- (Functions.OneHot, test_var, {'mode':kw.MIN_INDICATOR}, [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),
- (Functions.OneHot, test_var, {'mode':kw.MIN_ABS_INDICATOR}, [0., 0., 0., 1.,0., 0., 0., 0., 0., 0.]),
- (Functions.OneHot, [test_var, test_prob], {'mode':kw.PROB}, [0., 0., 0., 0.08976636599379373, 0., 0., 0., 0., 0., 0.]),
- (Functions.OneHot, [test_var, test_prob], {'mode':kw.PROB_INDICATOR}, [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]),
- (Functions.OneHot, [test_var, test_philox], {'mode':kw.PROB}, [0., 0.43037873274483895, 0., 0., 0., 0., 0., 0., 0., 0.]),
- (Functions.OneHot, [test_var, test_philox], {'mode':kw.PROB_INDICATOR}, [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),
+ (Functions.OneHot, test_var, {'mode':kw.MAX_VAL}, (0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.)),
+ (Functions.OneHot, test_var, {'mode':kw.MAX_ABS_VAL}, (0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.)),
+ (Functions.OneHot, -test_var, {'mode':kw.MAX_ABS_VAL}, (0., 0., 0., 0., 0., 0., 0., 0., 0.92732552, 0.)),
+ (Functions.OneHot, test_var, {'mode':kw.MAX_INDICATOR}, (0., 0., 0., 0., 0., 0., 0., 0., 1., 0.)),
+ (Functions.OneHot, test_var, {'mode':kw.MAX_ABS_INDICATOR}, (0., 0., 0., 0., 0., 0., 0., 0., 1., 0.)),
+ (Functions.OneHot, test_var, {'mode':kw.MIN_VAL}, (0., 0., 0., 0., 0., 0., 0., 0., 0., -0.23311696)),
+ (Functions.OneHot, test_var, {'mode':kw.MIN_ABS_VAL}, (0., 0., 0., 0.08976637, 0., 0., 0., 0., 0., 0.)),
+ (Functions.OneHot, test_var, {'mode':kw.MIN_INDICATOR}, (0., 0., 0., 0., 0., 0., 0., 0., 0., 1.)),
+ (Functions.OneHot, test_var, {'mode':kw.MIN_ABS_INDICATOR}, (0., 0., 0., 1.,0., 0., 0., 0., 0., 0.)),
+ (Functions.OneHot, [test_var, test_prob], {'mode':kw.PROB}, (0., 0., 0., 0.08976636599379373, 0., 0., 0., 0., 0., 0.)),
+ (Functions.OneHot, [test_var, test_prob], {'mode':kw.PROB_INDICATOR}, (0., 0., 0., 1., 0., 0., 0., 0., 0., 0.)),
+ (Functions.OneHot, [test_var, test_philox], {'mode':kw.PROB}, expected_philox_prob),
+ (Functions.OneHot, [test_var, test_philox], {'mode':kw.PROB_INDICATOR}, expected_philox_ind),
]
# use list, naming function produces ugly names
@@ -45,8 +52,8 @@
"OneHot MIN_ABS_INDICATOR",
"OneHot PROB",
"OneHot PROB_INDICATOR",
- "OneHot PROB PHILOX",
- "OneHot PROB_INDICATOR PHILOX",
+ "OneHot PROB Philox",
+ "OneHot PROB_INDICATOR Philox",
]
GROUP_PREFIX="SelectionFunction "
@@ -62,10 +69,15 @@ def test_basic(func, variable, params, expected, benchmark, func_mode):
if len(variable) == 2 and variable[1] is test_philox:
f.parameters.random_state.set(_SeededPhilox([0]))
+ if func_mode != 'Python':
+ precision = pytest.helpers.llvm_current_fp_precision()
+ expected = llvm_res[precision].get(expected, expected)
+
EX = pytest.helpers.get_func_execution(f, func_mode)
EX(variable)
res = EX(variable)
+
assert np.allclose(res, expected)
if benchmark.enabled:
benchmark(EX, variable)
diff --git a/tests/functions/test_transfer.py b/tests/functions/test_transfer.py
index b526a0cb4ed..5f71ee55d35 100644
--- a/tests/functions/test_transfer.py
+++ b/tests/functions/test_transfer.py
@@ -7,6 +7,7 @@
from math import e, pi, sqrt
SIZE=10
+np.random.seed(0)
test_var = np.random.rand(SIZE)
test_matrix = np.random.rand(SIZE, SIZE)
test_matrix_s = np.random.rand(SIZE, SIZE // 4)
@@ -35,26 +36,46 @@ def gaussian_distort_helper(seed):
test_data = [
- (Functions.Linear, test_var, {'slope':RAND1, 'intercept':RAND2}, None, test_var * RAND1 + RAND2),
- (Functions.Exponential, test_var, {'scale':RAND1, 'rate':RAND2}, None, RAND1 * np.exp(RAND2 * test_var)),
- (Functions.Logistic, test_var, {'gain':RAND1, 'x_0':RAND2, 'offset':RAND3, 'scale':RAND4}, None, RAND4 / (1 + np.exp(-(RAND1 * (test_var - RAND2)) + RAND3))),
- (Functions.Tanh, test_var, {'gain':RAND1, 'bias':RAND2, 'x_0':RAND3, 'offset':RAND4}, None, tanh_helper),
- (Functions.ReLU, test_var, {'gain':RAND1, 'bias':RAND2, 'leak':RAND3}, None, np.maximum(RAND1 * (test_var - RAND2), RAND3 * RAND1 *(test_var - RAND2))),
- (Functions.Angle, [0.5488135, 0.71518937, 0.60276338, 0.54488318, 0.4236548,
- 0.64589411, 0.43758721, 0.891773, 0.96366276, 0.38344152], {}, None,
- [0.85314409, 0.00556188, 0.01070476, 0.0214405, 0.05559454,
- 0.08091079, 0.21657281, 0.19296643, 0.21343805, 0.92738261, 0.00483101]),
- (Functions.Gaussian, test_var, {'standard_deviation':RAND1, 'bias':RAND2, 'scale':RAND3, 'offset':RAND4}, None, gaussian_helper),
- (Functions.GaussianDistort, test_var.tolist(), {'bias': RAND1, 'variance':RAND2, 'offset':RAND3, 'scale':RAND4 }, None, gaussian_distort_helper(0)),
- (Functions.GaussianDistort, test_var.tolist(), {'bias': RAND1, 'variance':RAND2, 'offset':RAND3, 'scale':RAND4, 'seed':0 }, None, gaussian_distort_helper(0)),
- (Functions.SoftMax, test_var, {'gain':RAND1, 'per_item': False}, None, softmax_helper),
- (Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_VAL}, 'per_item': False}, None, np.where(softmax_helper == np.max(softmax_helper), np.max(softmax_helper), 0)),
- (Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_INDICATOR}, 'per_item': False}, None, np.where(softmax_helper == np.max(softmax_helper), 1, 0)),
- (Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix.tolist()}, None, np.dot(test_var, test_matrix)),
- (Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix_l.tolist()}, None, np.dot(test_var, test_matrix_l)),
- (Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix_s.tolist()}, None, np.dot(test_var, test_matrix_s)),
+ pytest.param(Functions.Linear, test_var, {'slope':RAND1, 'intercept':RAND2}, test_var * RAND1 + RAND2, id="LINEAR"),
+ pytest.param(Functions.Exponential, test_var, {'scale':RAND1, 'rate':RAND2}, RAND1 * np.exp(RAND2 * test_var), id="EXPONENTIAL"),
+ pytest.param(Functions.Logistic, test_var, {'gain':RAND1, 'x_0':RAND2, 'offset':RAND3, 'scale':RAND4}, RAND4 / (1 + np.exp(-(RAND1 * (test_var - RAND2)) + RAND3)), id="LOGISTIC"),
+ pytest.param(Functions.Tanh, test_var, {'gain':RAND1, 'bias':RAND2, 'x_0':RAND3, 'offset':RAND4}, tanh_helper, id="TANH"),
+ pytest.param(Functions.ReLU, test_var, {'gain':RAND1, 'bias':RAND2, 'leak':RAND3}, np.maximum(RAND1 * (test_var - RAND2), RAND3 * RAND1 *(test_var - RAND2)), id="RELU"),
+ pytest.param(Functions.Angle, [0.5488135, 0.71518937, 0.60276338, 0.54488318, 0.4236548,
+ 0.64589411, 0.43758721, 0.891773, 0.96366276, 0.38344152], {},
+ [0.85314409, 0.00556188, 0.01070476, 0.0214405, 0.05559454,
+ 0.08091079, 0.21657281, 0.19296643, 0.21343805, 0.92738261, 0.00483101],
+ id="ANGLE"),
+ pytest.param(Functions.Gaussian, test_var, {'standard_deviation':RAND1, 'bias':RAND2, 'scale':RAND3, 'offset':RAND4}, gaussian_helper, id="GAUSSIAN"),
+ pytest.param(Functions.GaussianDistort, test_var.tolist(), {'bias': RAND1, 'variance':RAND2, 'offset':RAND3, 'scale':RAND4 }, gaussian_distort_helper(0), id="GAUSSIAN DISTORT GLOBAL SEED"),
+ pytest.param(Functions.GaussianDistort, test_var.tolist(), {'bias': RAND1, 'variance':RAND2, 'offset':RAND3, 'scale':RAND4, 'seed':0 }, gaussian_distort_helper(0), id="GAUSSIAN DISTORT"),
+ pytest.param(Functions.SoftMax, test_var, {'gain':RAND1, 'per_item': False}, softmax_helper, id="SOFT_MAX ALL"),
+ pytest.param(Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_VAL}, 'per_item': False}, np.where(softmax_helper == np.max(softmax_helper), np.max(softmax_helper), 0), id="SOFT_MAX MAX_VAL"),
+ pytest.param(Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_INDICATOR}, 'per_item': False}, np.where(softmax_helper == np.max(softmax_helper), 1, 0), id="SOFT_MAX MAX_INDICATOR"),
+ pytest.param(Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.PROB}, 'per_item': False},
+ [0.0, 0.0, 0.0, 0.0, test_var[4], 0.0, 0.0, 0.0, 0.0, 0.0], id="SOFT_MAX PROB"),
+ pytest.param(Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix.tolist()}, np.dot(test_var, test_matrix), id="LINEAR_MATRIX SQUARE"),
+ pytest.param(Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix_l.tolist()}, np.dot(test_var, test_matrix_l), id="LINEAR_MATRIX WIDE"),
+ pytest.param(Functions.LinearMatrix, test_var.tolist(), {'matrix':test_matrix_s.tolist()}, np.dot(test_var, test_matrix_s), id="LINEAR_MATRIX TALL"),
]
+@pytest.mark.function
+@pytest.mark.transfer_function
+@pytest.mark.benchmark
+@pytest.mark.parametrize("func, variable, params, expected", test_data)
+def test_execute(func, variable, params, expected, benchmark, func_mode):
+ if 'Angle' in func.componentName and func_mode != 'Python':
+ pytest.skip('Angle not yet supported by LLVM or PTX')
+ benchmark.group = "TransferFunction " + func.componentName
+ f = func(default_variable=variable, **params)
+ ex = pytest.helpers.get_func_execution(f, func_mode)
+
+ res = ex(variable)
+ assert np.allclose(res, expected)
+ if benchmark.enabled:
+ benchmark(ex, variable)
+
+
relu_derivative_helper = lambda x : RAND1 if x > 0 else RAND1 * RAND3
logistic_helper = RAND4 / (1 + np.exp(-(RAND1 * (test_var - RAND2)) + RAND3))
tanh_derivative_helper = (RAND1 * (test_var + RAND2) + RAND3)
@@ -67,25 +88,6 @@ def gaussian_distort_helper(seed):
(Functions.Tanh, test_var, {'gain':RAND1, 'bias':RAND2, 'offset':RAND3, 'scale':RAND4}, tanh_derivative_helper),
]
-# use list, naming function produces ugly names
-names = [
- "LINEAR",
- "EXPONENTIAL",
- "LOGISTIC",
- "TANH",
- "RELU",
- "ANGLE",
- "GAUSIAN",
- "GAUSSIAN DISTORT GLOBAL SEED",
- "GAUSSIAN DISTORT",
- "SOFT_MAX ALL",
- "SOFT_MAX MAX_VAL",
- "SOFT_MAX MAX_INDICATOR",
- "LINEAR_MATRIX SQUARE",
- "LINEAR_MATRIX WIDE",
- "LINEAR_MATRIX TALL",
-]
-
derivative_names = [
"LINEAR_DERIVATIVE",
"EXPONENTIAL_DERIVATIVE",
@@ -94,23 +96,6 @@ def gaussian_distort_helper(seed):
"TANH_DERIVATIVE",
]
-@pytest.mark.function
-@pytest.mark.transfer_function
-@pytest.mark.benchmark
-@pytest.mark.parametrize("func, variable, params, fail, expected", test_data, ids=names)
-def test_execute(func, variable, params, fail, expected, benchmark, func_mode):
- if 'Angle' in func.componentName and func_mode != 'Python':
- pytest.skip('Angle not yet supported by LLVM or PTX')
- benchmark.group = "TransferFunction " + func.componentName
- f = func(default_variable=variable, **params)
- ex = pytest.helpers.get_func_execution(f, func_mode)
-
- res = ex(variable)
- assert np.allclose(res, expected)
- if benchmark.enabled:
- benchmark(ex, variable)
-
-
@pytest.mark.function
@pytest.mark.transfer_function
@pytest.mark.benchmark
diff --git a/tests/llvm/test_builtins_intrinsics.py b/tests/llvm/test_builtins_intrinsics.py
index dad65836dd8..307ccdabc5d 100644
--- a/tests/llvm/test_builtins_intrinsics.py
+++ b/tests/llvm/test_builtins_intrinsics.py
@@ -10,12 +10,23 @@
@pytest.mark.benchmark(group="Builtins")
@pytest.mark.parametrize("op, args, builtin, result", [
(np.exp, (x,), "__pnl_builtin_exp", np.exp(x)),
+ #~900 is the limit after which exp returns inf
+ (np.exp, (900.0,), "__pnl_builtin_exp", np.exp(900.0)),
(np.log, (x,), "__pnl_builtin_log", np.log(x)),
(np.power, (x,y), "__pnl_builtin_pow", np.power(x, y)),
(np.tanh, (x,), "__pnl_builtin_tanh", np.tanh(x)),
+ #~450 is the limit after which exp(2x) used in tanh formula returns inf
+ (np.tanh, (450.0,), "__pnl_builtin_tanh", np.tanh(450)),
(lambda x: 1.0 / np.tanh(x), (x,), "__pnl_builtin_coth", 1 / np.tanh(x)),
+ #~450 is the limit after which exp(2x) used in coth formula returns inf
+ (lambda x: 1.0 / np.tanh(x), (450,), "__pnl_builtin_coth", 1 / np.tanh(450)),
(lambda x: 1.0 / np.sinh(x), (x,), "__pnl_builtin_csch", 1 / np.sinh(x)),
- ], ids=["EXP", "LOG", "POW", "TANH", "COTH", "CSCH"])
+ #~450 is the limit after which exp(2x) used in csch formula returns inf
+ (lambda x: 1.0 / np.sinh(x), (450,), "__pnl_builtin_csch", 1 / np.sinh(450)),
+ #~900 is the limit after which exp(x) used in csch formula returns inf
+ (lambda x: 1.0 / np.sinh(x), (900,), "__pnl_builtin_csch", 1 / np.sinh(900)),
+ ], ids=["EXP", "Large EXP", "LOG", "POW", "TANH", "Large TANH", "COTH", "Large COTH",
+ "CSCH", "Large CSCH", "xLarge CSCH"])
def test_builtin_op(benchmark, op, args, builtin, result, func_mode):
if func_mode == 'Python':
f = op
@@ -34,10 +45,15 @@ def test_builtin_op(benchmark, op, args, builtin, result, func_mode):
builder.ret_void()
bin_f = pnlvm.LLVMBinaryFunction.get(wrap_name)
- ptx_res = np.asarray(type(result)(0))
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ ptx_res = np.empty_like(result, dtype=dty)
ptx_res_arg = pnlvm.jit_engine.pycuda.driver.Out(ptx_res)
def f(*a):
- bin_f.cuda_call(*(np.double(p) for p in a), ptx_res_arg)
+ bin_f.cuda_call(*(dty.type(p) for p in a), ptx_res_arg)
return ptx_res
res = benchmark(f, *args)
- assert np.allclose(res, result)
+
+ if pytest.helpers.llvm_current_fp_precision() == 'fp32':
+ assert np.allclose(res, result)
+ else:
+ np.testing.assert_allclose(res, result)
diff --git a/tests/llvm/test_builtins_matrix.py b/tests/llvm/test_builtins_matrix.py
index 8010f3d317c..f3b485468f5 100644
--- a/tests/llvm/test_builtins_matrix.py
+++ b/tests/llvm/test_builtins_matrix.py
@@ -8,9 +8,12 @@
DIM_X = 1000
DIM_Y = 2000
-u = np.random.rand(DIM_X, DIM_Y)
+# These are just basic tests to check that matrix indexing and operations
+# work correctly when compiled. The values don't matter much.
+# Might as well make them representable in fp32 for single precision testing.
+u = np.random.rand(DIM_X, DIM_Y).astype(np.float32).astype(np.float64)
+v = np.random.rand(DIM_X, DIM_Y).astype(np.float32).astype(np.float64)
trans_u = u.transpose()
-v = np.random.rand(DIM_X, DIM_Y)
vector = np.random.rand(DIM_X)
trans_vector = np.random.rand(DIM_Y)
scalar = np.random.rand()
@@ -29,183 +32,87 @@
mat_sadd_res = np.add(u, scalar)
mat_smul_res = np.multiply(u, scalar)
-
-ct_u = u.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_v = v.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_vec = vector.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_tvec = trans_vector.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_mat_res = llvm_mat_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_vec_res = llvm_vec_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_tvec_res = llvm_tvec_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-
-
-@pytest.mark.benchmark(group="Hadamard")
-@pytest.mark.parametrize("op, builtin, result", [
- (np.add, "__pnl_builtin_mat_add", mat_add_res),
- (np.subtract, "__pnl_builtin_mat_sub", mat_sub_res),
- (np.multiply, "__pnl_builtin_mat_hadamard", mat_mul_res),
- ], ids=["ADD", "SUB", "MUL"])
-def test_mat_hadamard(benchmark, op, builtin, result, func_mode):
- if func_mode == 'Python':
- def ex():
- return op(u, v)
- elif func_mode == 'LLVM':
- bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
- def ex():
- bin_f(ct_u, ct_v, DIM_X, DIM_Y, ct_mat_res)
- return llvm_mat_res
- elif func_mode == 'PTX':
- bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
- cuda_u = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_v = pnlvm.jit_engine.pycuda.driver.In(v)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_mat_res)
- def ex():
- bin_f.cuda_call(cuda_u, cuda_v, np.int32(DIM_X), np.int32(DIM_Y), cuda_res)
- return llvm_mat_res
-
- res = benchmark(ex)
- assert np.allclose(res, result)
-
-
-@pytest.mark.benchmark(group="Scalar")
-@pytest.mark.parametrize("op, builtin, result", [
- (np.add, "__pnl_builtin_mat_scalar_add", mat_sadd_res),
- (np.multiply, "__pnl_builtin_mat_scalar_mult", mat_smul_res),
- ], ids=["ADD", "MUL"])
-def test_mat_scalar(benchmark, op, builtin, result, func_mode):
- if func_mode == 'Python':
- def ex():
- return op(u, scalar)
- elif func_mode == 'LLVM':
- bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
- def ex():
- bin_f(ct_u, scalar, DIM_X, DIM_Y, ct_mat_res)
- return llvm_mat_res
- elif func_mode == 'PTX':
- bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
- cuda_u = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_mat_res)
- def ex():
- bin_f.cuda_call(cuda_u, np.float64(scalar), np.int32(DIM_X), np.int32(DIM_Y), cuda_res)
- return llvm_mat_res
-
- res = benchmark(ex)
- assert np.allclose(res, result)
-
-
-@pytest.mark.benchmark(group="Dot")
-def test_dot(benchmark, func_mode):
- if func_mode == 'Python':
- def ex():
- return np.dot(vector, u)
- elif func_mode == 'LLVM':
- bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vxm")
- def ex():
- bin_f(ct_vec, ct_u, DIM_X, DIM_Y, ct_vec_res)
- return llvm_vec_res
- elif func_mode == 'PTX':
- bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vxm")
- cuda_vec = pnlvm.jit_engine.pycuda.driver.In(vector)
- cuda_mat = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_vec_res)
- def ex():
- bin_f.cuda_call(cuda_vec, cuda_mat, np.int32(DIM_X), np.int32(DIM_Y), cuda_res)
- return llvm_vec_res
-
- res = benchmark(ex)
- assert np.allclose(res, dot_res)
-
-
-@pytest.mark.llvm
-@pytest.mark.benchmark(group="Dot")
-@pytest.mark.parametrize('mode', ['CPU',
- pytest.param('PTX', marks=pytest.mark.cuda)])
-def test_dot_llvm_constant_dim(benchmark, mode):
- custom_name = None
-
+def _get_const_dim_func(builtin, *dims):
with pnlvm.LLVMBuilderContext.get_current() as ctx:
- custom_name = ctx.get_unique_name("vxsqm")
- double_ptr_ty = ctx.float_ty.as_pointer()
- func_ty = ir.FunctionType(ir.VoidType(), (double_ptr_ty, double_ptr_ty, double_ptr_ty))
+ custom_name = ctx.get_unique_name("cont_dim" + builtin)
+ # get builtin function
+ builtin = ctx.import_llvm_function(builtin)
+ pointer_arg_types = [a for a in builtin.type.pointee.args if pnlvm.helpers.is_pointer(a)]
+
+ func_ty = ir.FunctionType(ir.VoidType(), pointer_arg_types)
- # get builtin IR
- builtin = ctx.import_llvm_function("__pnl_builtin_vxm")
# Create square vector matrix multiply
- function = ir.Function(ctx.module, func_ty, name=custom_name)
- _x = ctx.int32_ty(DIM_X)
- _y = ctx.int32_ty(DIM_Y)
- _v, _m, _o = function.args
+ function = ir.Function(ctx.module, builtin.type.pointee, name=custom_name)
+ const_dims = (ctx.int32_ty(d) for d in dims)
+ *inputs, output = (a for a in function.args if pnlvm.helpers.is_floating_point(a))
block = function.append_basic_block(name="entry")
builder = ir.IRBuilder(block)
- builder.call(builtin, [_v, _m, _x, _y, _o])
+ builder.call(builtin, [*inputs, *const_dims, output])
builder.ret_void()
- binf2 = pnlvm.LLVMBinaryFunction.get(custom_name)
- if mode == 'CPU':
- benchmark(binf2, ct_vec, ct_u, ct_vec_res)
- else:
- cuda_vec = pnlvm.jit_engine.pycuda.driver.In(vector)
- cuda_mat = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_vec_res)
- benchmark(binf2.cuda_call, cuda_vec, cuda_mat, cuda_res)
- assert np.allclose(llvm_vec_res, dot_res)
-
-
-@pytest.mark.benchmark(group="Dot")
-def test_dot_transposed(benchmark, func_mode):
+ return custom_name
+
+@pytest.mark.benchmark
+@pytest.mark.parametrize("op, x, y, builtin, result", [
+ (np.add, u, v, "__pnl_builtin_mat_add", mat_add_res),
+ (np.subtract, u, v, "__pnl_builtin_mat_sub", mat_sub_res),
+ (np.multiply, u, v, "__pnl_builtin_mat_hadamard", mat_mul_res),
+ (np.add, u, scalar, "__pnl_builtin_mat_scalar_add", mat_sadd_res),
+ (np.multiply, u, scalar, "__pnl_builtin_mat_scalar_mult", mat_smul_res),
+ (np.dot, vector, u, "__pnl_builtin_vxm", dot_res),
+ (np.dot, trans_vector, trans_u, "__pnl_builtin_vxm_transposed", trans_dot_res),
+ ], ids=["ADD", "SUB", "MUL", "ADDS", "MULS", "DOT", "TRANS DOT"])
+@pytest.mark.parametrize("dims", [(DIM_X, DIM_Y), (0, 0)], ids=["VAR-DIM", "CONST-DIM"])
+def test_matrix_op(benchmark, op, x, y, builtin, result, func_mode, dims):
if func_mode == 'Python':
def ex():
- return np.dot(trans_vector, trans_u)
- elif func_mode == 'LLVM':
- bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vxm_transposed")
- def ex():
- bin_f(ct_tvec, ct_u, DIM_X, DIM_Y, ct_tvec_res)
- return llvm_tvec_res
- elif func_mode == 'PTX':
- bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vxm_transposed")
- cuda_vec = pnlvm.jit_engine.pycuda.driver.In(trans_vector)
- cuda_mat = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_tvec_res)
- def ex():
- bin_f.cuda_call(cuda_vec, cuda_mat, np.int32(DIM_X), np.int32(DIM_Y), cuda_res)
- return llvm_tvec_res
+ return op(x, y)
- res = benchmark(ex)
- assert np.allclose(res, trans_dot_res)
+ elif func_mode == 'LLVM':
+ if dims == (0, 0):
+ func_name = _get_const_dim_func(builtin, DIM_X, DIM_Y)
+ else:
+ func_name = builtin
+ bin_f = pnlvm.LLVMBinaryFunction.get(func_name)
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ assert dty == np.dtype(bin_f.byref_arg_types[1])
+ assert dty == np.dtype(bin_f.byref_arg_types[4])
-@pytest.mark.llvm
-@pytest.mark.benchmark(group="Dot")
-@pytest.mark.parametrize('mode', ['CPU',
- pytest.param('PTX', marks=pytest.mark.cuda)])
-def test_dot_transposed_llvm_constant_dim(benchmark, mode):
- custom_name = None
+ lx = x.astype(dty)
+ ly = dty.type(y) if np.isscalar(y) else y.astype(dty)
+ lres = np.empty_like(result, dtype=dty)
- with pnlvm.LLVMBuilderContext.get_current() as ctx:
- custom_name = ctx.get_unique_name("vxsqm")
- double_ptr_ty = ctx.float_ty.as_pointer()
- func_ty = ir.FunctionType(ir.VoidType(), (double_ptr_ty, double_ptr_ty, double_ptr_ty))
+ ct_x = lx.ctypes.data_as(bin_f.c_func.argtypes[0])
+ ct_y = ly if np.isscalar(ly) else ly.ctypes.data_as(bin_f.c_func.argtypes[1])
+ ct_res = lres.ctypes.data_as(bin_f.c_func.argtypes[4])
- # get builtin IR
- builtin = ctx.import_llvm_function("__pnl_builtin_vxm_transposed")
+ def ex():
+ bin_f(ct_x, ct_y, *dims, ct_res)
+ return lres
- # Create square vector matrix multiply
- function = ir.Function(ctx.module, func_ty, name=custom_name)
- _x = ctx.int32_ty(DIM_X)
- _y = ctx.int32_ty(DIM_Y)
- _v, _m, _o = function.args
- block = function.append_basic_block(name="entry")
- builder = ir.IRBuilder(block)
- builder.call(builtin, [_v, _m, _x, _y, _o])
- builder.ret_void()
+ elif func_mode == 'PTX':
+ if dims == (0, 0):
+ func_name = _get_const_dim_func(builtin, DIM_X, DIM_Y)
+ else:
+ func_name = builtin
+
+ bin_f = pnlvm.LLVMBinaryFunction.get(func_name)
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ assert dty == np.dtype(bin_f.byref_arg_types[1])
+ assert dty == np.dtype(bin_f.byref_arg_types[4])
+
+ lx = x.astype(dty)
+ ly = dty.type(y) if np.isscalar(y) else y.astype(dty)
+ lres = np.empty_like(result, dtype=dty)
+
+ cuda_x = pnlvm.jit_engine.pycuda.driver.In(lx)
+ cuda_y = ly if np.isscalar(ly) else pnlvm.jit_engine.pycuda.driver.In(ly)
+ cuda_res = pnlvm.jit_engine.pycuda.driver.Out(lres)
+ def ex():
+ bin_f.cuda_call(cuda_x, cuda_y, np.int32(dims[0]), np.int32(dims[1]), cuda_res)
+ return lres
- binf2 = pnlvm.LLVMBinaryFunction.get(custom_name)
- if mode == 'CPU':
- benchmark(binf2, ct_tvec, ct_u, ct_tvec_res)
- else:
- cuda_vec = pnlvm.jit_engine.pycuda.driver.In(trans_vector)
- cuda_mat = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_tvec_res)
- benchmark(binf2.cuda_call, cuda_vec, cuda_mat, cuda_res)
- assert np.allclose(llvm_tvec_res, trans_dot_res)
+ res = benchmark(ex)
+ assert np.allclose(res, result)
diff --git a/tests/llvm/test_builtins_mt_random.py b/tests/llvm/test_builtins_mt_random.py
index 86a02ff8627..19dbeb7b818 100644
--- a/tests/llvm/test_builtins_mt_random.py
+++ b/tests/llvm/test_builtins_mt_random.py
@@ -10,7 +10,7 @@
@pytest.mark.benchmark(group="Mersenne Twister integer PRNG")
@pytest.mark.parametrize('mode', ['Python', 'numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
def test_random_int(benchmark, mode):
res = []
if mode == 'Python':
@@ -53,7 +53,7 @@ def f():
@pytest.mark.benchmark(group="Mersenne Twister floating point PRNG")
@pytest.mark.parametrize('mode', ['Python', 'numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
def test_random_float(benchmark, mode):
res = []
if mode == 'Python':
@@ -72,7 +72,7 @@ def f():
init_fun(state, SEED)
gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_double')
- out = ctypes.c_double()
+ out = gen_fun.byref_arg_types[1]()
def f():
gen_fun(state, out)
return out.value
@@ -83,7 +83,7 @@ def f():
init_fun.cuda_call(gpu_state, np.int32(SEED))
gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_double')
- out = np.asfarray([0.0], dtype=np.float64)
+ out = np.asfarray([0.0], dtype=np.dtype(gen_fun.byref_arg_types[1]))
gpu_out = pnlvm.jit_engine.pycuda.driver.Out(out)
def f():
gen_fun.cuda_call(gpu_state, gpu_out)
@@ -97,7 +97,7 @@ def f():
@pytest.mark.benchmark(group="Marsenne Twister Normal distribution")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
# Python uses different algorithm so skip it in this test
def test_random_normal(benchmark, mode):
if mode == 'numpy':
@@ -111,7 +111,7 @@ def f():
init_fun(state, SEED)
gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_normal')
- out = ctypes.c_double()
+ out = gen_fun.byref_arg_types[1]()
def f():
gen_fun(state, out)
return out.value
@@ -122,7 +122,7 @@ def f():
init_fun.cuda_call(gpu_state, np.int32(SEED))
gen_fun = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_mt_rand_normal')
- out = np.asfarray([0.0], dtype=np.float64)
+ out = np.asfarray([0.0], dtype=np.dtype(gen_fun.byref_arg_types[1]))
gpu_out = pnlvm.jit_engine.pycuda.driver.Out(out)
def f():
gen_fun.cuda_call(gpu_state, gpu_out)
diff --git a/tests/llvm/test_builtins_philox_random.py b/tests/llvm/test_builtins_philox_random.py
index 1117fcc3605..479e91379e7 100644
--- a/tests/llvm/test_builtins_philox_random.py
+++ b/tests/llvm/test_builtins_philox_random.py
@@ -9,7 +9,7 @@
@pytest.mark.benchmark(group="Philox integer PRNG")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
@pytest.mark.parametrize('seed, expected', [
(0, [259491006799949737, 4754966410622352325, 8698845897610382596, 1686395276220330909, 18061843536446043542, 4723914225006068263]),
(-5, [4936860362606747269, 11611290354192475889, 2015254117581537576, 4620074701282684350, 9574602527017877750, 2811009141214824706]),
@@ -57,7 +57,7 @@ def f():
@pytest.mark.benchmark(group="Philox integer PRNG")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
def test_random_int32(benchmark, mode):
res = []
if mode == 'numpy':
@@ -99,7 +99,7 @@ def f():
@pytest.mark.benchmark(group="Philox floating point PRNG")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
def test_random_double(benchmark, mode):
res = []
if mode == 'numpy':
@@ -138,7 +138,7 @@ def f():
@pytest.mark.benchmark(group="Philox floating point PRNG")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
def test_random_float(benchmark, mode):
res = []
if mode == 'numpy':
@@ -177,7 +177,7 @@ def f():
@pytest.mark.benchmark(group="Philox Normal distribution")
@pytest.mark.parametrize('mode', ['numpy',
pytest.param('LLVM', marks=pytest.mark.llvm),
- pytest.param('PTX', marks=pytest.mark.cuda)])
+ pytest.helpers.cuda_param('PTX')])
@pytest.mark.parametrize('fp_type', [pnlvm.ir.DoubleType(), pnlvm.ir.FloatType()],
ids=lambda x: str(x))
def test_random_normal(benchmark, mode, fp_type):
diff --git a/tests/llvm/test_builtins_vector.py b/tests/llvm/test_builtins_vector.py
index cf101848eca..7bb1f472cae 100644
--- a/tests/llvm/test_builtins_vector.py
+++ b/tests/llvm/test_builtins_vector.py
@@ -5,50 +5,66 @@
from psyneulink.core import llvm as pnlvm
-DIM_X=1000
-
-
-u = np.random.rand(DIM_X)
-v = np.random.rand(DIM_X)
+DIM_X=1500
+# These are just basic tests to check that vector indexing and operations
+# work correctly when compiled. The values don't matter much.
+# Might as well make them representable in fp32 for single precision testing.
+u = np.random.rand(DIM_X).astype(np.float32).astype(np.float64)
+v = np.random.rand(DIM_X).astype(np.float32).astype(np.float64)
scalar = np.random.rand()
-llvm_res = np.random.rand(DIM_X)
add_res = np.add(u, v)
sub_res = np.subtract(u, v)
mul_res = np.multiply(u, v)
smul_res = np.multiply(u, scalar)
-ct_u = u.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_v = v.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_res = llvm_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-
-
@pytest.mark.benchmark(group="Hadamard")
-@pytest.mark.parametrize("op, y, llvm_y, builtin, result", [
- (np.add, v, ct_v, "__pnl_builtin_vec_add", add_res),
- (np.subtract, v, ct_v, "__pnl_builtin_vec_sub", sub_res),
- (np.multiply, v, ct_v, "__pnl_builtin_vec_hadamard", mul_res),
- (np.multiply, scalar, scalar, "__pnl_builtin_vec_scalar_mult", smul_res),
+@pytest.mark.parametrize("op, v, builtin, result", [
+ (np.add, v, "__pnl_builtin_vec_add", add_res),
+ (np.subtract, v, "__pnl_builtin_vec_sub", sub_res),
+ (np.multiply, v, "__pnl_builtin_vec_hadamard", mul_res),
+ (np.multiply, scalar, "__pnl_builtin_vec_scalar_mult", smul_res),
], ids=["ADD", "SUB", "MUL", "SMUL"])
-def test_vector_op(benchmark, op, y, llvm_y, builtin, result, func_mode):
+def test_vector_op(benchmark, op, v, builtin, result, func_mode):
if func_mode == 'Python':
def ex():
- return op(u, y)
+ return op(u, v)
elif func_mode == 'LLVM':
bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ assert dty == np.dtype(bin_f.byref_arg_types[1])
+ assert dty == np.dtype(bin_f.byref_arg_types[3])
+
+ lu = u.astype(dty)
+ lv = dty.type(v) if np.isscalar(v) else v.astype(dty)
+ lres = np.empty_like(lu)
+
+ ct_u = lu.ctypes.data_as(bin_f.c_func.argtypes[0])
+ ct_v = lv if np.isscalar(lv) else lv.ctypes.data_as(bin_f.c_func.argtypes[1])
+ ct_res = lres.ctypes.data_as(bin_f.c_func.argtypes[3])
+
def ex():
- bin_f(ct_u, llvm_y, DIM_X, ct_res)
- return llvm_res
+ bin_f(ct_u, ct_v, DIM_X, ct_res)
+ return lres
+
elif func_mode == 'PTX':
bin_f = pnlvm.LLVMBinaryFunction.get(builtin)
- cuda_u = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_y = np.float64(y) if np.isscalar(y) else pnlvm.jit_engine.pycuda.driver.In(y)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_res)
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ assert dty == np.dtype(bin_f.byref_arg_types[1])
+ assert dty == np.dtype(bin_f.byref_arg_types[3])
+
+ lu = u.astype(dty)
+ lv = dty.type(v) if np.isscalar(v) else v.astype(dty)
+ lres = np.empty_like(lu)
+
+ cuda_u = pnlvm.jit_engine.pycuda.driver.In(lu)
+ cuda_v = lv if np.isscalar(lv) else pnlvm.jit_engine.pycuda.driver.In(lv)
+ cuda_res = pnlvm.jit_engine.pycuda.driver.Out(lres)
def ex():
- bin_f.cuda_call(cuda_u, cuda_y, np.int32(DIM_X), cuda_res)
- return llvm_res
+ bin_f.cuda_call(cuda_u, cuda_v, np.int32(DIM_X), cuda_res)
+ return lres
res = benchmark(ex)
assert np.allclose(res, result)
@@ -61,16 +77,25 @@ def ex():
return np.sum(u)
elif func_mode == 'LLVM':
bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vec_sum")
+
+ lu = u.astype(np.dtype(bin_f.byref_arg_types[0]))
+ llvm_res = np.empty(1, dtype=lu.dtype)
+
+ ct_u = lu.ctypes.data_as(bin_f.c_func.argtypes[0])
+ ct_res = llvm_res.ctypes.data_as(bin_f.c_func.argtypes[2])
+
def ex():
bin_f(ct_u, DIM_X, ct_res)
return llvm_res[0]
elif func_mode == 'PTX':
bin_f = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vec_sum")
- cuda_u = pnlvm.jit_engine.pycuda.driver.In(u)
- cuda_res = pnlvm.jit_engine.pycuda.driver.Out(llvm_res)
+ lu = u.astype(np.dtype(bin_f.byref_arg_types[0]))
+ cuda_u = pnlvm.jit_engine.pycuda.driver.In(lu)
+ res = np.empty(1, dtype=lu.dtype)
+ cuda_res = pnlvm.jit_engine.pycuda.driver.Out(res)
def ex():
bin_f.cuda_call(cuda_u, np.int32(DIM_X), cuda_res)
- return llvm_res[0]
+ return res[0]
res = benchmark(ex)
assert np.allclose(res, sum(u))
diff --git a/tests/llvm/test_compile.py b/tests/llvm/test_compile.py
index ed012f037b5..406fc1e2430 100644
--- a/tests/llvm/test_compile.py
+++ b/tests/llvm/test_compile.py
@@ -8,20 +8,25 @@
DIM_X=1000
DIM_Y=2000
-matrix = np.random.rand(DIM_X, DIM_Y)
-vector = np.random.rand(DIM_X)
-llvm_res = np.random.rand(DIM_Y)
-
-ct_vec = vector.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-ct_mat = matrix.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
-x, y = matrix.shape
-
@pytest.mark.llvm
def test_recompile():
# The original builtin mxv function
binf = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_vxm')
+ dty = np.dtype(binf.byref_arg_types[0])
+ assert dty == np.dtype(binf.byref_arg_types[1])
+ assert dty == np.dtype(binf.byref_arg_types[4])
+
+ matrix = np.random.rand(DIM_X, DIM_Y).astype(dty)
+ vector = np.random.rand(DIM_X).astype(dty)
+ llvm_res = np.empty(DIM_Y, dtype=dty)
+
+ x, y = matrix.shape
+
+ ct_vec = vector.ctypes.data_as(binf.c_func.argtypes[0])
+ ct_mat = matrix.ctypes.data_as(binf.c_func.argtypes[1])
+
orig_res = np.empty_like(llvm_res)
- ct_res = orig_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+ ct_res = orig_res.ctypes.data_as(binf.c_func.argtypes[4])
binf.c_func(ct_vec, ct_mat, x, y, ct_res)
@@ -30,7 +35,7 @@ def test_recompile():
pnlvm._llvm_build()
rebuild_res = np.empty_like(llvm_res)
- ct_res = rebuild_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+ ct_res = rebuild_res.ctypes.data_as(binf.c_func.argtypes[4])
binf.c_func(ct_vec, ct_mat, x, y, ct_res)
assert np.array_equal(orig_res, rebuild_res)
@@ -38,13 +43,13 @@ def test_recompile():
# Get a new pointer
binf2 = pnlvm.LLVMBinaryFunction.get('__pnl_builtin_vxm')
new_res = np.empty_like(llvm_res)
- ct_res = new_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+ ct_res = new_res.ctypes.data_as(binf2.c_func.argtypes[4])
- binf.c_func(ct_vec, ct_mat, x, y, ct_res)
+ binf2.c_func(ct_vec, ct_mat, x, y, ct_res)
assert np.array_equal(rebuild_res, new_res)
callable_res = np.empty_like(llvm_res)
- ct_res = callable_res.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+ ct_res = callable_res.ctypes.data_as(binf.c_func.argtypes[4])
- binf(ct_vec, ct_mat, x, y, ct_res)
+ binf2(ct_vec, ct_mat, x, y, ct_res)
assert np.array_equal(new_res, callable_res)
diff --git a/tests/llvm/test_custom_func.py b/tests/llvm/test_custom_func.py
index 5435b9f3013..406beb937c3 100644
--- a/tests/llvm/test_custom_func.py
+++ b/tests/llvm/test_custom_func.py
@@ -4,67 +4,6 @@
from psyneulink.core import llvm as pnlvm
-from llvmlite import ir
-
-
-ITERATIONS=100
-DIM_X=1000
-
-matrix = np.random.rand(DIM_X, DIM_X)
-vector = np.random.rand(DIM_X)
-llvm_res = np.random.rand(DIM_X)
-
-x, y = matrix.shape
-
-@pytest.mark.llvm
-@pytest.mark.parametrize('mode', ['CPU',
- pytest.param('PTX', marks=pytest.mark.cuda)])
-def test_fixed_dimensions__pnl_builtin_vxm(mode):
- # The original builtin mxv function
- binf = pnlvm.LLVMBinaryFunction.get("__pnl_builtin_vxm")
- orig_res = np.empty_like(llvm_res)
- if mode == 'CPU':
- ct_in_ty, ct_mat_ty, _, _, ct_res_ty = binf.byref_arg_types
-
- ct_vec = vector.ctypes.data_as(ctypes.POINTER(ct_in_ty))
- ct_mat = matrix.ctypes.data_as(ctypes.POINTER(ct_mat_ty))
- ct_res = orig_res.ctypes.data_as(ctypes.POINTER(ct_res_ty))
-
- binf.c_func(ct_vec, ct_mat, x, y, ct_res)
- else:
- binf.cuda_wrap_call(vector, matrix, np.int32(x), np.int32(y), orig_res)
-
- custom_name = None
-
- with pnlvm.LLVMBuilderContext.get_current() as ctx:
- custom_name = ctx.get_unique_name("vxsqm")
- double_ptr_ty = ctx.convert_python_struct_to_llvm_ir(1.0).as_pointer()
- func_ty = ir.FunctionType(ir.VoidType(), (double_ptr_ty, double_ptr_ty, double_ptr_ty))
-
- # get builtin IR
- builtin = ctx.import_llvm_function("__pnl_builtin_vxm")
-
- # Create square vector matrix multiply
- function = ir.Function(ctx.module, func_ty, name=custom_name)
- _x = ctx.int32_ty(x)
- _v, _m, _o = function.args
- block = function.append_basic_block(name="entry")
- builder = ir.IRBuilder(block)
- builder.call(builtin, [_v, _m, _x, _x, _o])
- builder.ret_void()
-
- binf2 = pnlvm.LLVMBinaryFunction.get(custom_name)
- new_res = np.empty_like(llvm_res)
-
- if mode == 'CPU':
- ct_res = new_res.ctypes.data_as(ctypes.POINTER(ct_res_ty))
-
- binf2(ct_vec, ct_mat, ct_res)
- else:
- binf2.cuda_wrap_call(vector, matrix, new_res)
-
- assert np.array_equal(orig_res, new_res)
-
@pytest.mark.llvm
@pytest.mark.parametrize('mode', ['CPU',
@@ -79,14 +18,15 @@ def test_integer_broadcast(mode, val):
with pnlvm.LLVMBuilderContext.get_current() as ctx:
custom_name = ctx.get_unique_name("broadcast")
int_ty = ctx.convert_python_struct_to_llvm_ir(val)
- int_array_ty = ir.ArrayType(int_ty, 8)
- func_ty = ir.FunctionType(ir.VoidType(), (int_ty.as_pointer(),
- int_array_ty.as_pointer()))
- function = ir.Function(ctx.module, func_ty, name=custom_name)
+ int_array_ty = pnlvm.ir.ArrayType(int_ty, 8)
+ func_ty = pnlvm.ir.FunctionType(pnlvm.ir.VoidType(),
+ (int_ty.as_pointer(),
+ int_array_ty.as_pointer()))
+ function = pnlvm.ir.Function(ctx.module, func_ty, name=custom_name)
i, o = function.args
block = function.append_basic_block(name="entry")
- builder = ir.IRBuilder(block)
+ builder = pnlvm.ir.IRBuilder(block)
ival = builder.load(i)
ival = builder.add(ival, ival.type(1))
with pnlvm.helpers.array_ptr_loop(builder, o, "broadcast") as (b, i):
diff --git a/tests/llvm/test_debug_composition.py b/tests/llvm/test_debug_composition.py
index 5555e601791..e84ba68c1c8 100644
--- a/tests/llvm/test_debug_composition.py
+++ b/tests/llvm/test_debug_composition.py
@@ -10,7 +10,7 @@
from psyneulink.core.compositions.composition import Composition
debug_options=["const_input=[[[7]]]", "const_input", "const_data", "const_params", "const_data", "const_state", "stat", "time_stat", "unaligned_copy"]
-options_combinations = (";".join(("debug_info", *c)) for i in range(len(debug_options) + 1) for c in combinations(debug_options, i))
+options_combinations = (";".join(("", *c)) for i in range(len(debug_options) + 1) for c in combinations(debug_options, i))
@pytest.mark.composition
@pytest.mark.parametrize("mode", [pytest.param(pnlvm.ExecutionMode.LLVMRun, marks=pytest.mark.llvm),
diff --git a/tests/llvm/test_helpers.py b/tests/llvm/test_helpers.py
index 6862c784d00..56dff0df215 100644
--- a/tests/llvm/test_helpers.py
+++ b/tests/llvm/test_helpers.py
@@ -108,7 +108,11 @@ def test_helper_fclamp_const(mode):
[[1, 1], [1, 100], [1,2], [-4,5], [0, -100], [-1,-2],
[[1,1,1,-4,0,-1], [1,100,2,5,-100,-2]]
])
-def test_helper_is_close(mode, var1, var2, rtol, atol):
+@pytest.mark.parametrize('fp_type', [ir.DoubleType, ir.FloatType])
+def test_helper_is_close(mode, var1, var2, rtol, atol, fp_type):
+
+ # Instantiate LLVMBuilderContext using the preferred fp type
+ pnlvm.builder_context.LLVMBuilderContext(fp_type())
tolerance = {}
if rtol is not None:
@@ -116,11 +120,10 @@ def test_helper_is_close(mode, var1, var2, rtol, atol):
if atol is not None:
tolerance['atol'] = atol
-
with pnlvm.LLVMBuilderContext.get_current() as ctx:
- double_ptr_ty = ir.DoubleType().as_pointer()
- func_ty = ir.FunctionType(ir.VoidType(), [double_ptr_ty, double_ptr_ty,
- double_ptr_ty, ctx.int32_ty])
+ float_ptr_ty = ctx.float_ty.as_pointer()
+ func_ty = ir.FunctionType(ir.VoidType(), [float_ptr_ty, float_ptr_ty,
+ float_ptr_ty, ctx.int32_ty])
custom_name = ctx.get_unique_name("is_close")
function = ir.Function(ctx.module, func_ty, name=custom_name)
@@ -143,13 +146,15 @@ def test_helper_is_close(mode, var1, var2, rtol, atol):
builder.ret_void()
- vec1 = np.atleast_1d(np.asfarray(var1))
- vec2 = np.atleast_1d(np.asfarray(var2))
+ bin_f = pnlvm.LLVMBinaryFunction.get(custom_name)
+
+ dty = np.dtype(bin_f.byref_arg_types[0])
+ vec1 = np.atleast_1d(np.asfarray(var1, dtype=dty))
+ vec2 = np.atleast_1d(np.asfarray(var2, dtype=dty))
assert len(vec1) == len(vec2)
res = np.empty_like(vec2)
ref = np.isclose(vec1, vec2, **tolerance)
- bin_f = pnlvm.LLVMBinaryFunction.get(custom_name)
if mode == 'CPU':
ct_ty = ctypes.POINTER(bin_f.byref_arg_types[0])
ct_vec1 = vec1.ctypes.data_as(ct_ty)
@@ -459,8 +464,8 @@ def test_helper_numerical(mode, op, var, expected, fp_type):
@pytest.mark.parametrize('mode', ['CPU',
pytest.param('PTX', marks=pytest.mark.cuda)])
@pytest.mark.parametrize('var,expected', [
- (np.array([1,2,3], dtype=np.float64), np.array([2,3,4], dtype=np.float64)),
- (np.array([[1,2],[3,4]], dtype=np.float64), np.array([[2,3],[4,5]], dtype=np.float64)),
+ (np.asfarray([1,2,3]), np.asfarray([2,3,4])),
+ (np.asfarray([[1,2],[3,4]]), np.asfarray([[2,3],[4,5]])),
], ids=["vector", "matrix"])
def test_helper_elementwise_op(mode, var, expected):
with pnlvm.LLVMBuilderContext.get_current() as ctx:
@@ -479,12 +484,18 @@ def test_helper_elementwise_op(mode, var, expected):
builder.ret_void()
bin_f = pnlvm.LLVMBinaryFunction.get(custom_name)
+
+ # convert input to the right type
+ dt = np.dtype(bin_f.byref_arg_types[0])
+ dt = np.empty(1, dtype=dt).flatten().dtype
+ var = var.astype(dt)
+
if mode == 'CPU':
ct_vec = np.ctypeslib.as_ctypes(var)
res = bin_f.byref_arg_types[1]()
bin_f(ct_vec, ctypes.byref(res))
else:
- res = copy.deepcopy(var)
+ res = np.empty_like(var)
bin_f.cuda_wrap_call(var, res)
assert np.array_equal(res, expected)
@@ -524,13 +535,64 @@ def test_helper_recursive_iterate_arrays(mode, var1, var2, expected):
builder.ret_void()
bin_f = pnlvm.LLVMBinaryFunction.get(custom_name)
+
+ # convert input to the right type
+ dt = np.dtype(bin_f.byref_arg_types[0])
+ dt = np.empty(1, dtype=dt).flatten().dtype
+ var1 = var1.astype(dt)
+ var2 = var2.astype(dt)
+
if mode == 'CPU':
ct_vec = np.ctypeslib.as_ctypes(var1)
ct_vec_2 = np.ctypeslib.as_ctypes(var2)
res = bin_f.byref_arg_types[2]()
bin_f(ct_vec, ct_vec_2, ctypes.byref(res))
else:
- res = copy.deepcopy(var1)
+ res = np.empty_like(var1)
bin_f.cuda_wrap_call(var1, var2, res)
assert np.array_equal(res, expected)
+
+
+_fp_types = [ir.DoubleType, ir.FloatType, ir.HalfType]
+
+
+@pytest.mark.llvm
+@pytest.mark.parametrize('mode', ['CPU',
+ pytest.param('PTX', marks=pytest.mark.cuda)])
+@pytest.mark.parametrize('t1', _fp_types)
+@pytest.mark.parametrize('t2', _fp_types)
+@pytest.mark.parametrize('val', [1.0, '-Inf', 'Inf', 'NaN', 16777216, 16777217, -1.0])
+def test_helper_convert_fp_type(t1, t2, mode, val):
+ with pnlvm.LLVMBuilderContext.get_current() as ctx:
+ func_ty = ir.FunctionType(ir.VoidType(), [t1().as_pointer(), t2().as_pointer()])
+ custom_name = ctx.get_unique_name("fp_convert")
+ function = ir.Function(ctx.module, func_ty, name=custom_name)
+ x, y = function.args
+ block = function.append_basic_block(name="entry")
+ builder = ir.IRBuilder(block)
+
+ x_val = builder.load(x)
+ conv_x = pnlvm.helpers.convert_type(builder, x_val, y.type.pointee)
+ builder.store(conv_x, y)
+ builder.ret_void()
+
+ bin_f = pnlvm.LLVMBinaryFunction.get(custom_name)
+
+ # Convert type to numpy dtype
+ npt1, npt2 = (np.dtype(bin_f.byref_arg_types[x]) for x in (0, 1))
+ npt1, npt2 = (np.float16().dtype if x == np.uint16 else x for x in (npt1, npt2))
+
+ # instantiate value, result and reference
+ x = np.asfarray(val, dtype=npt1)
+ y = np.asfarray(np.random.rand(), dtype=npt2)
+ ref = x.astype(npt2)
+
+ if mode == 'CPU':
+ ct_x = x.ctypes.data_as(bin_f.c_func.argtypes[0])
+ ct_y = y.ctypes.data_as(bin_f.c_func.argtypes[1])
+ bin_f(ct_x, ct_y)
+ else:
+ bin_f.cuda_wrap_call(x, y)
+
+ assert np.allclose(y, ref, equal_nan=True)
diff --git a/tests/json/model_backprop.py b/tests/mdf/model_backprop.py
similarity index 100%
rename from tests/json/model_backprop.py
rename to tests/mdf/model_backprop.py
diff --git a/tests/json/model_basic.py b/tests/mdf/model_basic.py
similarity index 100%
rename from tests/json/model_basic.py
rename to tests/mdf/model_basic.py
diff --git a/tests/json/model_basic_non_identity.py b/tests/mdf/model_basic_non_identity.py
similarity index 100%
rename from tests/json/model_basic_non_identity.py
rename to tests/mdf/model_basic_non_identity.py
diff --git a/tests/json/model_integrators.py b/tests/mdf/model_integrators.py
similarity index 100%
rename from tests/json/model_integrators.py
rename to tests/mdf/model_integrators.py
diff --git a/tests/json/model_nested_comp_with_scheduler.py b/tests/mdf/model_nested_comp_with_scheduler.py
similarity index 100%
rename from tests/json/model_nested_comp_with_scheduler.py
rename to tests/mdf/model_nested_comp_with_scheduler.py
diff --git a/tests/json/model_udfs.py b/tests/mdf/model_udfs.py
similarity index 100%
rename from tests/json/model_udfs.py
rename to tests/mdf/model_udfs.py
diff --git a/tests/json/model_varied_matrix_sizes.py b/tests/mdf/model_varied_matrix_sizes.py
similarity index 100%
rename from tests/json/model_varied_matrix_sizes.py
rename to tests/mdf/model_varied_matrix_sizes.py
diff --git a/tests/json/model_with_control.py b/tests/mdf/model_with_control.py
similarity index 100%
rename from tests/json/model_with_control.py
rename to tests/mdf/model_with_control.py
diff --git a/tests/json/model_with_two_conjoint_comps.py b/tests/mdf/model_with_two_conjoint_comps.py
similarity index 100%
rename from tests/json/model_with_two_conjoint_comps.py
rename to tests/mdf/model_with_two_conjoint_comps.py
diff --git a/tests/json/model_with_two_disjoint_comps.py b/tests/mdf/model_with_two_disjoint_comps.py
similarity index 100%
rename from tests/json/model_with_two_disjoint_comps.py
rename to tests/mdf/model_with_two_disjoint_comps.py
diff --git a/tests/json/stroop_conflict_monitoring.py b/tests/mdf/stroop_conflict_monitoring.py
similarity index 100%
rename from tests/json/stroop_conflict_monitoring.py
rename to tests/mdf/stroop_conflict_monitoring.py
diff --git a/tests/json/test_json.py b/tests/mdf/test_mdf.py
similarity index 76%
rename from tests/json/test_json.py
rename to tests/mdf/test_mdf.py
index 091e55def88..150f7c1964a 100644
--- a/tests/json/test_json.py
+++ b/tests/mdf/test_mdf.py
@@ -2,13 +2,14 @@
import os
import psyneulink as pnl
import pytest
-import sys
pytest.importorskip(
'modeci_mdf',
- reason='JSON methods require modeci_mdf package'
+ reason='MDF methods require modeci_mdf package'
)
+from modeci_mdf.execution_engine import evaluate_onnx_expr # noqa: E402
+
# stroop stimuli
red = [1, 0]
@@ -76,7 +77,7 @@ def test_json_results_equivalence(
simple_edge_format,
):
# Get python script from file and execute
- filename = f'{os.path.dirname(__file__)}/{filename}'
+ filename = os.path.join(os.path.dirname(__file__), filename)
with open(filename, 'r') as orig_file:
exec(orig_file.read())
exec(f'{composition_name}.run(inputs={input_dict_str})')
@@ -103,7 +104,7 @@ def test_write_json_file(
simple_edge_format,
):
# Get python script from file and execute
- filename = f'{os.path.dirname(__file__)}/{filename}'
+ filename = os.path.join(os.path.dirname(__file__), filename)
with open(filename, 'r') as orig_file:
exec(orig_file.read())
exec(f'{composition_name}.run(inputs={input_dict_str})')
@@ -140,7 +141,7 @@ def test_write_json_file_multiple_comps(
orig_results = {}
# Get python script from file and execute
- filename = f'{os.path.dirname(__file__)}/{filename}'
+ filename = os.path.join(os.path.dirname(__file__), filename)
with open(filename, 'r') as orig_file:
exec(orig_file.read())
@@ -168,37 +169,36 @@ def test_write_json_file_multiple_comps(
# Values are generated from running onnx function RandomUniform and
# RandomNormal with parameters used in model_integrators.py (seed 0).
# RandomNormal values are different on mac versus linux and windows
-if sys.platform == 'linux':
- onnx_integrators_fixed_seeded_noise = {
- 'A': [[-0.9999843239784241]],
- 'B': [[-1.1295466423034668]],
- 'C': [[-0.0647732987999916]],
- 'D': [[-0.499992161989212]],
- 'E': [[-0.2499941289424896]],
- }
-elif sys.platform == 'win32':
- onnx_integrators_fixed_seeded_noise = {
- 'A': [[0.0976270437240601]],
- 'B': [[-0.4184607267379761]],
- 'C': [[0.290769636631012]],
- 'D': [[0.04881352186203]],
- 'E': [[0.1616101264953613]],
- }
-else:
- assert sys.platform == 'darwin'
- onnx_integrators_fixed_seeded_noise = {
- 'A': [[-0.9999550580978394]],
- 'B': [[-0.8846577405929565]],
- 'C': [[0.0576711297035217]],
- 'D': [[-0.4999775290489197]],
- 'E': [[-0.2499831467866898]],
+onnx_noise_data = {
+ 'onnx_ops.randomuniform': {
+ 'A': {'low': -1.0, 'high': 1.0, 'seed': 0, 'shape': (1, 1)},
+ 'D': {'low': -0.5, 'high': 0.5, 'seed': 0, 'shape': (1, 1)},
+ 'E': {'low': -0.25, 'high': 0.5, 'seed': 0, 'shape': (1, 1)}
+ },
+ 'onnx_ops.randomnormal': {
+ 'B': {'mean': -1.0, 'scale': 0.5, 'seed': 0, 'shape': (1, 1)},
+ 'C': {'mean': 0.0, 'scale': 0.25, 'seed': 0, 'shape': (1, 1)},
}
+}
+onnx_integrators_fixed_seeded_noise = {}
+integrators_runtime_params = None
-integrators_runtime_params = (
- 'runtime_params={'
- + ','.join([f'{k}: {{ "noise": {v} }}' for k, v in onnx_integrators_fixed_seeded_noise.items()])
- + '}'
-)
+for func_type in onnx_noise_data:
+ for node, args in onnx_noise_data[func_type].items():
+ # generates output from onnx noise functions with seed 0 to be
+ # passed in in runtime_params during psyneulink execution
+ onnx_integrators_fixed_seeded_noise[node] = evaluate_onnx_expr(
+ func_type, base_parameters=args, evaluated_parameters=args
+ )
+
+# high precision printing needed because script will be executed from string
+# 16 is insufficient on windows
+with np.printoptions(precision=32):
+ integrators_runtime_params = (
+ 'runtime_params={'
+ + ','.join([f'{k}: {{ "noise": {v} }}' for k, v in onnx_integrators_fixed_seeded_noise.items()])
+ + '}'
+ )
@pytest.mark.parametrize(
@@ -219,7 +219,7 @@ def test_mdf_equivalence(filename, composition_name, input_dict, simple_edge_for
import modeci_mdf.execution_engine as ee
# Get python script from file and execute
- filename = f'{os.path.dirname(__file__)}/{filename}'
+ filename = os.path.join(os.path.dirname(__file__), filename)
with open(filename, 'r') as orig_file:
exec(orig_file.read())
inputs_str = str(input_dict).replace("'", '')
@@ -240,3 +240,20 @@ def test_mdf_equivalence(filename, composition_name, input_dict, simple_edge_for
]
assert pnl.safe_equals(orig_results, mdf_results)
+
+
+@pytest.mark.parametrize('filename', ['model_basic.py'])
+@pytest.mark.parametrize('fmt', ['json', 'yml'])
+def test_generate_script_from_mdf(filename, fmt):
+ filename = os.path.join(os.path.dirname(__file__), filename)
+ outfi = filename.replace('.py', f'.{fmt}')
+
+ with open(filename, 'r') as orig_file:
+ exec(orig_file.read())
+ serialized = eval(f'pnl.get_mdf_serialized(comp, fmt="{fmt}")')
+
+ with open(outfi, 'w') as f:
+ f.write(serialized)
+
+ with open(outfi, 'r') as f:
+ assert pnl.generate_script_from_mdf(f.read()) == pnl.generate_script_from_mdf(outfi)
diff --git a/tests/mechanisms/test_control_mechanism.py b/tests/mechanisms/test_control_mechanism.py
index f43fdaf04b4..d5fdfd66204 100644
--- a/tests/mechanisms/test_control_mechanism.py
+++ b/tests/mechanisms/test_control_mechanism.py
@@ -109,6 +109,8 @@ def test_lc_control_modulated_mechanisms_all(self):
assert T_1.parameter_ports[pnl.SLOPE].mod_afferents[0] in LC.control_signals[0].efferents
assert T_2.parameter_ports[pnl.SLOPE].mod_afferents[0] in LC.control_signals[0].efferents
+
+class TestControlMechanism:
def test_control_modulation(self):
Tx = pnl.TransferMechanism(name='Tx')
Ty = pnl.TransferMechanism(name='Ty')
@@ -124,9 +126,11 @@ def test_control_modulation(self):
# comp.show_graph()
assert Tz.parameter_ports[pnl.SLOPE].mod_afferents[0].sender.owner == C
+ assert C.parameters.control_allocation.get() == [1]
result = comp.run(inputs={Tx:[1,1], Ty:[4,4]})
assert comp.results == [[[4.], [4.]], [[4.], [4.]]]
+
def test_identicalness_of_control_and_gating(self):
"""Tests same configuration as gating in tests/mechansims/test_gating_mechanism"""
Input_Layer = pnl.TransferMechanism(name='Input Layer', function=pnl.Logistic, size=2)
@@ -168,6 +172,8 @@ def test_identicalness_of_control_and_gating(self):
# c.add_linear_processing_pathway(pathway=z)
comp.add_node(Control_Mechanism)
+ assert np.allclose(Control_Mechanism.parameters.control_allocation.get(), [0, 0, 0])
+
stim_list = {
Input_Layer: [[-1, 30]],
Control_Mechanism: [1.0],
@@ -190,14 +196,18 @@ def test_identicalness_of_control_and_gating(self):
expected_results = [[0.96941429, 0.9837254 , 0.99217549]]
assert np.allclose(results, expected_results)
+
def test_control_of_all_input_ports(self, comp_mode):
mech = pnl.ProcessingMechanism(input_ports=['A','B','C'])
control_mech = pnl.ControlMechanism(control=mech.input_ports)
comp = pnl.Composition()
comp.add_nodes([(mech, pnl.NodeRole.INPUT), (control_mech, pnl.NodeRole.INPUT)])
results = comp.run(inputs={mech:[[2],[2],[2]], control_mech:[2]}, num_trials=2, execution_mode=comp_mode)
+
+ assert np.allclose(control_mech.parameters.control_allocation.get(), [1, 1, 1])
np.allclose(results, [[4],[4],[4]])
+
def test_control_of_all_output_ports(self, comp_mode):
mech = pnl.ProcessingMechanism(output_ports=[{pnl.VARIABLE: (pnl.OWNER_VALUE, 0)},
{pnl.VARIABLE: (pnl.OWNER_VALUE, 0)},
@@ -206,6 +216,8 @@ def test_control_of_all_output_ports(self, comp_mode):
comp = pnl.Composition()
comp.add_nodes([(mech, pnl.NodeRole.INPUT), (control_mech, pnl.NodeRole.INPUT)])
results = comp.run(inputs={mech:[[2]], control_mech:[3]}, num_trials=2, execution_mode=comp_mode)
+
+ assert np.allclose(control_mech.parameters.control_allocation.get(), [1, 1, 1])
np.allclose(results, [[6],[6],[6]])
def test_control_signal_default_allocation_specification(self):
@@ -227,6 +239,7 @@ def test_control_signal_default_allocation_specification(self):
comp = pnl.Composition()
comp.add_nodes([m1,m2,m3])
comp.add_controller(c1)
+ assert np.allclose(c1.parameters.control_allocation.get(), [10, 10, 10])
assert c1.control_signals[0].value == [10] # defaultControlAllocation should be assigned
# (as no default_allocation from pnl.ControlMechanism)
assert m1.parameter_ports[pnl.SLOPE].value == [1]
@@ -266,6 +279,7 @@ def test_control_signal_default_allocation_specification(self):
comp = pnl.Composition()
comp.add_nodes([m1,m2,m3])
comp.add_controller(c2)
+ assert np.allclose(c2.parameters.control_allocation.get(), [10, 10, 10])
assert c2.control_signals[0].value == [4] # default_allocation from pnl.ControlMechanism assigned
assert m1.parameter_ports[pnl.SLOPE].value == [10] # has not yet received pnl.ControlSignal value
assert c2.control_signals[1].value == [5] # default_allocation from pnl.ControlSignal assigned (converted scalar)
diff --git a/tests/mechanisms/test_processing_mechanism.py b/tests/mechanisms/test_processing_mechanism.py
index ced6f68ae8a..4780b575692 100644
--- a/tests/mechanisms/test_processing_mechanism.py
+++ b/tests/mechanisms/test_processing_mechanism.py
@@ -248,6 +248,7 @@ class TestProcessingMechanismStandardOutputPorts:
(MAX_ABS_INDICATOR, [0, 0, 1]),
(MAX_ABS_ONE_HOT, [0, 0, 4]),
(MAX_VAL, [2]),
+ (PROB, [0, 2, 0]),
],
ids=lambda x: x if isinstance(x, str) else "")
def test_output_ports(self, mech_mode, op, expected, benchmark):
@@ -265,7 +266,6 @@ def test_output_ports(self, mech_mode, op, expected, benchmark):
(STANDARD_DEVIATION, [1.24721913]),
(VARIANCE, [1.55555556]),
(MAX_ABS_VAL, [4]),
- (PROB, [0, 2, 0]),
],
ids=lambda x: x if isinstance(x, str) else "")
def test_output_ports2(self, op, expected):
diff --git a/tests/misc/test_parameters.py b/tests/misc/test_parameters.py
index 7f9cf8828c8..98af182a686 100644
--- a/tests/misc/test_parameters.py
+++ b/tests/misc/test_parameters.py
@@ -6,6 +6,11 @@
import warnings
+NO_PARAMETERS = "NO_PARAMETERS"
+NO_INIT = "NO_INIT"
+NO_VALUE = "NO_VALUE"
+
+
def shared_parameter_warning_regex(param_name, shared_name=None):
if shared_name is None:
shared_name = param_name
@@ -83,6 +88,19 @@ def test_parameter_values_overriding(ancestor, child, should_override, reset_var
assert child.parameters.variable.default_value == original_child_variable
+def test_unspecified_inheritance():
+ class NewTM(pnl.TransferMechanism):
+ class Parameters(pnl.TransferMechanism.Parameters):
+ pass
+
+ assert NewTM.parameters.variable._inherited
+ NewTM.parameters.variable.default_value = -1
+ assert not NewTM.parameters.variable._inherited
+
+ NewTM.parameters.variable.reset()
+ assert NewTM.parameters.variable._inherited
+
+
@pytest.mark.parametrize('obj, param_name, alias_name', param_alias_data)
def test_aliases(obj, param_name, alias_name):
obj = obj()
@@ -245,11 +263,15 @@ def test_copy():
[
(pnl.AdaptiveIntegrator, {'rate': None}, 'rate', False),
(pnl.AdaptiveIntegrator, {'rate': None}, 'multiplicative_param', False),
+ (pnl.AdaptiveIntegrator, {'rate': 0.5}, 'additive_param', False),
(pnl.AdaptiveIntegrator, {'rate': 0.5}, 'rate', True),
(pnl.AdaptiveIntegrator, {'rate': 0.5}, 'multiplicative_param', True),
(pnl.TransferMechanism, {'integration_rate': None}, 'integration_rate', False),
(pnl.TransferMechanism, {'integration_rate': 0.5}, 'integration_rate', True),
- ]
+ (pnl.TransferMechanism, {'initial_value': 0}, 'initial_value', True),
+ (pnl.TransferMechanism, {'initial_value': None}, 'initial_value', False),
+ (pnl.TransferMechanism, {}, 'initial_value', False),
+ ],
)
def test_user_specified(cls_, kwargs, parameter, is_user_specified):
c = cls_(**kwargs)
@@ -269,6 +291,17 @@ def test_function_user_specified(kwargs, parameter, is_user_specified):
assert getattr(t.function.parameters, parameter)._user_specified == is_user_specified
+# sort param names or pytest-xdist may cause failure
+# see https://github.com/pytest-dev/pytest/issues/4101
+@pytest.mark.parametrize('attr', sorted(pnl.Parameter._additional_param_attr_properties))
+def test_additional_param_attrs(attr):
+ assert hasattr(pnl.Parameter, f'_set_{attr}'), (
+ f'To include {attr} in Parameter._additional_param_attr_properties, you'
+ f' must add a _set_{attr} method on Parameter. If this is unneeded,'
+ ' remove it from Parameter._additional_param_attr_properties.'
+ )
+
+
class TestSharedParameters:
recurrent_mech = pnl.RecurrentTransferMechanism(default_variable=[0, 0], enable_learning=True)
@@ -418,3 +451,185 @@ def test_conflict_no_warning_parser(self):
raise
delattr(pnl.AdaptiveIntegrator.Parameters, '_parse_noise')
+
+
+class TestSpecificationType:
+ @staticmethod
+ def _create_params_class_variant(cls_param, init_param, parent_class=pnl.Component):
+ # init_param as Parameter doesn't make sense, only check cls_param
+ if cls_param is pnl.Parameter:
+ cls_param = pnl.Parameter()
+
+ if cls_param is NO_PARAMETERS:
+ if init_param is NO_INIT:
+
+ class TestComponent(parent_class):
+ pass
+
+ else:
+
+ class TestComponent(parent_class):
+ @pnl.core.globals.parameters.check_user_specified
+ def __init__(self, p=init_param):
+ super().__init__(p=p)
+
+ elif cls_param is NO_VALUE:
+ if init_param is NO_INIT:
+
+ class TestComponent(parent_class):
+ class Parameters(parent_class.Parameters):
+ pass
+
+ else:
+
+ class TestComponent(parent_class):
+ class Parameters(parent_class.Parameters):
+ pass
+
+ @pnl.core.globals.parameters.check_user_specified
+ def __init__(self, p=init_param):
+ super().__init__(p=p)
+
+ else:
+ if init_param is NO_INIT:
+
+ class TestComponent(parent_class):
+ class Parameters(parent_class.Parameters):
+ p = cls_param
+
+ else:
+
+ class TestComponent(parent_class):
+ class Parameters(parent_class.Parameters):
+ p = cls_param
+
+ @pnl.core.globals.parameters.check_user_specified
+ def __init__(self, p=init_param):
+ super().__init__(p=p)
+
+ return TestComponent
+
+ @pytest.mark.parametrize(
+ "cls_param, init_param, param_default",
+ [
+ (1, 1, 1),
+ (1, None, 1),
+ (None, 1, 1),
+ (1, NO_INIT, 1),
+ ("foo", "foo", "foo"),
+ (np.array(1), np.array(1), np.array(1)),
+ (np.array([1]), np.array([1]), np.array([1])),
+ ],
+ )
+ def test_valid_assignment(self, cls_param, init_param, param_default):
+ TestComponent = TestSpecificationType._create_params_class_variant(cls_param, init_param)
+ assert TestComponent.defaults.p == param_default
+ assert TestComponent.parameters.p.default_value == param_default
+
+ @pytest.mark.parametrize(
+ "cls_param, init_param",
+ [
+ (1, 2),
+ (2, 1),
+ (1, 1.0),
+ (np.array(1), 1),
+ (np.array([1]), 1),
+ (np.array([1]), np.array(1)),
+ ("foo", "bar"),
+ ],
+ )
+ def test_conflicting_assignments(self, cls_param, init_param):
+ with pytest.raises(AssertionError, match="Conflicting default parameter"):
+ TestSpecificationType._create_params_class_variant(cls_param, init_param)
+
+ @pytest.mark.parametrize(
+ "child_cls_param, child_init_param, parent_value, child_value",
+ [
+ (NO_PARAMETERS, NO_INIT, 1, 1),
+ (NO_VALUE, NO_INIT, 1, 1),
+ (2, NO_INIT, 1, 2),
+ (NO_PARAMETERS, 2, 1, 2),
+ (NO_VALUE, 2, 1, 2),
+ (2, 2, 1, 2),
+ ],
+ )
+ @pytest.mark.parametrize(
+ "parent_cls_param, parent_init_param",
+ [(1, 1), (1, None), (None, 1), (pnl.Parameter, 1)],
+ )
+ def test_inheritance(
+ self,
+ parent_cls_param,
+ parent_init_param,
+ child_cls_param,
+ child_init_param,
+ parent_value,
+ child_value,
+ ):
+ TestParent = TestSpecificationType._create_params_class_variant(
+ parent_cls_param, parent_init_param
+ )
+ TestChild = TestSpecificationType._create_params_class_variant(
+ child_cls_param, child_init_param, parent_class=TestParent
+ )
+
+ assert TestParent.defaults.p == parent_value
+ assert TestParent.parameters.p.default_value == parent_value
+
+ assert TestChild.defaults.p == child_value
+ assert TestChild.parameters.p.default_value == child_value
+
+ @pytest.mark.parametrize("set_from_defaults", [True, False])
+ @pytest.mark.parametrize(
+ "child_cls_param, child_init_param",
+ [(1, 1), (1, None), (None, 1), (NO_PARAMETERS, 1), (1, NO_INIT)],
+ )
+ @pytest.mark.parametrize("parent_cls_param, parent_init_param", [(0, 0), (0, None)])
+ def test_set_and_reset(
+ self,
+ parent_cls_param,
+ parent_init_param,
+ child_cls_param,
+ child_init_param,
+ set_from_defaults,
+ ):
+ def set_p_default(obj, val):
+ if set_from_defaults:
+ obj.defaults.p = val
+ else:
+ obj.parameters.p.default_value = val
+
+ TestParent = TestSpecificationType._create_params_class_variant(
+ parent_cls_param, parent_init_param
+ )
+ TestChild = TestSpecificationType._create_params_class_variant(
+ child_cls_param, child_init_param, parent_class=TestParent
+ )
+ TestGrandchild = TestSpecificationType._create_params_class_variant(
+ NO_PARAMETERS, NO_INIT, parent_class=TestChild
+ )
+
+ set_p_default(TestChild, 10)
+ assert TestParent.defaults.p == 0
+ assert TestChild.defaults.p == 10
+ assert TestGrandchild.defaults.p == 10
+
+ set_p_default(TestGrandchild, 20)
+ assert TestParent.defaults.p == 0
+ assert TestChild.defaults.p == 10
+ assert TestGrandchild.defaults.p == 20
+
+ TestChild.parameters.p.reset()
+ assert TestParent.defaults.p == 0
+ assert TestChild.defaults.p == 1
+ assert TestGrandchild.defaults.p == 20
+
+ TestGrandchild.parameters.p.reset()
+ assert TestParent.defaults.p == 0
+ assert TestChild.defaults.p == 1
+ assert TestGrandchild.defaults.p == 1
+
+ set_p_default(TestGrandchild, 20)
+ assert TestParent.defaults.p == 0
+ assert TestChild.defaults.p == 1
+ assert TestGrandchild.defaults.p == 20
diff --git a/tests/models/test_bi_percepts.py b/tests/models/test_bi_percepts.py
index 2b4dbc2df43..5d819d890bf 100644
--- a/tests/models/test_bi_percepts.py
+++ b/tests/models/test_bi_percepts.py
@@ -126,7 +126,10 @@ def get_node(percept, node_id):
# run the model
res = bp_comp.run(input_dict, num_trials=n_time_steps, execution_mode=comp_mode)
- np.testing.assert_allclose(res, expected)
+ if pytest.helpers.llvm_current_fp_precision() == 'fp32':
+ assert np.allclose(res, expected)
+ else:
+ np.testing.assert_allclose(res, expected)
# Test that order of CIM ports follows order of Nodes in self.nodes
for i in range(n_nodes):
diff --git a/tests/models/test_greedy_agent.py b/tests/models/test_greedy_agent.py
index 676150e3e1d..1ee9c192628 100644
--- a/tests/models/test_greedy_agent.py
+++ b/tests/models/test_greedy_agent.py
@@ -119,7 +119,8 @@ def test_simplified_greedy_agent_random(benchmark, comp_mode):
pytest.param([a / 10.0 for a in range(0, 101)], marks=pytest.mark.stress),
], ids=lambda x: len(x))
@pytest.mark.parametrize('prng', ['Default', 'Philox'])
-def test_predator_prey(benchmark, mode, prng, samples):
+@pytest.mark.parametrize('fp_type', [pnl.core.llvm.ir.DoubleType, pnl.core.llvm.ir.FloatType])
+def test_predator_prey(benchmark, mode, prng, samples, fp_type):
if len(samples) > 10 and mode not in {pnl.ExecutionMode.LLVM,
pnl.ExecutionMode.LLVMExec,
pnl.ExecutionMode.LLVMRun,
@@ -132,6 +133,9 @@ def test_predator_prey(benchmark, mode, prng, samples):
# OCM default mode is Python
ocm_mode = 'Python'
+ # Instantiate LLVMBuilderContext using the preferred fp type
+ pnl.core.llvm.builder_context.LLVMBuilderContext(fp_type())
+
benchmark.group = "Predator-Prey " + str(len(samples))
obs_len = 3
obs_coords = 2
@@ -234,11 +238,16 @@ def action_fn(variable):
if prng == 'Default':
assert np.allclose(run_results[0], [[0.9705216285127504, -0.1343332460369043]])
elif prng == 'Philox':
- assert np.allclose(run_results[0], [[-0.16882940384606543, -0.07280074899749223]])
+ if mode == pnl.ExecutionMode.Python or pytest.helpers.llvm_current_fp_precision() == 'fp64':
+ assert np.allclose(run_results[0], [[-0.16882940384606543, -0.07280074899749223]])
+ elif pytest.helpers.llvm_current_fp_precision() == 'fp32':
+ assert np.allclose(run_results[0], [[-0.8639436960220337, 0.4983368515968323]])
+ else:
+ assert False, "Unkown FP type!"
else:
assert False, "Unknown PRNG!"
- if mode is pnl.ExecutionMode.Python:
+ if mode == pnl.ExecutionMode.Python:
# FIXEM: The results are 'close' for both Philox and MT,
# because they're dominated by costs
assert np.allclose(np.asfarray(ocm.function.saved_values).flatten(),
diff --git a/tests/projections/test_projection_specifications.py b/tests/projections/test_projection_specifications.py
index 52358f04f52..02edd207534 100644
--- a/tests/projections/test_projection_specifications.py
+++ b/tests/projections/test_projection_specifications.py
@@ -28,14 +28,15 @@ def test_projection_specification_formats(self):
M3_M4_matrix_A = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 5)
M3_M4_matrix_B = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
- M1_M2_proj = pnl.MappingProjection(matrix=M1_M2_matrix)
+ M1_M2_proj = pnl.MappingProjection(matrix=M1_M2_matrix, name='M1_M2_matrix')
M2_M3_proj = pnl.MappingProjection(sender=M2,
receiver=M3,
matrix={pnl.VALUE: M2_M3_matrix,
pnl.FUNCTION: pnl.AccumulatorIntegrator,
pnl.FUNCTION_PARAMS: {pnl.DEFAULT_VARIABLE: M2_M3_matrix,
- pnl.INITIALIZER: M2_M3_matrix}})
- M3_M4_proj_A = pnl.MappingProjection(sender=M3, receiver=M4, matrix=M3_M4_matrix_A)
+ pnl.INITIALIZER: M2_M3_matrix}},
+ name='M2_M3_proj')
+ M3_M4_proj_A = pnl.MappingProjection(sender=M3, receiver=M4, matrix=M3_M4_matrix_A, name='M3_M4_proj_A')
c = pnl.Composition()
c.add_linear_processing_pathway(pathway=[M1,
M1_M2_proj,
diff --git a/tutorial_requirements.txt b/tutorial_requirements.txt
index 728aa0c0eab..6d141f739cd 100644
--- a/tutorial_requirements.txt
+++ b/tutorial_requirements.txt
@@ -1,3 +1,3 @@
-graphviz<0.20.0
+graphviz<0.21.0
jupyter<=1.0.0
-matplotlib<3.5.2
+matplotlib<3.5.3