Baunsgaard commented on a change in pull request #1234:
URL: https://github.com/apache/systemds/pull/1234#discussion_r617303102
##########
File path: src/main/python/systemds/operator/__init__.py
##########
@@ -20,6 +20,7 @@
# -------------------------------------------------------------
from systemds.operator.operation_node import OperationNode
+from systemds.operator.operation_node2 import OperationNode2
from systemds.operator import algorithm
-__all__ = [OperationNode, algorithm]
+__all__ = [OperationNode, OperationNode2, algorithm]
Review comment:
maybe call it MultiNode.
##########
File path: src/main/python/systemds/matrix/matrix.py
##########
@@ -25,15 +25,15 @@
import numpy as np
from py4j.java_gateway import JavaObject, JVMView
from systemds.context import SystemDSContext
-from systemds.operator import OperationNode
+from systemds.operator import OperationNode2
from systemds.utils.consts import VALID_INPUT_TYPES
from systemds.utils.converters import numpy_to_matrix_block
# TODO maybe instead of having a new class we could have a function `matrix`
instead, adding behavior to
-# `OperationNode` would be necessary
+# `OperationNode2` would be necessary
-class Matrix(OperationNode):
+class Matrix(OperationNode2):
Review comment:
It should not be necessary to change matrix at all, i would like it to
stay as a OperationNode
since it is the 'lowest' primitive.
##########
File path: src/main/python/systemds/operator/operation_node2.py
##########
@@ -0,0 +1,527 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+from typing import Union, Optional, Iterable, Dict, Sequence, Tuple,
TYPE_CHECKING, List
+from multiprocessing import Process
+
+import numpy as np
+from py4j.java_gateway import JVMView, JavaObject
+
+from systemds.utils.consts import VALID_INPUT_TYPES, BINARY_OPERATIONS,
VALID_ARITHMETIC_TYPES
+from systemds.utils.helpers import create_params_string
+from systemds.utils.converters import matrix_block_to_numpy
+from systemds.script_building.script import DMLScript
+from systemds.script_building.dag import OutputType, DAGNode
+
+if TYPE_CHECKING:
+ # to avoid cyclic dependencies during runtime
+ from systemds.context import SystemDSContext
+
+
+class OperationNode2(DAGNode):
+ """A Node representing an operation in SystemDS
+ This is a temporary clone of OperationNode"""
+ shape: Optional[Tuple[int]]
+ _result_var: Optional[Union[float, np.array]]
+ _lineage_trace: Optional[str]
+ _script: Optional[DMLScript]
+
+ def __init__(self, sds_context: 'SystemDSContext', operation: str,
+ unnamed_input_nodes: Iterable[VALID_INPUT_TYPES] = None,
+ named_input_nodes: Dict[str, VALID_INPUT_TYPES] = None,
+ outputs: List[Tuple[str, OutputType]] = [("_1",
OutputType.MATRIX)],
Review comment:
Make it a list of tuples of Operation Nodes, and leverage the previous
operation node to construct the individual returns.
##########
File path: src/main/python/systemds/script_building/script.py
##########
@@ -178,24 +178,30 @@ def _dfs_dag_nodes(self, dag_node: VALID_INPUT_TYPES) ->
str:
:param dag_node: current DAG node
:return: the variable name the current DAG node operation created
"""
+
if not isinstance(dag_node, DAGNode):
if isinstance(dag_node, bool):
return 'TRUE' if dag_node else 'FALSE'
return str(dag_node)
- # for each node do the dfs operation and save the variable names in
`input_var_names`
+
+ if dag_node.dml_name != "":
+ return dag_node.dml_name
+
# get variable names of unnamed parameters
- unnamed_input_vars = [self._dfs_dag_nodes(
- input_node) for input_node in dag_node.unnamed_input_nodes]
+ unnamed_input_vars = [self._dfs_dag_nodes(input_node) for input_node
in dag_node.unnamed_input_nodes]
# get variable names of named parameters
- named_input_vars = {name: self._dfs_dag_nodes(input_node) for name,
input_node in
- dag_node.named_input_nodes.items()}
- curr_var_name = self._next_unique_var()
+ named_input_vars = {}
+ for name, input_node in dag_node.named_input_nodes.items():
+ named_input_vars[name] = self._dfs_dag_nodes(input_node)
+ if isinstance(input_node, DAGNode) and
len(input_node.output_nodes) > 1:
+ named_input_vars[name] = named_input_vars[name] + name
Review comment:
this here seems oddly designed,
if multi return, append the name again?
can you explain?
##########
File path: src/main/python/systemds/script_building/dag.py
##########
@@ -45,9 +45,10 @@ class DAGNode(ABC):
sds_context: 'SystemDSContext'
_unnamed_input_nodes: Sequence[Union['DAGNode', str, int, float, bool]]
_named_input_nodes: Dict[str, Union['DAGNode', str, int, float, bool]]
- _output_type: OutputType
+ _outputs: List[Tuple[str, OutputType]]
+ _output_nodes: Dict[str, Union['DAGNode']]
_is_python_local_data: bool
- _number_of_outputs: int
+ _dml_name: str
Review comment:
this seems bad because it change the allocation to a List rather than
single element, maybe we should have a multi dag node.
If we chose to have a multi dag node then make two classes one called:
SingleDAGNode another MultiDAGNode that both inherit from DAGNode.
opinion?
##########
File path: src/main/python/tests/algorithms/test_pca.py
##########
@@ -49,10 +51,38 @@ def test_500x2(self):
m1 = self.generate_matrices_for_pca(30, seed=1304)
X = Matrix(self.sds, m1)
# print(features)
- [res, model, _, _ ] = pca(X, K=1, scale="FALSE",
center="FALSE").compute()
+ [res, model, _, _] = pca(X, K=1, scale="FALSE",
center="FALSE").compute()
for (x, y) in zip(m1, res):
self.assertTrue((x[0] > 0 and y > 0) or (x[0] < 0 and y < 0))
+ def test_500x2b(self):
+ """
+ This test constructs a line of values in 2d space.
+ That if fit correctly maps perfectly to 1d space.
+ The check is simply if the input value was positive
+ then the output value should be similar.
+ """
+ m1 = self.generate_matrices_for_pca(30, seed=1304)
+ X = Matrix(self.sds, m1)
+ # print(features)
+ X._check_matrix_op()
+ params_dict = {'X': X, 'K': 1, 'scale': "FALSE", 'center': "FALSE"}
+ nodeC = OperationNode2(self.sds, 'pca', named_input_nodes=params_dict,
+ outputs=[("res", OutputType.MATRIX), ("model",
OutputType.MATRIX), ("scale", OutputType.MATRIX), ("center",
OutputType.MATRIX)])
+ nodeD = OperationNode2(self.sds, 'abs',
unnamed_input_nodes=[nodeC.output_nodes["model"]])
Review comment:
In the end the tests should reflect what a user should write, therefore
in this case
The goal become these three:
```python
[res, model, _, _] = pca(...)
res = abs(res)
```
or
```python
m= pca(...)
res = abs(m[0])
```
or
```python
m= pca(...)
res = abs(m["model"])
```
Now this is challenging since it require some change in our generated
algorithms.
Maybe to keep the project simple, you could make a new test file and do the
following:
```python
X = self.sds.rand(rows=1, cols=2, seed = 3)
Y = self.sds.rand(rows=2, cols=1, seed=3)
// I want the default to be unnamed input nodes if the argument is a list,
// or if it is a dictionary named input nodes.
M = multinode(sds, 'list', [X,Y])
print(M[1]).compute()
```
this should result in a script if you execute with looking something like:
```dml
X = rand(rows = 1, cols = 2, seed = 3)
Y = rand(rows = 2, cols = 1, seed = 3)
m = list(X,Y)
print(toString(m[1]))
```
If it is not clear please say so :)
##########
File path: src/main/python/systemds/operator/operation_node2.py
##########
@@ -0,0 +1,527 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+from typing import Union, Optional, Iterable, Dict, Sequence, Tuple,
TYPE_CHECKING, List
+from multiprocessing import Process
+
+import numpy as np
+from py4j.java_gateway import JVMView, JavaObject
+
+from systemds.utils.consts import VALID_INPUT_TYPES, BINARY_OPERATIONS,
VALID_ARITHMETIC_TYPES
+from systemds.utils.helpers import create_params_string
+from systemds.utils.converters import matrix_block_to_numpy
+from systemds.script_building.script import DMLScript
+from systemds.script_building.dag import OutputType, DAGNode
+
+if TYPE_CHECKING:
+ # to avoid cyclic dependencies during runtime
+ from systemds.context import SystemDSContext
+
+
+class OperationNode2(DAGNode):
+ """A Node representing an operation in SystemDS
+ This is a temporary clone of OperationNode"""
+ shape: Optional[Tuple[int]]
+ _result_var: Optional[Union[float, np.array]]
+ _lineage_trace: Optional[str]
+ _script: Optional[DMLScript]
+
+ def __init__(self, sds_context: 'SystemDSContext', operation: str,
+ unnamed_input_nodes: Iterable[VALID_INPUT_TYPES] = None,
+ named_input_nodes: Dict[str, VALID_INPUT_TYPES] = None,
+ outputs: List[Tuple[str, OutputType]] = [("_1",
OutputType.MATRIX)],
+ is_python_local_data: bool = False,
+ shape: Tuple[int] = ()):
+ """
+ Create general `OperationNode2`
+
+ :param sds_context: The SystemDS context for performing the operations
+ :param operation: The name of the DML function to execute
+ :param unnamed_input_nodes: inputs identified by their position, not
name
+ :param named_input_nodes: inputs with their respective parameter name
+ :param output_type: type of the output in DML (double, matrix etc.)
+ :param is_python_local_data: if the data is local in python e.g. Numpy
arrays
+ :param number_of_outputs: If set to other value than 1 then it is
expected
+ that this operation node returns multiple values. If set remember
to set the output_types value as well.
+ :param output_types: The types of output in a multi output scenario.
+ Default is None, and means every multi output is a matrix.
+ """
+ self.sds_context = sds_context
+ self.shape = shape
+ if unnamed_input_nodes is None:
+ unnamed_input_nodes = []
+ if named_input_nodes is None:
+ named_input_nodes = {}
+ self.operation = operation
+ self._unnamed_input_nodes = unnamed_input_nodes
+ self._named_input_nodes = named_input_nodes
+ self._outputs = outputs
+ self._output_nodes = {}
+ if self.operation is not None:
+ for idx, output in enumerate(outputs):
+ if output[1] == OutputType.MATRIX:
+ self.output_nodes[output[0]] = OperationNode2(sds_context,
operation=None, named_input_nodes={f"_{idx}": self})
+ # TODO add output types
+ self._is_python_local_data = is_python_local_data
+ self._result_var = None
+ self._lineage_trace = None
+ self._script = None
+ self.dml_name = ""
+
+ def compute(self, verbose: bool = False, lineage: bool = False) -> \
+ Union[float, np.array, Tuple[Union[float, np.array], str]]:
+
+ if self._result_var is None or self._lineage_trace is None:
+ self._script = DMLScript(self.sds_context)
+ self._script.build_code(self)
+ if verbose:
+ print("SCRIPT:")
+ print(self._script.dml_script)
+
+ if lineage:
+ result_variables, self._lineage_trace =
self._script.execute_with_lineage()
+ else:
+ result_variables = self._script.execute()
+
+ self._result_var =
self.__parse_output_result_variables(result_variables)
+
+ if verbose:
+ for x in self.sds_context.get_stdout():
+ print(x)
+ for y in self.sds_context.get_stderr():
+ print(y)
+
+ if lineage:
+ return self._result_var, self._lineage_trace
+ else:
+ return self._result_var
+
+ def __parse_output_result_variables(self, result_variables):
+ result_var = []
+ for idx, v in enumerate(self._script.out_var_name):
+ if self.outputs[idx][1] == OutputType.MATRIX:
+
result_var.append(self.__parse_output_result_matrix(result_variables, v))
+ else:
+
result_var.append(result_variables.getDouble(self._script.out_var_name[idx]))
+ return result_var
+
+ def __parse_output_result_matrix(self, result_variables, var_name):
+ return matrix_block_to_numpy(self.sds_context.java_gateway.jvm,
+ result_variables.getMatrixBlock(var_name))
+
+ def get_lineage_trace(self) -> str:
+ """Get the lineage trace for this node.
+
+ :return: Lineage trace
+ """
+ if self._lineage_trace is None:
+ self._script = DMLScript(self.sds_context)
+ self._script.build_code(self)
+ self._lineage_trace = self._script.get_lineage()
+
+ return self._lineage_trace
+
+ def code_line(self, var_name: str, unnamed_input_vars: Sequence[str],
+ named_input_vars: Dict[str, str]) -> str:
+ if self.operation is None:
+ return f'{var_name}={list(named_input_vars.values())[0]}'
+
+ if self.operation in BINARY_OPERATIONS:
+ assert len(
+ named_input_vars) == 0, 'Named parameters can not be used with
binary operations'
+ assert len(
+ unnamed_input_vars) == 2, 'Binary Operations need exactly two
input variables'
+ return
f'{var_name}={unnamed_input_vars[0]}{self.operation}{unnamed_input_vars[1]}'
+
+ inputs_comma_sep = create_params_string(unnamed_input_vars,
named_input_vars)
+
+ if len(self._outputs) > 1:
+ output = "["
+ for idx in range(len(self._outputs)):
+ output += f'{var_name}_{idx},'
+ output = output[:-1] + "]"
+ return f'{output}={self.operation}({inputs_comma_sep});'
+ elif self._outputs[0][1] == OutputType.NONE:
+ return f'{self.operation}({inputs_comma_sep});'
+ elif self._outputs[0][1] == OutputType.ASSIGN:
+ return f'{var_name}={self.operation};'
+ else:
+ return f'{var_name}={self.operation}({inputs_comma_sep});'
+
+ def pass_python_data_to_prepared_script(self, jvm: JVMView, var_name: str,
prepared_script: JavaObject) -> None:
+ raise NotImplementedError(
+ 'Operation node has no python local data. Missing implementation
in derived class?')
+
+ def _check_matrix_op(self):
+ """Perform checks to assure operation is allowed to be performed on
data type of this `OperationNode2`
+
+ :raise: AssertionError
+ """
+ assert len(self._outputs) == 1 and self._outputs[0][1] ==
OutputType.MATRIX, f'{self.operation} only supported for matrices'
+
+ def __add__(self, other: VALID_ARITHMETIC_TYPES) -> 'OperationNode2':
+ return OperationNode2(self.sds_context, '+', [self, other],
shape=self.shape)
Review comment:
most of these are invalid, since you cant + a value on a list of
heterogeneous outputs.
I would suggest going through them and validate if they can be done.
##########
File path: src/main/python/systemds/operator/operation_node2.py
##########
@@ -0,0 +1,527 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+from typing import Union, Optional, Iterable, Dict, Sequence, Tuple,
TYPE_CHECKING, List
+from multiprocessing import Process
+
+import numpy as np
+from py4j.java_gateway import JVMView, JavaObject
+
+from systemds.utils.consts import VALID_INPUT_TYPES, BINARY_OPERATIONS,
VALID_ARITHMETIC_TYPES
+from systemds.utils.helpers import create_params_string
+from systemds.utils.converters import matrix_block_to_numpy
+from systemds.script_building.script import DMLScript
+from systemds.script_building.dag import OutputType, DAGNode
+
+if TYPE_CHECKING:
+ # to avoid cyclic dependencies during runtime
+ from systemds.context import SystemDSContext
+
+
+class OperationNode2(DAGNode):
+ """A Node representing an operation in SystemDS
+ This is a temporary clone of OperationNode"""
+ shape: Optional[Tuple[int]]
+ _result_var: Optional[Union[float, np.array]]
+ _lineage_trace: Optional[str]
+ _script: Optional[DMLScript]
+
+ def __init__(self, sds_context: 'SystemDSContext', operation: str,
+ unnamed_input_nodes: Iterable[VALID_INPUT_TYPES] = None,
+ named_input_nodes: Dict[str, VALID_INPUT_TYPES] = None,
+ outputs: List[Tuple[str, OutputType]] = [("_1",
OutputType.MATRIX)],
+ is_python_local_data: bool = False,
Review comment:
It will never be needed that it is pythonlocal data, since it should
contain OperationNodes.
##########
File path: src/main/python/systemds/script_building/script.py
##########
@@ -178,24 +178,30 @@ def _dfs_dag_nodes(self, dag_node: VALID_INPUT_TYPES) ->
str:
:param dag_node: current DAG node
:return: the variable name the current DAG node operation created
"""
+
if not isinstance(dag_node, DAGNode):
if isinstance(dag_node, bool):
return 'TRUE' if dag_node else 'FALSE'
return str(dag_node)
- # for each node do the dfs operation and save the variable names in
`input_var_names`
+
+ if dag_node.dml_name != "":
+ return dag_node.dml_name
Review comment:
nice, early termination !
##########
File path: src/main/python/systemds/operator/operation_node2.py
##########
@@ -0,0 +1,527 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+from typing import Union, Optional, Iterable, Dict, Sequence, Tuple,
TYPE_CHECKING, List
+from multiprocessing import Process
+
+import numpy as np
+from py4j.java_gateway import JVMView, JavaObject
+
+from systemds.utils.consts import VALID_INPUT_TYPES, BINARY_OPERATIONS,
VALID_ARITHMETIC_TYPES
+from systemds.utils.helpers import create_params_string
+from systemds.utils.converters import matrix_block_to_numpy
+from systemds.script_building.script import DMLScript
+from systemds.script_building.dag import OutputType, DAGNode
+
+if TYPE_CHECKING:
+ # to avoid cyclic dependencies during runtime
+ from systemds.context import SystemDSContext
+
+
+class OperationNode2(DAGNode):
+ """A Node representing an operation in SystemDS
+ This is a temporary clone of OperationNode"""
+ shape: Optional[Tuple[int]]
+ _result_var: Optional[Union[float, np.array]]
+ _lineage_trace: Optional[str]
+ _script: Optional[DMLScript]
+
+ def __init__(self, sds_context: 'SystemDSContext', operation: str,
+ unnamed_input_nodes: Iterable[VALID_INPUT_TYPES] = None,
+ named_input_nodes: Dict[str, VALID_INPUT_TYPES] = None,
+ outputs: List[Tuple[str, OutputType]] = [("_1",
OutputType.MATRIX)],
+ is_python_local_data: bool = False,
+ shape: Tuple[int] = ()):
+ """
+ Create general `OperationNode2`
+
+ :param sds_context: The SystemDS context for performing the operations
+ :param operation: The name of the DML function to execute
+ :param unnamed_input_nodes: inputs identified by their position, not
name
+ :param named_input_nodes: inputs with their respective parameter name
+ :param output_type: type of the output in DML (double, matrix etc.)
+ :param is_python_local_data: if the data is local in python e.g. Numpy
arrays
+ :param number_of_outputs: If set to other value than 1 then it is
expected
+ that this operation node returns multiple values. If set remember
to set the output_types value as well.
+ :param output_types: The types of output in a multi output scenario.
+ Default is None, and means every multi output is a matrix.
+ """
+ self.sds_context = sds_context
+ self.shape = shape
+ if unnamed_input_nodes is None:
+ unnamed_input_nodes = []
+ if named_input_nodes is None:
+ named_input_nodes = {}
+ self.operation = operation
+ self._unnamed_input_nodes = unnamed_input_nodes
+ self._named_input_nodes = named_input_nodes
+ self._outputs = outputs
+ self._output_nodes = {}
+ if self.operation is not None:
+ for idx, output in enumerate(outputs):
+ if output[1] == OutputType.MATRIX:
+ self.output_nodes[output[0]] = OperationNode2(sds_context,
operation=None, named_input_nodes={f"_{idx}": self})
+ # TODO add output types
+ self._is_python_local_data = is_python_local_data
+ self._result_var = None
+ self._lineage_trace = None
+ self._script = None
+ self.dml_name = ""
+
+ def compute(self, verbose: bool = False, lineage: bool = False) -> \
+ Union[float, np.array, Tuple[Union[float, np.array], str]]:
+
+ if self._result_var is None or self._lineage_trace is None:
+ self._script = DMLScript(self.sds_context)
+ self._script.build_code(self)
+ if verbose:
+ print("SCRIPT:")
+ print(self._script.dml_script)
+
+ if lineage:
+ result_variables, self._lineage_trace =
self._script.execute_with_lineage()
+ else:
+ result_variables = self._script.execute()
+
+ self._result_var =
self.__parse_output_result_variables(result_variables)
+
+ if verbose:
+ for x in self.sds_context.get_stdout():
+ print(x)
+ for y in self.sds_context.get_stderr():
+ print(y)
+
+ if lineage:
+ return self._result_var, self._lineage_trace
+ else:
+ return self._result_var
+
+ def __parse_output_result_variables(self, result_variables):
+ result_var = []
+ for idx, v in enumerate(self._script.out_var_name):
+ if self.outputs[idx][1] == OutputType.MATRIX:
+
result_var.append(self.__parse_output_result_matrix(result_variables, v))
+ else:
+
result_var.append(result_variables.getDouble(self._script.out_var_name[idx]))
+ return result_var
+
+ def __parse_output_result_matrix(self, result_variables, var_name):
+ return matrix_block_to_numpy(self.sds_context.java_gateway.jvm,
+ result_variables.getMatrixBlock(var_name))
+
+ def get_lineage_trace(self) -> str:
+ """Get the lineage trace for this node.
+
+ :return: Lineage trace
+ """
+ if self._lineage_trace is None:
+ self._script = DMLScript(self.sds_context)
+ self._script.build_code(self)
+ self._lineage_trace = self._script.get_lineage()
+
+ return self._lineage_trace
+
+ def code_line(self, var_name: str, unnamed_input_vars: Sequence[str],
+ named_input_vars: Dict[str, str]) -> str:
+ if self.operation is None:
+ return f'{var_name}={list(named_input_vars.values())[0]}'
+
+ if self.operation in BINARY_OPERATIONS:
+ assert len(
+ named_input_vars) == 0, 'Named parameters can not be used with
binary operations'
+ assert len(
+ unnamed_input_vars) == 2, 'Binary Operations need exactly two
input variables'
+ return
f'{var_name}={unnamed_input_vars[0]}{self.operation}{unnamed_input_vars[1]}'
+
+ inputs_comma_sep = create_params_string(unnamed_input_vars,
named_input_vars)
+
+ if len(self._outputs) > 1:
+ output = "["
+ for idx in range(len(self._outputs)):
+ output += f'{var_name}_{idx},'
+ output = output[:-1] + "]"
+ return f'{output}={self.operation}({inputs_comma_sep});'
+ elif self._outputs[0][1] == OutputType.NONE:
+ return f'{self.operation}({inputs_comma_sep});'
+ elif self._outputs[0][1] == OutputType.ASSIGN:
+ return f'{var_name}={self.operation};'
+ else:
+ return f'{var_name}={self.operation}({inputs_comma_sep});'
+
+ def pass_python_data_to_prepared_script(self, jvm: JVMView, var_name: str,
prepared_script: JavaObject) -> None:
+ raise NotImplementedError(
+ 'Operation node has no python local data. Missing implementation
in derived class?')
+
+ def _check_matrix_op(self):
+ """Perform checks to assure operation is allowed to be performed on
data type of this `OperationNode2`
+
+ :raise: AssertionError
+ """
+ assert len(self._outputs) == 1 and self._outputs[0][1] ==
OutputType.MATRIX, f'{self.operation} only supported for matrices'
+
+ def __add__(self, other: VALID_ARITHMETIC_TYPES) -> 'OperationNode2':
+ return OperationNode2(self.sds_context, '+', [self, other],
shape=self.shape)
Review comment:
Actually double checking all operations from
pass_python_data_to_prepared_script are invalid.
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