def ConcatV2(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict): inputsRef = curNode.getInputsRef() N = curNode.getAttrMapRef()["\"N\""].getI() assert(len(inputsRef) == N+1) #One extra for axis #TODO : Since the axis of concat is constant, therefore, its known here - the input's sizes along that dim should be # passed as input to the below function. # For now hardcoding. retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0], TFNodesAST.UninterpFuncCallNames.Concat.name + str(N) + 'T', list(map(lambda x : AST.ID(dictNodeNameToOutVarStr[x]), inputsRef)), outputDiffInpDims=1 ) return (None, retAST)
def Mean(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
attrMapRef = curNode.getAttrMapRef()
assert(len(inputsRef) == 2)
keepdims = False
if ("\"keep_dims\"" in attrMapRef):
keepdims = attrMapRef["\"keep_dims\""].getB()
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
return (None, AST.Reduce(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
AST.Int(int(keepdims), 32, isSecret=False),
curNodeShapeLi,
TFNodesAST.getOperatorsIdx('mean')))
def GlobalAveragePool(
node,
value_info,
node_name_to_out_var_dict,
innermost_let_ast_node,
out_var_count,
mtdAST,
):
node = OnnxNode(node)
if DEBUG:
print(node)
inputsRef = node.inputs
assert len(inputsRef) == 1
reshaped_input_name = get_new_var_name(out_var_count)
reshaped_input = get_reshaped_input_ast(inputsRef[0], value_info,
node_name_to_out_var_dict)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, reshaped_input, reshaped_input_name,
mtdAST)
out_var_count += 1
seedot_output_ast = AST.Pool(
AST.Pool.PoolType.AvgPool,
AST.ID(reshaped_input_name),
{
AST.PaddingKeysDict.FH: value_info[inputsRef[0]][1][2],
AST.PaddingKeysDict.FW: value_info[inputsRef[0]][1][3],
AST.PaddingKeysDict.zPadHLeft: 0,
AST.PaddingKeysDict.zPadHRight: 0,
AST.PaddingKeysDict.zPadWLeft: 0,
AST.PaddingKeysDict.zPadWRight: 0,
AST.PaddingKeysDict.strideH: 1,
AST.PaddingKeysDict.strideW: 1,
},
)
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
reshaped_output_name = get_new_var_name(out_var_count)
onnx_output_ast = get_reshaped_output_ast(node.outputs[0], value_info,
output_name)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, onnx_output_ast, reshaped_output_name,
mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[0]] = reshaped_output_name
return (innermost_let_ast_node, out_var_count)
def Transpose(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
permNodeName = inputsRef[1]
# We need to fetch the tensor value of the perm Node
permNode = graph.__getitem__(permNodeName)
permTensor = permNode.getAttrVal("value").getTensor()
permList = permTensor.getContentAsValArr()
assert (permTensor.getDType().kind == "i")
assert (permTensor.getShapeRef().getRank() == 1)
return (None,
AST.Transpose(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
permList))
def Cast(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
sourceType = curNode.getAttrMapRef()["\"SrcT\""].getDataType()
destType = curNode.getAttrMapRef()["\"DstT\""].getDataType()
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Cast.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(sourceType.name),
AST.ID(destType.name)
]))
def Split(node, value_info, node_name_to_out_var_dict, innermost_let_ast_node, out_var_count, mtdAST): node = OnnxNode(node) inputsRef = node.inputs output_count = len(node.outputs) for cur_count in range(output_count): seedot_output_ast = AST.UninterpFuncCall(list(value_info[node.outputs[cur_count]][1]), 'Split', [AST.ID(node_name_to_out_var_dict[inputsRef[0]]), AST.Int(node.attrs['axis'], 32, False), AST.Int(cur_count, 32, False), AST.Int(output_count, 32, False)]) output_name = get_new_var_name(out_var_count) innermost_let_ast_node = update_program_with_new_node(innermost_let_ast_node, seedot_output_ast, output_name, mtdAST) out_var_count += 1 node_name_to_out_var_dict[node.outputs[cur_count]] = output_name return (innermost_let_ast_node, out_var_count)
def Slice(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 3)
curNodeDataType = curNode.getAttrMapRef()["\"T\""].getDataType()
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateCopy.name,
[
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]), # of this
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]), # begin idx
AST.ID(dictNodeNameToOutVarStr[inputsRef[2]]) # size
])
return (None, retAST)
def fillSymbolTable(self):
if self.functions is not None:
for function in self.functions:
entry = SymbolTableEntry(function.id, 'FUNCTION', {'args': function.args, 'asssignments': function.assignments})
AST.addToSymbolTable(entry)
for assignment in self.assignments:
sym_table_entry = assignment.fillSymbolTable()
if sym_table_entry is not None:
AST.addToSymbolTable(sym_table_entry)
return self.symbolTable
def ExpandDims(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 2
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.ExpandDims.name,
list(map(lambda x: AST.ID(dictNodeNameToOutVarStr[x]), inputsRef)),
)
return (None, {curNode.getName(): retAST})
def enterIterationStatement(self, ctx: CParser.IterationStatementContext):
node = None
if ctx.For():
node = AST.ASTForStmtNode("For", ctx=ctx)
elif ctx.While():
node = AST.ASTWhileStmtNode("While", ctx=ctx)
node.parent = self.current_node
outer_for_scope = STT.STTNode()
outer_for_scope.parent = self.current_node.scope
outer_for_scope.depth = outer_for_scope.parent.depth + 1
node.scope = outer_for_scope
self.current_node.children.append(node)
self.current_node.scope.children.append(outer_for_scope)
self.current_node = node
def Conv2D(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 2
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert stridesUsed[0] == 1 and stridesUsed[3] == 1
strideH = stridesUsed[1]
strideW = stridesUsed[2]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgH = inputShape[1]
imgW = inputShape[2]
filterShape = extraNodeInfoDict[inputsRef[1]][0]
FH = filterShape[0]
FW = filterShape[1]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, FH, FW,
strideH, strideW,
paddingUsedStr)
options = {}
options[AST.PaddingKeysDict.FH] = FH
options[AST.PaddingKeysDict.FW] = FW
options[AST.PaddingKeysDict.zPadHLeft] = zPadHLeft
options[AST.PaddingKeysDict.zPadHRight] = zPadHRight
options[AST.PaddingKeysDict.zPadWLeft] = zPadWLeft
options[AST.PaddingKeysDict.zPadWRight] = zPadWRight
options[AST.PaddingKeysDict.strideH] = strideH
options[AST.PaddingKeysDict.strideW] = strideW
return (
None,
{
curNode.getName():
AST.BOp(
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
TFNodesAST.getOperatorsIdx("#"),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
options,
)
},
)
def get_reshaped_bias_ast(bias_name, value_info, node_name_to_out_var_dict,
dim):
if dim == 2:
return AST.Reshape(
AST.ID(node_name_to_out_var_dict[bias_name]),
[1, 1, 1, value_info[bias_name][1][0]],
None,
)
else:
return AST.Reshape(
AST.ID(node_name_to_out_var_dict[bias_name]),
[1, 1, 1, 1, value_info[bias_name][1][0]],
None,
)
def Fill(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
assert(len(inputsRef) == 2)
curNodeOutputShape = extraNodeInfoDict[inputsRef[0]][0]
assert(len(curNodeOutputShape) == 1) #inputsRef[0] denotes a shape and should have a rank of 1
curNodeOutputType = curNode.getAttrMapRef()["T"].getDataType()
assert(curNodeOutputType is not Graph.DataTypeEnum.DT_INVALID)
retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]) ],
isSecret=False)
return (None, { curNode.getName() : retAST})
def Conv3D(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert (stridesUsed[0] == 1 and stridesUsed[4] == 1)
strideD = stridesUsed[1]
strideH = stridesUsed[2]
strideW = stridesUsed[3]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgD = inputShape[1]
imgH = inputShape[2]
imgW = inputShape[3]
filterShape = extraNodeInfoDict[inputsRef[1]][0]
FD = filterShape[0]
FH = filterShape[1]
FW = filterShape[2]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadDLeft, zPadDRight, zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, FH, FW,
strideH, strideW,
paddingUsedStr, imgD, FD,
strideD)
options = {}
options[AST.PaddingKeysDict.FD] = FD
options[AST.PaddingKeysDict.FH] = FH
options[AST.PaddingKeysDict.FW] = FW
options[AST.PaddingKeysDict.zPadDLeft] = zPadDLeft
options[AST.PaddingKeysDict.zPadDRight] = zPadDRight
options[AST.PaddingKeysDict.zPadHLeft] = zPadHLeft
options[AST.PaddingKeysDict.zPadHRight] = zPadHRight
options[AST.PaddingKeysDict.zPadWLeft] = zPadWLeft
options[AST.PaddingKeysDict.zPadWRight] = zPadWRight
options[AST.PaddingKeysDict.strideD] = strideD
options[AST.PaddingKeysDict.strideH] = strideH
options[AST.PaddingKeysDict.strideW] = strideW
options[AST.PaddingKeysDict.ConvDim] = 3
return (None, {
curNode.getName():
AST.BOp(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
TFNodesAST.getOperatorsIdx('#'),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]), options)
})
def update_program_with_new_node(innermost_let_ast_node, new_node,
new_node_name, mtdAST):
cur_out_var_ast_node = AST.ID(new_node_name)
new_let_node = AST.Let(cur_out_var_ast_node, new_node,
cur_out_var_ast_node)
mtdAST.visit(new_let_node, {
AST.ASTNode.mtdKeyTFOpName: 'no',
AST.ASTNode.mtdKeyTFNodeName: 'no'
})
# Updating the innermost Let AST node and the expression for previous Let Node
innermost_let_ast_node.expr = new_let_node
innermost_let_ast_node = new_let_node
# node_name_to_out_var_dict[node.outputs[0]] = new_node_name
return innermost_let_ast_node
def Transpose(node, value_info, node_name_to_out_var_dict, innermost_let_ast_node, out_var_count, mtdAST): node = OnnxNode(node) if(DEBUG): print(node) inputsRef = node.inputs assert(len(inputsRef)==1) seedot_output_ast = AST.Transpose(AST.ID(node_name_to_out_var_dict[inputsRef[0]]), node.attrs['perm']) output_name = get_new_var_name(out_var_count) innermost_let_ast_node = update_program_with_new_node(innermost_let_ast_node, seedot_output_ast, output_name, mtdAST) out_var_count += 1 node_name_to_out_var_dict[node.outputs[0]] = output_name return (innermost_let_ast_node, out_var_count)
def Flatten(node, value_info, node_name_to_out_var_dict, innermost_let_ast_node, out_var_count, mtdAST): node = OnnxNode(node) if(DEBUG): print(node) inputsRef = node.inputs assert(len(inputsRef)==1) seedot_output_ast = AST.Reshape(AST.ID(node_name_to_out_var_dict[inputsRef[0]]), list(value_info[node.outputs[0]][1]), None) output_name = get_new_var_name(out_var_count) innermost_let_ast_node = update_program_with_new_node(innermost_let_ast_node, seedot_output_ast, output_name, mtdAST) out_var_count += 1 node_name_to_out_var_dict[node.outputs[0]] = output_name return (innermost_let_ast_node, out_var_count)
def Identity(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
#In SeeDot, J2=J1 creates a new reference for J1 -- so
# the corresponding code in Seedot cannot simply be J2 = J1.
# Instead create a new tensor first and then assign the old one to the new one.
inputsRef = curNode.getInputsRef()
assert(len(inputsRef)==1)
curNodeDataType = curNode.getAttrMapRef()["T"].getDataType()
assert(curNodeDataType is not Graph.DataTypeEnum.DT_INVALID)
curNodeShape = extraNodeInfoDict[curNode.getName()][0]
retAST = AST.UninterpFuncCall(curNodeShape,
TFNodesAST.UninterpFuncCallNames.CreateIdentity.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])])
return (None, { curNode.getName() : retAST})
def Squeeze(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
inputTensorShape = extraNodeInfoDict[inputsRef[0]][0]
inputTensorRank = len(inputTensorShape)
squeezeDims = curNode.getAttrMapRef()["squeeze_dims"].getList().getILi()
squeezeDimsRank = len(squeezeDims)
return (None, { curNode.getName() : AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Squeeze.name,
list(map(lambda x : AST.Int(x, 32, isSecret=False), squeezeDims)) +
[
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])
]
)})
def Add(node, value_info, node_name_to_out_var_dict,
innermost_let_ast_node, out_var_count, mtdAST):
node = OnnxNode(node)
if (DEBUG):
print(node)
inputsRef = node.inputs
assert (len(inputsRef) == 2)
reshaped_input_name = get_new_var_name(out_var_count)
reshaped_input = get_reshaped_input_ast(inputsRef[0], value_info,
node_name_to_out_var_dict)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, reshaped_input, reshaped_input_name,
mtdAST)
out_var_count += 1
reshaped_input_name1 = get_new_var_name(out_var_count)
reshaped_input1 = get_reshaped_input_ast(inputsRef[1], value_info,
node_name_to_out_var_dict)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, reshaped_input1, reshaped_input_name1,
mtdAST)
out_var_count += 1
seedot_output_ast = AST.BOp(AST.ID(reshaped_input_name),
getOperatorsIdx('+'),
AST.ID(reshaped_input_name1))
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
reshaped_output_name = get_new_var_name(out_var_count)
onnx_output_ast = get_reshaped_output_ast(node.outputs[0], value_info,
output_name)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, onnx_output_ast, reshaped_output_name,
mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[0]] = reshaped_output_name
if (DEBUG):
print(node.outputs[0])
print(onnx_input_shape, onnx_input_shape1, '->',
seedot_input_shape, seedot_input_shape1, '->',
onnx_output_shape)
return (innermost_let_ast_node, out_var_count)
def helper_processPool(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict, typeOfPool: str):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
options = {}
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert ((stridesUsed[0] == 1) and (stridesUsed[3] == 1))
strideH = stridesUsed[1]
strideW = stridesUsed[2]
kSizeUsed = curNode.getAttrMapRef()["ksize"].getList().getILi()
assert ((kSizeUsed[0] == 1) and (kSizeUsed[3] == 1))
kSizeH = kSizeUsed[1]
kSizeW = kSizeUsed[2]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgH = inputShape[1]
imgW = inputShape[2]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, kSizeH,
kSizeW, strideH, strideW,
paddingUsedStr)
poolType = None
if typeOfPool == 'MAXPOOL': poolType = AST.Pool.PoolType.MaxPool
elif typeOfPool == 'AVGPOOL': poolType = AST.Pool.PoolType.AvgPool
else:
print("Unknown type of pooling layer.", file=sys.stderr)
assert (False)
return (None, {
curNode.getName():
AST.Pool(
poolType, AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]), {
AST.PaddingKeysDict.FH: kSizeH,
AST.PaddingKeysDict.FW: kSizeW,
AST.PaddingKeysDict.zPadHLeft: zPadHLeft,
AST.PaddingKeysDict.zPadHRight: zPadHRight,
AST.PaddingKeysDict.zPadWLeft: zPadWLeft,
AST.PaddingKeysDict.zPadWRight: zPadWRight,
AST.PaddingKeysDict.strideH: strideH,
AST.PaddingKeysDict.strideW: strideW
})
})
def Input(node, value_info, node_name_to_out_var_dict): if(DEBUG): print(node.outputs[0]) # There are two types of inputs dims = list(node.dims if hasattr(node, 'dims') else ([val.dim_value for val in node.type.tensor_type.shape.dim])) data_type = node.data_type if hasattr (node, 'data_type') else node.type.tensor_type.elem_type return AST.Input(dims, onnx2seedot(data_type))
def enterDeclaration(self, ctx: CParser.DeclarationContext):
node = AST.ASTDeclarationNode(c_idx=len(self.current_node.children),
ctx=ctx)
node.parent = self.current_node
node.scope = self.current_node.scope
self.current_node.children.append(node)
self.current_node = node
def enterReturn(self, ctx: CParser.ReturnContext):
node = AST.ASTReturnNode(c_idx=len(self.current_node.children),
ctx=ctx)
node.parent = self.current_node
node.scope = self.current_node.scope
self.current_node.children.append(node)
self.current_node = node
def Placeholder(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
curNodeInputType = curNode.getAttrMapRef()["dtype"].getDataType()
assert curNodeInputType is not Graph.DataTypeEnum.DT_INVALID
# NOTE: There has to be some way for Athos to differentiate model from image, since in the compiled code
# (in the scenario of secure inference), model is input by server and image by client.
# We assume in the following that the PlaceHolder op node represents the image and
# all model parameters are represented using Variable op nodes.
# Hence, in the call to AST.Input, we pass inputByParty=1.
return (
None,
{
curNode.getName():
AST.Input(
curNodeShapeLi,
curNodeInputType.name,
isSecret=True,
inputByParty=AST.Party.CLIENT,
)
},
)
def enterLabeledStatement(self, ctx: CParser.LabeledStatementContext):
label = ctx.Identifier()
node = AST.ASTLabelStmtNode(label, ctx=ctx)
node.parent = self.current_node
node.scope = self.current_node.scope
self.current_node.children.append(node)
self.current_node = node
def VariableV2(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
curNodeShapeLi = curNode.getAttrMapRef()["shape"].getShape().getDimRef(
)[:]
curNodeInputType = curNode.getAttrMapRef()["dtype"].getDataType()
# NOTE : since this becomes an input node right now, i have also added to be prefixed at top in ProcessTFGraph::prefixAllPlaceHolderNodes()
# NOTE: There has to be some way for Athos to differentiate model from image, since in the compiled code
# (in the scenario of secure inference), model is input by server and image by client.
# We assume in the following that the PlaceHolder op node represents the image and
# all model parameters are represented using Variable op nodes.
# Hence, in the call to AST.Input, we pass inputByParty as SERVER.
return (
None,
{
curNode.getName():
AST.Input(
curNodeShapeLi,
curNodeInputType.name,
isSecret=True,
inputByParty=AST.Party.SERVER,
)
},
)
def StopGradient(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
return (None, {
curNode.getName():
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])
})
def searchUpHandleNode(self, mergeList, AST):
'''
在ASTBeftore中向上搜索
'''
structureNode = set()
for i in mergeList:
tempNode = AST.getNodeByID(i)
index = self.getIndexInParent(tempNode.id, AST)
tempNodeO = tempNode
tempNode = tempNode.parent
while True:
if tempNode == None:
break
elif tempNode.typeLabel == "MethodDeclaration":
# print(tempNodeO.id)
structureNode.add((tempNode.id, index))
break
# print(tempNode.typeLabel,"----------")
elif tempNode.typeLabel in self.structureHandle:
# print(tempNodeO.id)
structureNode.add((tempNode.id, index))
# print(tempNode.typeLabel, '123123123123123')
# print(index)
index = self.getIndexInParent(tempNode.id, AST)
tempNode = tempNode.parent
# print(structureNode)
return structureNode
def fillSymbolTable(self):
if self.expr:
expr_type, expr_value = self.expr.fillSymbolTable()
if AST.getEntry(self.id) is not None:
if AST.getEntry(self.id).type != expr_type:
raise Exception('Error when trying to assign type {0} to {1}'.format(expr_type, AST.getEntry(self.id).type))
return SymbolTableEntry(self.id, expr_type, expr_value)
else:
# check if self.id is of type group
if AST.getEntry(self.id) is None:
raise Exception('Undefined variable {}'.format(self.id))
elif AST.getEntry(self.id).type != 'Group' :
raise Exception('Expected variable type Group but got {}'.format(AST.getEntry(self.id).type))
self.cmd.generateInstruction()
def doeval(ele, env):
if type(ele) == str:
ele = AST([ele])
# print env, ele
cmd = ele.get_cmd()
args = ele.get_args()
if isinstance(cmd, AST):
cur_env = Env(env.name + '_0', env)
opt = doeval(cmd, cur_env)
else :
opt = env.search_symbol(cmd)
if opt is None:
print 'symbol %s not found!' % cmd
return None
rs = opt.apply(env, args)
# print 'eval result: %s' % str(rs)
return rs
