clean up gen_doc.py (#59)
Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
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chentong319
GitHub
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ce8594fc60
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6959cf4586
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@ -18,6 +18,44 @@ from onnx.backend.sample.ops import collect_sample_implementations
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from typing import Any, Text, Sequence, Dict, List, Type, Set, Tuple
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#controls on ONNF code gen
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#specify attr default value
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special_attr_defaults = dict([
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# ("AveragePool "+"kernel_shape", ('ints', '{}')),
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# ("MaxPool "+"kernel_shape", ('ints', '{}')),
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# ("Cast "+"to", ('int', '0')),
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# ("Concat "+"axis", ('int', '0')),
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# ("Conv "+"group", ('int', '1')),
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# ("Unsqueeze "+"axes", ('ints', '{}')),
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# ("RNN "+"activation_alpha", ('floats', '{}')),
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# ("RNN "+"activation_beta", ('floats', '{}')),
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])
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#specify the function name in src/builder/frontend_dialect_transformer.cpp
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#the reason for Conv and MaPool is to handled optional arguments
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special_op_handler = dict([
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("Conv", "ImportNodeConv"),
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("MaxPool", "ImportNodeMaxPool"),
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#("Transpose", "ImportNodeTranspose")
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])
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#add an Op in this list if ShapeInterference is defined for this Op
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ShapeInferenceList=['Exp', 'Tanh', 'Sinh', 'Cosh', 'Sigmoid', 'Relu',
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'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
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'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
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'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
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'Identity', 'Cos', 'Log', 'Transpose', 'Softmax',
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'Softplus', 'Softsign', 'Sqrt', 'Unsqueeze']
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CanonicalList=['Add', 'Identity']
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manual_code_in_op_def = dict([
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('DummyExample', ' let extraClassDeclaration = [{ \n'+
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' static StringRef getPermAttrName() { return "perm"; }\n'+
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' }];\n')
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])
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SNIPPETS = collect_snippets()
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SAMPLE_IMPLEMENTATIONS = collect_sample_implementations()
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ONNX_ML = not bool(os.getenv('ONNX_ML') == '0')
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@ -263,18 +301,6 @@ def collect_types(schema, input) :
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return allowedTypeStr
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def gen_schema(schema) :
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ShapeInferenceList=['Exp', 'Tanh', 'Sinh', 'Cosh', 'Sigmoid', 'Relu',
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'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
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'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
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'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
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'Identity', 'Cos', 'Log', 'Transpose', 'Softmax',
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'Softplus', 'Softsign', 'Sqrt', 'Unsqueeze']
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CanonicalList=['Add', 'Identity']
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manual_code = dict([
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('DummyExample', ' let extraClassDeclaration = [{ \n'+
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' static StringRef getPermAttrName() { return "perm"; }\n'+
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' }];\n')
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])
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skip_attr_gen = []
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line_indent = ' '
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@ -362,8 +388,8 @@ def gen_schema(schema) :
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#s+= 'let hasCanonicalizer = 1;'
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#add special code
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if schema.name in manual_code :
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s += manual_code[schema.name]
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if schema.name in manual_code_in_op_def :
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s += manual_code_in_op_def[schema.name]
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s += '}\n\n'
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@ -378,19 +404,14 @@ special cases:
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"""
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def gen_code(schema,fefile) :
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special_handler = dict([
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("Conv", "ImportNodeConv"),
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("MaxPool", "ImportNodeMaxPool"),
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#("Transpose", "ImportNodeTranspose")
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])
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handle_variadic = False
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line_indent = ' '
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fefile.write(' '+'}else if (OpName == "'+schema.name+'") {\n')
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op_type_str='mlir::ONNX'+schema.name+'Op'
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if schema.name in special_handler :
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fefile.write(' '+special_handler[schema.name]+'(node, '
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if schema.name in special_op_handler :
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fefile.write(' '+special_op_handler[schema.name]+'(node, '
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+str(len(schema.inputs))
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+', ' +str(len(schema.outputs)))
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elif len(schema.outputs) > 1 :
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@ -419,16 +440,6 @@ def gen_code(schema,fefile) :
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fefile.write(', '+variadicIn+', '+variadicOut+');\n')
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def gen_attr_ins(schema, isfirst) :
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special_defaults = dict([
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("AveragePool "+"kernel_shape", ('ints', '{}')),
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("MaxPool "+"kernel_shape", ('ints', '{}')),
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("Cast "+"to", ('int', '0')),
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("Concat "+"axis", ('int', '0')),
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("Conv "+"group", ('int', '1')),
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("Unsqueeze "+"axes", ('ints', '{}')),
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("RNN "+"activation_alpha", ('floats', '{}')),
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("RNN "+"activation_beta", ('floats', '{}')),
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])
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def get_attr_type_basic(attr_type) :
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if attr_type == 'int' :
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@ -469,8 +480,8 @@ def gen_attr_ins(schema, isfirst) :
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else :
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isfirst = False
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if schema.name+' '+attr.name in special_defaults:
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(attr_type_str, attr_default_str) = special_defaults[schema.name+' '+attr.name]
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if schema.name+' '+attr.name in special_attr_defaults:
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(attr_type_str, attr_default_str) = special_attr_defaults[schema.name+' '+attr.name]
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attr_line += get_attr_type_with_default(attr_type_str, attr_default_str)
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attr_line += ':$'+attr.name
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elif attr.required:
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@ -614,9 +625,21 @@ def main(args): # type: (Type[Args]) -> None
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fout.write('\n')
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tdfile= io.open(args.tdfile, 'w', newline='')
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tdfile.write('//********************************************************\n'+
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'// Warning: Do not modify this file directly\n'+
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'// This file is automatically generated via script\n'+
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'// Details can be found in doc/readonnxdefs.md\n'+
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'//********************************************************\n\n'
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)
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fefile=io.open('op_build_table.inc', 'w', newline='')
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firstfunc = True
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fefile.write('//********************************************************\n'+
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'// Warning: Do not modify this file directly\n'+
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'// This file is automatically generated via script\n'+
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'// Details can be found in doc/readonnxdefs.md\n'+
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'//********************************************************\n\n'
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)
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fefile.write(' '+'if (OpName == "DUMMY") {\n')
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for domain, supportmap in operator_schemas:
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s = '## {}\n'.format(display_domain_short(domain))
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@ -1,3 +1,9 @@
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//********************************************************
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// Warning: Do not modify this file directly
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// This file is automatically generated via script
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// Details can be found in doc/readonnxdefs.md
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//********************************************************
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if (OpName == "DUMMY") {
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}else if (OpName == "Abs") {
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ImportNodeOneOut<mlir::ONNXAbsOp>(node, 1, 1);
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@ -1,3 +1,9 @@
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//********************************************************
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// Warning: Do not modify this file directly
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// This file is automatically generated via script
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// Details can be found in doc/readonnxdefs.md
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//********************************************************
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def ONNXAbsOp:ONNX_Op<"Abs",
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[NoSideEffect]> {
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let summary = "ONNX Abs operation";
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@ -166,7 +172,7 @@ def ONNXAveragePoolOp:ONNX_Op<"AveragePool",
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DefaultValuedAttr<StrAttr, "NOTSET">:$auto_pad,
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DefaultValuedAttr<I64Attr, "0">:$ceil_mode,
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DefaultValuedAttr<I64Attr, "0">:$count_include_pad,
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DefaultValuedAttr<I64ArrayAttr, "{}">:$kernel_shape,
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I64ArrayAttr:$kernel_shape,
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OptionalAttr<I64ArrayAttr>:$pads,
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OptionalAttr<I64ArrayAttr>:$strides);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
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@ -245,7 +251,7 @@ def ONNXCastOp:ONNX_Op<"Cast",
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"an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type."
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}];
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let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input,
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DefaultValuedAttr<I64Attr, "0">:$to);
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I64Attr:$to);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_output);
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}
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@ -297,7 +303,7 @@ def ONNXConcatOp:ONNX_Op<"Concat",
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"Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on."
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}];
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let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$inputs,
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DefaultValuedAttr<I64Attr, "0">:$axis);
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I64Attr:$axis);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_concat_result);
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}
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@ -1621,7 +1627,7 @@ def ONNXMaxPoolOp:ONNX_Op<"MaxPool",
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DefaultValuedAttr<StrAttr, "NOTSET">:$auto_pad,
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DefaultValuedAttr<I64Attr, "0">:$ceil_mode,
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OptionalAttr<I64ArrayAttr>:$dilations,
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DefaultValuedAttr<I64ArrayAttr, "{}">:$kernel_shape,
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I64ArrayAttr:$kernel_shape,
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OptionalAttr<I64ArrayAttr>:$pads,
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DefaultValuedAttr<I64Attr, "0">:$storage_order,
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OptionalAttr<I64ArrayAttr>:$strides);
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@ -2122,8 +2128,8 @@ def ONNXRNNOp:ONNX_Op<"RNN",
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AnyTypeOf<[AnyMemRef, AnyTensor]>:$B,
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AnyTypeOf<[AnyMemRef, AnyTensor]>:$sequence_lens,
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AnyTypeOf<[AnyMemRef, AnyTensor]>:$initial_h,
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DefaultValuedAttr<F32ArrayAttr, "{}">:$activation_alpha,
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DefaultValuedAttr<F32ArrayAttr, "{}">:$activation_beta,
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OptionalAttr<F32ArrayAttr>:$activation_alpha,
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OptionalAttr<F32ArrayAttr>:$activation_beta,
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DefaultValuedAttr<StrArrayAttr, "{\"Tanh\", \"Tanh\"}">:$activations,
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OptionalAttr<F32Attr>:$clip,
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DefaultValuedAttr<StrAttr, "forward">:$direction,
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@ -3519,7 +3525,7 @@ def ONNXUnsqueezeOp:ONNX_Op<"Unsqueeze",
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""
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}];
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let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data,
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DefaultValuedAttr<I64ArrayAttr, "{}">:$axes);
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I64ArrayAttr:$axes);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_expanded);
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}
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