147 lines
5.6 KiB
TableGen
147 lines
5.6 KiB
TableGen
// RUN: llvm-tblgen -dump-json %s | %python %S/JSON-check.py %s
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// CHECK: data['!tablegen_json_version'] == 1
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// CHECK: all(data[s]['!name'] == s for s in data if not s.startswith("!"))
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class Base {}
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class Intermediate : Base {}
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class Derived : Intermediate {}
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def D : Intermediate {}
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// CHECK: 'D' in data['!instanceof']['Base']
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// CHECK: 'D' in data['!instanceof']['Intermediate']
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// CHECK: 'D' not in data['!instanceof']['Derived']
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// CHECK: 'Base' in data['D']['!superclasses']
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// CHECK: 'Intermediate' in data['D']['!superclasses']
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// CHECK: 'Derived' not in data['D']['!superclasses']
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def ExampleDagOp;
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def FieldKeywordTest {
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int a;
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field int b;
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// CHECK: 'a' not in data['FieldKeywordTest']['!fields']
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// CHECK: 'b' in data['FieldKeywordTest']['!fields']
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}
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class Variables {
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int i;
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string s;
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bit b;
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bits<8> bs;
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code c;
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list<int> li;
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Base base;
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dag d;
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}
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def VarNull : Variables {
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// A variable not filled in at all has its value set to JSON
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// 'null', which translates to Python None
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// CHECK: data['VarNull']['i'] is None
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}
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def VarPrim : Variables {
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// Test initializers that map to primitive JSON types
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int i = 3;
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// CHECK: data['VarPrim']['i'] == 3
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// Integer literals should be emitted in the JSON at full 64-bit
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// precision, for the benefit of JSON readers that preserve that
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// much information. Python's is one such.
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int enormous_pos = 9123456789123456789;
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int enormous_neg = -9123456789123456789;
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// CHECK: data['VarPrim']['enormous_pos'] == 9123456789123456789
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// CHECK: data['VarPrim']['enormous_neg'] == -9123456789123456789
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string s = "hello, world";
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// CHECK: data['VarPrim']['s'] == 'hello, world'
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bit b = 0;
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// CHECK: data['VarPrim']['b'] == 0
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// bits<> arrays are stored in logical order (array[i] is the same
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// bit identified in .td files as bs{i}), which means the _visual_
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// order of the list (in default rendering) is reversed.
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bits<8> bs = { 0,0,0,1,0,1,1,1 };
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// CHECK: data['VarPrim']['bs'] == [ 1,1,1,0,1,0,0,0 ]
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code c = [{ \" }];
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// CHECK: data['VarPrim']['c'] == r' \" '
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list<int> li = [ 1, 2, 3, 4 ];
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// CHECK: data['VarPrim']['li'] == [ 1, 2, 3, 4 ]
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}
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def VarObj : Variables {
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// Test initializers that map to JSON objects containing a 'kind'
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// discriminator
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Base base = D;
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// CHECK: data['VarObj']['base']['kind'] == 'def'
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// CHECK: data['VarObj']['base']['def'] == 'D'
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// CHECK: data['VarObj']['base']['printable'] == 'D'
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dag d = (ExampleDagOp 22, "hello":$foo);
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// CHECK: data['VarObj']['d']['kind'] == 'dag'
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// CHECK: data['VarObj']['d']['operator']['kind'] == 'def'
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// CHECK: data['VarObj']['d']['operator']['def'] == 'ExampleDagOp'
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// CHECK: data['VarObj']['d']['operator']['printable'] == 'ExampleDagOp'
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// CHECK: data['VarObj']['d']['args'] == [[22, None], ["hello", "foo"]]
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// CHECK: data['VarObj']['d']['printable'] == '(ExampleDagOp 22, "hello":$foo)'
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int undef_int;
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field int ref_int = undef_int;
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// CHECK: data['VarObj']['ref_int']['kind'] == 'var'
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// CHECK: data['VarObj']['ref_int']['var'] == 'undef_int'
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// CHECK: data['VarObj']['ref_int']['printable'] == 'undef_int'
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bits<2> undef_bits;
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bits<4> ref_bits;
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let ref_bits{3...2} = 0b10;
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let ref_bits{1...0} = undef_bits{1...0};
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// CHECK: data['VarObj']['ref_bits'][3] == 1
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// CHECK: data['VarObj']['ref_bits'][2] == 0
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// CHECK: data['VarObj']['ref_bits'][1]['kind'] == 'varbit'
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// CHECK: data['VarObj']['ref_bits'][1]['var'] == 'undef_bits'
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// CHECK: data['VarObj']['ref_bits'][1]['index'] == 1
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// CHECK: data['VarObj']['ref_bits'][1]['printable'] == 'undef_bits{1}'
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// CHECK: data['VarObj']['ref_bits'][0]['kind'] == 'varbit'
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// CHECK: data['VarObj']['ref_bits'][0]['var'] == 'undef_bits'
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// CHECK: data['VarObj']['ref_bits'][0]['index'] == 0
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// CHECK: data['VarObj']['ref_bits'][0]['printable'] == 'undef_bits{0}'
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field int complex_ref_int = !add(undef_int, 2);
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// CHECK: data['VarObj']['complex_ref_int']['kind'] == 'complex'
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// CHECK: data['VarObj']['complex_ref_int']['printable'] == '!add(undef_int, 2)'
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}
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// Test the !anonymous member. This is tricky because when a def is
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// anonymous, almost by definition, the test can't reliably predict
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// the name it will be stored under! So we have to search all the defs
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// in the JSON output looking for the one that has the test integer
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// field set to the right value.
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def Named { int AnonTestField = 1; }
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// CHECK: data['Named']['AnonTestField'] == 1
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// CHECK: data['Named']['!anonymous'] is False
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def { int AnonTestField = 2; }
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// CHECK: next(rec for rec in data.values() if isinstance(rec, dict) and rec.get('AnonTestField') == 2)['!anonymous'] is True
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multiclass AnonTestMulticlass<int base> {
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def _plus_one { int AnonTestField = !add(base,1); }
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def { int AnonTestField = !add(base,2); }
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}
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defm NamedDefm : AnonTestMulticlass<10>;
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// CHECK: data['NamedDefm_plus_one']['!anonymous'] is False
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// CHECK: data['NamedDefm_plus_one']['AnonTestField'] == 11
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// CHECK: next(rec for rec in data.values() if isinstance(rec, dict) and rec.get('AnonTestField') == 12)['!anonymous'] is True
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// D47431 clarifies that a named def inside a multiclass gives a
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// *non*-anonymous output record, even if the defm that instantiates
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// that multiclass is anonymous.
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defm : AnonTestMulticlass<20>;
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// CHECK: next(rec for rec in data.values() if isinstance(rec, dict) and rec.get('AnonTestField') == 21)['!anonymous'] is False
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// CHECK: next(rec for rec in data.values() if isinstance(rec, dict) and rec.get('AnonTestField') == 22)['!anonymous'] is True
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