2995d29bb4
In several cases, operation result types can be unambiguously inferred from operands and attributes at operation construction time. Stop requiring the user to provide these types as arguments in the ODS-generated constructors in Python bindings. In particular, handle the SameOperandAndResultTypes and FirstAttrDerivedResultType traits as well as InferTypeOpInterface using the recently added interface support. This is a significant usability improvement for IR construction, similar to what C++ ODS provides. Depends On D111656 Reviewed By: gysit Differential Revision: https://reviews.llvm.org/D111811
30 lines
755 B
Python
30 lines
755 B
Python
# RUN: %PYTHON %s | FileCheck %s
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from mlir.ir import *
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import numpy as np
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import mlir.dialects.builtin as builtin
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import mlir.dialects.shape as shape
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def run(f):
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print("\nTEST:", f.__name__)
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f()
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return f
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# CHECK-LABEL: TEST: testConstShape
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@run
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def testConstShape():
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with Context() as ctx, Location.unknown():
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module = Module.create()
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f32 = F32Type.get()
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with InsertionPoint(module.body):
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((12, -1), f32))
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def const_shape_tensor(arg):
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return shape.ConstShapeOp(DenseElementsAttr.get(np.array([10, 20])))
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# CHECK-LABEL: func @const_shape_tensor(%arg0: tensor<12x?xf32>)
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# CHECK: shape.const_shape [10, 20] : tensor<2xindex>
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print(module)
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