cf05668c17
The revision renames `PrimFn` to `ArithFn`. The name resembles the newly introduced arith dialect that implements most of the arithmetic functions. An exception are log/exp that are part of the math dialect. Depends On D115239 Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D115240
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
# RUN: %PYTHON -m mlir.dialects.linalg.opdsl.dump_oplib --file %s | FileCheck %s
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from mlir.dialects.linalg.opdsl.lang import *
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# CHECK: ---
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# CHECK-LABEL: matmul
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# CHECK: assignments:
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# CHECK: -
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# CHECK: arg: C
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# CHECK: value:
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# CHECK: arith_fn:
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# CHECK: fn_name: add
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# CHECK: operands:
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# CHECK: arith_fn:
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# CHECK: fn_name: mul
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# CHECK: operands:
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# CHECK: type_fn:
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# CHECK: type_var: U
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# CHECK: operands:
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# CHECK: scalar_arg: A
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# CHECK: type_fn:
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# CHECK: type_var: U
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# CHECK: operands:
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# CHECK: scalar_arg: B
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@linalg_structured_op
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def matmul(
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A=TensorDef(T, S.M, S.K),
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B=TensorDef(T, S.K, S.N),
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C=TensorDef(U, S.M, S.N, output=True)):
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C[D.m, D.n] += TypeFn.cast(U, A[D.m, D.k]) * TypeFn.cast(U, B[D.k, D.n])
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# CHECK: ---
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# CHECK-LABEL: constants
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# CHECK: assignments:
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# CHECK: -
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# CHECK: arg: O
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# CHECK: arith_fn:
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# CHECK: fn_name: sub
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# CHECK: operands:
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# CHECK: arith_fn:
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# CHECK: fn_name: add
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# CHECK: operands:
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# CHECK: type_fn:
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# CHECK: type_var: T
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# CHECK: operands:
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# CHECK: scalar_const: '3.1415926535897931 : f64'
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# CHECK: type_fn:
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# CHECK: type_var: T
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# CHECK: operands:
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# CHECK: scalar_const: '42 : i64'
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# CHECK: type_fn:
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# CHECK: type_var: T
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# CHECK: operands:
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# CHECK: scalar_const: '1.{{[0]*}}e+03 : f64'
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@linalg_structured_op
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def constants(O=TensorDef(T, S.M, S.K, output=True)):
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pi = TypeFn.cast(T, const(3.1415926535897931))
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cst42 = TypeFn.cast(T, const(42))
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cst1000 = TypeFn.cast(T, const(1e+3))
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O[D.m, D.n] = pi + cst42 - cst1000
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# CHECK: ---
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# CHECK-LABEL: indices
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# CHECK: assignments:
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# CHECK: -
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# CHECK: arg: O
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# CHECK: arith_fn:
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# CHECK: fn_name: add
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# CHECK: operands:
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# CHECK: scalar_index: 1
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# CHECK: scalar_index: 0
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@linalg_structured_op
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def indices(O=TensorDef(T, S.M, S.K, output=True)):
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O[D.m, D.n] = index(D.n) + index(D.m)
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# CHECK: ---
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# CHECK-LABEL: fill
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# CHECK: assignments:
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# CHECK: -
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# CHECK: arg: O
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# CHECK: scalar_arg: value
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@linalg_structured_op
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def fill(value=ScalarDef(T), O=TensorDef(T, S.M, S.K, output=True)):
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O[D.m, D.n] = value
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