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# RUN: %PYTHON -m mlir.dialects.linalg.opdsl.dump_oplib --file %s | FileCheck %s
from mlir.dialects.linalg.opdsl.lang import *
# CHECK: ---
# CHECK-LABEL: matmul
# CHECK: args:
# CHECK: name: A
# CHECK: usage: InputOperand
# CHECK: type_var: T
# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
# CHECK: name: B
# CHECK: usage: InputOperand
# CHECK: type_var: T
# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s1, s2)>
# CHECK: name: C
# CHECK: usage: OutputOperand
# CHECK: type_var: U
# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
@linalg_structured_op
def matmul(
A=TensorDef(T, S.M, S.K),
B=TensorDef(T, S.K, S.N),
C=TensorDef(U, S.M, S.N, output=True)):
C[D.m, D.n] += TypeFn.cast(U, A[D.m, D.k]) * TypeFn.cast(U, B[D.k, D.n])
# CHECK: ---
# CHECK-LABEL: fill
# CHECK: args:
# CHECK: name: value
# CHECK: usage: InputOperand
# CHECK-NOT: shape_map:
# CHECK: type_var: T
@linalg_structured_op
def fill(value=ScalarDef(T), O=TensorDef(T, S.M, S.K, output=True)):
O[D.m, D.n] = value
# CHECK: ---
# CHECK-LABEL: strided_copy
# CHECK: args:
# CHECK: name: I
# CHECK: usage: InputOperand
# CHECK: type_var: T
# CHECK: shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s0, s1)>
# CHECK: name: O
# CHECK: usage: OutputOperand
# CHECK: type_var: T
# CHECK: shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s2, s3)>
# CHECK: name: strides
# CHECK: usage: IndexAttribute
# CHECK: type_var: I64
# CHECK: attribute_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s4, s5)>
@linalg_structured_op
def strided_copy(
I=TensorDef(T, S.IH, S.IW),
O=TensorDef(T, S.OH, S.OW, output=True),
strides=IndexAttrDef(S.SH, S.SW)):
O[D.oh, D.ow] = I[D.oh * S.SH, D.ow * S.SW]
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