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func.func @depthwise_conv_1d_nwc_wcm(%input: tensor<1x12x8xf32>, %filter: tensor<3x8x8xf32>)
// <- function.builtin
// ^ function
// ^ variable.parameter
// ^ type.builtin
// ^ variable.parameter
// ^ type.builtin
-> tensor<1x10x8x8xf32> {
// ^ operator
// ^ type.builtin
%zero = arith.constant 0.000000e+00 : f32
// ^ variable
// ^ function.builtin
// ^ number
// ^ type.builtin
%init = tensor.empty() : tensor<1x10x8x8xf32>
// ^ variable
// ^ function.builtin
// ^ type.builtin
%fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
// ^ variable
// ^ function.builtin
// ^ keyword
// ^ variable
// ^ type.builtin
// ^ keyword
%0 = linalg.depthwise_conv_1d_nwc_wcm {dilations = dense<1> : tensor<1xi64>,
// ^ variable
// ^ function.builtin
// ^ attribute
// ^ constant.builtin
strides = dense<1> : tensor<1xi64>}
// ^ constant.builtin
ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8x8xf32>)
// ^ variable.parameter
// ^ variable.parameter
outs(%fill : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
// ^ variable
return %0 : tensor<1x10x8x8xf32>
// ^ function.builtin
// ^ variable
}
func.func @fastmath(%arg0: f32, %arg1: f32) {
// <- function.builtin
// ^ function
// ^ variable.parameter
// ^ type.builtin
// ^ variable.parameter
// ^ type.builtin
%5 = arith.negf %arg0 fastmath<fast> : f32
// ^ function.builtin
// ^ attribute
%6 = arith.addf %arg0, %arg1 fastmath<none> : f32
// ^ function.builtin
// ^ attribute
%8 = arith.mulf %arg0, %arg1 fastmath<reassoc,nnan,ninf,nsz,arcp,contract,afn> : f32
// ^ function.builtin
// ^ attribute
return
// ^ function.builtin
}
#map0 = affine_map<(d0, d1) -> (d0, d1)>
// <- attribute
// ^ attribute
#map1 = affine_map<(d0, d1) -> (d0)>
// <- attribute
// ^ attribute
#map2 = affine_map<(d0) -> (d0)>
// <- attribute
// ^ attribute
func.func @add_broadcast_mul_fusion(%arg0: tensor<?xf32>, %arg1 : tensor<?xf32>,
%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
{
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = tensor.dim %arg0, %c0 : tensor<?xf32>
%1 = tensor.empty(%0) : tensor<?xf32>
%2 = linalg.generic {indexing_maps = [#map2, #map2, #map2], iterator_types = ["parallel"]}
// ^ attribute
// ^ attribute
// ^ attribute
ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
// ^ keyword
outs(%1 : tensor<?xf32>) {
// ^ keyword
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
%3 = arith.addf %arg3, %arg4 : f32
linalg.yield %3 : f32
} -> tensor<?xf32>
%3 = tensor.dim %arg2, %c1 : tensor<?x?xf32>
%4 = tensor.empty(%0, %3) : tensor<?x?xf32>
%5 = linalg.generic {indexing_maps = [#map1, #map0, #map0], iterator_types = ["parallel", "parallel"]}
// ^ function.builtin
ins(%2, %arg2 : tensor<?xf32>, tensor<?x?xf32>)
outs(%4 : tensor<?x?xf32>){
^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
%6 = arith.mulf %arg5, %arg6 : f32
linalg.yield %6 : f32
} -> tensor<?x?xf32>
return %5 : tensor<?x?xf32>
}
func.func @broadcast(%input: tensor<8x32xf32>,
%init: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
%bcast = linalg.broadcast
// ^ function.builtin
ins(%input:tensor<8x32xf32>)
// ^ keyword
outs(%init:tensor<8x16x32xf32>)
// ^ keyword
dimensions = [1]
// ^ attribute
func.return %bcast : tensor<8x16x32xf32>
}
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