Example
ad_basic.kina
Source
let f = fn(x: tensor<F32, [4, 4], @host>) -> tensor<F32, [4, 4], @host> {
let W = tensor<F32, [4, 4], @host> { 1.0 } in
let b = tensor<F32, [4, 4], @host> { 1.0 } in
let mm = W @ x in
let linear = mm + b in
let act = relu(linear) in
act
} in
let x_input = tensor<F32, [4, 4], @host> { 1.0 } in
handle f(x_input) with reverseMode
Emitted MLIR (golden)
module {
func.func @fn_val_0(%1: tensor<1x32x64xf32>, %2: tensor<1x1x64xf32>) -> tensor<1x32x64xf32> attributes {llvm.emit_c_interface} {
%3 = tensor.empty() : tensor<1x32x64xf32>
%4 = linalg.mul ins(%1, %1 : tensor<1x32x64xf32>, tensor<1x32x64xf32>) outs(%3 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%7 = arith.constant 0.000000e+00 : f32
%5 = tensor.empty() : tensor<1x32x1xf32>
%6 = linalg.fill ins(%7 : f32) outs(%5 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%8 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, 0)>],
iterator_types = ["parallel", "parallel", "reduction"]
} ins(%4 : tensor<1x32x64xf32>) outs(%6 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%9 = arith.addf %a0, %a1 : f32
linalg.yield %9 : f32
} -> tensor<1x32x1xf32>
%10 = arith.constant dense<0.000000e+00> : tensor<1x1x1xf32>
%11 = tensor.empty() : tensor<1x32x1xf32>
%12 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, 0, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%10 : tensor<1x1x1xf32>) outs(%11 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x1xf32>
%13 = tensor.empty() : tensor<1x32x1xf32>
%14 = linalg.mul ins(%8, %12 : tensor<1x32x1xf32>, tensor<1x32x1xf32>) outs(%13 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%15 = arith.constant dense<0.000000e+00> : tensor<1x1x1xf32>
%16 = tensor.empty() : tensor<1x32x1xf32>
%17 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, 0, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%15 : tensor<1x1x1xf32>) outs(%16 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x1xf32>
%18 = tensor.empty() : tensor<1x32x1xf32>
%19 = linalg.add ins(%14, %17 : tensor<1x32x1xf32>, tensor<1x32x1xf32>) outs(%18 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%23 = tensor.empty() : tensor<1x32x1xf32>
%24 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%19 : tensor<1x32x1xf32>) outs(%23 : tensor<1x32x1xf32>) {
^bb0(%x: f32, %_: f32):
%20 = math.sqrt %x : f32
%21 = arith.constant 1.000000e+00 : f32
%22 = arith.divf %21, %20 : f32
linalg.yield %22 : f32
} -> tensor<1x32x1xf32>
%25 = tensor.empty() : tensor<1x32x64xf32>
%26 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, d1, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%24 : tensor<1x32x1xf32>) outs(%25 : tensor<1x32x64xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x64xf32>
%27 = tensor.empty() : tensor<1x32x64xf32>
%28 = linalg.mul ins(%1, %26 : tensor<1x32x64xf32>, tensor<1x32x64xf32>) outs(%27 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%29 = tensor.empty() : tensor<1x32x64xf32>
%30 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, 0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%2 : tensor<1x1x64xf32>) outs(%29 : tensor<1x32x64xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x64xf32>
%31 = tensor.empty() : tensor<1x32x64xf32>
%32 = linalg.mul ins(%28, %30 : tensor<1x32x64xf32>, tensor<1x32x64xf32>) outs(%31 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
return %32 : tensor<1x32x64xf32>
}
func.func @fn_val_33(%34: tensor<1x32x64xf32>, %35: tensor<1x64x128xf32>, %36: tensor<1x128x64xf32>) -> tensor<1x32x64xf32> attributes {llvm.emit_c_interface} {
%37 = tensor.empty() : tensor<1x32x128xf32>
%38 = linalg.batch_matmul ins(%34, %35 : tensor<1x32x64xf32>, tensor<1x64x128xf32>) outs(%37 : tensor<1x32x128xf32>) -> tensor<1x32x128xf32>
%44 = tensor.empty() : tensor<1x32x128xf32>
%45 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%38 : tensor<1x32x128xf32>) outs(%44 : tensor<1x32x128xf32>) {
^bb0(%a0: f32, %a1: f32):
%39 = arith.constant 1.000000e+00 : f32
%40 = arith.negf %a0 : f32
%41 = math.exp %40 : f32
%42 = arith.addf %39, %41 : f32
%43 = arith.divf %39, %42 : f32
linalg.yield %43 : f32
} -> tensor<1x32x128xf32>
%46 = tensor.empty() : tensor<1x32x128xf32>
%47 = linalg.mul ins(%38, %45 : tensor<1x32x128xf32>, tensor<1x32x128xf32>) outs(%46 : tensor<1x32x128xf32>) -> tensor<1x32x128xf32>
%48 = tensor.empty() : tensor<1x32x64xf32>
%49 = linalg.batch_matmul ins(%47, %36 : tensor<1x32x128xf32>, tensor<1x128x64xf32>) outs(%48 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
return %49 : tensor<1x32x64xf32>
}
func.func @fn_val_50(%51: tensor<1x32x64xf32>, %52: tensor<1x64x64xf32>, %53: tensor<1x64x64xf32>, %54: tensor<1x64x64xf32>, %55: tensor<1x64x64xf32>) -> tensor<1x32x64xf32> attributes {llvm.emit_c_interface} {
%56 = tensor.empty() : tensor<1x32x64xf32>
%57 = linalg.batch_matmul ins(%51, %52 : tensor<1x32x64xf32>, tensor<1x64x64xf32>) outs(%56 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%58 = tensor.empty() : tensor<1x32x64xf32>
%59 = linalg.batch_matmul ins(%51, %53 : tensor<1x32x64xf32>, tensor<1x64x64xf32>) outs(%58 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%60 = tensor.empty() : tensor<1x32x64xf32>
%61 = linalg.batch_matmul ins(%51, %54 : tensor<1x32x64xf32>, tensor<1x64x64xf32>) outs(%60 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%62 = tensor.empty() : tensor<1x64x32xf32>
%63 = linalg.transpose ins(%59 : tensor<1x32x64xf32>) outs(%62 : tensor<1x64x32xf32>) permutation = [0, 2, 1]
%64 = tensor.empty() : tensor<1x32x32xf32>
%65 = linalg.batch_matmul ins(%57, %63 : tensor<1x32x64xf32>, tensor<1x64x32xf32>) outs(%64 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%66 = arith.constant dense<0.000000e+00> : tensor<1x1x1xf32>
%67 = tensor.empty() : tensor<1x32x32xf32>
%68 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, 0, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%66 : tensor<1x1x1xf32>) outs(%67 : tensor<1x32x32xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x32xf32>
%69 = tensor.empty() : tensor<1x32x32xf32>
%70 = linalg.mul ins(%65, %68 : tensor<1x32x32xf32>, tensor<1x32x32xf32>) outs(%69 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%73 = arith.constant 0xFF800000 : f32
%71 = tensor.empty() : tensor<1x32x1xf32>
%72 = linalg.fill ins(%73 : f32) outs(%71 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%74 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, 0)>],
iterator_types = ["parallel", "parallel", "reduction"]
} ins(%70 : tensor<1x32x32xf32>) outs(%72 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%75 = arith.maximumf %a0, %a1 : f32
linalg.yield %75 : f32
} -> tensor<1x32x1xf32>
%76 = tensor.empty() : tensor<1x32x32xf32>
%77 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, d1, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%74 : tensor<1x32x1xf32>) outs(%76 : tensor<1x32x32xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x32xf32>
%78 = tensor.empty() : tensor<1x32x32xf32>
%79 = linalg.sub ins(%70, %77 : tensor<1x32x32xf32>, tensor<1x32x32xf32>) outs(%78 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%80 = tensor.empty() : tensor<1x32x32xf32>
%81 = linalg.exp ins(%79 : tensor<1x32x32xf32>) outs(%80 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%84 = arith.constant 0xFF800000 : f32
%82 = tensor.empty() : tensor<1x32x1xf32>
%83 = linalg.fill ins(%84 : f32) outs(%82 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%85 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, 0)>],
iterator_types = ["parallel", "parallel", "reduction"]
} ins(%70 : tensor<1x32x32xf32>) outs(%83 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%86 = arith.maximumf %a0, %a1 : f32
linalg.yield %86 : f32
} -> tensor<1x32x1xf32>
%87 = tensor.empty() : tensor<1x32x32xf32>
%88 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, d1, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%85 : tensor<1x32x1xf32>) outs(%87 : tensor<1x32x32xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x32xf32>
%89 = tensor.empty() : tensor<1x32x32xf32>
%90 = linalg.sub ins(%70, %88 : tensor<1x32x32xf32>, tensor<1x32x32xf32>) outs(%89 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%91 = tensor.empty() : tensor<1x32x32xf32>
%92 = linalg.exp ins(%90 : tensor<1x32x32xf32>) outs(%91 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%95 = arith.constant 0.000000e+00 : f32
%93 = tensor.empty() : tensor<1x32x1xf32>
%94 = linalg.fill ins(%95 : f32) outs(%93 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%96 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, 0)>],
iterator_types = ["parallel", "parallel", "reduction"]
} ins(%92 : tensor<1x32x32xf32>) outs(%94 : tensor<1x32x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%97 = arith.addf %a0, %a1 : f32
linalg.yield %97 : f32
} -> tensor<1x32x1xf32>
%98 = tensor.empty() : tensor<1x32x32xf32>
%99 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (0, d1, 0)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%96 : tensor<1x32x1xf32>) outs(%98 : tensor<1x32x32xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x32x32xf32>
%100 = tensor.empty() : tensor<1x32x32xf32>
%101 = linalg.div ins(%81, %99 : tensor<1x32x32xf32>, tensor<1x32x32xf32>) outs(%100 : tensor<1x32x32xf32>) -> tensor<1x32x32xf32>
%102 = tensor.empty() : tensor<1x32x64xf32>
%103 = linalg.batch_matmul ins(%101, %61 : tensor<1x32x32xf32>, tensor<1x32x64xf32>) outs(%102 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%104 = tensor.empty() : tensor<1x32x64xf32>
%105 = linalg.batch_matmul ins(%103, %55 : tensor<1x32x64xf32>, tensor<1x64x64xf32>) outs(%104 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
return %105 : tensor<1x32x64xf32>
}
func.func @fn_val_106(%107: tensor<1x32x64xf32>, %108: tensor<1x1x64xf32>, %109: tensor<1x64x64xf32>, %110: tensor<1x64x64xf32>, %111: tensor<1x64x64xf32>, %112: tensor<1x64x64xf32>, %113: tensor<1x1x64xf32>, %114: tensor<1x64x128xf32>, %115: tensor<1x128x64xf32>) -> tensor<1x32x64xf32> attributes {llvm.emit_c_interface} {
%116 = func.call @fn_val_0(%107, %108) : (tensor<1x32x64xf32>, tensor<1x1x64xf32>) -> tensor<1x32x64xf32>
%117 = func.call @fn_val_50(%116, %109, %110, %111, %112) : (tensor<1x32x64xf32>, tensor<1x64x64xf32>, tensor<1x64x64xf32>, tensor<1x64x64xf32>, tensor<1x64x64xf32>) -> tensor<1x32x64xf32>
%118 = tensor.empty() : tensor<1x32x64xf32>
%119 = linalg.add ins(%107, %117 : tensor<1x32x64xf32>, tensor<1x32x64xf32>) outs(%118 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
%120 = func.call @fn_val_0(%119, %113) : (tensor<1x32x64xf32>, tensor<1x1x64xf32>) -> tensor<1x32x64xf32>
%121 = func.call @fn_val_33(%120, %114, %115) : (tensor<1x32x64xf32>, tensor<1x64x128xf32>, tensor<1x128x64xf32>) -> tensor<1x32x64xf32>
%122 = tensor.empty() : tensor<1x32x64xf32>
%123 = linalg.add ins(%119, %121 : tensor<1x32x64xf32>, tensor<1x32x64xf32>) outs(%122 : tensor<1x32x64xf32>) -> tensor<1x32x64xf32>
return %123 : tensor<1x32x64xf32>
}
func.func @fn_val_124(%125: tensor<4x4xf32>) -> tensor<4x4xf32> attributes {llvm.emit_c_interface} {
%126 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%127 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%128 = tensor.empty() : tensor<4x4xf32>
%129 = linalg.matmul ins(%126, %125 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%128 : tensor<4x4xf32>) -> tensor<4x4xf32>
%130 = tensor.empty() : tensor<4x4xf32>
%131 = linalg.add ins(%129, %127 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%130 : tensor<4x4xf32>) -> tensor<4x4xf32>
%132 = arith.constant 0.000000e+00 : f32
%135 = tensor.empty() : tensor<4x4xf32>
%133 = linalg.fill ins(%132 : f32) outs(%135 : tensor<4x4xf32>) -> tensor<4x4xf32>
%134 = tensor.empty() : tensor<4x4xf32>
%136 = linalg.max ins(%131, %133 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%134 : tensor<4x4xf32>) -> tensor<4x4xf32>
return %136 : tensor<4x4xf32>
}
func.func @main() {
%137 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%138 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%139 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%140 = tensor.empty() : tensor<4x4xf32>
%141 = linalg.matmul ins(%138, %137 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%140 : tensor<4x4xf32>) -> tensor<4x4xf32>
%142 = tensor.empty() : tensor<4x4xf32>
%143 = linalg.add ins(%141, %139 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%142 : tensor<4x4xf32>) -> tensor<4x4xf32>
%144 = arith.constant 0.000000e+00 : f32
%147 = tensor.empty() : tensor<4x4xf32>
%145 = linalg.fill ins(%144 : f32) outs(%147 : tensor<4x4xf32>) -> tensor<4x4xf32>
%146 = tensor.empty() : tensor<4x4xf32>
%148 = linalg.max ins(%143, %145 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%146 : tensor<4x4xf32>) -> tensor<4x4xf32>
%149 = arith.constant dense<1.000000e+00> : tensor<4x4xf32>
%150 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%151 = tensor.empty() : tensor<4x4xi1>
%152 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} ins(%143, %150 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%151 : tensor<4x4xi1>) {
^bb0(%x: f32, %y: f32, %_: i1):
%153 = arith.cmpf ogt, %x, %y : f32
linalg.yield %153 : i1
} -> tensor<4x4xi1>
%154 = arith.extui %152 : tensor<4x4xi1> to tensor<4x4xi32>
%155 = arith.sitofp %154 : tensor<4x4xi32> to tensor<4x4xf32>
%156 = arith.constant dense<1.000000e+00> : tensor<4x4xf32>
%157 = tensor.empty() : tensor<4x4xf32>
%158 = linalg.mul ins(%156, %155 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%157 : tensor<4x4xf32>) -> tensor<4x4xf32>
%159 = tensor.empty() : tensor<4x4xf32>
%160 = linalg.transpose ins(%138 : tensor<4x4xf32>) outs(%159 : tensor<4x4xf32>) permutation = [1, 0]
%161 = tensor.empty() : tensor<4x4xf32>
%162 = linalg.matmul ins(%160, %158 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%161 : tensor<4x4xf32>) -> tensor<4x4xf32>
%163 = tensor.empty() : tensor<4x4xf32>
%164 = linalg.transpose ins(%137 : tensor<4x4xf32>) outs(%163 : tensor<4x4xf32>) permutation = [1, 0]
%165 = tensor.empty() : tensor<4x4xf32>
%166 = linalg.matmul ins(%158, %164 : tensor<4x4xf32>, tensor<4x4xf32>) outs(%165 : tensor<4x4xf32>) -> tensor<4x4xf32>
return
}
}