Example
storylm0_forward.kina
Source
let storylm0_forward = fn(
token_ids: tensor<I64, [1, 4], @host>,
token_embedding: tensor<F32, [8, 4], @host>,
causal_mask: tensor<F32, [1, 1, 4, 4], @host>,
attention_scale: tensor<F32, [1, 1, 1, 1], @host>,
wq: tensor<F32, [4, 4], @host>,
wk: tensor<F32, [4, 4], @host>,
wv: tensor<F32, [4, 4], @host>,
wo: tensor<F32, [4, 4], @host>,
lm_head: tensor<F32, [4, 8], @host>
) -> tensor<F32, [1, 4, 8], @host> {
let x = token_embedding.gather(token_ids) in
// Matmul never broadcasts implicitly; parameters use canonical 2D layouts.
let wq_broadcast = wq.broadcast([1, 4, 4]) in
let wk_broadcast = wk.broadcast([1, 4, 4]) in
let wv_broadcast = wv.broadcast([1, 4, 4]) in
let wo_broadcast = wo.broadcast([1, 4, 4]) in
let lm_head_broadcast = lm_head.broadcast([1, 4, 8]) in
// Split D=4 into H=2 heads of Dh=2.
let q_flat = x @ wq_broadcast in
let k_flat = x @ wk_broadcast in
let v_flat = x @ wv_broadcast in
let q = q_flat.reshape([1, 4, 2, 2]).transpose([0, 2, 1, 3]) in
let k_t = k_flat.reshape([1, 4, 2, 2]).transpose([0, 2, 3, 1]) in
let v = v_flat.reshape([1, 4, 2, 2]).transpose([0, 2, 1, 3]) in
// Both attention matmuls are rank 4 with leading batch/head dimensions.
let scores = q @ k_t in
let scale_broadcast = attention_scale.broadcast([1, 2, 4, 4]) in
let mask_broadcast = causal_mask.broadcast([1, 2, 4, 4]) in
let masked_scores = (scores * scale_broadcast) + mask_broadcast in
let probs = masked_scores.softmax(axis=3) in
let context = probs @ v in
let merged = context.transpose([0, 2, 1, 3]).reshape([1, 4, 4]) in
let projected = merged @ wo_broadcast in
let residual = x + projected in
let logits = residual @ lm_head_broadcast in
logits
} in
// Host placeholders keep the example compile-only. The numerical validator
// invokes the emitted C wrapper directly with non-uniform tensor contents.
let token_ids = tensor<I64, [1, 4], @host> in
let token_embedding = tensor<F32, [8, 4], @host> in
let causal_mask = tensor<F32, [1, 1, 4, 4], @host> in
let attention_scale = tensor<F32, [1, 1, 1, 1], @host> in
let wq = tensor<F32, [4, 4], @host> in
let wk = tensor<F32, [4, 4], @host> in
let wv = tensor<F32, [4, 4], @host> in
let wo = tensor<F32, [4, 4], @host> in
let lm_head = tensor<F32, [4, 8], @host> in
storylm0_forward(token_ids, token_embedding, causal_mask, attention_scale, wq, wk, wv, wo, lm_head)
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<1x4xi64>, %126: tensor<8x4xf32>, %127: tensor<1x1x4x4xf32>, %128: tensor<1x1x1x1xf32>, %129: tensor<4x4xf32>, %130: tensor<4x4xf32>, %131: tensor<4x4xf32>, %132: tensor<4x4xf32>, %133: tensor<4x8xf32>) -> tensor<1x4x8xf32> attributes {llvm.emit_c_interface} {
%134 = arith.constant 0 : i64
%135 = arith.constant 8 : i64
%136 = tensor.empty() : tensor<1x4xi64>
%137 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} ins(%125 : tensor<1x4xi64>) outs(%136 : tensor<1x4xi64>) {
^bb0(%candidate: i64, %_: i64):
%138 = arith.cmpi sge, %candidate, %134 : i64
%139 = arith.cmpi slt, %candidate, %135 : i64
%140 = arith.andi %138, %139 : i1
cf.assert %140, "gather index out of bounds"
linalg.yield %candidate : i64
} -> tensor<1x4xi64>
%141 = tensor.empty() : tensor<1x4x4xf32>
%142 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%137 : tensor<1x4xi64>) outs(%141 : tensor<1x4x4xf32>) {
^bb0(%idx: i64, %_: f32):
%143 = arith.index_cast %idx : i64 to index
%144 = linalg.index 2 : index
%145 = tensor.extract %126[%143, %144] : tensor<8x4xf32>
linalg.yield %145 : f32
} -> tensor<1x4x4xf32>
%146 = tensor.empty() : tensor<1x4x4xf32>
%147 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%129 : tensor<4x4xf32>) outs(%146 : tensor<1x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x4x4xf32>
%148 = tensor.empty() : tensor<1x4x4xf32>
%149 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%130 : tensor<4x4xf32>) outs(%148 : tensor<1x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x4x4xf32>
%150 = tensor.empty() : tensor<1x4x4xf32>
%151 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%131 : tensor<4x4xf32>) outs(%150 : tensor<1x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x4x4xf32>
%152 = tensor.empty() : tensor<1x4x4xf32>
%153 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%132 : tensor<4x4xf32>) outs(%152 : tensor<1x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x4x4xf32>
%154 = tensor.empty() : tensor<1x4x8xf32>
%155 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]
} ins(%133 : tensor<4x8xf32>) outs(%154 : tensor<1x4x8xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x4x8xf32>
%156 = tensor.empty() : tensor<1x4x4xf32>
%157 = linalg.batch_matmul ins(%142, %147 : tensor<1x4x4xf32>, tensor<1x4x4xf32>) outs(%156 : tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
%158 = tensor.empty() : tensor<1x4x4xf32>
%159 = linalg.batch_matmul ins(%142, %149 : tensor<1x4x4xf32>, tensor<1x4x4xf32>) outs(%158 : tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
%160 = tensor.empty() : tensor<1x4x4xf32>
%161 = linalg.batch_matmul ins(%142, %151 : tensor<1x4x4xf32>, tensor<1x4x4xf32>) outs(%160 : tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
%162 = arith.constant dense<[1, 4, 2, 2]> : tensor<4xi32>
%163 = tensor.reshape %157(%162) : (tensor<1x4x4xf32>, tensor<4xi32>) -> tensor<1x4x2x2xf32>
%164 = tensor.empty() : tensor<1x2x4x2xf32>
%165 = linalg.transpose ins(%163 : tensor<1x4x2x2xf32>) outs(%164 : tensor<1x2x4x2xf32>) permutation = [0, 2, 1, 3]
%166 = arith.constant dense<[1, 4, 2, 2]> : tensor<4xi32>
%167 = tensor.reshape %159(%166) : (tensor<1x4x4xf32>, tensor<4xi32>) -> tensor<1x4x2x2xf32>
%168 = tensor.empty() : tensor<1x2x2x4xf32>
%169 = linalg.transpose ins(%167 : tensor<1x4x2x2xf32>) outs(%168 : tensor<1x2x2x4xf32>) permutation = [0, 2, 3, 1]
%170 = arith.constant dense<[1, 4, 2, 2]> : tensor<4xi32>
%171 = tensor.reshape %161(%170) : (tensor<1x4x4xf32>, tensor<4xi32>) -> tensor<1x4x2x2xf32>
%172 = tensor.empty() : tensor<1x2x4x2xf32>
%173 = linalg.transpose ins(%171 : tensor<1x4x2x2xf32>) outs(%172 : tensor<1x2x4x2xf32>) permutation = [0, 2, 1, 3]
%174 = tensor.collapse_shape %165 [[0, 1], [2], [3]] : tensor<1x2x4x2xf32> into tensor<2x4x2xf32>
%175 = tensor.collapse_shape %169 [[0, 1], [2], [3]] : tensor<1x2x2x4xf32> into tensor<2x2x4xf32>
%176 = tensor.empty() : tensor<2x4x4xf32>
%177 = linalg.batch_matmul ins(%174, %175 : tensor<2x4x2xf32>, tensor<2x2x4xf32>) outs(%176 : tensor<2x4x4xf32>) -> tensor<2x4x4xf32>
%178 = tensor.expand_shape %177 [[0, 1], [2], [3]] : tensor<2x4x4xf32> into tensor<1x2x4x4xf32>
%179 = tensor.empty() : tensor<1x2x4x4xf32>
%180 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0, 0, 0, 0)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
} ins(%128 : tensor<1x1x1x1xf32>) outs(%179 : tensor<1x2x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x2x4x4xf32>
%181 = tensor.empty() : tensor<1x2x4x4xf32>
%182 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0, 0, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
} ins(%127 : tensor<1x1x4x4xf32>) outs(%181 : tensor<1x2x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x2x4x4xf32>
%183 = tensor.empty() : tensor<1x2x4x4xf32>
%184 = linalg.mul ins(%178, %180 : tensor<1x2x4x4xf32>, tensor<1x2x4x4xf32>) outs(%183 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%185 = tensor.empty() : tensor<1x2x4x4xf32>
%186 = linalg.add ins(%184, %182 : tensor<1x2x4x4xf32>, tensor<1x2x4x4xf32>) outs(%185 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%189 = arith.constant 0xFF800000 : f32
%187 = tensor.empty() : tensor<1x2x4x1xf32>
%188 = linalg.fill ins(%189 : f32) outs(%187 : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>
%190 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>],
iterator_types = ["parallel", "parallel", "parallel", "reduction"]
} ins(%186 : tensor<1x2x4x4xf32>) outs(%188 : tensor<1x2x4x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%191 = arith.maximumf %a0, %a1 : f32
linalg.yield %191 : f32
} -> tensor<1x2x4x1xf32>
%192 = tensor.empty() : tensor<1x2x4x4xf32>
%193 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0, d1, d2, 0)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
} ins(%190 : tensor<1x2x4x1xf32>) outs(%192 : tensor<1x2x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x2x4x4xf32>
%194 = tensor.empty() : tensor<1x2x4x4xf32>
%195 = linalg.sub ins(%186, %193 : tensor<1x2x4x4xf32>, tensor<1x2x4x4xf32>) outs(%194 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%196 = tensor.empty() : tensor<1x2x4x4xf32>
%197 = linalg.exp ins(%195 : tensor<1x2x4x4xf32>) outs(%196 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%200 = arith.constant 0xFF800000 : f32
%198 = tensor.empty() : tensor<1x2x4x1xf32>
%199 = linalg.fill ins(%200 : f32) outs(%198 : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>
%201 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>],
iterator_types = ["parallel", "parallel", "parallel", "reduction"]
} ins(%186 : tensor<1x2x4x4xf32>) outs(%199 : tensor<1x2x4x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%202 = arith.maximumf %a0, %a1 : f32
linalg.yield %202 : f32
} -> tensor<1x2x4x1xf32>
%203 = tensor.empty() : tensor<1x2x4x4xf32>
%204 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0, d1, d2, 0)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
} ins(%201 : tensor<1x2x4x1xf32>) outs(%203 : tensor<1x2x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x2x4x4xf32>
%205 = tensor.empty() : tensor<1x2x4x4xf32>
%206 = linalg.sub ins(%186, %204 : tensor<1x2x4x4xf32>, tensor<1x2x4x4xf32>) outs(%205 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%207 = tensor.empty() : tensor<1x2x4x4xf32>
%208 = linalg.exp ins(%206 : tensor<1x2x4x4xf32>) outs(%207 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%211 = arith.constant 0.000000e+00 : f32
%209 = tensor.empty() : tensor<1x2x4x1xf32>
%210 = linalg.fill ins(%211 : f32) outs(%209 : tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>
%212 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>],
iterator_types = ["parallel", "parallel", "parallel", "reduction"]
} ins(%208 : tensor<1x2x4x4xf32>) outs(%210 : tensor<1x2x4x1xf32>) {
^bb0(%a0: f32, %a1: f32):
%213 = arith.addf %a0, %a1 : f32
linalg.yield %213 : f32
} -> tensor<1x2x4x1xf32>
%214 = tensor.empty() : tensor<1x2x4x4xf32>
%215 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (0, d1, d2, 0)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
} ins(%212 : tensor<1x2x4x1xf32>) outs(%214 : tensor<1x2x4x4xf32>) {
^bb0(%a0: f32, %a1: f32):
linalg.yield %a0 : f32
} -> tensor<1x2x4x4xf32>
%216 = tensor.empty() : tensor<1x2x4x4xf32>
%217 = linalg.div ins(%197, %215 : tensor<1x2x4x4xf32>, tensor<1x2x4x4xf32>) outs(%216 : tensor<1x2x4x4xf32>) -> tensor<1x2x4x4xf32>
%218 = tensor.collapse_shape %217 [[0, 1], [2], [3]] : tensor<1x2x4x4xf32> into tensor<2x4x4xf32>
%219 = tensor.collapse_shape %173 [[0, 1], [2], [3]] : tensor<1x2x4x2xf32> into tensor<2x4x2xf32>
%220 = tensor.empty() : tensor<2x4x2xf32>
%221 = linalg.batch_matmul ins(%218, %219 : tensor<2x4x4xf32>, tensor<2x4x2xf32>) outs(%220 : tensor<2x4x2xf32>) -> tensor<2x4x2xf32>
%222 = tensor.expand_shape %221 [[0, 1], [2], [3]] : tensor<2x4x2xf32> into tensor<1x2x4x2xf32>
%223 = tensor.empty() : tensor<1x4x2x2xf32>
%224 = linalg.transpose ins(%222 : tensor<1x2x4x2xf32>) outs(%223 : tensor<1x4x2x2xf32>) permutation = [0, 2, 1, 3]
%225 = arith.constant dense<[1, 4, 4]> : tensor<3xi32>
%226 = tensor.reshape %224(%225) : (tensor<1x4x2x2xf32>, tensor<3xi32>) -> tensor<1x4x4xf32>
%227 = tensor.empty() : tensor<1x4x4xf32>
%228 = linalg.batch_matmul ins(%226, %153 : tensor<1x4x4xf32>, tensor<1x4x4xf32>) outs(%227 : tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
%229 = tensor.empty() : tensor<1x4x4xf32>
%230 = linalg.add ins(%142, %228 : tensor<1x4x4xf32>, tensor<1x4x4xf32>) outs(%229 : tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
%231 = tensor.empty() : tensor<1x4x8xf32>
%232 = linalg.batch_matmul ins(%230, %155 : tensor<1x4x4xf32>, tensor<1x4x8xf32>) outs(%231 : tensor<1x4x8xf32>) -> tensor<1x4x8xf32>
return %232 : tensor<1x4x8xf32>
}
func.func @main() -> tensor<1x4x8xf32> {
%233 = arith.constant dense<0> : tensor<1x4xi64>
%234 = arith.constant dense<0.000000e+00> : tensor<8x4xf32>
%235 = arith.constant dense<0.000000e+00> : tensor<1x1x4x4xf32>
%236 = arith.constant dense<0.000000e+00> : tensor<1x1x1x1xf32>
%237 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%238 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%239 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%240 = arith.constant dense<0.000000e+00> : tensor<4x4xf32>
%241 = arith.constant dense<0.000000e+00> : tensor<4x8xf32>
%242 = func.call @fn_val_124(%233, %234, %235, %236, %237, %238, %239, %240, %241) : (tensor<1x4xi64>, tensor<8x4xf32>, tensor<1x1x4x4xf32>, tensor<1x1x1x1xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x8xf32>) -> tensor<1x4x8xf32>
return %242 : tensor<1x4x8xf32>
}
}