.triton/dump/63ac7476060ddeef758fa13ad6ed58f5/triton_.ttir

module {
  tt.func public @triton__0d1d2d3d4d5d6de7de(%arg0: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<bf16, 1> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg4: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<bf16, 1> {tt.divisibility = 16 : i32}, %arg6: i32 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}) attributes {noinline = false} {
    %c256_i32 = arith.constant 256 : i32
    %cst = arith.constant dense<0.000000e+00> : tensor<256xbf16>
    %cst_0 = arith.constant 0.000000e+00 : f32
    %cst_1 = arith.constant dense<0.000000e+00> : tensor<256xf32>
    %cst_2 = arith.constant dense<2.560000e+02> : tensor<256xf32>
    %cst_3 = arith.constant dense<2.560000e+02> : tensor<1xf32>
    %cst_4 = arith.constant dense<256> : tensor<256xi32>
    %0 = tt.get_program_id x : i32
    %1 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32>
    %2 = arith.cmpi slt, %1, %cst_4 : tensor<256xi32>
    %3 = arith.muli %0, %c256_i32 : i32
    %4 = tt.splat %3 : (i32) -> tensor<256xi32>
    %5 = arith.addi %1, %4 : tensor<256xi32>
    %6 = tt.splat %arg1 : (!tt.ptr<bf16, 1>) -> tensor<256x!tt.ptr<bf16, 1>>
    %7 = tt.addptr %6, %5 : tensor<256x!tt.ptr<bf16, 1>>, tensor<256xi32>
    %8 = tt.load %7, %2, %cst {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<256xbf16>
    %9 = arith.extf %8 : tensor<256xbf16> to tensor<256xf32>
    %10 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>>
    %11 = tt.addptr %10, %1 : tensor<256x!tt.ptr<f32, 1>>, tensor<256xi32>
    %12 = tt.load %11, %2, %cst_1 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<256xf32>
    %13 = tt.splat %arg3 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>>
    %14 = tt.addptr %13, %5 : tensor<256x!tt.ptr<f32, 1>>, tensor<256xi32>
    %15 = tt.load %14, %2, %cst_1 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<256xf32>
    %16 = tt.splat %arg0 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>>
    %17 = tt.addptr %16, %5 : tensor<256x!tt.ptr<f32, 1>>, tensor<256xi32>
    %18 = tt.load %17, %2, %cst_1 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<256xf32>
    %19 = tt.addptr %arg4, %0 : !tt.ptr<f32, 1>, i32
    %20 = tt.splat %19 : (!tt.ptr<f32, 1>) -> tensor<1x!tt.ptr<f32, 1>>
    %21 = tt.load %20 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1xf32>
    %22 = arith.mulf %9, %12 : tensor<256xf32>
    %23 = arith.select %2, %22, %cst_1 : tensor<256xi1>, tensor<256xf32>
    %24 = "tt.reduce"(%23) <{axis = 0 : i32}> ({
    ^bb0(%arg8: f32, %arg9: f32):
      %43 = arith.addf %arg8, %arg9 : f32
      tt.reduce.return %43 : f32
    }) : (tensor<256xf32>) -> f32
    %25 = arith.addf %24, %cst_0 : f32
    %26 = arith.mulf %22, %15 : tensor<256xf32>
    %27 = arith.select %2, %26, %cst_1 : tensor<256xi1>, tensor<256xf32>
    %28 = "tt.reduce"(%27) <{axis = 0 : i32}> ({
    ^bb0(%arg8: f32, %arg9: f32):
      %43 = arith.addf %arg8, %arg9 : f32
      tt.reduce.return %43 : f32
    }) : (tensor<256xf32>) -> f32
    %29 = arith.addf %28, %cst_0 : f32
    %30 = arith.divf %21, %cst_3 : tensor<1xf32>
    %31 = arith.mulf %22, %cst_2 : tensor<256xf32>
    %32 = tt.splat %25 : (f32) -> tensor<256xf32>
    %33 = arith.subf %31, %32 : tensor<256xf32>
    %34 = tt.splat %29 : (f32) -> tensor<256xf32>
    %35 = arith.mulf %15, %34 : tensor<256xf32>
    %36 = arith.subf %33, %35 : tensor<256xf32>
    %37 = tt.broadcast %30 : (tensor<1xf32>) -> tensor<256xf32>
    %38 = arith.mulf %37, %36 : tensor<256xf32>
    %39 = arith.addf %18, %38 : tensor<256xf32>
    tt.store %17, %39, %2 {cache = 1 : i32, evict = 1 : i32} : tensor<256xf32>
    %40 = tt.splat %arg5 : (!tt.ptr<bf16, 1>) -> tensor<256x!tt.ptr<bf16, 1>>
    %41 = tt.addptr %40, %5 : tensor<256x!tt.ptr<bf16, 1>>, tensor<256xi32>
    %42 = arith.truncf %39 : tensor<256xf32> to tensor<256xbf16>
    tt.store %41, %42, %2 {cache = 1 : i32, evict = 1 : i32} : tensor<256xbf16>
    tt.return
  }
}