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#blocked = #triton_gpu.blocked<{sizePerThread = [4], threadsPerWarp = [32], warpsPerCTA = [2], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}>
#blocked1 = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [32], warpsPerCTA = [2], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}>
module attributes {"triton_gpu.compute-capability" = 89 : i32, "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 2 : i32, "triton_gpu.threads-per-warp" = 32 : i32} {
tt.func public @triton__0d1d2d3d4d5d6d7d8de9de(%arg0: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<i64, 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<f32, 1> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<bf16, 1> {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}) attributes {noinline = false} {
%cst = arith.constant dense<256> : tensor<256xi32, #blocked>
%cst_0 = arith.constant dense<0> : tensor<1xi64, #blocked>
%cst_1 = arith.constant dense<50257> : tensor<1xi64, #blocked>
%cst_2 = arith.constant dense<256> : tensor<1xi64, #blocked>
%cst_3 = arith.constant 9.99999974E-6 : f32
%cst_4 = arith.constant 2.560000e+02 : f32
%cst_5 = arith.constant 0.000000e+00 : f32
%c256_i32 = arith.constant 256 : i32
%c512_i32 = arith.constant 512 : i32
%cst_6 = arith.constant dense<50257> : tensor<1xi64, #blocked1>
%cst_7 = arith.constant dense<0> : tensor<1xi64, #blocked1>
%cst_8 = arith.constant dense<0.000000e+00> : tensor<256xf32, #blocked>
%0 = tt.get_program_id x : i32
%1 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32, #blocked>
%2 = arith.cmpi slt, %1, %cst : tensor<256xi32, #blocked>
%3 = arith.remsi %0, %c512_i32 : i32
%4 = tt.addptr %arg1, %0 : !tt.ptr<i64, 1>, i32
%5 = tt.splat %4 : (!tt.ptr<i64, 1>) -> tensor<1x!tt.ptr<i64, 1>, #blocked>
%6 = tt.splat %4 : (!tt.ptr<i64, 1>) -> tensor<1x!tt.ptr<i64, 1>, #blocked1>
%7 = tt.load %5 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1xi64, #blocked>
%8 = tt.load %6 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1xi64, #blocked1>
%9 = arith.muli %3, %c256_i32 : i32
%10 = tt.splat %9 : (i32) -> tensor<256xi32, #blocked>
%11 = arith.addi %1, %10 : tensor<256xi32, #blocked>
%12 = tt.splat %arg3 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>, #blocked>
%13 = tt.addptr %12, %11 : tensor<256x!tt.ptr<f32, 1>, #blocked>, tensor<256xi32, #blocked>
%14 = tt.load %13, %2, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<256xf32, #blocked>
%15 = tt.splat %arg4 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>, #blocked>
%16 = tt.addptr %15, %1 : tensor<256x!tt.ptr<f32, 1>, #blocked>, tensor<256xi32, #blocked>
%17 = tt.load %16, %2, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<256xf32, #blocked>
%18 = arith.addi %7, %cst_1 : tensor<1xi64, #blocked>
%19 = arith.addi %8, %cst_6 : tensor<1xi64, #blocked1>
%20 = arith.cmpi slt, %7, %cst_0 : tensor<1xi64, #blocked>
%21 = arith.cmpi slt, %8, %cst_7 : tensor<1xi64, #blocked1>
%22 = arith.select %20, %18, %7 : tensor<1xi1, #blocked>, tensor<1xi64, #blocked>
%23 = arith.select %21, %19, %8 : tensor<1xi1, #blocked1>, tensor<1xi64, #blocked1>
%24 = arith.cmpi sge, %23, %cst_7 : tensor<1xi64, #blocked1>
%25 = arith.cmpi slt, %23, %cst_6 : tensor<1xi64, #blocked1>
%26 = arith.andi %24, %25 : tensor<1xi1, #blocked1>
tt.assert %26, "index out of bounds: 0 <= tmp3 < 50257", "<frozen importlib._bootstrap_external>", "_call_with_frames_removed", 883 : tensor<1xi1, #blocked1>
%27 = arith.muli %22, %cst_2 : tensor<1xi64, #blocked>
%28 = tt.broadcast %27 : (tensor<1xi64, #blocked>) -> tensor<256xi64, #blocked>
%29 = arith.extsi %1 : tensor<256xi32, #blocked> to tensor<256xi64, #blocked>
%30 = arith.addi %29, %28 : tensor<256xi64, #blocked>
%31 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>, #blocked>
%32 = tt.addptr %31, %30 : tensor<256x!tt.ptr<f32, 1>, #blocked>, tensor<256xi64, #blocked>
%33 = tt.load %32, %2, %cst_8 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<256xf32, #blocked>
%34 = arith.addf %33, %14 : tensor<256xf32, #blocked>
%35 = arith.select %2, %34, %cst_8 : tensor<256xi1, #blocked>, tensor<256xf32, #blocked>
%36 = "tt.reduce"(%35) <{axis = 0 : i32}> ({
^bb0(%arg10: f32, %arg11: f32):
%65 = arith.addf %arg10, %arg11 : f32
tt.reduce.return %65 : f32
}) : (tensor<256xf32, #blocked>) -> f32
%37 = arith.addf %36, %cst_5 : f32
%38 = arith.divf %37, %cst_4 : f32
%39 = tt.splat %38 : (f32) -> tensor<1xf32, #blocked1>
%40 = tt.splat %38 : (f32) -> tensor<256xf32, #blocked>
%41 = arith.subf %34, %40 : tensor<256xf32, #blocked>
%42 = arith.mulf %41, %41 : tensor<256xf32, #blocked>
%43 = arith.select %2, %42, %cst_8 : tensor<256xi1, #blocked>, tensor<256xf32, #blocked>
%44 = "tt.reduce"(%43) <{axis = 0 : i32}> ({
^bb0(%arg10: f32, %arg11: f32):
%65 = arith.addf %arg10, %arg11 : f32
tt.reduce.return %65 : f32
}) : (tensor<256xf32, #blocked>) -> f32
%45 = arith.addf %44, %cst_5 : f32
%46 = arith.divf %45, %cst_4 : f32
%47 = arith.addf %46, %cst_3 : f32
%48 = tt.extern_elementwise %47 {libname = "libdevice", libpath = "/usr/local/lib/python3.10/dist-packages/triton/language/../third_party/cuda/lib/libdevice.10.bc", pure = true, symbol = "__nv_rsqrtf"} : (f32) -> f32
%49 = tt.splat %48 : (f32) -> tensor<1xf32, #blocked1>
%50 = tt.splat %48 : (f32) -> tensor<256xf32, #blocked>
%51 = arith.mulf %41, %50 : tensor<256xf32, #blocked>
%52 = arith.mulf %51, %17 : tensor<256xf32, #blocked>
%53 = arith.muli %0, %c256_i32 : i32
%54 = tt.splat %53 : (i32) -> tensor<256xi32, #blocked>
%55 = arith.addi %1, %54 : tensor<256xi32, #blocked>
%56 = tt.splat %arg5 : (!tt.ptr<f32, 1>) -> tensor<256x!tt.ptr<f32, 1>, #blocked>
%57 = tt.addptr %56, %55 : tensor<256x!tt.ptr<f32, 1>, #blocked>, tensor<256xi32, #blocked>
tt.store %57, %34, %2 {cache = 1 : i32, evict = 1 : i32} : tensor<256xf32, #blocked>
gpu.barrier
%58 = tt.addptr %arg0, %0 : !tt.ptr<f32, 1>, i32
%59 = tt.splat %58 : (!tt.ptr<f32, 1>) -> tensor<1x!tt.ptr<f32, 1>, #blocked1>
tt.store %59, %49 {cache = 1 : i32, evict = 1 : i32} : tensor<1xf32, #blocked1>
%60 = tt.splat %arg7 : (!tt.ptr<bf16, 1>) -> tensor<256x!tt.ptr<bf16, 1>, #blocked>
%61 = tt.addptr %60, %55 : tensor<256x!tt.ptr<bf16, 1>, #blocked>, tensor<256xi32, #blocked>
%62 = arith.truncf %52 : tensor<256xf32, #blocked> to tensor<256xbf16, #blocked>
tt.store %61, %62, %2 {cache = 1 : i32, evict = 1 : i32} : tensor<256xbf16, #blocked>
%63 = tt.addptr %arg6, %0 : !tt.ptr<f32, 1>, i32
%64 = tt.splat %63 : (!tt.ptr<f32, 1>) -> tensor<1x!tt.ptr<f32, 1>, #blocked1>
tt.store %64, %39 {cache = 1 : i32, evict = 1 : i32} : tensor<1xf32, #blocked1>
tt.return
}
}
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