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.triton/dump/76fb48b96c75cb8e388c291a18ef9b02/triton_.ttir

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+module {
+  tt.func public @triton__0d1d2d3d4d5d6de7de(%arg0: !tt.ptr<i64, 1> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg3: !tt.ptr<bf16, 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} {
+    %cst = arith.constant dense<0.000000e+00> : tensor<2x128xbf16>
+    %cst_0 = arith.constant 0.000000e+00 : f32
+    %cst_1 = arith.constant dense<1.000000e+00> : tensor<2x128xf32>
+    %c256_i32 = arith.constant 256 : i32
+    %c128_i32 = arith.constant 128 : i32
+    %c0_i32 = arith.constant 0 : i32
+    %cst_2 = arith.constant dense<256> : tensor<2x1xi64>
+    %cst_3 = arith.constant dense<0> : tensor<2x1xi64>
+    %cst_4 = arith.constant dense<50257> : tensor<2x1xi64>
+    %cst_5 = arith.constant dense<9.99999974E-6> : tensor<2x1xf32>
+    %cst_6 = arith.constant dense<2.560000e+02> : tensor<2x1xf32>
+    %cst_7 = arith.constant dense<0.000000e+00> : tensor<1x128xf32>
+    %cst_8 = arith.constant dense<0.000000e+00> : tensor<2x128xf32>
+    %cst_9 = arith.constant dense<256> : tensor<2x1xi32>
+    %cst_10 = arith.constant dense<256> : tensor<1x128xi32>
+    %cst_11 = arith.constant dense<512> : tensor<2x1xi32>
+    %c2_i32 = arith.constant 2 : i32
+    %0 = tt.get_program_id x : i32
+    %1 = arith.muli %0, %c2_i32 : i32
+    %2 = tt.make_range {end = 2 : i32, start = 0 : i32} : tensor<2xi32>
+    %3 = tt.expand_dims %2 {axis = 1 : i32} : (tensor<2xi32>) -> tensor<2x1xi32>
+    %4 = tt.splat %1 : (i32) -> tensor<2x1xi32>
+    %5 = arith.addi %4, %3 : tensor<2x1xi32>
+    %6 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32>
+    %7 = tt.expand_dims %6 {axis = 0 : i32} : (tensor<128xi32>) -> tensor<1x128xi32>
+    %8 = tt.splat %arg0 : (!tt.ptr<i64, 1>) -> tensor<2x1x!tt.ptr<i64, 1>>
+    %9 = tt.addptr %8, %5 : tensor<2x1x!tt.ptr<i64, 1>>, tensor<2x1xi32>
+    %10 = tt.load %9 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x1xi64>
+    %11 = arith.remsi %5, %cst_11 : tensor<2x1xi32>
+    %12 = arith.muli %11, %cst_9 : tensor<2x1xi32>
+    %13 = tt.broadcast %12 : (tensor<2x1xi32>) -> tensor<2x128xi32>
+    %14 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<2x128x!tt.ptr<f32, 1>>
+    %15 = arith.muli %5, %cst_9 : tensor<2x1xi32>
+    %16 = tt.broadcast %15 : (tensor<2x1xi32>) -> tensor<2x128xi32>
+    %17 = tt.splat %arg3 : (!tt.ptr<bf16, 1>) -> tensor<2x128x!tt.ptr<bf16, 1>>
+    %18 = arith.addi %10, %cst_4 : tensor<2x1xi64>
+    %19 = arith.cmpi slt, %10, %cst_3 : tensor<2x1xi64>
+    %20 = arith.select %19, %18, %10 : tensor<2x1xi1>, tensor<2x1xi64>
+    %21 = arith.cmpi sge, %20, %cst_3 : tensor<2x1xi64>
+    %22 = arith.cmpi slt, %20, %cst_4 : tensor<2x1xi64>
+    %23 = arith.andi %21, %22 : tensor<2x1xi1>
+    %24 = arith.muli %20, %cst_2 : tensor<2x1xi64>
+    %25 = tt.broadcast %24 : (tensor<2x1xi64>) -> tensor<2x128xi64>
+    %26 = tt.splat %arg1 : (!tt.ptr<f32, 1>) -> tensor<2x128x!tt.ptr<f32, 1>>
+    %27:3 = scf.for %arg8 = %c0_i32 to %c256_i32 step %c128_i32 iter_args(%arg9 = %cst_8, %arg10 = %cst_8, %arg11 = %cst_8) -> (tensor<2x128xf32>, tensor<2x128xf32>, tensor<2x128xf32>)  : i32 {
+      %51 = tt.splat %arg8 : (i32) -> tensor<1x128xi32>
+      %52 = arith.addi %51, %7 : tensor<1x128xi32>
+      %53 = arith.cmpi slt, %52, %cst_10 : tensor<1x128xi32>
+      %54 = tt.broadcast %52 : (tensor<1x128xi32>) -> tensor<2x128xi32>
+      %55 = arith.addi %54, %13 : tensor<2x128xi32>
+      %56 = tt.addptr %14, %55 : tensor<2x128x!tt.ptr<f32, 1>>, tensor<2x128xi32>
+      %57 = tt.broadcast %53 : (tensor<1x128xi1>) -> tensor<2x128xi1>
+      %58 = tt.load %56, %57, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x128xf32>
+      %59 = arith.addi %54, %16 : tensor<2x128xi32>
+      %60 = tt.addptr %17, %59 : tensor<2x128x!tt.ptr<bf16, 1>>, tensor<2x128xi32>
+      %61 = tt.load %60, %57, %cst {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x128xbf16>
+      %62 = arith.extf %61 : tensor<2x128xbf16> to tensor<2x128xf32>
+      tt.assert %23, "index out of bounds: 0 <= tmp3 < 50257", "/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py", "<module>", 1892 : tensor<2x1xi1>
+      %63 = arith.extsi %52 : tensor<1x128xi32> to tensor<1x128xi64>
+      %64 = tt.broadcast %63 : (tensor<1x128xi64>) -> tensor<2x128xi64>
+      %65 = arith.addi %64, %25 : tensor<2x128xi64>
+      %66 = tt.addptr %26, %65 : tensor<2x128x!tt.ptr<f32, 1>>, tensor<2x128xi64>
+      %67 = tt.load %66, %57, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x128xf32>
+      %68 = arith.addf %67, %58 : tensor<2x128xf32>
+      %69 = arith.addf %68, %62 : tensor<2x128xf32>
+      %70 = arith.subf %69, %arg9 : tensor<2x128xf32>
+      %71 = arith.addf %arg11, %cst_1 : tensor<2x128xf32>
+      %72 = arith.divf %70, %71 : tensor<2x128xf32>
+      %73 = arith.addf %arg9, %72 : tensor<2x128xf32>
+      %74 = arith.subf %69, %73 : tensor<2x128xf32>
+      %75 = arith.mulf %70, %74 : tensor<2x128xf32>
+      %76 = arith.addf %arg10, %75 : tensor<2x128xf32>
+      %77 = arith.select %57, %73, %arg9 : tensor<2x128xi1>, tensor<2x128xf32>
+      %78 = arith.select %57, %76, %arg10 : tensor<2x128xi1>, tensor<2x128xf32>
+      %79 = arith.select %57, %71, %arg11 : tensor<2x128xi1>, tensor<2x128xf32>
+      scf.yield %77, %78, %79 : tensor<2x128xf32>, tensor<2x128xf32>, tensor<2x128xf32>
+    }
+    %28:3 = "tt.reduce"(%27#0, %27#1, %27#2) <{axis = 1 : i32}> ({
+    ^bb0(%arg8: f32, %arg9: f32, %arg10: f32, %arg11: f32, %arg12: f32, %arg13: f32):
+      %51 = arith.subf %arg11, %arg8 : f32
+      %52 = arith.addf %arg10, %arg13 : f32
+      %53 = arith.cmpf oeq, %52, %cst_0 : f32
+      %54 = arith.divf %arg13, %52 : f32
+      %55 = arith.select %53, %cst_0, %54 : f32
+      %56 = arith.mulf %51, %55 : f32
+      %57 = arith.addf %arg8, %56 : f32
+      %58 = arith.addf %arg9, %arg12 : f32
+      %59 = arith.mulf %51, %51 : f32
+      %60 = arith.mulf %59, %arg10 : f32
+      %61 = arith.mulf %60, %55 : f32
+      %62 = arith.addf %58, %61 : f32
+      tt.reduce.return %57, %62, %52 : f32, f32, f32
+    }) : (tensor<2x128xf32>, tensor<2x128xf32>, tensor<2x128xf32>) -> (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>)
+    %29 = tt.expand_dims %28#0 {axis = 1 : i32} : (tensor<2xf32>) -> tensor<2x1xf32>
+    %30 = tt.expand_dims %28#1 {axis = 1 : i32} : (tensor<2xf32>) -> tensor<2x1xf32>
+    %31 = arith.muli %11, %cst_9 : tensor<2x1xi32>
+    %32 = tt.broadcast %31 : (tensor<2x1xi32>) -> tensor<2x128xi32>
+    %33 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<2x128x!tt.ptr<f32, 1>>
+    %34 = arith.muli %5, %cst_9 : tensor<2x1xi32>
+    %35 = tt.broadcast %34 : (tensor<2x1xi32>) -> tensor<2x128xi32>
+    %36 = tt.splat %arg3 : (!tt.ptr<bf16, 1>) -> tensor<2x128x!tt.ptr<bf16, 1>>
+    %37 = tt.splat %arg4 : (!tt.ptr<f32, 1>) -> tensor<1x128x!tt.ptr<f32, 1>>
+    %38 = arith.addi %10, %cst_4 : tensor<2x1xi64>
+    %39 = arith.cmpi slt, %10, %cst_3 : tensor<2x1xi64>
+    %40 = arith.select %39, %38, %10 : tensor<2x1xi1>, tensor<2x1xi64>
+    %41 = arith.cmpi sge, %40, %cst_3 : tensor<2x1xi64>
+    %42 = arith.cmpi slt, %40, %cst_4 : tensor<2x1xi64>
+    %43 = arith.andi %41, %42 : tensor<2x1xi1>
+    %44 = arith.muli %40, %cst_2 : tensor<2x1xi64>
+    %45 = tt.broadcast %44 : (tensor<2x1xi64>) -> tensor<2x128xi64>
+    %46 = tt.splat %arg1 : (!tt.ptr<f32, 1>) -> tensor<2x128x!tt.ptr<f32, 1>>
+    %47 = tt.broadcast %29 : (tensor<2x1xf32>) -> tensor<2x128xf32>
+    %48 = arith.divf %30, %cst_6 : tensor<2x1xf32>
+    %49 = arith.addf %48, %cst_5 : tensor<2x1xf32>
+    %50 = tt.splat %arg5 : (!tt.ptr<bf16, 1>) -> tensor<2x128x!tt.ptr<bf16, 1>>
+    scf.for %arg8 = %c0_i32 to %c256_i32 step %c128_i32  : i32 {
+      %51 = tt.splat %arg8 : (i32) -> tensor<1x128xi32>
+      %52 = arith.addi %51, %7 : tensor<1x128xi32>
+      %53 = arith.cmpi slt, %52, %cst_10 : tensor<1x128xi32>
+      %54 = tt.broadcast %52 : (tensor<1x128xi32>) -> tensor<2x128xi32>
+      %55 = arith.addi %54, %32 : tensor<2x128xi32>
+      %56 = tt.addptr %33, %55 : tensor<2x128x!tt.ptr<f32, 1>>, tensor<2x128xi32>
+      %57 = tt.broadcast %53 : (tensor<1x128xi1>) -> tensor<2x128xi1>
+      %58 = tt.load %56, %57, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x128xf32>
+      %59 = arith.addi %54, %35 : tensor<2x128xi32>
+      %60 = tt.addptr %36, %59 : tensor<2x128x!tt.ptr<bf16, 1>>, tensor<2x128xi32>
+      %61 = tt.load %60, %57, %cst {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<2x128xbf16>
+      %62 = arith.extf %61 : tensor<2x128xbf16> to tensor<2x128xf32>
+      %63 = tt.addptr %37, %52 : tensor<1x128x!tt.ptr<f32, 1>>, tensor<1x128xi32>
+      %64 = tt.load %63, %53, %cst_7 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1x128xf32>
+      tt.assert %43, "index out of bounds: 0 <= tmp16 < 50257", "/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py", "<module>", 1892 : tensor<2x1xi1>
+      %65 = arith.extsi %52 : tensor<1x128xi32> to tensor<1x128xi64>
+      %66 = tt.broadcast %65 : (tensor<1x128xi64>) -> tensor<2x128xi64>
+      %67 = arith.addi %66, %45 : tensor<2x128xi64>
+      %68 = tt.addptr %46, %67 : tensor<2x128x!tt.ptr<f32, 1>>, tensor<2x128xi64>
+      %69 = tt.load %68, %57, %cst_8 {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<2x128xf32>
+      %70 = arith.addf %69, %58 : tensor<2x128xf32>
+      %71 = arith.addf %70, %62 : tensor<2x128xf32>
+      %72 = arith.subf %71, %47 : tensor<2x128xf32>
+      %73 = tt.extern_elementwise %49 {libname = "libdevice", libpath = "/usr/local/lib/python3.10/dist-packages/triton/language/../third_party/cuda/lib/libdevice.10.bc", pure = true, symbol = "__nv_rsqrtf"} : (tensor<2x1xf32>) -> tensor<2x1xf32>
+      %74 = tt.broadcast %73 : (tensor<2x1xf32>) -> tensor<2x128xf32>
+      %75 = arith.mulf %72, %74 : tensor<2x128xf32>
+      %76 = tt.broadcast %64 : (tensor<1x128xf32>) -> tensor<2x128xf32>
+      %77 = arith.mulf %75, %76 : tensor<2x128xf32>
+      %78 = tt.addptr %50, %59 : tensor<2x128x!tt.ptr<bf16, 1>>, tensor<2x128xi32>
+      %79 = arith.truncf %77 : tensor<2x128xf32> to tensor<2x128xbf16>
+      tt.store %78, %79, %57 {cache = 1 : i32, evict = 1 : i32} : tensor<2x128xbf16>
+    }
+    tt.return
+  }
+}

.triton/dump/89f8cc1079aa03024e56dc2aee42813a/triton_.ttir

@@ -0,0 +1,91 @@
+module {
+  tt.func public @triton__0d1d2d3d4d5d6e7de(%arg0: !tt.ptr<i64, 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<i64, 1> {tt.divisibility = 16 : i32}, %arg6: i64 {tt.max_divisibility = 8 : i32}, %arg7: i64 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}) attributes {noinline = false} {
+    %c8_i64 = arith.constant 8 : i64
+    %c7680_i64 = arith.constant 7680 : i64
+    %c385973760_i64 = arith.constant 385973760 : i64
+    %cst = arith.constant dense<0.000000e+00> : tensor<1x2048xbf16>
+    %cst_0 = arith.constant dense<50257> : tensor<1x2048xi64>
+    %cst_1 = arith.constant dense<7680> : tensor<1x2048xi64>
+    %c2048_i32 = arith.constant 2048 : i32
+    %c7680_i32 = arith.constant 7680 : i32
+    %c0_i32 = arith.constant 0 : i32
+    %cst_2 = arith.constant dense<-1> : tensor<1x2048xi64>
+    %cst_3 = arith.constant dense<0> : tensor<1x2048xi64>
+    %cst_4 = arith.constant dense<0.000000e+00> : tensor<1x2048xf32>
+    %0 = tt.get_program_id x : i32
+    %1 = arith.extsi %0 : i32 to i64
+    %2 = arith.cmpi slt, %1, %c8_i64 : i64
+    %3 = tt.splat %2 : (i1) -> tensor<1x1xi1>
+    %4 = tt.make_range {end = 2048 : i32, start = 0 : i32} : tensor<2048xi32>
+    %5 = tt.expand_dims %4 {axis = 0 : i32} : (tensor<2048xi32>) -> tensor<1x2048xi32>
+    %6 = arith.extsi %5 : tensor<1x2048xi32> to tensor<1x2048xi64>
+    %7 = arith.muli %1, %c7680_i64 : i64
+    %8 = tt.splat %7 : (i64) -> tensor<1x2048xi64>
+    %9 = tt.splat %arg0 : (!tt.ptr<i64, 1>) -> tensor<1x2048x!tt.ptr<i64, 1>>
+    %10 = tt.splat %2 : (i1) -> tensor<1x2048xi1>
+    %11 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<1x2048x!tt.ptr<f32, 1>>
+    %12 = tt.splat %arg3 : (!tt.ptr<f32, 1>) -> tensor<1x2048x!tt.ptr<f32, 1>>
+    %13 = arith.muli %1, %c385973760_i64 : i64
+    %14 = tt.splat %13 : (i64) -> tensor<1x2048xi64>
+    %15 = tt.splat %arg1 : (!tt.ptr<bf16, 1>) -> tensor<1x2048x!tt.ptr<bf16, 1>>
+    %16:2 = scf.for %arg8 = %c0_i32 to %c7680_i32 step %c2048_i32 iter_args(%arg9 = %cst_4, %arg10 = %cst_3) -> (tensor<1x2048xf32>, tensor<1x2048xi64>)  : i32 {
+      %25 = arith.extsi %arg8 : i32 to i64
+      %26 = tt.splat %25 : (i64) -> tensor<1x2048xi64>
+      %27 = arith.addi %26, %6 : tensor<1x2048xi64>
+      %28 = arith.cmpi slt, %27, %cst_1 : tensor<1x2048xi64>
+      %29 = arith.addi %27, %8 : tensor<1x2048xi64>
+      %30 = tt.addptr %9, %29 : tensor<1x2048x!tt.ptr<i64, 1>>, tensor<1x2048xi64>
+      %31 = arith.andi %28, %10 : tensor<1x2048xi1>
+      %32 = tt.load %30, %31, %cst_3 {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<1x2048xi64>
+      %33 = tt.addptr %11, %29 : tensor<1x2048x!tt.ptr<f32, 1>>, tensor<1x2048xi64>
+      %34 = tt.load %33, %31, %cst_4 {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<1x2048xf32>
+      %35 = tt.addptr %12, %29 : tensor<1x2048x!tt.ptr<f32, 1>>, tensor<1x2048xi64>
+      %36 = tt.load %35, %31, %cst_4 {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<1x2048xf32>
+      %37 = arith.cmpi ne, %32, %cst_2 : tensor<1x2048xi64>
+      %38 = arith.select %37, %32, %cst_3 : tensor<1x2048xi1>, tensor<1x2048xi64>
+      %39 = arith.addi %38, %cst_0 : tensor<1x2048xi64>
+      %40 = arith.cmpi slt, %38, %cst_3 : tensor<1x2048xi64>
+      %41 = arith.select %40, %39, %38 : tensor<1x2048xi1>, tensor<1x2048xi64>
+      %42 = arith.cmpi sge, %41, %cst_3 : tensor<1x2048xi64>
+      %43 = arith.cmpi slt, %41, %cst_0 : tensor<1x2048xi64>
+      %44 = arith.andi %42, %43 : tensor<1x2048xi1>
+      tt.assert %44, "index out of bounds: 0 <= tmp7 < 50257", "<frozen importlib._bootstrap_external>", "_call_with_frames_removed", 883 : tensor<1x2048xi1>
+      %45 = arith.muli %27, %cst_0 : tensor<1x2048xi64>
+      %46 = arith.addi %41, %45 : tensor<1x2048xi64>
+      %47 = arith.addi %46, %14 : tensor<1x2048xi64>
+      %48 = tt.addptr %15, %47 : tensor<1x2048x!tt.ptr<bf16, 1>>, tensor<1x2048xi64>
+      %49 = tt.load %48, %31, %cst {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1x2048xbf16>
+      %50 = arith.extf %49 : tensor<1x2048xbf16> to tensor<1x2048xf32>
+      %51 = arith.subf %50, %34 : tensor<1x2048xf32>
+      %52 = math.log %36 : tensor<1x2048xf32>
+      %53 = arith.subf %51, %52 : tensor<1x2048xf32>
+      %54 = arith.subf %cst_4, %53 : tensor<1x2048xf32>
+      %55 = arith.select %37, %54, %cst_4 : tensor<1x2048xi1>, tensor<1x2048xf32>
+      %56 = arith.addf %arg9, %55 : tensor<1x2048xf32>
+      %57 = arith.select %31, %56, %arg9 : tensor<1x2048xi1>, tensor<1x2048xf32>
+      %58 = arith.extui %37 : tensor<1x2048xi1> to tensor<1x2048xi64>
+      %59 = arith.addi %arg10, %58 : tensor<1x2048xi64>
+      %60 = arith.select %31, %59, %arg10 : tensor<1x2048xi1>, tensor<1x2048xi64>
+      scf.yield %57, %60 : tensor<1x2048xf32>, tensor<1x2048xi64>
+    }
+    %17 = "tt.reduce"(%16#0) <{axis = 1 : i32}> ({
+    ^bb0(%arg8: f32, %arg9: f32):
+      %25 = arith.addf %arg8, %arg9 : f32
+      tt.reduce.return %25 : f32
+    }) : (tensor<1x2048xf32>) -> tensor<1xf32>
+    %18 = tt.expand_dims %17 {axis = 1 : i32} : (tensor<1xf32>) -> tensor<1x1xf32>
+    %19 = tt.addptr %arg4, %1 : !tt.ptr<f32, 1>, i64
+    %20 = tt.splat %19 : (!tt.ptr<f32, 1>) -> tensor<1x1x!tt.ptr<f32, 1>>
+    tt.store %20, %18, %3 {cache = 1 : i32, evict = 1 : i32} : tensor<1x1xf32>
+    %21 = "tt.reduce"(%16#1) <{axis = 1 : i32}> ({
+    ^bb0(%arg8: i64, %arg9: i64):
+      %25 = arith.addi %arg8, %arg9 : i64
+      tt.reduce.return %25 : i64
+    }) : (tensor<1x2048xi64>) -> tensor<1xi64>
+    %22 = tt.expand_dims %21 {axis = 1 : i32} : (tensor<1xi64>) -> tensor<1x1xi64>
+    %23 = tt.addptr %arg5, %1 : !tt.ptr<i64, 1>, i64
+    %24 = tt.splat %23 : (!tt.ptr<i64, 1>) -> tensor<1x1x!tt.ptr<i64, 1>>
+    tt.store %24, %22, %3 {cache = 1 : i32, evict = 1 : i32} : tensor<1x1xi64>
+    tt.return
+  }
+}