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module { |
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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} { |
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%cst = arith.constant dense<0.000000e+00> : tensor<2x256xbf16> |
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%cst_0 = arith.constant dense<1.000000e+00> : tensor<1x256xf32> |
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%cst_1 = arith.constant dense<0.000000e+00> : tensor<1x256xf32> |
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%cst_2 = arith.constant 0.000000e+00 : f32 |
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%cst_3 = arith.constant dense<256> : tensor<2x1xi64> |
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%cst_4 = arith.constant dense<50257> : tensor<2x1xi64> |
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%cst_5 = arith.constant dense<0> : tensor<2x1xi64> |
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%cst_6 = arith.constant dense<9.99999974E-6> : tensor<2x1xf32> |
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%cst_7 = arith.constant dense<2.560000e+02> : tensor<2x1xf32> |
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%cst_8 = arith.constant dense<0.000000e+00> : tensor<2x256xf32> |
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%cst_9 = arith.constant dense<256> : tensor<2x1xi32> |
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%cst_10 = arith.constant dense<256> : tensor<1x256xi32> |
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%cst_11 = arith.constant dense<512> : tensor<2x1xi32> |
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%c2_i32 = arith.constant 2 : i32 |
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%0 = tt.get_program_id x : i32 |
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%1 = arith.muli %0, %c2_i32 : i32 |
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%2 = tt.make_range {end = 2 : i32, start = 0 : i32} : tensor<2xi32> |
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%3 = tt.expand_dims %2 {axis = 1 : i32} : (tensor<2xi32>) -> tensor<2x1xi32> |
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%4 = tt.splat %1 : (i32) -> tensor<2x1xi32> |
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%5 = arith.addi %4, %3 : tensor<2x1xi32> |
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%6 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32> |
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%7 = tt.expand_dims %6 {axis = 0 : i32} : (tensor<256xi32>) -> tensor<1x256xi32> |
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%8 = tt.splat %arg0 : (!tt.ptr<i64, 1>) -> tensor<2x1x!tt.ptr<i64, 1>> |
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%9 = tt.addptr %8, %5 : tensor<2x1x!tt.ptr<i64, 1>>, tensor<2x1xi32> |
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%10 = tt.load %9 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x1xi64> |
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%11 = arith.remsi %5, %cst_11 : tensor<2x1xi32> |
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%12 = arith.cmpi slt, %7, %cst_10 : tensor<1x256xi32> |
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%13 = arith.muli %11, %cst_9 : tensor<2x1xi32> |
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%14 = tt.broadcast %7 : (tensor<1x256xi32>) -> tensor<2x256xi32> |
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%15 = tt.broadcast %13 : (tensor<2x1xi32>) -> tensor<2x256xi32> |
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%16 = arith.addi %14, %15 : tensor<2x256xi32> |
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%17 = tt.splat %arg2 : (!tt.ptr<f32, 1>) -> tensor<2x256x!tt.ptr<f32, 1>> |
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%18 = tt.addptr %17, %16 : tensor<2x256x!tt.ptr<f32, 1>>, tensor<2x256xi32> |
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%19 = tt.broadcast %12 : (tensor<1x256xi1>) -> tensor<2x256xi1> |
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%20 = tt.load %18, %19, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x256xf32> |
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%21 = arith.muli %5, %cst_9 : tensor<2x1xi32> |
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%22 = tt.broadcast %21 : (tensor<2x1xi32>) -> tensor<2x256xi32> |
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%23 = arith.addi %14, %22 : tensor<2x256xi32> |
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%24 = tt.splat %arg3 : (!tt.ptr<bf16, 1>) -> tensor<2x256x!tt.ptr<bf16, 1>> |
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%25 = tt.addptr %24, %23 : tensor<2x256x!tt.ptr<bf16, 1>>, tensor<2x256xi32> |
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%26 = tt.load %25, %19, %cst {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x256xbf16> |
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%27 = arith.extf %26 : tensor<2x256xbf16> to tensor<2x256xf32> |
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%28 = arith.addi %10, %cst_4 : tensor<2x1xi64> |
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%29 = arith.cmpi slt, %10, %cst_5 : tensor<2x1xi64> |
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%30 = arith.select %29, %28, %10 : tensor<2x1xi1>, tensor<2x1xi64> |
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%31 = arith.cmpi sge, %30, %cst_5 : tensor<2x1xi64> |
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%32 = arith.cmpi slt, %30, %cst_4 : tensor<2x1xi64> |
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%33 = arith.andi %31, %32 : tensor<2x1xi1> |
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tt.assert %33, "index out of bounds: 0 <= tmp3 < 50257", "<frozen importlib._bootstrap_external>", "_call_with_frames_removed", 883 : tensor<2x1xi1> |
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%34 = arith.muli %30, %cst_3 : tensor<2x1xi64> |
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%35 = tt.broadcast %34 : (tensor<2x1xi64>) -> tensor<2x256xi64> |
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%36 = arith.extsi %7 : tensor<1x256xi32> to tensor<1x256xi64> |
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%37 = tt.broadcast %36 : (tensor<1x256xi64>) -> tensor<2x256xi64> |
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%38 = arith.addi %37, %35 : tensor<2x256xi64> |
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%39 = tt.splat %arg1 : (!tt.ptr<f32, 1>) -> tensor<2x256x!tt.ptr<f32, 1>> |
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%40 = tt.addptr %39, %38 : tensor<2x256x!tt.ptr<f32, 1>>, tensor<2x256xi64> |
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%41 = tt.load %40, %19, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x256xf32> |
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%42 = arith.addf %41, %20 : tensor<2x256xf32> |
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%43 = arith.addf %42, %27 : tensor<2x256xf32> |
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%44 = arith.addf %43, %cst_8 : tensor<2x256xf32> |
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%45 = arith.subf %43, %44 : tensor<2x256xf32> |
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%46 = arith.mulf %43, %45 : tensor<2x256xf32> |
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%47 = arith.addf %46, %cst_8 : tensor<2x256xf32> |
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%48 = arith.select %19, %44, %cst_8 : tensor<2x256xi1>, tensor<2x256xf32> |
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%49 = arith.select %19, %47, %cst_8 : tensor<2x256xi1>, tensor<2x256xf32> |
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%50 = arith.select %12, %cst_0, %cst_1 : tensor<1x256xi1>, tensor<1x256xf32> |
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%51 = tt.broadcast %50 : (tensor<1x256xf32>) -> tensor<2x256xf32> |
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%52:3 = "tt.reduce"(%48, %49, %51) <{axis = 1 : i32}> ({ |
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^bb0(%arg8: f32, %arg9: f32, %arg10: f32, %arg11: f32, %arg12: f32, %arg13: f32): |
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%76 = arith.subf %arg11, %arg8 : f32 |
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%77 = arith.addf %arg10, %arg13 : f32 |
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%78 = arith.cmpf oeq, %77, %cst_2 : f32 |
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%79 = arith.divf %arg13, %77 : f32 |
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%80 = arith.select %78, %cst_2, %79 : f32 |
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%81 = arith.mulf %76, %80 : f32 |
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%82 = arith.addf %arg8, %81 : f32 |
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%83 = arith.addf %arg9, %arg12 : f32 |
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%84 = arith.mulf %76, %76 : f32 |
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%85 = arith.mulf %84, %arg10 : f32 |
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%86 = arith.mulf %85, %80 : f32 |
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%87 = arith.addf %83, %86 : f32 |
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tt.reduce.return %82, %87, %77 : f32, f32, f32 |
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}) : (tensor<2x256xf32>, tensor<2x256xf32>, tensor<2x256xf32>) -> (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) |
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%53 = tt.expand_dims %52#0 {axis = 1 : i32} : (tensor<2xf32>) -> tensor<2x1xf32> |
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%54 = tt.expand_dims %52#1 {axis = 1 : i32} : (tensor<2xf32>) -> tensor<2x1xf32> |
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%55 = tt.load %18, %19, %cst_8 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<2x256xf32> |
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%56 = tt.load %25, %19, %cst {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<2x256xbf16> |
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%57 = arith.extf %56 : tensor<2x256xbf16> to tensor<2x256xf32> |
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%58 = tt.splat %arg4 : (!tt.ptr<f32, 1>) -> tensor<1x256x!tt.ptr<f32, 1>> |
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%59 = tt.addptr %58, %7 : tensor<1x256x!tt.ptr<f32, 1>>, tensor<1x256xi32> |
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%60 = tt.load %59, %12, %cst_1 {cache = 1 : i32, evict = 3 : i32, isVolatile = false} : tensor<1x256xf32> |
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tt.assert %33, "index out of bounds: 0 <= tmp16 < 50257", "<frozen importlib._bootstrap_external>", "_call_with_frames_removed", 883 : tensor<2x1xi1> |
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%61 = tt.load %40, %19, %cst_8 {cache = 1 : i32, evict = 2 : i32, isVolatile = false} : tensor<2x256xf32> |
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%62 = arith.addf %61, %55 : tensor<2x256xf32> |
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%63 = arith.addf %62, %57 : tensor<2x256xf32> |
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%64 = tt.broadcast %53 : (tensor<2x1xf32>) -> tensor<2x256xf32> |
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%65 = arith.subf %63, %64 : tensor<2x256xf32> |
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%66 = arith.divf %54, %cst_7 : tensor<2x1xf32> |
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%67 = arith.addf %66, %cst_6 : tensor<2x1xf32> |
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%68 = tt.extern_elementwise %67 {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> |
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%69 = tt.broadcast %68 : (tensor<2x1xf32>) -> tensor<2x256xf32> |
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%70 = arith.mulf %65, %69 : tensor<2x256xf32> |
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%71 = tt.broadcast %60 : (tensor<1x256xf32>) -> tensor<2x256xf32> |
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%72 = arith.mulf %70, %71 : tensor<2x256xf32> |
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%73 = tt.splat %arg5 : (!tt.ptr<bf16, 1>) -> tensor<2x256x!tt.ptr<bf16, 1>> |
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%74 = tt.addptr %73, %23 : tensor<2x256x!tt.ptr<bf16, 1>>, tensor<2x256xi32> |
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%75 = arith.truncf %72 : tensor<2x256xf32> to tensor<2x256xbf16> |
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tt.store %74, %75, %19 {cache = 1 : i32, evict = 1 : i32} : tensor<2x256xbf16> |
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tt.return |
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} |
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} |
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