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#blocked = #triton_gpu.blocked<{sizePerThread = [8], threadsPerWarp = [32], warpsPerCTA = [4], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}> |
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#blocked1 = #triton_gpu.blocked<{sizePerThread = [4], threadsPerWarp = [32], warpsPerCTA = [4], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}> |
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module attributes {"triton_gpu.compute-capability" = 89 : i32, "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 32 : i32} { |
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tt.func public @triton__0d1d2de(%arg0: !tt.ptr<bf16, 1> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f32, 1> {tt.divisibility = 16 : i32}, %arg2: i32 {tt.divisibility = 16 : i32, tt.max_divisibility = 16 : i32}) attributes {noinline = false} { |
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%c1024_i32 = arith.constant 1024 : i32 |
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%0 = tt.get_program_id x : i32 |
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%1 = arith.muli %0, %c1024_i32 : i32 |
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%2 = tt.make_range {end = 1024 : i32, start = 0 : i32} : tensor<1024xi32, #blocked> |
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%3 = tt.make_range {end = 1024 : i32, start = 0 : i32} : tensor<1024xi32, #blocked1> |
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%4 = tt.splat %1 : (i32) -> tensor<1024xi32, #blocked> |
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%5 = tt.splat %1 : (i32) -> tensor<1024xi32, #blocked1> |
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%6 = arith.addi %4, %2 : tensor<1024xi32, #blocked> |
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%7 = arith.addi %5, %3 : tensor<1024xi32, #blocked1> |
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%8 = tt.splat %arg0 : (!tt.ptr<bf16, 1>) -> tensor<1024x!tt.ptr<bf16, 1>, #blocked> |
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%9 = tt.addptr %8, %6 : tensor<1024x!tt.ptr<bf16, 1>, #blocked>, tensor<1024xi32, #blocked> |
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%10 = tt.load %9 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<1024xbf16, #blocked> |
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%11 = triton_gpu.convert_layout %10 : (tensor<1024xbf16, #blocked>) -> tensor<1024xbf16, #blocked1> |
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%12 = arith.extf %11 : tensor<1024xbf16, #blocked1> to tensor<1024xf32, #blocked1> |
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%13 = tt.splat %arg1 : (!tt.ptr<f32, 1>) -> tensor<1024x!tt.ptr<f32, 1>, #blocked1> |
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%14 = tt.addptr %13, %7 : tensor<1024x!tt.ptr<f32, 1>, #blocked1>, tensor<1024xi32, #blocked1> |
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tt.store %14, %12 {cache = 1 : i32, evict = 1 : i32} : tensor<1024xf32, #blocked1> |
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tt.return |
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} |
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} |
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