feat: initial triton setup #1702

avik-pal · 2025-09-25T03:16:48Z

import triton
import triton.language as tl


@triton.jit
def add_kernel(
    x_ptr,  # *Pointer* to first input vector.
    y_ptr,  # *Pointer* to second input vector.
    output_ptr,  # *Pointer* to output vector.
    n_elements,  # Size of the vector.
    BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process.
    # NOTE: `constexpr` so it can be used as a shape value.
):
    # There are multiple 'programs' processing different data. We identify which program
    # we are here:
    pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
    # This program will process inputs that are offset from the initial data.
    # For instance, if you had a vector of length 256 and block_size of 64, the programs
    # would each access the elements [0:64, 64:128, 128:192, 192:256].
    # Note that offsets is a list of pointers:
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    # Create a mask to guard memory operations against out-of-bounds accesses.
    mask = offsets < n_elements
    # Load x and y from DRAM, masking out any extra elements in case the input is not a
    # multiple of the block size.
    x = tl.load(x_ptr + offsets, mask=mask)
    y = tl.load(y_ptr + offsets, mask=mask)
    output = x + y
    # Write x + y back to DRAM.
    tl.store(output_ptr + offsets, output, mask=mask)

using PythonCall, Reactant

pyimport("sys").path.append(@__DIR__)
kernel = pyimport("vector_add").add_kernel

x = Reactant.to_rarray(rand(Float32, 1024));
y = Reactant.to_rarray(rand(Float32, 1024));
out = Reactant.to_rarray(zeros(Float32, 1024));

@code_hlo kernel(
    x,
    y,
    out,
    length(x),
    64;
    grid=cld(length(x), 64),
    num_warps=1,
    num_stages=3,
    hints=Dict(1 => 16),
)

avik-pal · 2025-09-27T13:56:52Z

module @reactant_JITFunc... attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
  module @tt_module_0 {
    tt.func @add_kernel_call_e72661bb113efd0f(%arg0: !tt.ptr<f32>, %arg1: !tt.ptr<f32>, %arg2: !tt.ptr<f32>) attributes {noinline = false} {
      %0 = tt.get_program_id x : i32
      %c64_i32 = arith.constant 64 : i32
      %c64_i32_0 = arith.constant 64 : i32
      %1 = arith.extsi %0 : i32 to i64
      %2 = arith.extsi %c64_i32_0 : i32 to i64
      %3 = arith.muli %1, %2 : i64
      %c2147483647_i64 = arith.constant 2147483647 : i64
      %c-2147483648_i64 = arith.constant -2147483648 : i64
      %4 = arith.cmpi sle, %3, %c2147483647_i64 : i64
      %5 = arith.cmpi sge, %3, %c-2147483648_i64 : i64
      %6 = arith.andi %4, %5 : i1
      %7 = arith.muli %0, %c64_i32_0 : i32
      %8 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
      %9 = tt.splat %7 : i32 -> tensor<64xi32>
      %10 = arith.extsi %9 : tensor<64xi32> to tensor<64xi64>
      %11 = arith.extsi %8 : tensor<64xi32> to tensor<64xi64>
      %12 = arith.addi %10, %11 : tensor<64xi64>
      %c2147483647_i64_1 = arith.constant 2147483647 : i64
      %c-2147483648_i64_2 = arith.constant -2147483648 : i64
      %cst = arith.constant dense<2147483647> : tensor<64xi64>
      %13 = arith.cmpi sle, %12, %cst : tensor<64xi64>
      %cst_3 = arith.constant dense<-2147483648> : tensor<64xi64>
      %14 = arith.cmpi sge, %12, %cst_3 : tensor<64xi64>
      %15 = arith.andi %13, %14 : tensor<64xi1>
      %16 = arith.addi %9, %8 : tensor<64xi32>
      %c1024_i32 = arith.constant 1024 : i32
      %cst_4 = arith.constant dense<1024> : tensor<64xi32>
      %17 = arith.cmpi slt, %16, %cst_4 : tensor<64xi32>
      %18 = tt.splat %arg0 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
      %19 = tt.addptr %18, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
      %20 = tt.load %19, %17 : tensor<64x!tt.ptr<f32>>
      %21 = tt.splat %arg1 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
      %22 = tt.addptr %21, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
      %23 = tt.load %22, %17 : tensor<64x!tt.ptr<f32>>
      %24 = arith.addf %20, %23 : tensor<64xf32>
      %25 = tt.splat %arg2 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
      %26 = tt.addptr %25, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
      tt.store %26, %24, %17 : tensor<64x!tt.ptr<f32>>
      tt.return
    }
  }
  func.func @main(%arg0: tensor<1024xf32> {tf.aliasing_output = 0 : i32}, %arg1: tensor<1024xf32> {tf.aliasing_output = 1 : i32}, %arg2: tensor<1024xf32> {tf.aliasing_output = 2 : i32}) -> (tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) {
    %0 = stablehlo.transpose %arg0, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %1 = stablehlo.transpose %arg1, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %2 = stablehlo.transpose %arg2, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %c = stablehlo.constant dense<16> : tensor<i64>
    %c_0 = stablehlo.constant dense<1> : tensor<i64>
    %c_1 = stablehlo.constant dense<1> : tensor<i64>
    %c_2 = stablehlo.constant dense<0> : tensor<i64>
    enzymexla.triton_call @tt_module_0::@add_kernel_call_e72661bb113efd0f blocks in(%c, %c_0, %c_1) shmem = %c_2 (%0, %1, %2) : (tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> ()
    %3 = stablehlo.transpose %0, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %4 = stablehlo.transpose %1, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %5 = stablehlo.transpose %2, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    return %3, %4, %5 : tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>
  }
}

avik-pal · 2025-09-28T01:40:59Z

https://github.com/triton-lang/triton/blob/8ee584014e9570ba608809c42dc2060fdd214a98/python/src/passes.cc

avik-pal · 2025-09-29T18:50:54Z

module @reactant_JITFunc... attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
  triton_ext.module @add_kernel_tt_module_e72661bb113efd0f {
    builtin.module @add_kernel_module_e72661bb113efd0f {
      tt.func private @add_kernel_call_e72661bb113efd0f(%arg0: !tt.ptr<f32>, %arg1: !tt.ptr<f32>, %arg2: !tt.ptr<f32>) attributes {enzymexla.memory_effects = ["read", "write"], noinline = false} {
        %0 = tt.get_program_id x : i32
        %c64_i32 = arith.constant 64 : i32
        %c64_i32_0 = arith.constant 64 : i32
        %1 = arith.extsi %0 : i32 to i64
        %2 = arith.extsi %c64_i32_0 : i32 to i64
        %3 = arith.muli %1, %2 : i64
        %c2147483647_i64 = arith.constant 2147483647 : i64
        %c-2147483648_i64 = arith.constant -2147483648 : i64
        %4 = arith.cmpi sle, %3, %c2147483647_i64 : i64
        %5 = arith.cmpi sge, %3, %c-2147483648_i64 : i64
        %6 = arith.andi %4, %5 : i1
        %7 = arith.muli %0, %c64_i32_0 : i32
        %8 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
        %9 = tt.splat %7 : i32 -> tensor<64xi32>
        %10 = arith.extsi %9 : tensor<64xi32> to tensor<64xi64>
        %11 = arith.extsi %8 : tensor<64xi32> to tensor<64xi64>
        %12 = arith.addi %10, %11 : tensor<64xi64>
        %c2147483647_i64_1 = arith.constant 2147483647 : i64
        %c-2147483648_i64_2 = arith.constant -2147483648 : i64
        %cst = arith.constant dense<2147483647> : tensor<64xi64>
        %13 = arith.cmpi sle, %12, %cst : tensor<64xi64>
        %cst_3 = arith.constant dense<-2147483648> : tensor<64xi64>
        %14 = arith.cmpi sge, %12, %cst_3 : tensor<64xi64>
        %15 = arith.andi %13, %14 : tensor<64xi1>
        %16 = arith.addi %9, %8 : tensor<64xi32>
        %c1024_i32 = arith.constant 1024 : i32
        %cst_4 = arith.constant dense<1024> : tensor<64xi32>
        %17 = arith.cmpi slt, %16, %cst_4 : tensor<64xi32>
        %18 = tt.splat %arg0 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
        %19 = tt.addptr %18, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
        %20 = tt.load %19, %17 : tensor<64x!tt.ptr<f32>>
        %21 = tt.splat %arg1 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
        %22 = tt.addptr %21, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
        %23 = tt.load %22, %17 : tensor<64x!tt.ptr<f32>>
        %24 = arith.addf %20, %23 : tensor<64xf32>
        %25 = tt.splat %arg2 : !tt.ptr<f32> -> tensor<64x!tt.ptr<f32>>
        %26 = tt.addptr %25, %16 : tensor<64x!tt.ptr<f32>>, tensor<64xi32>
        tt.store %26, %24, %17 : tensor<64x!tt.ptr<f32>>
        tt.return
      }
    }
  }
  func.func @main(%arg0: tensor<1024xf32> {tf.aliasing_output = 0 : i32}, %arg1: tensor<1024xf32> {tf.aliasing_output = 1 : i32}, %arg2: tensor<1024xf32> {tf.aliasing_output = 2 : i32}) -> (tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) attributes {enzymexla.memory_effects = ["read", "write", "allocate", "free"]} {
    %0 = stablehlo.transpose %arg0, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %1 = stablehlo.transpose %arg1, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %2 = stablehlo.transpose %arg2, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %c = stablehlo.constant dense<16> : tensor<i64>
    %c_0 = stablehlo.constant dense<1> : tensor<i64>
    %c_1 = stablehlo.constant dense<1> : tensor<i64>
    %c_2 = stablehlo.constant dense<0> : tensor<i64>
    triton_ext.call @add_kernel_tt_module_e72661bb113efd0f::@add_kernel_module_e72661bb113efd0f::@add_kernel_call_e72661bb113efd0f blocks in(%c, %c_0, %c_1) shmem = %c_2 (%0, %1, %2) : (tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> ()
    %3 = stablehlo.transpose %0, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %4 = stablehlo.transpose %1, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    %5 = stablehlo.transpose %2, dims = [0] : (tensor<1024xf32>) -> tensor<1024xf32>
    return %3, %4, %5 : tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>
  }
}

avik-pal · 2025-09-30T00:17:14Z

module @reactant_JITFunc... attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
  triton_ext.module @add_kernel_tt_module_e72661bb113efd0f {
    builtin.module @add_kernel_module_e72661bb113efd0f attributes {ttg.global_scratch_memory_alignment = 1 : i32, ttg.global_scratch_memory_size = 0 : i32, "ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 1 : i32, ttg.shared = 0 : i32, ttg.target = "cuda:120", ttg.tensor_memory_size = 0 : i32, "ttg.threads-per-warp" = 32 : i32, "ttg.total-num-warps" = 1 : i32} {
      llvm.mlir.global external @global_smem() {addr_space = 3 : i32, alignment = 16 : i64} : !llvm.array<0 x i8>
      llvm.func @add_kernel_call_e72661bb113efd0f(%arg0: !llvm.ptr<1>, %arg1: !llvm.ptr<1>, %arg2: !llvm.ptr<1>, %arg3: !llvm.ptr<1>) attributes {enzymexla.memory_effects = ["read", "write", "allocate", "free"], noinline = false, nvvm.kernel = 1 : ui1, nvvm.reqntid = array<i32: 32>, ttg.global_scratch_memory_alignment = 1 : i32, ttg.global_scratch_memory_size = 0 : i32} {
        %0 = llvm.mlir.undef : vector<1xf32>
        %1 = llvm.mlir.constant(0 : i32) : i32
        %2 = llvm.mlir.constant(32 : i32) : i32
        %3 = llvm.mlir.constant(31 : i32) : i32
        %4 = llvm.mlir.constant(0 : index) : i32
        %5 = llvm.mlir.constant(1024 : i32) : i32
        %6 = llvm.mlir.constant(64 : i32) : i32
        %7 = llvm.call_intrinsic "llvm.nvvm.read.ptx.sreg.ctaid.x"() : () -> i32
        %8 = llvm.mul %7, %6 : i32
        %9 = nvvm.read.ptx.sreg.tid.x : i32
        %10 = llvm.and %9, %3 : i32
        %11 = llvm.shl %10, %1 : i32
        %12 = llvm.or %1, %11 : i32
        %13 = llvm.or %12, %1 : i32
        %14 = llvm.and %13, %3 : i32
        %15 = llvm.lshr %14, %1 : i32
        %16 = llvm.xor %1, %15 : i32
        %17 = llvm.xor %1, %16 : i32
        %18 = llvm.xor %17, %1 : i32
        %19 = llvm.xor %17, %2 : i32
        %20 = llvm.add %18, %4 : i32
        %21 = llvm.add %19, %4 : i32
        %22 = llvm.add %8, %20 : i32
        %23 = llvm.add %8, %21 : i32
        %24 = llvm.icmp "slt" %22, %5 : i32
        %25 = llvm.icmp "slt" %23, %5 : i32
        %26 = llvm.getelementptr %arg0[%22] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %27 = llvm.getelementptr %arg0[%23] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %28 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "mov.u32 $0, 0x0;\0A\09@$2 ld.global.b32 { $0 }, [ $1 + 0 ];", "=r,l,b" %26, %24 : (!llvm.ptr<1>, i1) -> i32
        %29 = llvm.bitcast %28 : i32 to vector<1xf32>
        %30 = llvm.extractelement %29[%4 : i32] : vector<1xf32>
        %31 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "mov.u32 $0, 0x0;\0A\09@$2 ld.global.b32 { $0 }, [ $1 + 0 ];", "=r,l,b" %27, %25 : (!llvm.ptr<1>, i1) -> i32
        %32 = llvm.bitcast %31 : i32 to vector<1xf32>
        %33 = llvm.extractelement %32[%4 : i32] : vector<1xf32>
        %34 = llvm.getelementptr %arg1[%22] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %35 = llvm.getelementptr %arg1[%23] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %36 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "mov.u32 $0, 0x0;\0A\09@$2 ld.global.b32 { $0 }, [ $1 + 0 ];", "=r,l,b" %34, %24 : (!llvm.ptr<1>, i1) -> i32
        %37 = llvm.bitcast %36 : i32 to vector<1xf32>
        %38 = llvm.extractelement %37[%4 : i32] : vector<1xf32>
        %39 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "mov.u32 $0, 0x0;\0A\09@$2 ld.global.b32 { $0 }, [ $1 + 0 ];", "=r,l,b" %35, %25 : (!llvm.ptr<1>, i1) -> i32
        %40 = llvm.bitcast %39 : i32 to vector<1xf32>
        %41 = llvm.extractelement %40[%4 : i32] : vector<1xf32>
        %42 = llvm.fadd %30, %38 : f32
        %43 = llvm.fadd %33, %41 : f32
        %44 = llvm.getelementptr %arg2[%22] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %45 = llvm.getelementptr %arg2[%23] : (!llvm.ptr<1>, i32) -> !llvm.ptr<1>, f32
        %46 = llvm.insertelement %42, %0[%1 : i32] : vector<1xf32>
        %47 = llvm.bitcast %46 : vector<1xf32> to i32
        %48 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "@$2 st.global.b32 [ $1 + 0 ], { $0 };", "r,l,b" %47, %44, %24 : (i32, !llvm.ptr<1>, i1) -> !llvm.void
        %49 = llvm.insertelement %43, %0[%1 : i32] : vector<1xf32>
        %50 = llvm.bitcast %49 : vector<1xf32> to i32
        %51 = llvm.inline_asm has_side_effects asm_dialect = att operand_attrs = [] "@$2 st.global.b32 [ $1 + 0 ], { $0 };", "r,l,b" %50, %45, %25 : (i32, !llvm.ptr<1>, i1) -> !llvm.void
        llvm.return
      }
    }
  }
  func.func @main(%arg0: tensor<1024xf32>, %arg1: tensor<1024xf32>, %arg2: tensor<1024xf32>) attributes {enzymexla.memory_effects = ["read", "write", "allocate", "free"]} {
    %c = stablehlo.constant dense<0> : tensor<i64>
    %c_0 = stablehlo.constant dense<1> : tensor<i64>
    %c_1 = stablehlo.constant dense<16> : tensor<i64>
    triton_ext.call @add_kernel_tt_module_e72661bb113efd0f::@add_kernel_module_e72661bb113efd0f::@add_kernel_call_e72661bb113efd0f blocks in(%c_1, %c_0, %c_0) shmem = %c (%arg0, %arg1, %arg2) : (tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> ()
    return
  }
}

test/integration/triton/softmax.jl

feat: auto-trace triton code feat: copy tt.func into main module [skip ci] feat: tracing fully functional fix: hlo_call feat: more triton passes + keep triton func in a separate module feat: put the tt func in a separate module and use symbol ref feat: new triton_ext dialect feat: triton tracing works now finally fix: kind of working fix: new API feat: return values feat: lowering triton now works feat: triton working end to end fix: extra export + naming feat: allow grid/blocks via a function [skip ci] feat: use new device properties [skip ci] feat: correctly set strides + get n_regs test: add some triton tests test: layer_norm + libdevice fix: partial fix to the blocks fix: correct launch configuration test: missing vars chore: bump workspace fix: cluster dims fix: bump version chore: bump

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