feat: tracing Random.jl functionality correctly

[avik-pal]

No description provided.

[avik-pal]

julia> using Reactant, Random

julia> fn() = randn(Random.default_rng(), 2, 3)
fn (generic function with 1 method)

julia> @code_hlo optimize = false fn()
module {
  func.func @main() -> tensor<3x2xf64> {
    %c = stablehlo.constant dense<[9454987348304227925, 11257230962712577529]> : tensor<2xui64>
    %output_state, %output = stablehlo.rng_bit_generator %c, algorithm =  DEFAULT : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x3xui64>)
    %0 = stablehlo.convert %output : (tensor<2x3xui64>) -> tensor<2x3xf64>
    %cst = stablehlo.constant dense<1.8446744073709552E+19> : tensor<2x3xf64>
    %1 = stablehlo.divide %0, %cst : tensor<2x3xf64>
    %cst_0 = stablehlo.constant dense<2.000000e+00> : tensor<2x3xf64>
    %2 = stablehlo.multiply %1, %cst_0 : tensor<2x3xf64>
    %cst_1 = stablehlo.constant dense<1.000000e+00> : tensor<2x3xf64>
    %3 = stablehlo.subtract %2, %cst_1 : tensor<2x3xf64>
    %4 = chlo.erf_inv %3 : tensor<2x3xf64> -> tensor<2x3xf64>
    %cst_2 = stablehlo.constant dense<1.4142135623730951> : tensor<2x3xf64>
    %5 = stablehlo.multiply %4, %cst_2 : tensor<2x3xf64>
    %6 = stablehlo.transpose %5, dims = [1, 0] : (tensor<2x3xf64>) -> tensor<3x2xf64>
    return %6 : tensor<3x2xf64>
  }
}

julia> @code_hlo fn()
module {
  func.func @main() -> tensor<3x2xf64> {
    %cst = stablehlo.constant dense<1.4142135623730951> : tensor<2x3xf64>
    %cst_0 = stablehlo.constant dense<1.000000e+00> : tensor<2x3xf64>
    %cst_1 = stablehlo.constant dense<2.000000e+00> : tensor<2x3xf64>
    %cst_2 = stablehlo.constant dense<1.8446744073709552E+19> : tensor<2x3xf64>
    %c = stablehlo.constant dense<[17523564455668573441, 5342821220909967229]> : tensor<2xui64>
    %output_state, %output = stablehlo.rng_bit_generator %c, algorithm =  DEFAULT : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x3xui64>)
    %0 = stablehlo.convert %output : (tensor<2x3xui64>) -> tensor<2x3xf64>
    %1 = stablehlo.divide %0, %cst_2 : tensor<2x3xf64>
    %2 = stablehlo.multiply %1, %cst_1 : tensor<2x3xf64>
    %3 = stablehlo.subtract %2, %cst_0 : tensor<2x3xf64>
    %4 = chlo.erf_inv %3 : tensor<2x3xf64> -> tensor<2x3xf64>
    %5 = stablehlo.multiply %4, %cst : tensor<2x3xf64>
    %6 = stablehlo.transpose %5, dims = [1, 0] : (tensor<2x3xf64>) -> tensor<3x2xf64>
    return %6 : tensor<3x2xf64>
  }
}

[avik-pal]

This is kind of working now. Can I get an initial review?

[avik-pal]

This will have to be updated once the new CUDA interpreter stuff lands