mirror of https://github.com/tracel-ai/burn.git
Refactor/wgpu/prng (#576)
This commit is contained in:
parent
73fb0eaa7e
commit
d5f9f69cea
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@ -41,6 +41,7 @@ burn-tensor = {path = "../burn-tensor", version = "0.9.0", default-features = fa
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"export_tests",
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]}
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burn-ndarray = {path = "../burn-ndarray", version = "0.9.0" }
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serial_test = "0.5.0"
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[[bench]]
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name = "unary"
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@ -188,6 +188,20 @@ pub(crate) fn elemwise_workgroup(num_elems: usize, workgroup_size: usize) -> Wor
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WorkGroup::new(workgroup_x as u32, workgroup_y as u32, 1)
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}
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pub(crate) fn prng_workgroup(
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num_elems: usize,
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workgroup_size: usize,
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n_values_per_thread: usize,
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) -> WorkGroup {
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let num_threads = f32::ceil(num_elems as f32 / n_values_per_thread as f32);
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let num_elem_per_invocation = workgroup_size * workgroup_size;
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let num_invocations = f32::ceil(num_threads / num_elem_per_invocation as f32);
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let workgroup_x = f32::ceil(f32::sqrt(num_invocations));
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let workgroup_y = f32::ceil(num_invocations / workgroup_x);
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WorkGroup::new(workgroup_x as u32, workgroup_y as u32, 1)
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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@ -1,7 +1,13 @@
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use burn_common::rand::get_seeded_rng;
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use rand::Rng;
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use std::sync::Arc;
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use crate::SEED;
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use burn_common::rand::get_seeded_rng;
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use burn_tensor::Shape;
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use rand::Rng;
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use wgpu::Buffer;
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use crate::{context::Context, element::WgpuElement, kernel_wgsl, tensor::WgpuTensor, SEED};
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kernel_wgsl!(Prng, "../../template/prng/prng.wgsl");
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pub(crate) fn get_seeds() -> Vec<u32> {
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let mut seed = SEED.lock().unwrap();
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@ -17,6 +23,24 @@ pub(crate) fn get_seeds() -> Vec<u32> {
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seeds
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}
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pub(crate) fn make_output_tensor<E: WgpuElement, const D: usize>(
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context: Arc<Context>,
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shape: Shape<D>,
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) -> WgpuTensor<E, D> {
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let buffer = context.create_buffer(shape.num_elements() * core::mem::size_of::<E>());
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WgpuTensor::new(context.clone(), shape, buffer)
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}
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pub(crate) fn make_info_buffer(context: Arc<Context>, n_values_per_thread: usize) -> Arc<Buffer> {
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let mut info = get_seeds();
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info.insert(0, n_values_per_thread as u32);
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context.create_buffer_with_data(bytemuck::cast_slice(&info))
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}
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pub(crate) fn make_args_buffer<E: WgpuElement>(context: Arc<Context>, args: &[E]) -> Arc<Buffer> {
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context.create_buffer_with_data(E::as_bytes(args))
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}
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#[cfg(test)]
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pub mod tests {
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use burn_tensor::Element;
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@ -1,16 +1,31 @@
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use burn_tensor::Shape;
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use crate::{
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context::WorkGroup,
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element::WgpuElement,
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kernel::{prng::base::get_seeds, KernelSettings},
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kernel_wgsl,
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kernel::{
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prng::base::{make_args_buffer, make_info_buffer, make_output_tensor},
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prng_workgroup, KernelSettings, SourceTemplate, StaticKernel,
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},
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pool::get_context,
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tensor::WgpuTensor,
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GraphicsApi, WgpuDevice,
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};
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kernel_wgsl!(BernoulliPRNG, "../../template/prng/bernoulli.wgsl");
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use super::base::Prng;
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struct BernoulliPrng;
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impl StaticKernel for BernoulliPrng {
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fn source_template() -> SourceTemplate {
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Prng::source_template()
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.register("num_args", "1")
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.register(
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"prng_loop",
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include_str!("../../template/prng/bernoulli_inner_loop.wgsl"),
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)
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.add_template("fn cast_elem(e: bool) -> {{ elem }} {return {{elem}}(e);}")
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}
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}
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/// Pseudo-random generator for bernoulli
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pub fn random_bernoulli<G: GraphicsApi, E: WgpuElement, const D: usize>(
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@ -18,32 +33,17 @@ pub fn random_bernoulli<G: GraphicsApi, E: WgpuElement, const D: usize>(
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device: &WgpuDevice,
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prob: E,
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) -> WgpuTensor<E, D> {
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let context = get_context::<G>(device);
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const WORKGROUP: usize = 32;
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const N_VALUES_PER_THREAD: u32 = 128;
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let num_elems = shape.num_elements();
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let num_threads = f32::ceil(num_elems as f32 / N_VALUES_PER_THREAD as f32);
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let num_invocations = f32::ceil(num_threads / (WORKGROUP * WORKGROUP) as f32);
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let workgroup_x = f32::ceil(f32::sqrt(num_invocations));
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let workgroup_y = f32::ceil(num_invocations / workgroup_x);
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let workgroup = WorkGroup::new(workgroup_x as u32, workgroup_y as u32, 1);
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const N_VALUES_PER_THREAD: usize = 128;
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let buffer = context.create_buffer(num_elems * core::mem::size_of::<E>());
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let output = WgpuTensor::new(context.clone(), shape, buffer);
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let mut info = get_seeds();
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info.insert(0, N_VALUES_PER_THREAD);
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let info_buffer = context.create_buffer_with_data(bytemuck::cast_slice(&info));
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let args = [prob];
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let args_buffer = context.create_buffer_with_data(E::as_bytes(&args));
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let kernel =
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context.compile_static::<KernelSettings<BernoulliPRNG, E, i32, WORKGROUP, WORKGROUP, 1>>();
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let context = get_context::<G>(device);
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let output = make_output_tensor(context.clone(), shape.clone());
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let info_buffer = make_info_buffer(context.clone(), N_VALUES_PER_THREAD);
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let args_buffer = make_args_buffer(context.clone(), &[prob]);
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context.execute(
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workgroup,
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kernel,
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prng_workgroup(shape.num_elements(), WORKGROUP, N_VALUES_PER_THREAD),
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context.compile_static::<KernelSettings<BernoulliPrng, E, i32, WORKGROUP, WORKGROUP, 1>>(),
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&[&output.buffer, &info_buffer, &args_buffer],
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);
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@ -55,10 +55,12 @@ mod tests {
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use core::f32;
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use burn_tensor::{backend::Backend, Distribution, Shape, Tensor};
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use serial_test::serial;
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use crate::{kernel::prng::base::tests::calculate_bin_stats, tests::TestBackend, WgpuDevice};
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#[test]
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#[serial]
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fn subsequent_calls_give_different_tensors() {
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TestBackend::seed(0);
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let shape: Shape<2> = [40, 40].into();
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@ -85,6 +87,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn number_of_1_proportional_to_prob() {
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TestBackend::seed(0);
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let shape: Shape<2> = [40, 40].into();
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@ -101,11 +104,12 @@ mod tests {
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let bin_stats = calculate_bin_stats(tensor_1.into_data().value, 2, 0., 1.1);
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assert!(
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f32::abs((bin_stats[1].count as f32 / shape.num_elements() as f32) - prob as f32)
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< 0.01
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< 0.05
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);
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}
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#[test]
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#[serial]
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fn runs_test() {
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TestBackend::seed(0);
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let shape = Shape::new([512, 512]);
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@ -128,6 +132,7 @@ mod tests {
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let z = (n_runs - expectation) / variance.sqrt();
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// below 2 means we can have good confidence in the randomness
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assert!(z.abs() < 2.);
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// we put 2.5 to make sure it passes even when very unlucky
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assert!(z.abs() < 2.5);
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}
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}
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@ -1,16 +1,33 @@
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use burn_tensor::Shape;
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use crate::{
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context::WorkGroup,
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element::WgpuElement,
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kernel::{prng::base::get_seeds, KernelSettings},
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kernel_wgsl,
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kernel::{
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prng::base::{make_args_buffer, make_info_buffer, make_output_tensor},
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prng_workgroup, KernelSettings, SourceTemplate, StaticKernel,
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},
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pool::get_context,
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tensor::WgpuTensor,
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GraphicsApi, WgpuDevice,
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};
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kernel_wgsl!(NormalPRNG, "../../template/prng/normal.wgsl");
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use super::base::Prng;
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struct NormalPrng;
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impl StaticKernel for NormalPrng {
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fn source_template() -> SourceTemplate {
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Prng::source_template()
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.register("num_args", "2")
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.register(
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"prng_loop",
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include_str!("../../template/prng/normal_inner_loop.wgsl"),
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)
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.add_template(include_str!(
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"../../template/prng/box_muller_transform.wgsl"
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))
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}
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}
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/// Pseudo-random generator for normal distribution
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pub fn random_normal<G: GraphicsApi, E: WgpuElement, const D: usize>(
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@ -19,33 +36,17 @@ pub fn random_normal<G: GraphicsApi, E: WgpuElement, const D: usize>(
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mean: E,
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std: E,
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) -> WgpuTensor<E, D> {
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let context = get_context::<G>(device);
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const WORKGROUP: usize = 32;
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const N_VALUES_PER_THREAD: u32 = 128; // must be even
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const N_VALUES_PER_THREAD: usize = 128; // must be even
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let num_elems = shape.num_elements();
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let num_threads = f32::ceil(num_elems as f32 / N_VALUES_PER_THREAD as f32);
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let num_invocations = f32::ceil(num_threads / (WORKGROUP * WORKGROUP) as f32);
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let workgroup_x = f32::ceil(f32::sqrt(num_invocations));
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let workgroup_y = f32::ceil(num_invocations / workgroup_x);
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let workgroup = WorkGroup::new(workgroup_x as u32, workgroup_y as u32, 1);
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let buffer = context.create_buffer(num_elems * core::mem::size_of::<E>());
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let output = WgpuTensor::new(context.clone(), shape, buffer);
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let mut info = get_seeds();
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info.insert(0, N_VALUES_PER_THREAD);
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let info_buffer = context.create_buffer_with_data(bytemuck::cast_slice(&info));
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let args = [mean, std];
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let args_buffer = context.create_buffer_with_data(E::as_bytes(&args));
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let kernel =
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context.compile_static::<KernelSettings<NormalPRNG, E, i32, WORKGROUP, WORKGROUP, 1>>();
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let context = get_context::<G>(device);
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let output = make_output_tensor(context.clone(), shape.clone());
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let info_buffer = make_info_buffer(context.clone(), N_VALUES_PER_THREAD);
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let args_buffer = make_args_buffer(context.clone(), &[mean, std]);
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context.execute(
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workgroup,
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kernel,
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prng_workgroup(shape.num_elements(), WORKGROUP, N_VALUES_PER_THREAD),
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context.compile_static::<KernelSettings<NormalPrng, E, i32, WORKGROUP, WORKGROUP, 1>>(),
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&[&output.buffer, &info_buffer, &args_buffer],
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);
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@ -56,10 +57,12 @@ pub fn random_normal<G: GraphicsApi, E: WgpuElement, const D: usize>(
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mod tests {
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use burn_tensor::{backend::Backend, Data, Distribution, Shape, Tensor};
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use serial_test::serial;
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use crate::{kernel::prng::base::tests::calculate_bin_stats, tests::TestBackend, WgpuDevice};
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#[test]
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#[serial]
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fn subsequent_calls_give_different_tensors() {
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TestBackend::seed(0);
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let shape = [4, 5];
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@ -75,6 +78,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn empirical_mean_close_to_expectation() {
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TestBackend::seed(0);
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let shape = [128, 128];
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@ -87,6 +91,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn normal_respects_68_95_99_rule() {
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// https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule
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let shape: Shape<2> = [1000, 1000].into();
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@ -106,7 +111,7 @@ mod tests {
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);
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let assert_approx_eq = |count, percent| {
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let expected = percent * shape.num_elements() as f32 / 100.;
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assert!(f32::abs(count as f32 - expected) < 1000.);
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assert!(f32::abs(count as f32 - expected) < 2000.);
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};
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assert_approx_eq(stats[0].count, 2.1);
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assert_approx_eq(stats[1].count, 13.6);
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@ -1,16 +1,28 @@
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use burn_tensor::Shape;
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use crate::{
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context::WorkGroup,
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element::WgpuElement,
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kernel::{prng::base::get_seeds, KernelSettings},
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kernel_wgsl,
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kernel::{
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prng::base::{make_args_buffer, make_info_buffer, make_output_tensor},
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prng_workgroup, KernelSettings, SourceTemplate, StaticKernel,
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},
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pool::get_context,
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tensor::WgpuTensor,
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GraphicsApi, WgpuDevice,
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};
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kernel_wgsl!(UniformPRNG, "../../template/prng/uniform.wgsl");
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use super::base::Prng;
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struct UniformPrng;
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impl StaticKernel for UniformPrng {
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fn source_template() -> SourceTemplate {
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Prng::source_template().register("num_args", "2").register(
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"prng_loop",
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include_str!("../../template/prng/uniform_inner_loop.wgsl"),
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)
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}
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}
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/// Pseudo-random generator for uniform distribution
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pub fn random_uniform<G: GraphicsApi, E: WgpuElement, const D: usize>(
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@ -19,32 +31,17 @@ pub fn random_uniform<G: GraphicsApi, E: WgpuElement, const D: usize>(
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low: E,
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high: E,
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) -> WgpuTensor<E, D> {
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let context = get_context::<G>(device);
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const WORKGROUP: usize = 32;
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const N_VALUES_PER_THREAD: u32 = 128;
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let num_elems = shape.num_elements();
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let num_threads = f32::ceil(num_elems as f32 / N_VALUES_PER_THREAD as f32);
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let num_invocations = f32::ceil(num_threads / (WORKGROUP * WORKGROUP) as f32);
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let workgroup_x = f32::ceil(f32::sqrt(num_invocations));
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let workgroup_y = f32::ceil(num_invocations / workgroup_x);
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let workgroup = WorkGroup::new(workgroup_x as u32, workgroup_y as u32, 1);
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const N_VALUES_PER_THREAD: usize = 128;
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let buffer = context.create_buffer(num_elems * core::mem::size_of::<E>());
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let output = WgpuTensor::new(context.clone(), shape, buffer);
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let mut info = get_seeds();
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info.insert(0, N_VALUES_PER_THREAD);
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let info_buffer = context.create_buffer_with_data(bytemuck::cast_slice(&info));
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let args = [low, high];
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let args_buffer = context.create_buffer_with_data(E::as_bytes(&args));
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let kernel =
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context.compile_static::<KernelSettings<UniformPRNG, E, i32, WORKGROUP, WORKGROUP, 1>>();
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let context = get_context::<G>(device);
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let output = make_output_tensor(context.clone(), shape.clone());
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let info_buffer = make_info_buffer(context.clone(), N_VALUES_PER_THREAD);
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let args_buffer = make_args_buffer(context.clone(), &[low, high]);
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context.execute(
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workgroup,
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kernel,
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prng_workgroup(shape.num_elements(), WORKGROUP, N_VALUES_PER_THREAD),
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context.compile_static::<KernelSettings<UniformPrng, E, i32, WORKGROUP, WORKGROUP, 1>>(),
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&[&output.buffer, &info_buffer, &args_buffer],
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);
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|
@ -56,10 +53,12 @@ mod tests {
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use core::f32;
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use burn_tensor::{backend::Backend, Distribution, Shape, Tensor};
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use serial_test::serial;
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use crate::{kernel::prng::base::tests::calculate_bin_stats, tests::TestBackend, WgpuDevice};
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#[test]
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#[serial]
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fn subsequent_calls_give_different_tensors() {
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TestBackend::seed(0);
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let shape = [4, 5];
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|
@ -75,6 +74,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn values_all_within_interval_default() {
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TestBackend::seed(0);
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let shape = [24, 24];
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|
@ -85,6 +85,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn values_all_within_interval_uniform() {
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TestBackend::seed(0);
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let shape = [24, 24];
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|
@ -96,6 +97,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn at_least_one_value_per_bin_uniform() {
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TestBackend::seed(0);
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let shape = [64, 64];
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|
@ -114,6 +116,7 @@ mod tests {
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}
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#[test]
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#[serial]
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fn runs_test() {
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TestBackend::seed(0);
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let shape = Shape::new([512, 512]);
|
||||
|
@ -133,6 +136,7 @@ mod tests {
|
|||
let z = (n_runs - expectation) / variance.sqrt();
|
||||
|
||||
// below 2 means we can have good confidence in the randomness
|
||||
assert!(z.abs() < 2.);
|
||||
// we put 2.5 to make sure it passes even when very unlucky
|
||||
assert!(z.abs() < 2.5);
|
||||
}
|
||||
}
|
||||
|
|
|
@ -1,60 +0,0 @@
|
|||
@group(0)
|
||||
@binding(0)
|
||||
var<storage, read_write> output: array<{{ elem }}>;
|
||||
|
||||
@group(0)
|
||||
@binding(1)
|
||||
var<storage, read> info: array<u32, 5>;
|
||||
|
||||
@group(0)
|
||||
@binding(2)
|
||||
var<storage, read> args: array<{{ elem }}, 2>;
|
||||
|
||||
@compute
|
||||
@workgroup_size({{ workgroup_size_x }}, {{ workgroup_size_y }}, 1)
|
||||
fn main(
|
||||
@builtin(local_invocation_index) local_id: u32,
|
||||
@builtin(num_workgroups) num_workgroups: vec3<u32>,
|
||||
@builtin(workgroup_id) workgroup_id: vec3<u32>,
|
||||
) {
|
||||
let wg_size_x = {{ workgroup_size_x }}u;
|
||||
let wg_size_y = {{ workgroup_size_y }}u;
|
||||
let wg = workgroup_id.x * num_workgroups.y + workgroup_id.y;
|
||||
let n_threads_per_workgroup = wg_size_x * wg_size_y;
|
||||
let wg_offset = wg * n_threads_per_workgroup;
|
||||
let unique_thread_id = wg_offset + local_id;
|
||||
let large_prime = 1000000007u;
|
||||
let thread_seed = large_prime * unique_thread_id;
|
||||
|
||||
var state: array<u32, 4u>;
|
||||
for (var i = 0u; i < 4u; i++) {
|
||||
state[i] = info[i + 1u] + thread_seed;
|
||||
}
|
||||
|
||||
let n_values_per_thread = info[0u];
|
||||
for (var i = 0u; i < n_values_per_thread; i++) {
|
||||
state[0u] = taus_step(state[0u], 13u, 19u, 12u, 4294967294u);
|
||||
state[1u] = taus_step(state[1u], 2u, 25u, 4u, 4294967288u);
|
||||
state[2u] = taus_step(state[2u], 3u, 11u, 17u, 4294967280u);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let hybrid_taus = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let write_index = wg_offset * n_values_per_thread + local_id + i * n_threads_per_workgroup;
|
||||
let float = cast_float(hybrid_taus);
|
||||
|
||||
let prob = args[0];
|
||||
output[write_index] = {{ elem }}(float < prob);
|
||||
}
|
||||
}
|
||||
|
||||
fn lcg_step(z: u32) -> u32 {
|
||||
return (1664525u * z + 1013904223u); // modulo 2^32, not necessary in u32
|
||||
}
|
||||
|
||||
fn taus_step(z: u32, s1: u32, s2: u32, s3: u32, m: u32) -> u32 {
|
||||
let b = ((z << s1) ^ z) >> s2;
|
||||
return (z & m) << s3 ^ b;
|
||||
}
|
||||
|
||||
fn cast_float(number: u32) -> {{ elem }} {
|
||||
return 2.3283064365387e-10 * {{ elem }}(number);
|
||||
}
|
|
@ -0,0 +1,13 @@
|
|||
let prob = args[0];
|
||||
for (var i = 0u; i < n_values_per_thread; i++) {
|
||||
let write_index = write_index_base + i * n_threads_per_workgroup;
|
||||
|
||||
state[0u] = taus_step_0(state[0u]);
|
||||
state[1u] = taus_step_1(state[1u]);
|
||||
state[2u] = taus_step_2(state[2u]);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let random_u32 = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let float = cast_float(random_u32);
|
||||
|
||||
output[write_index] = cast_elem(float < prob);
|
||||
}
|
|
@ -0,0 +1,12 @@
|
|||
fn box_muller_transform(unit_1: {{ elem }}, unit_2: {{ elem }}) -> array<{{ elem }}, 2> {
|
||||
let mean = args[0];
|
||||
let stdev = args[1];
|
||||
let coeff = stdev * sqrt(-2.0 * log(unit_1));
|
||||
|
||||
let pi = 3.141592653589793238;
|
||||
let trigo_arg = 2.0 * pi * unit_2;
|
||||
let cos_ = cos(trigo_arg);
|
||||
let sin_ = sin(trigo_arg);
|
||||
|
||||
return array(coeff * cos_ + mean, coeff * sin_ + mean);
|
||||
}
|
|
@ -1,81 +0,0 @@
|
|||
@group(0)
|
||||
@binding(0)
|
||||
var<storage, read_write> output: array<{{ elem }}>;
|
||||
|
||||
@group(0)
|
||||
@binding(1)
|
||||
var<storage, read> info: array<u32, 5>;
|
||||
|
||||
@group(0)
|
||||
@binding(2)
|
||||
var<storage, read> args: array<{{ elem }}, 2>;
|
||||
|
||||
@compute
|
||||
@workgroup_size({{ workgroup_size_x }}, {{ workgroup_size_y }}, 1)
|
||||
fn main(
|
||||
@builtin(local_invocation_index) local_id: u32,
|
||||
@builtin(num_workgroups) num_workgroups: vec3<u32>,
|
||||
@builtin(workgroup_id) workgroup_id: vec3<u32>,
|
||||
) {
|
||||
let wg_size_x = {{ workgroup_size_x }}u;
|
||||
let wg_size_y = {{ workgroup_size_y }}u;
|
||||
let wg = workgroup_id.x * num_workgroups.y + workgroup_id.y;
|
||||
let n_threads_per_workgroup = wg_size_x * wg_size_y;
|
||||
let wg_offset = wg * n_threads_per_workgroup;
|
||||
let unique_thread_id = wg_offset + local_id;
|
||||
let large_prime = 1000000007u;
|
||||
let thread_seed = large_prime * unique_thread_id;
|
||||
|
||||
var state: array<u32, 4u>;
|
||||
for (var i = 0u; i < 4u; i++) {
|
||||
state[i] = info[i + 1u] + thread_seed;
|
||||
}
|
||||
|
||||
let n_values_per_thread = info[0u];
|
||||
// TODO ASSERT random_normal n threads is even
|
||||
for (var i = 0u; i < n_values_per_thread / 2u; i++) {
|
||||
var units: array<{{elem}}, 2>;
|
||||
for (var j = 0u; j < 2u; j++) {
|
||||
state[0u] = taus_step(state[0u], 13u, 19u, 12u, 4294967294u);
|
||||
state[1u] = taus_step(state[1u], 2u, 25u, 4u, 4294967288u);
|
||||
state[2u] = taus_step(state[2u], 3u, 11u, 17u, 4294967280u);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let hybrid_taus = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
units[j] = cast_float(hybrid_taus);
|
||||
}
|
||||
|
||||
let transformed = box_muller_transform(units[0], units[1]);
|
||||
|
||||
let write_index_0 = wg_offset * n_values_per_thread + local_id + (2u * i) * n_threads_per_workgroup;
|
||||
let write_index_1 = write_index_0 + n_threads_per_workgroup;
|
||||
|
||||
output[write_index_0] = transformed[0];
|
||||
output[write_index_1] = transformed[1];
|
||||
}
|
||||
}
|
||||
|
||||
fn lcg_step(z: u32) -> u32 {
|
||||
return (1664525u * z + 1013904223u);
|
||||
}
|
||||
|
||||
fn taus_step(z: u32, s1: u32, s2: u32, s3: u32, m: u32) -> u32 {
|
||||
let b = ((z << s1) ^ z) >> s2;
|
||||
return (z & m) << s3 ^ b;
|
||||
}
|
||||
|
||||
fn cast_float(number: u32) -> {{ elem }} {
|
||||
return 2.3283064365387e-10 * {{ elem }}(number);
|
||||
}
|
||||
|
||||
fn box_muller_transform(unit_1: {{ elem }}, unit_2: {{ elem }}) -> array<{{elem}}, 2> {
|
||||
let mean = args[0];
|
||||
let stdev = args[1];
|
||||
let coeff = stdev * sqrt(-2.0 * log(unit_1));
|
||||
|
||||
let pi = 3.141592653589793238;
|
||||
let trigo_arg = 2.0 * pi * unit_2;
|
||||
let cos_ = cos(trigo_arg);
|
||||
let sin_ = sin(trigo_arg);
|
||||
|
||||
return array(coeff * cos_ + mean, coeff * sin_ + mean);
|
||||
}
|
|
@ -0,0 +1,23 @@
|
|||
for (var i = 0u; i < n_values_per_thread / 2u; i++) {
|
||||
let write_index_0 = write_index_base + (2u * i) * n_threads_per_workgroup;
|
||||
let write_index_1 = write_index_0 + n_threads_per_workgroup;
|
||||
|
||||
state[0u] = taus_step_0(state[0u]);
|
||||
state[1u] = taus_step_1(state[1u]);
|
||||
state[2u] = taus_step_2(state[2u]);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let random_1_u32 = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let random_1 = cast_float(random_1_u32);
|
||||
|
||||
state[0u] = taus_step_0(state[0u]);
|
||||
state[1u] = taus_step_1(state[1u]);
|
||||
state[2u] = taus_step_2(state[2u]);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let random_2_u32 = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let random_2 = cast_float(random_2_u32);
|
||||
|
||||
let transformed = box_muller_transform(random_1, random_2);
|
||||
|
||||
output[write_index_0] = transformed[0];
|
||||
output[write_index_1] = transformed[1];
|
||||
}
|
|
@ -0,0 +1,60 @@
|
|||
@group(0)
|
||||
@binding(0)
|
||||
var<storage, read_write> output: array<{{ elem }}>;
|
||||
|
||||
@group(0)
|
||||
@binding(1)
|
||||
var<storage, read> info: array<u32, 5>;
|
||||
|
||||
@group(0)
|
||||
@binding(2)
|
||||
var<storage, read> args: array<{{ elem }}, {{ num_args }}>;
|
||||
|
||||
@compute
|
||||
@workgroup_size({{ workgroup_size_x }}, {{ workgroup_size_y }}, 1)
|
||||
fn main(
|
||||
@builtin(local_invocation_index) local_id: u32,
|
||||
@builtin(num_workgroups) num_workgroups: vec3<u32>,
|
||||
@builtin(workgroup_id) workgroup_id: vec3<u32>,
|
||||
) {
|
||||
// Thread preparation
|
||||
let n_threads_per_workgroup = {{ workgroup_size }}u;
|
||||
let workgroup_offset = (workgroup_id.x * num_workgroups.y + workgroup_id.y) * n_threads_per_workgroup;
|
||||
let n_values_per_thread = info[0u];
|
||||
let write_index_base = workgroup_offset * n_values_per_thread + local_id;
|
||||
|
||||
// Set state with unique seeds
|
||||
let thread_seed = 1000000007u * (workgroup_offset + local_id);
|
||||
var state: array<u32, 4u>;
|
||||
for (var i = 0u; i < 4u; i++) {
|
||||
state[i] = info[i + 1u] + thread_seed;
|
||||
}
|
||||
|
||||
// Creation of n_values_per_thread values, specific to the distribution
|
||||
{{ prng_loop }}
|
||||
}
|
||||
|
||||
fn taus_step(z: u32, s1: u32, s2: u32, s3: u32, m: u32) -> u32 {
|
||||
let b = ((z << s1) ^ z) >> s2;
|
||||
return (z & m) << s3 ^ b;
|
||||
}
|
||||
|
||||
fn taus_step_0(z: u32) -> u32 {
|
||||
return taus_step(z, 13u, 19u, 12u, 4294967294u);
|
||||
}
|
||||
|
||||
fn taus_step_1(z: u32) -> u32 {
|
||||
return taus_step(z, 2u, 25u, 4u, 4294967288u);
|
||||
}
|
||||
|
||||
fn taus_step_2(z: u32) -> u32 {
|
||||
return taus_step(z, 3u, 11u, 17u, 4294967280u);
|
||||
}
|
||||
|
||||
fn lcg_step(z: u32) -> u32 {
|
||||
return (1664525u * z + 1013904223u);
|
||||
}
|
||||
|
||||
fn cast_float(number: u32) -> {{ elem }} {
|
||||
return 2.3283064365387e-10 * {{ elem }}(number);
|
||||
}
|
|
@ -1,63 +0,0 @@
|
|||
@group(0)
|
||||
@binding(0)
|
||||
var<storage, read_write> output: array<{{ elem }}>;
|
||||
|
||||
@group(0)
|
||||
@binding(1)
|
||||
var<storage, read> info: array<u32, 5>;
|
||||
|
||||
@group(0)
|
||||
@binding(2)
|
||||
var<storage, read> args: array<{{ elem }}, 2>;
|
||||
|
||||
@compute
|
||||
@workgroup_size({{ workgroup_size_x }}, {{ workgroup_size_y }}, 1)
|
||||
fn main(
|
||||
@builtin(local_invocation_index) local_id: u32,
|
||||
@builtin(num_workgroups) num_workgroups: vec3<u32>,
|
||||
@builtin(workgroup_id) workgroup_id: vec3<u32>,
|
||||
) {
|
||||
let wg_size_x = {{ workgroup_size_x }}u;
|
||||
let wg_size_y = {{ workgroup_size_y }}u;
|
||||
let wg = workgroup_id.x * num_workgroups.y + workgroup_id.y;
|
||||
let n_threads_per_workgroup = wg_size_x * wg_size_y;
|
||||
let wg_offset = wg * n_threads_per_workgroup;
|
||||
let unique_thread_id = wg_offset + local_id;
|
||||
let large_prime = 1000000007u;
|
||||
let thread_seed = large_prime * unique_thread_id;
|
||||
|
||||
var state: array<u32, 4u>;
|
||||
for (var i = 0u; i < 4u; i++) {
|
||||
state[i] = info[i + 1u] + thread_seed;
|
||||
}
|
||||
|
||||
let n_values_per_thread = info[0u];
|
||||
for (var i = 0u; i < n_values_per_thread; i++) {
|
||||
state[0u] = taus_step(state[0u], 13u, 19u, 12u, 4294967294u);
|
||||
state[1u] = taus_step(state[1u], 2u, 25u, 4u, 4294967288u);
|
||||
state[2u] = taus_step(state[2u], 3u, 11u, 17u, 4294967280u);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let hybrid_taus = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let write_index = wg_offset * n_values_per_thread + local_id + i * n_threads_per_workgroup;
|
||||
let float = cast_float(hybrid_taus);
|
||||
|
||||
let low = args[0];
|
||||
let high = args[1];
|
||||
let scale = high - low;
|
||||
let bias = low;
|
||||
output[write_index] = float * scale + bias;
|
||||
}
|
||||
}
|
||||
|
||||
fn lcg_step(z: u32) -> u32 {
|
||||
return (1664525u * z + 1013904223u); // modulo 2^32, not necessary in u32
|
||||
}
|
||||
|
||||
fn taus_step(z: u32, s1: u32, s2: u32, s3: u32, m: u32) -> u32 {
|
||||
let b = ((z << s1) ^ z) >> s2;
|
||||
return (z & m) << s3 ^ b;
|
||||
}
|
||||
|
||||
fn cast_float(number: u32) -> {{ elem }} {
|
||||
return 2.3283064365387e-10 * {{ elem }}(number);
|
||||
}
|
|
@ -0,0 +1,16 @@
|
|||
let low = args[0];
|
||||
let high = args[1];
|
||||
let scale = high - low;
|
||||
let bias = low;
|
||||
for (var i = 0u; i < n_values_per_thread; i++) {
|
||||
let write_index = write_index_base + i * n_threads_per_workgroup;
|
||||
|
||||
state[0u] = taus_step_0(state[0u]);
|
||||
state[1u] = taus_step_1(state[1u]);
|
||||
state[2u] = taus_step_2(state[2u]);
|
||||
state[3u] = lcg_step(state[3u]);
|
||||
let random_u32 = state[0u] ^ state[1u] ^ state[2u] ^ state[3u];
|
||||
let float = cast_float(random_u32);
|
||||
|
||||
output[write_index] = float * scale + bias;
|
||||
}
|
Loading…
Reference in New Issue