numr 0.5.1

High-performance numerical computing with multi-backend GPU acceleration (CPU/CUDA/WebGPU)
Documentation 
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//! Basic linear algebra operations: trace, diag, diagflat, create_identity

use wgpu::{Buffer, Queue};

use super::check_dtype_f32;
use crate::dtype::DType;
use crate::error::Result;
use crate::runtime::wgpu::shaders::linalg_shaders::basic_ops::BASIC_OPS_SHADER;
use crate::runtime::wgpu::shaders::pipeline::{LayoutKey, PipelineCache, workgroup_count};

/// Launch trace kernel to compute sum of diagonal elements.
pub fn launch_trace(
    cache: &PipelineCache,
    queue: &Queue,
    input: &Buffer,
    output: &Buffer,
    params_buffer: &Buffer,
    n: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "trace");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 2,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline = cache.get_or_create_pipeline("linalg_basic_ops", "trace_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[input, output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("trace"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("trace"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(n), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}

/// Launch diag kernel to extract diagonal elements from matrix.
pub fn launch_diag(
    cache: &PipelineCache,
    queue: &Queue,
    input: &Buffer,
    output: &Buffer,
    params_buffer: &Buffer,
    min_dim: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "diag");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 2,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline = cache.get_or_create_pipeline("linalg_basic_ops", "diag_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[input, output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("diag"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("diag"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(min_dim), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}

/// Launch diagflat kernel to create diagonal matrix from vector.
pub fn launch_diagflat(
    cache: &PipelineCache,
    queue: &Queue,
    input: &Buffer,
    output: &Buffer,
    params_buffer: &Buffer,
    n: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "diagflat");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 2,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline =
        cache.get_or_create_pipeline("linalg_basic_ops", "diagflat_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[input, output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("diagflat"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("diagflat"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(n * n), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}

/// Create identity matrix on GPU.
pub fn launch_create_identity(
    cache: &PipelineCache,
    queue: &Queue,
    output: &Buffer,
    params_buffer: &Buffer,
    n: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "create_identity");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 1,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline =
        cache.get_or_create_pipeline("linalg_basic_ops", "create_identity_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("create_identity"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("create_identity"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(n * n), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}

/// Launch Kronecker product kernel: out = A ⊗ B
pub fn launch_kron(
    cache: &PipelineCache,
    queue: &Queue,
    a: &Buffer,
    b: &Buffer,
    output: &Buffer,
    params_buffer: &Buffer,
    total_elements: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "kron");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 3,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline = cache.get_or_create_pipeline("linalg_basic_ops", "kron_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[a, b, output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("kron"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("kron"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(total_elements), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}

/// Launch Khatri-Rao product kernel: out = A ⊙ B (column-wise Kronecker)
pub fn launch_khatri_rao(
    cache: &PipelineCache,
    queue: &Queue,
    a: &Buffer,
    b: &Buffer,
    output: &Buffer,
    params_buffer: &Buffer,
    total_elements: usize,
    dtype: DType,
) -> Result<()> {
    check_dtype_f32!(dtype, "khatri_rao");

    let module = cache.get_or_create_module("linalg_basic_ops", BASIC_OPS_SHADER);
    let layout = cache.get_or_create_layout(LayoutKey {
        num_storage_buffers: 3,
        num_uniform_buffers: 1,
        num_readonly_storage: 0,
    });
    let pipeline =
        cache.get_or_create_pipeline("linalg_basic_ops", "khatri_rao_f32", &module, &layout);

    let bind_group = cache.create_bind_group(&layout, &[a, b, output, params_buffer]);

    let mut encoder = cache
        .device()
        .create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("khatri_rao"),
        });

    {
        let mut pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("khatri_rao"),
            timestamp_writes: None,
        });
        pass.set_pipeline(&pipeline);
        pass.set_bind_group(0, Some(&bind_group), &[]);
        pass.dispatch_workgroups(workgroup_count(total_elements), 1, 1);
    }

    queue.submit(std::iter::once(encoder.finish()));
    Ok(())
}