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use crate::{prelude::{get_broadcast_shape, GpuOperations, GpuRunner}, tensor::Tensor};
impl GpuRunner{
/// Perform a broadcast addition operation on tensor using GpuRunner
/// No need to care about output shape while creating sample
/// It is managed by GpuRunner
///
/// # Example
/// ```
/// use flashlight_tensor::prelude::*;
///
/// #[tokio::main]
/// async fn main(){
/// if std::env::var("CI").is_ok() {
/// eprintln!("Skipping GPU test in CI");
/// return;
/// }
/// let mut runner: GpuRunner = GpuRunner::init(1, MemoryMetric::GB);
///
/// let sample = Sample::from_data(vec!{Tensor::fill(1.0, &[2, 1]), Tensor::fill(1.0, &[1, 2])}, vec!{}, &[]);
///
/// runner.append(sample);
///
/// //output shape: [2, 2]
/// let output_data: Vec<Tensor<f32>> = runner.tens_broadcast_add().await;
/// }
/// ```
pub async fn tens_broadcast_add(&mut self) -> Vec<Tensor<f32>>{
let flat_shapes_len = self.gpu_data.flat_shapes.len();
self.gpu_data.output_shape = get_broadcast_shape(&self.gpu_data.flat_shapes[0..flat_shapes_len/2].to_vec(), &self.gpu_data.flat_shapes[flat_shapes_len/2..].to_vec()).unwrap();
self.gpu_data.output_len = self.gpu_data.output_shape.iter().product::<u32>() as usize * self.gpu_data.samples_count as usize;
self.gpu_data.output_per_sample = self.gpu_data.output_shape.iter().product::<u32>() as usize;
self.gpu_data.enable_shapes();
self.gpu_data.disable_params();
self.gpu_data.disable_single_output();
self.gpu_data.prepare_chunking_alt(self.buffer_size);
let mut return_vec: Vec<Tensor<f32>> = self.run_ops(&GpuOperations::BroadcastAdd).await;
let (fix_needed, new_return_vec) = self.fix_for_single_output(&return_vec).await;
if fix_needed{
return new_return_vec;
}
return_vec
}
/// Perform a broadcast subtraction operation on tensor using GpuRunner
/// No need to care about output shape while creating sample
/// It is managed by GpuRunner
///
/// # Example
/// ```
/// use flashlight_tensor::prelude::*;
///
/// #[tokio::main]
/// async fn main(){
/// if std::env::var("CI").is_ok() {
/// eprintln!("Skipping GPU test in CI");
/// return;
/// }
/// let mut runner: GpuRunner = GpuRunner::init(1, MemoryMetric::GB);
///
/// let sample = Sample::from_data(vec!{Tensor::fill(1.0, &[2, 1]), Tensor::fill(1.0, &[1, 2])}, vec!{}, &[]);
///
/// runner.append(sample);
///
/// //output shape: [2, 2]
/// let output_data: Vec<Tensor<f32>> = runner.tens_broadcast_sub().await;
/// }
/// ```
pub async fn tens_broadcast_sub(&mut self) -> Vec<Tensor<f32>>{
let flat_shapes_len = self.gpu_data.flat_shapes.len();
self.gpu_data.output_shape = get_broadcast_shape(&self.gpu_data.flat_shapes[0..flat_shapes_len/2].to_vec(), &self.gpu_data.flat_shapes[flat_shapes_len/2..].to_vec()).unwrap();
self.gpu_data.output_len = self.gpu_data.output_shape.iter().product::<u32>() as usize * self.gpu_data.samples_count as usize;
self.gpu_data.output_per_sample = self.gpu_data.output_shape.iter().product::<u32>() as usize;
self.gpu_data.enable_shapes();
self.gpu_data.disable_params();
self.gpu_data.disable_single_output();
self.gpu_data.prepare_chunking_alt(self.buffer_size);
let mut return_vec: Vec<Tensor<f32>> = self.run_ops(&GpuOperations::BroadcastSub).await;
let (fix_needed, new_return_vec) = self.fix_for_single_output(&return_vec).await;
if fix_needed{
return new_return_vec;
}
return_vec
}
/// Perform a broadcast multiplication operation on tensor using GpuRunner
/// No need to care about output shape while creating sample
/// It is managed by GpuRunner
///
/// # Example
/// ```
/// use flashlight_tensor::prelude::*;
///
/// #[tokio::main]
/// async fn main(){
/// if std::env::var("CI").is_ok() {
/// eprintln!("Skipping GPU test in CI");
/// return;
/// }
/// let mut runner: GpuRunner = GpuRunner::init(1, MemoryMetric::GB);
///
/// let sample = Sample::from_data(vec!{Tensor::fill(1.0, &[2, 1]), Tensor::fill(1.0, &[1, 2])}, vec!{}, &[]);
///
/// runner.append(sample);
///
/// //output shape: [2, 2]
/// let output_data: Vec<Tensor<f32>> = runner.tens_broadcast_mul().await;
/// }
/// ```
pub async fn tens_broadcast_mul(&mut self) -> Vec<Tensor<f32>>{
let flat_shapes_len = self.gpu_data.flat_shapes.len();
self.gpu_data.output_shape = get_broadcast_shape(&self.gpu_data.flat_shapes[0..flat_shapes_len/2].to_vec(), &self.gpu_data.flat_shapes[flat_shapes_len/2..].to_vec()).unwrap();
self.gpu_data.output_len = self.gpu_data.output_shape.iter().product::<u32>() as usize * self.gpu_data.samples_count as usize;
self.gpu_data.output_per_sample = self.gpu_data.output_shape.iter().product::<u32>() as usize;
self.gpu_data.enable_shapes();
self.gpu_data.disable_params();
self.gpu_data.disable_single_output();
self.gpu_data.prepare_chunking_alt(self.buffer_size);
let mut return_vec: Vec<Tensor<f32>> = self.run_ops(&GpuOperations::BroadcastMul).await;
let (fix_needed, new_return_vec) = self.fix_for_single_output(&return_vec).await;
if fix_needed{
return new_return_vec;
}
return_vec
}
/// Perform a broadcast division operation on tensor using GpuRunner
/// No need to care about output shape while creating sample
/// It is managed by GpuRunner
///
/// # Example
/// ```
/// use flashlight_tensor::prelude::*;
///
/// #[tokio::main]
/// async fn main(){
/// if std::env::var("CI").is_ok() {
/// eprintln!("Skipping GPU test in CI");
/// return;
/// }
/// let mut runner: GpuRunner = GpuRunner::init(1, MemoryMetric::GB);
///
/// let sample = Sample::from_data(vec!{Tensor::fill(1.0, &[2, 1]), Tensor::fill(1.0, &[1, 2])}, vec!{}, &[]);
///
/// runner.append(sample);
///
/// //output shape: [2, 2]
/// let output_data: Vec<Tensor<f32>> = runner.tens_broadcast_div().await;
/// }
/// ```
pub async fn tens_broadcast_div(&mut self) -> Vec<Tensor<f32>>{
let flat_shapes_len = self.gpu_data.flat_shapes.len();
self.gpu_data.output_shape = get_broadcast_shape(&self.gpu_data.flat_shapes[0..flat_shapes_len/2].to_vec(), &self.gpu_data.flat_shapes[flat_shapes_len/2..].to_vec()).unwrap();
self.gpu_data.output_len = self.gpu_data.output_shape.iter().product::<u32>() as usize * self.gpu_data.samples_count as usize;
self.gpu_data.output_per_sample = self.gpu_data.output_shape.iter().product::<u32>() as usize;
self.gpu_data.enable_shapes();
self.gpu_data.disable_params();
self.gpu_data.disable_single_output();
self.gpu_data.prepare_chunking_alt(self.buffer_size);
let mut return_vec: Vec<Tensor<f32>> = self.run_ops(&GpuOperations::BroadcastDiv).await;
let (fix_needed, new_return_vec) = self.fix_for_single_output(&return_vec).await;
if fix_needed{
return new_return_vec;
}
return_vec
}
}