ONNXWhileOp

The ONNXWhileOp is a Rust crate defining a mathematical operator used in digital signal processing (DSP) and machine learning computations. This crate provides an implementation of a loop operator, as specified in the Open Neural Network Exchange (ONNX) standard.

Note: This crate is currently being translated from C++ to Rust, and some function bodies may still be in the process of translation.

The ONNXWhileOp allows a graph of operators to be iterated over multiple times, using a set of loop-carried dependencies (LCD) tensors. These tensors are updated in each iteration of the loop, and passed back into the loop for the next iteration. This creates a powerful tool for building complex models, such as recurrent neural networks and dynamic graphs.

The ONNXWhileOp also provides several convenience functions for working with LCD tensors, including set_iteration and OnnxWhileOpLocalScope. Additionally, the crate provides a workspace abstraction for managing the data used by the loop, and an allow_inplace flag for indicating when in-place updates are allowed.

The mathematics behind the ONNXWhileOp operator can be expressed using standard notation for iterative computations. Given an initial set of LCD tensors lcd_tensor, and a graph of operators net, the loop updates the LCD tensors and executes the graph in each iteration. This can be represented as follows:

for i in 1..iteration {
    set_iteration(i)
    lcd_tensor = do_run_with_type(&net, &workspace, &lcd_tensor, &allow_inplace)
}

Here, do_run_with_type is a function that executes the graph of operators net on the input tensors lcd_tensor, using the workspace to manage the data, and respecting the allow_inplace flag. The set_iteration function sets the current iteration number, and is used by other operators to determine their behavior.

Overall, the ONNXWhileOp crate provides a powerful tool for building iterative models in Rust, using a standardized format that can be easily integrated with other ONNX-compliant tools.

2> what are the use cases for running a network in a while loop?

There are several use cases where running a network in a while loop can be useful:

1. Recurrent Neural Networks (RNNs): RNNs are a type of neural network that are designed to process sequential data, such as time-series or natural language. RNNs typically use a loop structure to process each element of the sequence, and the ONNXWhileOp can be used to implement this loop.

2. Conditional Computation: In some cases, it may be necessary to run a network for a variable number of iterations, depending on some condition. For example, in image segmentation tasks, the number of objects in an image may vary, and the ONNXWhileOp can be used to segment each object in the image, with the number of iterations determined by the number of objects.

3. Reinforcement Learning: In reinforcement learning, an agent interacts with an environment and takes actions to maximize a reward signal. The environment can be modeled as a loop, where the agent takes actions and receives rewards in each iteration. The ONNXWhileOp can be used to implement this loop, allowing the agent to learn and adapt over time.

Overall, the ONNXWhileOp provides a flexible and powerful way to implement iterative computations in Rust, and can be used in a wide range of applications across DSP and machine learning.

3> could you show me an example of each of these?

Sure! Here are some examples of how the ONNXWhileOp can be used in each of the use cases I mentioned earlier:

Recurrent Neural Networks (RNNs)

One common use case for the ONNXWhileOp is to implement a recurrent neural network (RNN) in Rust. RNNs are a type of neural network that are designed to process sequential data, such as time-series or natural language. In an RNN, the same set of weights is applied to each element of the sequence, with the output from one step being fed back as input to the next step.

Here's an example of how the ONNXWhileOp can be used to implement a simple RNN in Rust, using the Gluon machine learning library:

use gluon::vm::api::{Hole, OpaqueValue, Userdata};
use gluon::{vm, Thread};

use onnxwhileop::{ONNXWhileOp, OnnxWhileOpLocalScope, run_on_device, workspace};

// Define the RNN graph
let mut net = ONNXWhileOp::new(vec![], vec![], vec![], vec![], vec![]);
net.append_op("linear", vec!["input", "weight", "bias"], vec!["output"]);
net.append_op("tanh", vec!["output"], vec!["output"]);

// Define the input and output tensors
let input = workspace()
    .new_tensor("input", &[1, 1, 10])
    .unwrap()
    .into();

let output = workspace()
    .new_tensor("output", &[1, 1, 10])
    .unwrap()
    .into();

// Run the RNN for 5 iterations
let iteration = 5;

for i in 1..=iteration {
    let iteration_hole = Hole::new::<i64>();
    let mut scope = OnnxWhileOpLocalScope::new();
    scope.add("input", input.clone());
    scope.add("weight", output.clone());
    scope.add("bias", output.clone());
    scope.add("output", output.clone());
    scope.add("iteration_number", iteration_hole.clone().into());

    let (ret, _, _) = run_on_device(
        &mut net,
        &workspace(),
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &scope,
        &iteration_hole,
    )
    .unwrap();
}

This example defines a simple RNN graph with a single linear layer followed by a hyperbolic tangent activation function. The ONNXWhileOp is used to run the graph for 5 iterations, with the output from each iteration being fed back as input to the next iteration.

Conditional Computation

Another use case for the ONNXWhileOp is to implement conditional computation, where the number of iterations is determined by some condition. For example, in image segmentation tasks, the number of objects in an image may vary, and the ONNXWhileOp can be used to segment each object in the image, with the number of iterations determined by the number of objects.

Here's an example of how the ONNXWhileOp can be used to implement conditional computation in Rust, using the Gluon machine learning library:

use gluon::vm::api::{Hole, OpaqueValue, Userdata};
use gluon::{vm, Thread};

use onnxwhileop::{ONNXWhileOp, OnnxWhileOpLocalScope, run_on_device, workspace};

// Define the object segmentation graph
let mut net = ONNXWhileOp::new(vec!

4> continue

let mut net = ONNXWhileOp::new(vec![], vec![], vec![], vec![], vec![]);
net.append_op("image_segmentation", vec!["image"], vec!["mask"]);
net.append_op("count_objects", vec!["mask"], vec!["num_objects"]);

// Define the input and output tensors
let image = workspace()
    .new_tensor("image", &[1, 3, 256, 256])
    .unwrap()
    .into();

let mask = workspace()
    .new_tensor("mask", &[1, 1, 256, 256])
    .unwrap()
    .into();

// Run the object segmentation graph until all objects have been segmented
let mut num_objects = 0;
let mut i = 0;

while num_objects < max_objects {
    let iteration_hole = Hole::new::<i64>();
    let mut scope = OnnxWhileOpLocalScope::new();
    scope.add("image", image.clone());
    scope.add("mask", mask.clone());
    scope.add("iteration_number", iteration_hole.clone().into());

    let (ret, _, _) = run_on_device(
        &mut net,
        &workspace(),
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &scope,
        &iteration_hole,
    )
    .unwrap();

    num_objects = ret[0].as_ref().unwrap().to_scalar::<f32>().unwrap() as usize;
    i += 1;
}

This example defines an object segmentation graph that takes an input image and produces a binary mask indicating which pixels belong to which object. The ONNXWhileOp is used to run the graph until a maximum number of objects have been segmented, with the number of iterations determined dynamically by the number of objects.

Custom Loops

Finally, the ONNXWhileOp can be used to implement custom loops that are not easily expressible using existing machine learning operations. For example, the ONNXWhileOp could be used to implement a custom optimization algorithm that requires iterating over a complex set of equations.

Here's an example of how the ONNXWhileOp can be used to implement a custom loop in Rust:

use onnxwhileop::{ONNXWhileOp, OnnxWhileOpLocalScope, run_on_device, workspace};

// Define the loop graph
let mut net = ONNXWhileOp::new(vec![], vec![], vec![], vec![], vec![]);
net.append_op("update_x", vec!["x", "v"], vec!["x"]);
net.append_op("update_v", vec!["x", "v"], vec!["v"]);

// Define the input and output tensors
let x = workspace().new_tensor("x", &[1]).unwrap().into();
let v = workspace().new_tensor("v", &[1]).unwrap().into();

// Define the loop condition
let mut condition = String::from("");
condition.push_str("cond = (i < max_iter)");

// Define the loop body
let mut body = String::from("");
body.push_str("v = v + lr * grad");
body.push_str("x = x - v");

// Run the custom loop
let max_iter = 10;
let lr = 0.1;
let grad = 0.5;

let iteration_hole = Hole::new::<i64>();
let mut scope = OnnxWhileOpLocalScope::new();
scope.add("x", x.clone());
scope.add("v", v.clone());
scope.add("i", 0i64.into());
scope.add("max_iter", max_iter.into());


5> continue

let mut i = 0i64;

while run_on_device(
    &mut net,
    &workspace(),
    &vec![],
    &vec![],
    &vec![],
    &vec![],
    &vec![],
    &vec![],
    &scope,
    &iteration_hole,
)
.is_ok()
{
    if !condition.eval(&scope).unwrap().to_scalar::<bool>().unwrap() {
        break;
    }

    scope.add("i", (i + 1).into());

    let (ret, _, _) = run_on_device(
        &mut net,
        &workspace(),
        &vec!["grad"],
        &vec![grad.clone()],
        &vec![],
        &vec![],
        &vec![],
        &vec![],
        &scope,
        &iteration_hole,
    )
    .unwrap();

    i += 1;
}

In this example, the ONNXWhileOp is used to implement a custom optimization algorithm that updates a variable x according to the gradient of a loss function. The loop has a fixed number of iterations determined by the max_iter parameter, and the learning rate is controlled by the lr parameter. The loop condition is defined by the string cond = (i < max_iter), and the loop body is defined by the string v = v + lr * grad; x = x - v. Inside the loop, the gradient is updated by the value of the grad parameter, and the iteration number is updated by adding 1 to the previous value of i.

Overall, the ONNXWhileOp provides a powerful and flexible way to implement custom loops in Rust that can be integrated with existing machine learning frameworks. By defining custom loop conditions and bodies, developers can implement a wide range of algorithms that are not easily expressible using existing machine learning operations.

6> what about an example of reinforcement learning?

Sure, here's an example of using the ONNXWhileOp to implement a simple reinforcement learning algorithm:

use onnxruntime::{environment::Environment, tensor::Tensor};
use onnxruntime::{Graph, LoggingLevel};
use rand::{distributions::Uniform, prelude::*};
use std::collections::HashMap;

fn main() {
    // Create the ONNX runtime environment
    let env = Environment::new(LoggingLevel::Warning).unwrap();

    // Load the ONNX model defining the reinforcement learning algorithm
    let mut graph = Graph::new();
    graph.import("reinforcement_learning.onnx").unwrap();

    // Initialize the state and reward variables
    let mut state = Tensor::from(vec![0f32]);
    let mut reward = Tensor::from(vec![0f32]);

    // Set up the initial action distribution
    let actions = vec!["move left", "move right", "stay"];
    let mut action_probs = HashMap::new();
    action_probs.insert("move left", 0.33);
    action_probs.insert("move right", 0.33);
    action_probs.insert("stay", 0.34);

    // Set up the loop variables
    let mut iteration = 0;
    let max_iterations = 100;

    // Define the loop body
    let mut body = String::new();
    body.push_str("state, reward, action_probs = f(state, reward, action_probs); ");
    body.push_str("action = sample_action(action_probs); ");
    body.push_str("state, reward = execute_action(action); ");

    // Define the loop condition
    let condition = "iteration < max_iterations";

    // Define the loop scope
    let mut scope = HashMap::new();
    scope.insert("state".to_string(), state.clone());
    scope.insert("reward".to_string(), reward.clone());
    scope.insert("action_probs".to_string(), Tensor::from(action_probs.values().cloned().collect::<Vec<f32>>()));
    scope.insert("max_iterations".to_string(), Tensor::from(max_iterations));
    scope.insert("iteration".to_string(), Tensor::from(iteration));
    scope.insert("f".to_string(), graph.get_function("f").unwrap());
    scope.insert("sample_action".to_string(), graph.get_function("sample_action").unwrap());
    scope.insert("execute_action".to_string(), graph.get_function("execute_action").unwrap());

    // Run the loop
    while onnx_while_op::run_while_op::<HashMap<String, Tensor>>(
        &mut graph,
        &env,
        &mut scope,
        &condition,
        &body,
    )
    .is_ok()
    {
        // Update the iteration counter
        iteration += 1;

        // Get the updated state and reward
        state = scope.get("state").unwrap().clone();
        reward = scope.get("reward").unwrap().clone();
    }

    // Print the final state and reward
    println!("Final state: {:?}", state);
    println!("Final reward: {:?}", reward);
}

In this example, the ONNXWhileOp is used to implement a simple reinforcement learning algorithm that learns to navigate a 1D environment. The loop body calls an ONNX function f that takes the current state, reward, and action probabilities as inputs, and returns updated values for the state, reward, and action probabilities. The body also samples an action from the current action probabilities, and executes the action to obtain a new state and reward. The loop condition is based on a fixed number of iterations, and the loop scope contains the loop variables and ONNX functions used in the loop body.

Overall, the ONNXWhileOp provides a powerful and

flexible way to define and run loops in ONNX models, which is useful for a variety of machine learning and data processing tasks. It allows users to define complex loop bodies and conditions using ONNX functions, and provides efficient memory management and parallel execution for large-scale datasets. By leveraging the power of the ONNX format and the ONNX runtime, users can build and deploy high-performance loop-based algorithms in a variety of environments, from edge devices to cloud clusters.

Some other examples of machine learning tasks that can be implemented using the ONNXWhileOp include:

• Gradient descent: In a typical neural network training pipeline, the weights of the network are updated iteratively using a gradient descent algorithm. The ONNXWhileOp can be used to implement this algorithm by defining a loop body that computes the gradients of the loss function with respect to the weights, and updates the weights accordingly.

• Attention mechanisms: Attention mechanisms are widely used in natural language processing and other applications to selectively focus on different parts of the input. The ONNXWhileOp can be used to implement attention mechanisms by defining a loop body that iteratively computes attention weights based on the current input and context, and updates the context based on the attention weights.

• Data augmentation: Data augmentation is a common technique used in machine learning to increase the size and diversity of training datasets. The ONNXWhileOp can be used to implement data augmentation algorithms by defining a loop body that applies a series of random transformations to the input data, and yields the augmented data as output.

Overall, the ONNXWhileOp provides a powerful and flexible way to implement complex iterative algorithms in ONNX models, and is a valuable tool for machine learning researchers and practitioners.

8> could you show me an example of each of these? (gradient descent, attention mechanisms, data augmentation)?

Sure, here are some examples of how the ONNXWhileOp can be used to implement gradient descent, attention mechanisms, and data augmentation in ONNX models:

Gradient Descent

Gradient descent is a popular optimization algorithm used in machine learning to train neural networks. Here's an example of how the ONNXWhileOp can be used to implement gradient descent:

import onnx
import numpy as np
from onnx import numpy_helper
from onnx import helper
from onnxruntime import InferenceSession, SessionOptions

# Define the inputs to the model
x = np.array([[1, 2], [3, 4]], dtype=np.float32)
y = np.array([[0], [1]], dtype=np.float32)

# Define the model graph
graph = helper.GraphProto()
graph.name = 'gradient_descent'
input_x = helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 2])
input_y = helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 1])
output_loss = helper.make_tensor_value_info('loss', onnx.TensorProto.FLOAT, [])
output_w = helper.make_tensor_value_info('w', onnx.TensorProto.FLOAT, [2, 1])
w = np.array([[0], [0]], dtype=np.float32)
init_w = helper.make_tensor('init_w', onnx.TensorProto.FLOAT, [2, 1], w.flatten())
step_size = np.array([0.1], dtype=np.float32)
init_step_size = helper.make_tensor('init_step_size', onnx.TensorProto.FLOAT, [1], step_size)
graph.initializer.extend([init_w, init_step_size])
loss_node = helper.make_node('ReduceSumSquare', ['y', 'w'], ['loss'], keepdims=0)
diff_node = helper.make_node('Sub', ['y', 'w'], ['diff'])
grad_node = helper.make_node('MatMul', ['x', 'diff'], ['grad'])
update_node = helper.make_node('Mul', ['grad', 'step_size'], ['update'])
new_w_node = helper.make_node('Add', ['w', 'update'], ['new_w'])
cond_node = helper.make_node('Less', ['loss', helper.make_tensor('', onnx.TensorProto.FLOAT, [], [0.001])], ['cond'])
body = helper.make_graph([loss_node, diff_node, grad_node, update_node, new_w_node, cond_node], 'gradient_descent_body', [input_x, input_y], [output_loss, output_w])
loop_inputs = [init_w, init_step_size]
loop_outputs = ['w', 'step_size']
loop_node = helper.make_node('Loop', ['x'], loop_outputs, body, loop_inputs)
graph.node.extend([loop_node])
graph.output.extend([helper.make_tensor_value_info('final_w', onnx.TensorProto.FLOAT, [2, 1])])

# Save the model graph to a file
onnx.save(graph, 'gradient_descent.onnx')

# Run the model with an ONNX runtime session
session_options = SessionOptions()
session_options.optimized_model_filepath = 'gradient_descent_optimized.onnx'
session = InferenceSession('gradient_descent.onnx', session_options)
input_feed = {'x': x, 'y': y}
output = session.run(None, input_feed)
final_w = numpy_helper.to_array(output[0])
print('Final weights:', final_w)

In this example, the ONNXWhileOp is used to implement a simple gradient descent algorithm to find the optimal weights w for a linear regression problem. The algorithm iteratively computes the gradient of the loss function with respect to

9> continue

the weights, updates the weights by subtracting the gradient multiplied by a step size, and repeats until the loss function falls below a certain threshold.

Attention Mechanisms

Attention mechanisms are a key component of many modern neural network architectures, especially those used for natural language processing and image classification. Here's an example of how the ONNXWhileOp can be used to implement an attention mechanism:

import onnx
import numpy as np
from onnx import helper
from onnxruntime import InferenceSession, SessionOptions

# Define the inputs to the model
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
h = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32)

# Define the model graph
graph = helper.GraphProto()
graph.name = 'attention_mechanism'
input_x = helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [3, 3])
input_h = helper.make_tensor_value_info('h', onnx.TensorProto.FLOAT, [2, 3])
output_y = helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
output_alpha = helper.make_tensor_value_info('alpha', onnx.TensorProto.FLOAT, [2, 3])
y0 = helper.make_node('MatMul', ['h', helper.make_tensor('', onnx.TensorProto.FLOAT, [3, 3], np.ones((3, 3)).flatten())], ['y0'])
alpha0 = helper.make_node('MatMul', ['h', helper.make_tensor('', onnx.TensorProto.FLOAT, [3, 1], np.ones((3, 1)).flatten())], ['alpha0'])
concat_input = helper.make_node('Concat', ['h', 'y0', alpha0], ['concat_input'], axis=1)
concat_output = helper.make_node('MatMul', ['concat_input', helper.make_tensor('', onnx.TensorProto.FLOAT, [7, 7], np.ones((7, 7)).flatten())], ['concat_output'])
y = helper.make_node('Slice', ['concat_output'], ['y'], starts=[0, 0], ends=[2, 3])
alpha = helper.make_node('Slice', ['concat_output'], ['alpha'], starts=[2, 0], ends=[4, 3])
cond_node = helper.make_node('Less', ['alpha', helper.make_tensor('', onnx.TensorProto.FLOAT, [], [0.9])], ['cond'])
body = helper.make_graph([y, alpha, cond_node], 'attention_mechanism_body', [input_x, input_h], [output_y, output_alpha])
loop_inputs = [helper.make_tensor('', onnx.TensorProto.FLOAT, [2, 3], np.zeros((2, 3)).flatten())]
loop_outputs = ['y', 'alpha']
loop_node = helper.make_node('Loop', ['x'], loop_outputs, body, loop_inputs)
graph.node.extend([loop_node])
graph.output.extend([output_y, output_alpha])

# Save the model graph to a file
onnx.save(graph, 'attention_mechanism.onnx')

# Run the model with an ONNX runtime session
sessionh_options = SessionOptions()
session_options.optimized_model_filepath = 'attention_mechanism_optimized.onnx'
session = InferenceSession('attention_mechanism.onnx', session_options)
input_feed = {'x': x, 'h': h}
output = session.run(None, input_feed)
y = output[0]

10> continue

Data Augmentation

Data augmentation is a technique used to increase the amount and diversity of data available for training a machine learning model. By applying various transformations to the training data, such as rotations, translations, and scaling, the model can learn to be more robust to variations in the input data. Here's an example of how the ONNXWhileOp can be used to implement data augmentation:

import onnx
import numpy as np
from onnx import helper
from onnxruntime import InferenceSession, SessionOptions

# Define the inputs to the model
x = np.array([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]], dtype=np.float32)

# Define the model graph
graph = helper.GraphProto()
graph.name = 'data_augmentation'
input_x = helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [1, 1, 3, 3])
output_y = helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [8, 1, 3, 3])
y0 = helper.make_node('Identity', ['x'], ['y0'])
cond_node = helper.make_node('Less', ['i', helper.make_tensor('', onnx.TensorProto.INT64, [], [8])], ['cond'])
flip_node = helper.make_node('Flip', ['y', helper.make_tensor('', onnx.TensorProto.INT64, [], [2])], ['flip_output'])
rot_node = helper.make_node('Rotate', ['y', helper.make_tensor('', onnx.TensorProto.FLOAT, [], [30])], ['rot_output'])
crop_node = helper.make_node('Crop', ['y', 'crop'], ['crop_output'])
pad_node = helper.make_node('Pad', ['y', 'pad'], ['pad_output'])
resize_node = helper.make_node('Resize', ['y', 'size'], ['resize_output'])
add_node = helper.make_node('Add', ['y', helper.make_tensor('', onnx.TensorProto.FLOAT, [1, 1, 1, 1], [1])], ['add_output'])
loop_body = helper.make_node('If', ['cond'], ['loop_body_output'], [flip_node, rot_node, crop_node, pad_node, resize_node, add_node])
loop_inputs = [helper.make_tensor('', onnx.TensorProto.INT64, [], [0])]
loop_outputs = ['y']
loop_node = helper.make_node('Loop', ['i'], loop_outputs, loop_body, loop_inputs)
output_y0 = helper.make_node('Identity', ['y'], ['output_y0'])
output_node = helper.make_node('Concat', ['output_y0', 'y'], ['y_concat'], axis=0)
graph.node.extend([y0, cond_node, loop_node, output_y0, output_node])
graph.output.extend([output_y])

# Save the model graph to a file
onnx.save(graph, 'data_augmentation.onnx')

# Run the model with an ONNX runtime session
session_options = SessionOptions()
session_options.optimized_model_filepath = 'data_augmentation_optimized.onnx'
session = InferenceSession('data_augmentation.onnx', session_options)
input_feed = {'x': x, 'i': np.array(0, dtype=np.int64), 'crop': np.array([0, 0], dtype=np.int64), 'pad': np.array([0, 0, 0, 0], dtype=np.int64), 'size': np.array([5, 5], dtype=np.int64)}
output = session.run(None, input_feed)
y = output[0]