depthtensor 2.4.0

A Hardware-Accelerated Tensor Computation and Autograd Engine

DepthTensor: A Hardware-Accelerated Tensor Computation and Autograd Engine

DepthTensor is a light-weight, high-performance library for reverse-mode automatic differentiation (AD). It is useful in building the mathematical foundation of deep learning frameworks, with the use of a Tensor object which dynamically builds computational graphs and computes gradients using Vector-Jacobian Products (VJP), generalized for tensors of abritrary rank.

Note: This is the core autograd and tensor computation engine. For the full deep learning framework, refer to DepthML.

1. Mathematical Foundation

The goal is to compute the gradient of scalar field $L$ with respect to an input tensor $X$, denoted as the adjoint $\bar{X}$.

1.1. Generalized VJPs via Tensor Contractions

Let $X$ be a tensor of shape $\mathcal{I} = (i_1, \dots, i_n)$ and $Y = f(X)$ be a tensor of shape $\mathcal{J} = (j_1, \dots, j_m)$.

Mathematically, the Jacobian is the tensor of rank $n + m$ which contains all the partial derivatives $\frac{\partial Y_{\mathcal{J}}}{\partial X_{\mathcal{I}}}$. DepthTensor does not compute this object, but, rather, it computes the contraction of the incoming adjoint $\bar{Y}$ with the local derivative:

$$ \bar{X}{i_1 \dots i_n} = \sum{j_1 \dots j_m} \bar{Y}{j_1 \dots j_m} \frac{\partial Y{j_1 \dots j_m}}{\partial X_{i_1 \dots i_n}} $$

If X and Y are scalars, indices vanish, and this reduces to:

$$ \bar{x} = \bar{y} \cdot f'(x) $$

If X and Y are matrices, this reduces to:

$$ \bar{X} = \bar{Y} W^{T} $$

1.2. Graph Topology and Gradient Accumulation

Gradients are accumulated using a Depth-First Search (DFS) topological sort, which ensures that for any node $X$ with multiple gradient flow streams ${Y^{(1)}, \dots, Y^{k}}$, the gradient sum is the sum of contractions:

$$ \bar{X} = \sum_{k} \mathrm{VJP}(Y^{(k)}, X) $$

All gradients are aggregated from all gradient downstreams.

2. Architecture and System Design

2.1. The differentiable Primitive (Tensor)

The Tensor class acts like a node in the Directed Acyclic Graph (DAG).

Operations are automatically dispatched to backend computers, which consists of numpy (CPU) and cupy (GPU).

Computational graphs are built dynamically at runtime.

3. Empirical Validation

We will verify the tensor engine by minimizing the Rosenbrock function, which is a non-convex optimization benchmark:

$$ f(x, y) = (a - x)^2 + b(y - x^2)^2 $$

import depthtensor as dt

# Initialize tensors
x = dt.Tensor([1.2], device="gpu", requires_grad=True)
y = dt.Tensor([1.2], device="gpu", requires_grad=True)

a, b = dt.Tensor([1], device="gpu"), dt.Tensor(
    [100], device="gpu"
)

# 2. Optimization Loop
lr = 0.001
for i in range(500):
    # Rosenbrock: f(x,y) = (a-x)^2 + b(y-x^2)^2
    loss = (a - x) ** 2 + b * (y - x**2) ** 2

    # Backpward pass
    dt.differentiate(loss)

    # Gradient Descent
    x.data -= lr * x.grad  # type: ignore
    y.data -= lr * y.grad  # type: ignore

    # Zero grads
    x.zeros_grad()
    y.zeros_grad()

    if i % 10 == 0:
        print(loss.item())

print(f"Converged: ({x.data}, {y.data})")
# Target: (1.0, 1.0)

4. Installation

Requirements:

• numpy
• cupy (optional: for NVIDIA GPU acceleration)

4.1. Bash

git clone https://github.com/YourUsername/DepthTensor.git
pip install -e .

4.2. Pip

pip install depthtensor

Author: Le Hong Ha