🔥 CGrad

⏭️🥅 Next goal:

• Grad engine -> new task: matmul/div autograd. pow-tensor/pow-scaler -> scaler part still remaining.
• randn Generator -> with seed
• still more operation is remaining on Tensors, add them.
• Make the Tensor fast: Check the tensor.c and tensor.pyx files again, and try to optimize them to make them faster -> still not done.
• stop using numpy -> add the reshape, and other stuff.
• Build a Tensor for Int, Double, Long, etc.
• Use the Fast matrix multiplication algorithm to reduce the time complexity.
• Make loss dir and make loss like "Tenh, ReLU, sigmoid, softmax" in a more optimistic way. -> Make the loss folder, but you also need to make the backward pass for it.
• Make Optimizer start with SGD in C not in pyx (aka cython) -> after SGD -> Adam ...

✨ Overview

Lightweight library for performing tensor operations. CGrad is a module designed to handle all gradient computations, and most matrix manipulation and numerical work generally required for tasks in machine learning and deep learning.

• Inspired by "Andrej Karpathy's micrograd and George Hotz's tinygrad."

💡 Release Feature (0.0.3)

• New methods .ones_like, .zeros_like, .ones, .zeros .sum, .mean, .median
• Now You can do the backprop for scaler .sum().backward() and also change backward pass, use your own custom size backward pass .backward(custom_grad=).
• Try to Optimize the Tensor and AutoGrad
• AutoGrad.no_grad() add from stop the grad caculation.

⚙️ Installation

For user:

pip install numpy
pip install cython
pip install cgrad

For Contributers

1. install MinGW for Windows user install latest MinGW.

2. install gcc for Mac or Linux user install latest gcc.

3. clone the repository and install manually:

   git clone https://github.com/Ruhaan838/CGrad
   
   python setup.py build_ext --inplace
   pip install .

🚀 Getting Started

Here’s a simple guide to get you started with CGrad:

📥 Importing the module

import cgrad

📦 Creating Tensors

You can create a tensor from a Python list or NumPy array:

# Creating a tensor from a list
tensor = cgrad.Tensor([1.0, 2.0, 3.0])

# Creating a tensor with a specified shape
tensor = cgrad.Tensor([[1.0, 2.0], [3.0, 4.0]])

🔄 Basic Tensor Operations

CGrad supports basic operations like addition, multiplication, etc.:

# Tensor addition 
a = cgrad.Tensor([1.0, 2.0, 3.0])
b = cgrad.Tensor([4.0, 5.0, 6.0])
result = a + b  # Element-wise addition

# Tensor multiplication 
c = cgrad.Tensor([[1.0, 2.0], [3.0, 4.0]])
d = cgrad.Tensor([[5.0, 6.0], [7.0, 8.0]])
result = c * d  # Element-wise multiplication

📐 Advance Tensor Operations

CGrad supports advanced operations like matrix multiplication etc.:

a = cgrad.rand((1,2,3))
b = cgrad.rand((5,3,2))
result = a @ b

Note: cgrad.matmul with axis is still underdevelopment.

🔥 Gradient Computation

CGrad automatically tracks operations and computes gradients for backpropagation:

Using Scaler Values

# Defining tensors with gradient tracking 
x = cgrad.Tensor([2.0, 3.0], requires_grad=True)
y = cgrad.Tensor([1.0, 4.0], requires_grad=True)

# Performing operations 
z = x * y

# Backpropagation to compute gradients 
z.sum().backward()

# Accessing gradients 
print(x.grad)  # Gradients of x
print(y.grad)  # Gradients of y

Using Tensor likes:

x = cgrad.Tensor([2.0, 3.0], requires_grad=True)
y = cgrad.Tensor([1.0, 4.0], requires_grad=True)

# Performing operations 
z = x + y

z.backward(custom_grad = cgrad.ones_like(x)) # allow to do the grad with you custom grad

print(x.grad)
print(y.grad)

Stop the Grad caculation

from cgrad import AutoGrad

x = cgrad.Tensor([2.0, 3.0], requires_grad=True)
y = cgrad.Tensor([1.0, 4.0], requires_grad=True)

with AutoGrad.no_grad():
    z = x + y #only caculate the value not grad
    print(z.requires_grad)

w = x * y
print(w.requires_grad)

📖 Documentation

For more detailed information, please visit our documentation website.

🤝 Contributing

I ❤️ contributions! If you’d like to contribute to CGrad, please:

• You can contribute to code improvement and documentation editing.

• If any issue is found, report it on the GitHub issue

  1. 🍴 Clone the repository or fork the repository.
  2. 🌱 Create a new branch for your feature or bugfix.
  3. ✉️ Submit a pull request.

📖 Reading

• Blog about how tensors work at the computer level. [link]
• Cython Documentation. [link]

📝 License

📜 See LICENSE for more details.