ENH: rename library

.gitattributes

@@ -1 +1 @@
-toydiff/_version.py export-subst
+avagrad/_version.py export-subst

ALTERNATIVES.md

@@ -6,3 +6,4 @@ Alternatives that do same or very similar stuff:
 * [TinyGrad](https://github.com/tinygrad/tinygrad)
 * [JAX](https://github.com/google/jax)
 * [MyGrad](https://github.com/rsokl/MyGrad)
+* [MXNet](https://github.com/apache/mxnet)

MANIFEST.in

@@ -1,7 +1,7 @@
 include pyproject.toml
 include versioneer.py
-include toydiff/_version.py
-include src/toydiff/_version.py
+include avagrad/_version.py
+include src/avagrad/_version.py
 
 # Include the README
 # include *.md

README.md

@@ -1,6 +1,8 @@
-# Toydiff
+<p align="center">
+  <img src="img/logo.png" width="500">
+</p>
 
-`toydiff` is a simple automatic differentiation library that I created to wrap
+`avagrad` is a simple automatic differentiation library that I created to wrap
 my head around how autodiff works. It is built using NumPy and SciPy and it has
 been tested using PyTorch as a reference.
 
@@ -10,35 +12,36 @@ networks (WIP, only linear layers for now).
 ## Installation
 Normal user:
 ```bash
-git clone https://github.com/Xylambda/toydiff.git
-pip install toydiff/.
+git clone https://github.com/Xylambda/avagrad.git
+pip install avagrad/.
 ```
 
 Developer:
 ```bash
-git clone https://github.com/Xylambda/toydiff.git
-pip install -e toydiff/. -r toydiff/requirements-dev.txt
+git clone https://github.com/Xylambda/avagrad.git
+
+pip install -e avagrad/. -r avagrad/requirements-dev.txt
 ```
 
 ## Tests
-To run test, you must install the library as a `developer`.
+To run tests you must install the library as a `developer`.
 
 ```bash
-cd toydiff/
+cd avagrad/
 pytest -v tests/
 ```
 
 ## Differentiable operations
 The use is almost the same as the one you would expect from PyTorch:
 
 ```python
->>> import toydiff as tdf
+>>> import avagrad as ag
 >>> # use `track_gradient=True` to allow backward to fill the gradients
->>> a = tdf.random.rand((3,3), track_gradient=True)
->>> b = tdf.random.rand((3,3), track_gradient=True)
->>> c = tdf.matmul(a, b)
->>> d = tdf.log(c)
->>> e = tdf.sum(d)
+>>> a = ag.random.rand((3,3), track_gradient=True)
+>>> b = ag.random.rand((3,3), track_gradient=True)
+>>> c = ag.matmul(a, b)
+>>> d = ag.log(c)
+>>> e = ag.sum(d)
 ```
 
 Variable `e` is a Tensor that allows to backpropagate:
@@ -69,10 +72,10 @@ basic neural networks:
 
 ```python
 import numpy as np
-import toydiff as tdf
-from toydiff.nn.blocks import Linear
-from toydiff.nn.optim import SGD
-from toydiff.nn.functional import mse_loss
+import avagrad as ag
+from avagrad.nn.blocks import Linear
+from avagrad.nn.optim import SGD
+from avagrad.nn.functional import mse_loss
 
 # generate data
 x = np.arange(-1, 1, 0.01).reshape(-1,1)
@@ -82,8 +85,8 @@ y = 2 * x + np.random.normal(size=(len(x), 1), scale=0.3)
 model = Linear(1, 1, bias=False)
 
 # wrap your data in Tensors with `track_gradient=True`
-feat = tdf.Tensor(X, track_gradient=True)
-labels = tdf.Tensor(y, track_gradient=True)
+feat = ag.Tensor(X, track_gradient=True)
+labels = ag.Tensor(y, track_gradient=True)
 
 # pass model to optimizer
 optimizer = SGD(model)

examples/fma.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c3b837b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import avagrad as ag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d3456753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = ag.random.rand((3,3), track_gradient=True)\n",
+    "b = ag.random.rand((3,3), track_gradient=True)\n",
+    "c = ag.random.rand((3,3), track_gradient=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8041ad56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tensor([[1.8777063 , 0.9567863 , 1.2879769 ],\n",
+       "       [0.62437826, 1.2251703 , 0.8385898 ],\n",
+       "       [0.835547  , 1.1380192 , 1.1682112 ]], dtype=float32, backward_fn=<FusedMatMulAdd(TernaryOp).Backward>)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag.fma(a, b, c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf6bbf6a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65b5a9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6f37935",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

profiling/ops.py

@@ -1,6 +1,6 @@
 import argparse
 import cProfile
-import toydiff as tdf
+import avagrad as ag
 from functools import partial
 from pathlib import Path
 
@@ -13,7 +13,7 @@ def prof_func(func: callable, name: str, size: int):
     profiler.enable()
     func()
     profiler.disable()
-    version = tdf.__version__
+    version = ag.__version__
     folder = PROFS_PATH / version
     folder.mkdir(exist_ok=True)
     profiler.dump_stats(folder / f"n={name}_s={size}.prof")
@@ -22,10 +22,10 @@ def prof_func(func: callable, name: str, size: int):
 # -----------------------------------------------------------------------------
 def test_matmul(size: int):
     def exec(a, b):
-        tdf.matmul(a, b)
+        ag.matmul(a, b)
 
-    a = tdf.rand((size, size))
-    b = tdf.rand((size, size))
+    a = ag.rand((size, size))
+    b = ag.rand((size, size))
     func = partial(exec, a=a, b=b)
     prof_func(func, "MatMul", size)
 
@@ -34,9 +34,9 @@ def test_matmul_backward(size: int):
     def exec(c):
         c.backward()
 
-    a = tdf.rand((size, size), track_gradient=True)
-    b = tdf.rand((size, size), track_gradient=True)
-    c = tdf.matmul(a, b)
+    a = ag.rand((size, size), track_gradient=True)
+    b = ag.rand((size, size), track_gradient=True)
+    c = ag.matmul(a, b)
     func = partial(exec, c=c)
     prof_func(func, "MatMul.Backward", size)
 

setup.cfg

@@ -6,14 +6,14 @@ license_files = LICENSE.txt
 [versioneer]
 VCS = git
 style = pep440
-versionfile_source = src/toydiff/_version.py
-versionfile_build = toydiff/_version.py
+versionfile_source = src/avagrad/_version.py
+versionfile_build = avagrad/_version.py
 tag_prefix = v
-parentdir_prefix = toydiff-
+parentdir_prefix = avagrad-
 
 [tool:pytest]
 minversion = 4.0.2
-testpaths = toydiff
+testpaths = avagrad
 
 
 [coverage:run]

setup.py

@@ -8,13 +8,13 @@
     long_description = f.read()
 
 setup(
-    name='toydiff',
+    name='avagrad',
     version=versioneer.get_version(),
     cmdclass=versioneer.get_cmdclass(),
     description="Tensor automatic differentiation and neural networks library",
     long_description=long_description,
     long_description_content_type='text/markdown',
-    url='https://github.com/Xylambda/toydiff',
+    url='https://github.com/Xylambda/avagrad',
     author='Alejandro Pérez-Sanjuán',
     classifiers=[
         'Development Status :: 3 - Alpha',
@@ -34,7 +34,6 @@
     install_requires=[
         "numpy",
         "scipy",
-        #"pyfma",  # fused multiply-add
     ],
     extras_require={
         "test": [
@@ -44,6 +43,10 @@
         "profile": [
             "snakeviz",
             "perfplot",
+        ],
+        "docs": [
+            "sphinx",
+            "furo",
         ]
     }
 )

src/toydiff/__init__.py → src/avagrad/__init__.py

@@ -1,4 +1,4 @@
-""" Small automatic differentiation package for scalars. """
+"""Tensor automatic differentiation and neural networks package. """
 
 # relative subpacackges import
 from . import exceptions, nn, random, utils
@@ -7,6 +7,6 @@
 __version__ = get_versions()["version"]
 del get_versions
 
-from toydiff.core import *
+from avagrad.core import *
 
 __all__ = ["exceptions", "utils", "nn", "Tensor", "random"]

src/toydiff/_version.py → src/avagrad/_version.py

@@ -42,7 +42,7 @@ def get_config():
     cfg.style = "pep440"
     cfg.tag_prefix = "v"
     cfg.parentdir_prefix = "None"
-    cfg.versionfile_source = "toydiff/_version.py"
+    cfg.versionfile_source = "avagrad/_version.py"
     cfg.verbose = False
     return cfg