diff --git a/Cargo.lock b/Cargo.lock
index 915ebf9..5deb985 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -8,13 +8,6 @@ version = "1.0.102"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7f202df86484c868dbad7eaa557ef785d5c66295e41b460ef922eca0723b842c"
-[[package]]
-name = "athena"
-version = "0.1.0"
-dependencies = [
- "rand",
-]
-
[[package]]
name = "bitflags"
version = "2.11.1"
@@ -23,9 +16,9 @@ checksum = "c4512299f36f043ab09a583e57bceb5a5aab7a73db1805848e8fef3c9e8c78b3"
[[package]]
name = "cfg-if"
-version = "1.0.0"
+version = "1.0.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
+checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801"
[[package]]
name = "chacha20"
@@ -47,6 +40,31 @@ dependencies = [
"libc",
]
+[[package]]
+name = "delta"
+version = "0.1.0"
+dependencies = [
+ "delta-macros",
+ "rand",
+]
+
+[[package]]
+name = "delta-macros"
+version = "0.1.0"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "delta-py"
+version = "0.1.0"
+dependencies = [
+ "delta",
+ "pyo3",
+]
+
[[package]]
name = "equivalent"
version = "1.0.2"
@@ -142,6 +160,18 @@ version = "2.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f8ca58f447f06ed17d5fc4043ce1b10dd205e060fb3ce5b979b8ed8e59ff3f79"
+[[package]]
+name = "once_cell"
+version = "1.21.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9f7c3e4beb33f85d45ae3e3a1792185706c8e16d043238c593331cc7cd313b50"
+
+[[package]]
+name = "portable-atomic"
+version = "1.13.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c33a9471896f1c69cecef8d20cbe2f7accd12527ce60845ff44c153bb2a21b49"
+
[[package]]
name = "prettyplease"
version = "0.2.37"
@@ -161,6 +191,64 @@ dependencies = [
"unicode-ident",
]
+[[package]]
+name = "pyo3"
+version = "0.28.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91fd8e38a3b50ed1167fb981cd6fd60147e091784c427b8f7183a7ee32c31c12"
+dependencies = [
+ "libc",
+ "once_cell",
+ "portable-atomic",
+ "pyo3-build-config",
+ "pyo3-ffi",
+ "pyo3-macros",
+]
+
+[[package]]
+name = "pyo3-build-config"
+version = "0.28.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e368e7ddfdeb98c9bca7f8383be1648fd84ab466bf2bc015e94008db6d35611e"
+dependencies = [
+ "target-lexicon",
+]
+
+[[package]]
+name = "pyo3-ffi"
+version = "0.28.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7f29e10af80b1f7ccaf7f69eace800a03ecd13e883acfacc1e5d0988605f651e"
+dependencies = [
+ "libc",
+ "pyo3-build-config",
+]
+
+[[package]]
+name = "pyo3-macros"
+version = "0.28.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "df6e520eff47c45997d2fc7dd8214b25dd1310918bbb2642156ef66a67f29813"
+dependencies = [
+ "proc-macro2",
+ "pyo3-macros-backend",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "pyo3-macros-backend"
+version = "0.28.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c4cdc218d835738f81c2338f822078af45b4afdf8b2e33cbb5916f108b813acb"
+dependencies = [
+ "heck",
+ "proc-macro2",
+ "pyo3-build-config",
+ "quote",
+ "syn",
+]
+
[[package]]
name = "quote"
version = "1.0.45"
@@ -252,6 +340,12 @@ dependencies = [
"unicode-ident",
]
+[[package]]
+name = "target-lexicon"
+version = "0.13.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "adb6935a6f5c20170eeceb1a3835a49e12e19d792f6dd344ccc76a985ca5a6ca"
+
[[package]]
name = "unicode-ident"
version = "1.0.24"
diff --git a/Cargo.toml b/Cargo.toml
index f810966..e84e50a 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,16 +1,5 @@
-[package]
-name = "athena"
-# versions
-version = "0.1.0"
-edition = "2024"
-rust-version = "1.95.0"
-# info
-authors = ["commanderxa"]
-readme = "README.md"
-repository = "https://github.com/CommanderXA/athena"
-license-file = "LICENSE.txt"
+[workspace]
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+members = ["delta", "delta-macros", "delta-py"]
-[dependencies]
-rand = "0.10.1"
+resolver = "2"
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..b5abac3
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,5 @@
+py_develop:
+ cd ./delta-py && maturin develop --uv
+
+py_release:
+ cd ./delta-py && maturin build --release
diff --git a/README.md b/README.md
index e19e41c..8dd3807 100644
--- a/README.md
+++ b/README.md
@@ -5,23 +5,23 @@
-The ATHENA Project
+The DELTA Project
diff --git a/SECURITY.md b/SECURITY.md
index e789524..770c428 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -2,6 +2,6 @@
If you have found a security vulnerability in this project, then please share your finding.
-Please, report security issues here: https://github.com/CommanderXA/athena/security/advisories/new
+Please, report security issues here: https://github.com/CommanderXA/delta/security/advisories/new
Describe the issue in detail as much as possible.
\ No newline at end of file
diff --git a/delta-macros/Cargo.toml b/delta-macros/Cargo.toml
new file mode 100644
index 0000000..f6185b0
--- /dev/null
+++ b/delta-macros/Cargo.toml
@@ -0,0 +1,12 @@
+[package]
+name = "delta-macros"
+version = "0.1.0"
+edition = "2024"
+
+[lib]
+proc-macro = true
+
+[dependencies]
+syn = { version = "2", features = ["full"] }
+quote = "1"
+proc-macro2 = "1"
diff --git a/delta-macros/src/lib.rs b/delta-macros/src/lib.rs
new file mode 100644
index 0000000..974b986
--- /dev/null
+++ b/delta-macros/src/lib.rs
@@ -0,0 +1,154 @@
+use proc_macro::TokenStream;
+use quote::quote;
+use syn::{parse_macro_input, Data, DeriveInput, Fields, Type};
+
+/// Derive macro for the `Module` trait.
+///
+/// Automatically implements:
+/// - `module_name()` → returns the struct's name as a `String`
+/// - `parameters()` → collects all fields of type `nn::Parameter`
+/// - `submodules()` → collects all fields that implement `Module`
+/// (detected via the `#[module]` field attribute)
+///
+/// The user must still implement `forward()` manually.
+///
+/// # Field Attributes
+///
+/// - `#[module]` — marks a field as a sub-module (must be `Box` or
+/// a concrete type implementing `Module`). The macro will include it in the
+/// `submodules()` return value.
+///
+/// Fields of type `nn::Parameter` are picked up **automatically** without any
+/// attribute, because the type itself is unambiguous.
+///
+/// # Example
+///
+/// ```ignore
+/// use your_crate::nn;
+/// use module_derive::Module;
+///
+/// #[derive(Module)]
+/// pub struct Linear {
+/// pub weight: nn::Parameter,
+/// pub bias: nn::Parameter,
+/// #[module]
+/// pub activation: Box,
+/// }
+///
+/// impl Module for Linear {
+/// fn forward(&self, args: Vec, kwargs: HashMap) -> IValue {
+/// todo!()
+/// }
+/// }
+/// ```
+#[proc_macro_derive(Module, attributes(module))]
+pub fn derive_module(input: TokenStream) -> TokenStream {
+ let input = parse_macro_input!(input as DeriveInput);
+ let struct_name = &input.ident;
+ let struct_name_str = struct_name.to_string();
+
+ let fields = match &input.data {
+ Data::Struct(data) => match &data.fields {
+ Fields::Named(f) => &f.named,
+ Fields::Unnamed(_) => {
+ return syn::Error::new_spanned(
+ struct_name,
+ "Module derive does not support tuple structs",
+ )
+ .to_compile_error()
+ .into()
+ }
+ Fields::Unit => {
+ // Unit struct — no parameters or submodules
+ return expand(struct_name, &struct_name_str, vec![], vec![]);
+ }
+ },
+ _ => {
+ return syn::Error::new_spanned(struct_name, "Module can only be derived for structs")
+ .to_compile_error()
+ .into()
+ }
+ };
+
+ let mut param_fields = Vec::new();
+ let mut submodule_fields = Vec::new();
+
+ for field in fields {
+ let field_name = field.ident.as_ref().expect("named field");
+
+ // Check for #[module] attribute → submodule
+ let is_submodule = field
+ .attrs
+ .iter()
+ .any(|a| a.path().is_ident("module"));
+
+ if is_submodule {
+ submodule_fields.push(field_name.clone());
+ } else if is_nn_parameter(&field.ty) {
+ // Automatically detect nn::Parameter fields
+ param_fields.push(field_name.clone());
+ }
+ }
+
+ expand(struct_name, &struct_name_str, param_fields, submodule_fields)
+}
+
+// ---------------------------------------------------------------------------
+// Helpers
+// ---------------------------------------------------------------------------
+
+/// Returns `true` when the type path ends in `Parameter` (covers both
+/// `nn::Parameter` and a bare `Parameter` import).
+fn is_nn_parameter(ty: &Type) -> bool {
+ if let Type::Path(type_path) = ty {
+ if let Some(last) = type_path.path.segments.last() {
+ return last.ident == "Parameter";
+ }
+ }
+ false
+}
+
+/// Emit the `module_name`, `parameters`, and `submodules` impl block.
+fn expand(
+ struct_name: &syn::Ident,
+ struct_name_str: &str,
+ param_fields: Vec,
+ submodule_fields: Vec,
+) -> TokenStream {
+ let parameters_body = if param_fields.is_empty() {
+ quote! { vec![] }
+ } else {
+ quote! {
+ vec![
+ #( self.#param_fields.clone() ),*
+ ]
+ }
+ };
+
+ let submodules_body = if submodule_fields.is_empty() {
+ quote! { vec![] }
+ } else {
+ quote! {
+ vec![
+ #( &self.#submodule_fields as &dyn Module ),*
+ ]
+ }
+ };
+
+ quote! {
+ impl Module for #struct_name {
+ fn module_name(&self) -> String {
+ #struct_name_str.to_string()
+ }
+
+ fn parameters(&self) -> Vec {
+ #parameters_body
+ }
+
+ fn submodules(&self) -> Vec<&dyn Module> {
+ #submodules_body
+ }
+ }
+ }
+ .into()
+}
\ No newline at end of file
diff --git a/delta-py/.github/workflows/CI.yml b/delta-py/.github/workflows/CI.yml
new file mode 100644
index 0000000..546037c
--- /dev/null
+++ b/delta-py/.github/workflows/CI.yml
@@ -0,0 +1,186 @@
+# This file is autogenerated by maturin v1.13.1
+# To update, run
+#
+# maturin generate-ci github
+#
+name: CI
+
+on:
+ push:
+ branches:
+ - main
+ - master
+ tags:
+ - '*'
+ pull_request:
+ workflow_dispatch:
+
+permissions:
+ contents: read
+
+jobs:
+ linux:
+ runs-on: ${{ matrix.platform.runner }}
+ strategy:
+ matrix:
+ platform:
+ - runner: ubuntu-22.04
+ target: x86_64
+ - runner: ubuntu-22.04
+ target: x86
+ - runner: ubuntu-22.04
+ target: aarch64
+ - runner: ubuntu-22.04
+ target: armv7
+ - runner: ubuntu-22.04
+ target: s390x
+ - runner: ubuntu-22.04
+ target: ppc64le
+ steps:
+ - uses: actions/checkout@v6
+ - uses: actions/setup-python@v6
+ with:
+ python-version: 3.x
+ - name: Build wheels
+ uses: PyO3/maturin-action@v1
+ with:
+ target: ${{ matrix.platform.target }}
+ args: --release --out dist --find-interpreter
+ sccache: ${{ !startsWith(github.ref, 'refs/tags/') }}
+ manylinux: auto
+ - name: Upload wheels
+ uses: actions/upload-artifact@v6
+ with:
+ name: wheels-linux-${{ matrix.platform.target }}
+ path: dist
+
+ musllinux:
+ runs-on: ${{ matrix.platform.runner }}
+ strategy:
+ matrix:
+ platform:
+ - runner: ubuntu-22.04
+ target: x86_64
+ - runner: ubuntu-22.04
+ target: x86
+ - runner: ubuntu-22.04
+ target: aarch64
+ - runner: ubuntu-22.04
+ target: armv7
+ steps:
+ - uses: actions/checkout@v6
+ - uses: actions/setup-python@v6
+ with:
+ python-version: 3.x
+ - name: Build wheels
+ uses: PyO3/maturin-action@v1
+ with:
+ target: ${{ matrix.platform.target }}
+ args: --release --out dist --find-interpreter
+ sccache: ${{ !startsWith(github.ref, 'refs/tags/') }}
+ manylinux: musllinux_1_2
+ - name: Upload wheels
+ uses: actions/upload-artifact@v6
+ with:
+ name: wheels-musllinux-${{ matrix.platform.target }}
+ path: dist
+
+ windows:
+ runs-on: ${{ matrix.platform.runner }}
+ strategy:
+ matrix:
+ platform:
+ - runner: windows-latest
+ target: x64
+ python_arch: x64
+ - runner: windows-latest
+ target: x86
+ python_arch: x86
+ - runner: windows-11-arm
+ target: aarch64
+ python_arch: arm64
+ steps:
+ - uses: actions/checkout@v6
+ - uses: actions/setup-python@v6
+ with:
+ python-version: 3.13
+ architecture: ${{ matrix.platform.python_arch }}
+ - name: Build wheels
+ uses: PyO3/maturin-action@v1
+ with:
+ target: ${{ matrix.platform.target }}
+ args: --release --out dist --find-interpreter
+ sccache: ${{ !startsWith(github.ref, 'refs/tags/') }}
+ - name: Upload wheels
+ uses: actions/upload-artifact@v6
+ with:
+ name: wheels-windows-${{ matrix.platform.target }}
+ path: dist
+
+ macos:
+ runs-on: ${{ matrix.platform.runner }}
+ strategy:
+ matrix:
+ platform:
+ - runner: macos-15-intel
+ target: x86_64
+ - runner: macos-latest
+ target: aarch64
+ steps:
+ - uses: actions/checkout@v6
+ - uses: actions/setup-python@v6
+ with:
+ python-version: 3.x
+ - name: Build wheels
+ uses: PyO3/maturin-action@v1
+ with:
+ target: ${{ matrix.platform.target }}
+ args: --release --out dist --find-interpreter
+ sccache: ${{ !startsWith(github.ref, 'refs/tags/') }}
+ - name: Upload wheels
+ uses: actions/upload-artifact@v6
+ with:
+ name: wheels-macos-${{ matrix.platform.target }}
+ path: dist
+
+ sdist:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v6
+ - name: Build sdist
+ uses: PyO3/maturin-action@v1
+ with:
+ command: sdist
+ args: --out dist
+ - name: Upload sdist
+ uses: actions/upload-artifact@v6
+ with:
+ name: wheels-sdist
+ path: dist
+
+ release:
+ name: Release
+ runs-on: ubuntu-latest
+ if: ${{ startsWith(github.ref, 'refs/tags/') || github.event_name == 'workflow_dispatch' }}
+ needs: [linux, musllinux, windows, macos, sdist]
+ permissions:
+ # Use to sign the release artifacts
+ id-token: write
+ # Used to upload release artifacts
+ contents: write
+ # Used to generate artifact attestation
+ attestations: write
+ steps:
+ - uses: actions/download-artifact@v7
+ - name: Generate artifact attestation
+ uses: actions/attest-build-provenance@v3
+ with:
+ subject-path: 'wheels-*/*'
+ - name: Install uv
+ if: ${{ startsWith(github.ref, 'refs/tags/') }}
+ uses: astral-sh/setup-uv@v7
+ - name: Publish to PyPI
+ if: ${{ startsWith(github.ref, 'refs/tags/') }}
+ run: uv publish 'wheels-*/*'
+ env:
+ UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/delta-py/.gitignore b/delta-py/.gitignore
new file mode 100644
index 0000000..c8f0442
--- /dev/null
+++ b/delta-py/.gitignore
@@ -0,0 +1,72 @@
+/target
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+.pytest_cache/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+.venv/
+env/
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+include/
+man/
+venv/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+pip-selfcheck.json
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Rope
+.ropeproject
+
+# Django stuff:
+*.log
+*.pot
+
+.DS_Store
+
+# Sphinx documentation
+docs/_build/
+
+# PyCharm
+.idea/
+
+# VSCode
+.vscode/
+
+# Pyenv
+.python-version
diff --git a/delta-py/Cargo.toml b/delta-py/Cargo.toml
new file mode 100644
index 0000000..fa72c0c
--- /dev/null
+++ b/delta-py/Cargo.toml
@@ -0,0 +1,15 @@
+[package]
+name = "delta-py"
+version = "0.1.0"
+edition = "2024"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+[lib]
+name = "delta_py"
+path = "src/lib.rs"
+crate-type = ["cdylib"]
+
+[dependencies]
+pyo3 = "0.28.2"
+
+delta = { path = "../delta" }
diff --git a/delta-py/pyproject.toml b/delta-py/pyproject.toml
new file mode 100644
index 0000000..f703197
--- /dev/null
+++ b/delta-py/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["maturin>=1.13,<2.0"]
+build-backend = "maturin"
+
+[project]
+name = "delta"
+requires-python = ">=3.10"
+classifiers = [
+ "Programming Language :: Rust",
+ "Programming Language :: Python :: Implementation :: CPython",
+ "Programming Language :: Python :: Implementation :: PyPy",
+]
+dynamic = ["version"]
+dependencies = ["ipykernel >= 7.2.0"]
+
+[tool.maturin]
+python-source = "python"
+module-name = "delta._delta"
diff --git a/delta-py/python/delta/__init__.py b/delta-py/python/delta/__init__.py
new file mode 100644
index 0000000..9cefd16
--- /dev/null
+++ b/delta-py/python/delta/__init__.py
@@ -0,0 +1 @@
+from ._delta import *
diff --git a/delta-py/python/delta/__init__.pyi b/delta-py/python/delta/__init__.pyi
new file mode 100644
index 0000000..0628689
--- /dev/null
+++ b/delta-py/python/delta/__init__.pyi
@@ -0,0 +1,78 @@
+# delta/_delta.pyi
+from __future__ import annotations
+from typing import Optional
+
+class Tensor:
+ shape: list[int]
+ stride: list[int]
+
+ # ── Constructors ────────────────────────────────────────────────────────
+ @staticmethod
+ def tensor(data: list[float], shape: list[int]) -> Tensor: ...
+ @staticmethod
+ def randn(shape: list[int]) -> Tensor: ...
+ @staticmethod
+ def zeros(shape: list[int]) -> Tensor: ...
+ @staticmethod
+ def zeros_like(tensor: Tensor) -> Tensor: ...
+ @staticmethod
+ def ones(shape: list[int]) -> Tensor: ...
+ @staticmethod
+ def ones_like(tensor: Tensor) -> Tensor: ...
+ @staticmethod
+ def arange(start: float, end: float, step: float) -> Tensor: ...
+ @staticmethod
+ def cat(tensors: list[Tensor], dim: int) -> Tensor: ...
+
+ # ── Metadata ────────────────────────────────────────────────────────────
+ def length(self) -> int: ...
+ def storage(self) -> list[float]: ...
+ def item(self) -> list[float]: ...
+ def grad(self) -> list[float] | None: ...
+ def requires_grad(self, value: bool) -> Tensor: ...
+
+ # ── Shape manipulation ───────────────────────────────────────────────────
+ def reshape(self, shape: list[int]) -> Tensor: ...
+ def view(self, shape: list[int]) -> Tensor: ...
+ def transpose(self, dim0: int, dim1: int) -> Tensor: ...
+ def t(self) -> Tensor: ...
+ def unsqueeze(self, dim: int) -> Tensor: ...
+ def squeeze(self, dim: list[int]) -> Tensor: ...
+ def expand(self, new_shape: list[int]) -> Tensor: ...
+
+ # ── Reduction ───────────────────────────────────────────────────────────
+ def sum(self, dim: Optional[int] = None, keepdim: bool = False) -> Tensor: ...
+ def mean(self, dim: Optional[int] = None, keepdim: bool = False) -> Tensor: ...
+
+ # ── Element-wise ops ────────────────────────────────────────────────────
+ def exp(self) -> Tensor: ...
+ def pow(self, n: int) -> Tensor: ...
+
+ # ── Autograd ────────────────────────────────────────────────────────────
+ def backward(self) -> None: ...
+
+ # ── Arithmetic operators ─────────────────────────────────────────────────
+ def __add__(self, other: float | int | Tensor) -> Tensor: ...
+ def __radd__(self, other: float | int | Tensor) -> Tensor: ...
+ def __sub__(self, other: float | int | Tensor) -> Tensor: ...
+ def __rsub__(self, other: float | int | Tensor) -> Tensor: ...
+ def __mul__(self, other: float | int | Tensor) -> Tensor: ...
+ def __rmul__(self, other: float | int | Tensor) -> Tensor: ...
+ def __truediv__(self, other: float | int | Tensor) -> Tensor: ...
+ def __rtruediv__(self, other: float | int | Tensor) -> Tensor: ...
+ def __neg__(self) -> Tensor: ...
+
+ # ── Display ──────────────────────────────────────────────────────────────
+ def __repr__(self) -> str: ...
+ def __str__(self) -> str: ...
+
+# ── Module-level functions ───────────────────────────────────────────────────
+def tensor(data: list[float], shape: list[int]) -> Tensor: ...
+def randn(*shape: int) -> Tensor: ...
+def zeros(*shape: int) -> Tensor: ...
+def ones(*shape: int) -> Tensor: ...
+def matmul(a: Tensor, b: Tensor) -> Tensor: ...
+def cross(a: Tensor, b: Tensor) -> Tensor: ...
+def sum(input: Tensor, dim: Optional[int] = None, keepdim: bool = False) -> Tensor: ...
+def mean(input: Tensor, dim: Optional[int] = None, keepdim: bool = False) -> Tensor: ...
+def cat(tensors: list[Tensor], dim: int) -> Tensor: ...
diff --git a/delta-py/python/delta/nn/__init__.py b/delta-py/python/delta/nn/__init__.py
new file mode 100644
index 0000000..c9c00b1
--- /dev/null
+++ b/delta-py/python/delta/nn/__init__.py
@@ -0,0 +1,17 @@
+import sys
+
+from .. import _delta as _C
+
+_mod = _C.nn
+
+from .module import Module
+
+for name in dir(_mod):
+ if not name.startswith("_") and name not in globals():
+ globals()[name] = getattr(_mod, name)
+
+sys.modules["delta.nn.functional"] = _mod.functional
+
+from .linear import Linear
+
+__all__ = [name for name in dir(_mod) if not name.startswith("_")]
diff --git a/delta-py/python/delta/nn/__init__.pyi b/delta-py/python/delta/nn/__init__.pyi
new file mode 100644
index 0000000..f833345
--- /dev/null
+++ b/delta-py/python/delta/nn/__init__.pyi
@@ -0,0 +1,25 @@
+from __future__ import annotations
+from enum import Enum
+from delta import Tensor
+
+class Parameter:
+ @property
+ def shape(self) -> list[int]: ...
+ @property
+ def data(self) -> Tensor: ...
+ def __new__(cls, tensor: Tensor) -> Parameter: ...
+ def grad(self) -> list[float] | None: ...
+ def item(self) -> list[float]: ...
+
+def relu(x: Tensor) -> Tensor: ...
+def sigmoid(x: Tensor) -> Tensor: ...
+def softmax(x: Tensor, dim: int = -1) -> Tensor: ...
+
+class Reduction(Enum):
+ SUM: Reduction
+ MEAN: Reduction
+
+class MSELoss:
+ def __new__(cls, reduction: Reduction | None = Reduction.MEAN) -> MSELoss: ...
+ def measure(self, a: Tensor, b: Tensor) -> Tensor: ...
+ def __call__(self, a: Tensor, b: Tensor) -> Tensor: ...
diff --git a/delta-py/python/delta/nn/linear.py b/delta-py/python/delta/nn/linear.py
new file mode 100644
index 0000000..e597fe5
--- /dev/null
+++ b/delta-py/python/delta/nn/linear.py
@@ -0,0 +1,20 @@
+from delta import Tensor
+from delta.nn import Module, Parameter
+import delta
+
+
+class Linear(Module):
+
+ def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
+ super().__init__()
+
+ self.use_bias = bias
+ if self.use_bias:
+ in_features += 1
+ self.weights = Parameter(delta.randn([in_features, out_features]))
+
+ def forward(self, x: Tensor) -> Tensor:
+ if self.use_bias:
+ ones_shape = list(x.shape[:-1]) + [1]
+ x = delta.cat([x, delta.ones(ones_shape)], dim=-1)
+ return x @ self.weights.data
diff --git a/delta-py/python/delta/nn/module.py b/delta-py/python/delta/nn/module.py
new file mode 100644
index 0000000..89064e0
--- /dev/null
+++ b/delta-py/python/delta/nn/module.py
@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+
+from delta import Tensor, nn
+
+
+class Module(ABC):
+
+ def __init__(self):
+ self._modules: dict[str, "Module"] = {}
+ self._parameters: dict[str, "nn.Parameter"] = {}
+
+ @abstractmethod
+ def forward(self, *args, **kwargs) -> ...: ...
+
+ def __call__(self, *args, **kwargs):
+ return self.forward(*args, **kwargs)
+
+ def __setattr__(self, name, value):
+ if isinstance(value, Module):
+ self._modules[name] = value
+ elif isinstance(value, nn.Parameter):
+ self._parameters[name] = value
+ super().__setattr__(name, value)
+
+ def parameters(self) -> list[Tensor]:
+ params = list(self._parameters.values())
+ for mod in self._modules.values():
+ params.extend(mod.parameters())
+ return params
diff --git a/delta-py/python/delta/optim/__init__.py b/delta-py/python/delta/optim/__init__.py
new file mode 100644
index 0000000..8150864
--- /dev/null
+++ b/delta-py/python/delta/optim/__init__.py
@@ -0,0 +1,9 @@
+from .. import _delta as _C
+
+_mod = _C.optim
+
+for name in dir(_mod):
+ if not name.startswith("_"):
+ globals()[name] = getattr(_mod, name)
+
+__all__ = [name for name in dir(_mod) if not name.startswith("_")]
\ No newline at end of file
diff --git a/delta-py/python/delta/optim/__init__.pyi b/delta-py/python/delta/optim/__init__.pyi
new file mode 100644
index 0000000..1e92cfc
--- /dev/null
+++ b/delta-py/python/delta/optim/__init__.pyi
@@ -0,0 +1,16 @@
+from typing import Sequence
+from delta import Tensor
+
+class SGD:
+ @property
+ def lr(self) -> float: ...
+
+ @property
+ def is_maximizing(self) -> bool: ...
+
+ def __init__(self, parameters: Sequence[Tensor], lr: float) -> None: ...
+ def step(self) -> None: ...
+ def zero_grad(self) -> None: ...
+ def change_lr(self, gamma: float) -> None: ...
+ def maximize(self) -> None: ...
+ def minimize(self) -> None: ...
\ No newline at end of file
diff --git a/delta-py/python/delta/py.typed b/delta-py/python/delta/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/delta-py/src/lib.rs b/delta-py/src/lib.rs
new file mode 100644
index 0000000..c11c7bf
--- /dev/null
+++ b/delta-py/src/lib.rs
@@ -0,0 +1,18 @@
+mod linalg;
+mod nn;
+mod operations;
+mod optim;
+mod tensor;
+
+use pyo3::prelude::*;
+
+#[pymodule]
+#[pyo3(name = "_delta")]
+fn _delta(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
+ linalg::register_submodule(_py, m)?;
+ nn::register_submodule(_py, m)?;
+ operations::register_submodule(_py, m)?;
+ optim::register_submodule(_py, m)?;
+ tensor::register_submodule(_py, m)?;
+ Ok(())
+}
diff --git a/delta-py/src/linalg.rs b/delta-py/src/linalg.rs
new file mode 100644
index 0000000..e6bf632
--- /dev/null
+++ b/delta-py/src/linalg.rs
@@ -0,0 +1,23 @@
+use pyo3::prelude::*;
+
+use crate::tensor::PyTensor;
+
+pub fn register_submodule(_: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ parent.add_function(wrap_pyfunction!(matmul, parent)?)?;
+ parent.add_function(wrap_pyfunction!(cross, parent)?)?;
+ Ok(())
+}
+
+#[pyfunction]
+pub fn matmul(a: PyRef<'_, PyTensor>, b: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: delta::linalg::matmul(a.inner.clone(), b.inner.clone()),
+ })
+}
+
+#[pyfunction]
+pub fn cross(a: PyRef<'_, PyTensor>, b: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: delta::linalg::cross(a.inner.clone(), b.inner.clone()),
+ })
+}
diff --git a/delta-py/src/nn.rs b/delta-py/src/nn.rs
new file mode 100644
index 0000000..5a33036
--- /dev/null
+++ b/delta-py/src/nn.rs
@@ -0,0 +1,16 @@
+pub mod criterions;
+pub mod functional;
+pub mod parameter;
+
+use pyo3::prelude::*;
+
+use crate::nn::parameter::PyParameter;
+
+pub fn register_submodule(py: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ let nn = PyModule::new(py, "_delta.nn")?;
+ nn.add_class::()?;
+ functional::register_submodule(py, &nn)?;
+ criterions::register_submodule(py, &nn)?;
+ parent.add_submodule(&nn)?;
+ Ok(())
+}
diff --git a/delta-py/src/nn/criterions.rs b/delta-py/src/nn/criterions.rs
new file mode 100644
index 0000000..959ca89
--- /dev/null
+++ b/delta-py/src/nn/criterions.rs
@@ -0,0 +1,65 @@
+use pyo3::prelude::*;
+
+use crate::tensor::PyTensor;
+use delta::nn::{MSELoss, criterions::Reduction};
+
+pub fn register_submodule(_py: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ parent.add_class::()?;
+ parent.add_class::()?;
+ Ok(())
+}
+
+#[pyclass(module = "delta.nn", name = "Reduction", eq, eq_int, from_py_object)]
+#[derive(Clone, Copy, PartialEq)]
+pub enum PyReduction {
+ SUM = 0,
+ MEAN = 1,
+}
+
+impl From for Reduction {
+ fn from(value: PyReduction) -> Self {
+ match value {
+ PyReduction::SUM => Reduction::SUM,
+ PyReduction::MEAN => Reduction::MEAN,
+ }
+ }
+}
+
+#[pyclass(
+ name = "MSELoss",
+ module = "delta.nn",
+ unsendable,
+ skip_from_py_object
+)]
+#[derive(Clone)]
+pub struct PyMSELoss {
+ pub(crate) inner: MSELoss,
+}
+
+#[pymethods]
+impl PyMSELoss {
+ #[new]
+ #[pyo3(signature = (reduction=PyReduction::MEAN))]
+ fn new(reduction: Option) -> Self {
+ Self {
+ inner: MSELoss::new(reduction.map(Into::into)),
+ }
+ }
+
+ #[pyo3(name = "measure")]
+ fn measure(&self, a: PyRef<'_, PyTensor>, b: PyRef<'_, PyTensor>) -> PyTensor {
+ PyTensor {
+ inner: self.inner.measure(a.inner.clone(), b.inner.clone()),
+ }
+ }
+
+ fn __call__(&self, a: PyRef<'_, PyTensor>, b: PyRef<'_, PyTensor>) -> PyTensor {
+ PyTensor {
+ inner: self.inner.measure(a.inner.clone(), b.inner.clone()),
+ }
+ }
+
+ fn __repr__(&self) -> String {
+ "MSELoss()".to_string()
+ }
+}
diff --git a/delta-py/src/nn/functional.rs b/delta-py/src/nn/functional.rs
new file mode 100644
index 0000000..b37cb4d
--- /dev/null
+++ b/delta-py/src/nn/functional.rs
@@ -0,0 +1,36 @@
+use pyo3::prelude::*;
+
+use delta::nn::functional as F;
+
+use crate::tensor::PyTensor;
+
+pub fn register_submodule(py: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ let functional = PyModule::new(py, "delta.nn.functional")?;
+ functional.add_function(wrap_pyfunction!(relu, &functional)?)?;
+ functional.add_function(wrap_pyfunction!(sigmoid, &functional)?)?;
+ functional.add_function(wrap_pyfunction!(softmax, &functional)?)?;
+ parent.add_submodule(&functional)?;
+ Ok(())
+}
+
+#[pyfunction]
+pub fn relu(x: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: F::relu(x.inner.clone()),
+ })
+}
+
+#[pyfunction]
+pub fn sigmoid(x: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: F::sigmoid(x.inner.clone()),
+ })
+}
+
+#[pyfunction]
+#[pyo3(signature = (x, dim=-1))]
+pub fn softmax(x: PyRef<'_, PyTensor>, dim: isize) -> PyResult {
+ Ok(PyTensor {
+ inner: F::softmax(x.inner.clone(), dim),
+ })
+}
diff --git a/delta-py/src/nn/parameter.rs b/delta-py/src/nn/parameter.rs
new file mode 100644
index 0000000..2132997
--- /dev/null
+++ b/delta-py/src/nn/parameter.rs
@@ -0,0 +1,46 @@
+use delta::nn::Parameter;
+use pyo3::prelude::*;
+
+use crate::tensor::PyTensor;
+
+#[pyclass(
+ name = "Parameter",
+ module = "delta.nn",
+ unsendable,
+ skip_from_py_object
+)]
+#[derive(Clone)]
+pub struct PyParameter {
+ pub(crate) inner: Parameter,
+}
+
+#[pymethods]
+impl PyParameter {
+ #[new]
+ fn new(tensor: PyRef<'_, PyTensor>) -> Self {
+ Self {
+ inner: Parameter(tensor.inner.clone()),
+ }
+ }
+
+ // Delegate tensor methods so it behaves like a Tensor in Python
+ fn grad(&self) -> Option> {
+ self.inner.0.grad()
+ }
+
+ fn item(&self) -> Vec {
+ self.inner.0.item()
+ }
+
+ #[getter]
+ fn shape(&self) -> Vec {
+ self.inner.0.shape.clone()
+ }
+
+ #[getter]
+ fn data(&self) -> PyTensor {
+ PyTensor {
+ inner: self.inner.0.clone(),
+ }
+ }
+}
diff --git a/delta-py/src/operations.rs b/delta-py/src/operations.rs
new file mode 100644
index 0000000..bbdc550
--- /dev/null
+++ b/delta-py/src/operations.rs
@@ -0,0 +1,36 @@
+use delta::Tensor;
+use pyo3::prelude::*;
+
+use crate::tensor::PyTensor;
+
+pub fn register_submodule(_: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ parent.add_function(wrap_pyfunction!(sum, parent)?)?;
+ parent.add_function(wrap_pyfunction!(mean, parent)?)?;
+ parent.add_function(wrap_pyfunction!(cat, parent)?)?;
+ Ok(())
+}
+
+#[pyfunction]
+#[pyo3(signature = (input, dim=None, keepdim=false))]
+pub fn sum(input: PyRef<'_, PyTensor>, dim: Option, keepdim: bool) -> PyTensor {
+ PyTensor {
+ inner: input.inner.sum(dim, keepdim),
+ }
+}
+
+#[pyfunction]
+#[pyo3(signature = (input, dim=None, keepdim=false))]
+pub fn mean(input: PyRef<'_, PyTensor>, dim: Option, keepdim: bool) -> PyTensor {
+ PyTensor {
+ inner: input.inner.mean(dim, keepdim),
+ }
+}
+
+#[pyfunction]
+#[pyo3(signature = (tensors, dim=0))]
+pub fn cat(tensors: Vec>, dim: isize) -> PyTensor {
+ let rust_tensors: Vec = tensors.iter().map(|t| t.inner.clone()).collect();
+ PyTensor {
+ inner: Tensor::cat(&rust_tensors, dim),
+ }
+}
diff --git a/delta-py/src/optim.rs b/delta-py/src/optim.rs
new file mode 100644
index 0000000..1bd8fac
--- /dev/null
+++ b/delta-py/src/optim.rs
@@ -0,0 +1,12 @@
+pub mod sgd;
+
+use pyo3::prelude::*;
+
+use crate::optim::sgd::PySGD;
+
+pub fn register_submodule(py: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ let optim = PyModule::new(py, "_delta.optim")?;
+ optim.add_class::()?;
+ parent.add_submodule(&optim)?;
+ Ok(())
+}
diff --git a/delta-py/src/optim/sgd.rs b/delta-py/src/optim/sgd.rs
new file mode 100644
index 0000000..6855489
--- /dev/null
+++ b/delta-py/src/optim/sgd.rs
@@ -0,0 +1,53 @@
+use pyo3::prelude::*;
+
+use delta::optim::Optim;
+use delta::optim::sgd::SGD;
+
+use crate::nn::parameter::PyParameter;
+
+#[pyclass(name = "SGD", module = "delta.optim", unsendable)]
+pub struct PySGD {
+ pub(crate) inner: SGD,
+}
+
+#[pymethods]
+impl PySGD {
+ #[new]
+ #[pyo3(signature = (parameters, lr))]
+ fn new(parameters: Vec>, lr: f64) -> Self {
+ let params = parameters.into_iter().map(|t| t.inner.clone()).collect();
+
+ Self {
+ inner: SGD::new(params, lr),
+ }
+ }
+ #[getter]
+ fn lr(&self) -> f64 {
+ self.inner.lr()
+ }
+
+ #[getter]
+ fn is_maximize(&self) -> bool {
+ self.inner.is_maximize()
+ }
+
+ fn step(&self) {
+ self.inner.step();
+ }
+
+ fn zero_grad(&self) {
+ self.inner.zero_grad();
+ }
+
+ fn change_lr(&mut self, gamma: f64) {
+ self.inner.change_lr(gamma);
+ }
+
+ fn maximize(&mut self) {
+ self.inner.maximize();
+ }
+
+ fn minimize(&mut self) {
+ self.inner.minimize();
+ }
+}
diff --git a/delta-py/src/tensor.rs b/delta-py/src/tensor.rs
new file mode 100644
index 0000000..cc8cc22
--- /dev/null
+++ b/delta-py/src/tensor.rs
@@ -0,0 +1,402 @@
+use pyo3::exceptions::{PyTypeError, PyValueError};
+use pyo3::prelude::*;
+use pyo3::types::{PyList, PyTuple};
+
+use delta::Tensor;
+
+pub fn register_submodule(_: Python<'_>, parent: &Bound<'_, PyModule>) -> PyResult<()> {
+ parent.add_class::()?;
+ parent.add_function(wrap_pyfunction!(tensor, parent)?)?;
+ parent.add_function(wrap_pyfunction!(randn, parent)?)?;
+ parent.add_function(wrap_pyfunction!(zeros, parent)?)?;
+ parent.add_function(wrap_pyfunction!(zeros_like, parent)?)?;
+ parent.add_function(wrap_pyfunction!(ones, parent)?)?;
+ parent.add_function(wrap_pyfunction!(ones_like, parent)?)?;
+ Ok(())
+}
+
+#[pyclass(name = "Tensor", module = "delta", unsendable, skip_from_py_object)]
+#[derive(Clone)]
+pub struct PyTensor {
+ pub(crate) inner: Tensor,
+}
+
+#[pymethods]
+impl PyTensor {
+ #[staticmethod]
+ fn zeros(shape: Vec) -> Self {
+ Self {
+ inner: Tensor::zeros(&shape),
+ }
+ }
+
+ #[allow(non_snake_case)]
+ #[getter]
+ fn T(&self) -> PyResult {
+ Ok(Self {
+ inner: self.inner.t(),
+ })
+ }
+
+ #[staticmethod]
+ fn zeros_like(a: PyRef<'_, PyTensor>) -> Self {
+ let shape = a.shape();
+ Self {
+ inner: Tensor::zeros(&shape),
+ }
+ }
+
+ #[staticmethod]
+ fn ones(shape: Vec) -> Self {
+ Self {
+ inner: Tensor::ones(&shape),
+ }
+ }
+
+ #[staticmethod]
+ fn ones_like(a: PyRef<'_, PyTensor>) -> Self {
+ let shape = a.shape();
+ Self {
+ inner: Tensor::ones(&shape),
+ }
+ }
+
+ #[staticmethod]
+ fn randn(shape: Vec) -> Self {
+ Self {
+ inner: Tensor::randn(&shape),
+ }
+ }
+
+ #[getter]
+ fn shape(&self) -> Vec {
+ self.inner.shape.clone()
+ }
+
+ #[getter]
+ fn ndim(&self) -> usize {
+ self.inner.shape.len()
+ }
+
+ #[getter]
+ fn length(&self) -> usize {
+ self.inner.length()
+ }
+
+ fn storage(&self) -> Vec {
+ self.inner.storage()
+ }
+
+ fn item(&self) -> Vec {
+ self.inner.item()
+ }
+
+ #[getter]
+ fn grad(&self) -> PyResult>> {
+ Ok(self.inner.grad())
+ }
+
+ #[pyo3(signature = (*shape))]
+ fn reshape(&self, shape: &Bound<'_, PyTuple>) -> PyResult {
+ let obj = if shape.len() == 1 {
+ shape.get_item(0)?
+ } else {
+ shape.as_any().clone()
+ };
+
+ let shape = parse_shape(&obj, self.inner.length())?;
+
+ Ok(Self {
+ inner: self.inner.reshape(&shape),
+ })
+ }
+
+ #[pyo3(signature = (dim=None))]
+ fn squeeze(&self, dim: Option>) -> PyResult {
+ let dim = dim.unwrap_or_default();
+ Ok(Self {
+ inner: self.inner.squeeze(&dim),
+ })
+ }
+
+ fn __repr__(&self) -> String {
+ format!("{}", self.inner)
+ }
+
+ fn backward(&self) -> () {
+ self.inner.backward();
+ }
+
+ #[staticmethod]
+ #[pyo3(signature = (tensors, dim=0))]
+ fn cat(tensors: Vec>, dim: Option) -> PyResult {
+ let rust_tensors: Vec = tensors.iter().map(|t| t.inner.clone()).collect();
+ let dim = dim.unwrap_or_default();
+ Ok(Self {
+ inner: Tensor::cat(&rust_tensors, dim),
+ })
+ }
+
+ #[pyo3(signature = (dim=None, keepdim=false))]
+ fn sum(&self, dim: Option, keepdim: bool) -> Self {
+ Self {
+ inner: self.inner.sum(dim, keepdim),
+ }
+ }
+
+ #[pyo3(signature = (dim=None, keepdim=false))]
+ fn mean(&self, dim: Option, keepdim: bool) -> Self {
+ Self {
+ inner: self.inner.mean(dim, keepdim),
+ }
+ }
+
+ fn __add__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ if let Ok(value) = other.extract::() {
+ Ok(Self {
+ inner: self.inner.clone() + value,
+ })
+ } else if let Ok(other_tensor) = other.extract::>() {
+ Ok(Self {
+ inner: self.inner.clone() + other_tensor.inner.clone(),
+ })
+ } else {
+ Err(PyTypeError::new_err(
+ "unsupported operand type(s) for +: 'delta.Tensor' and given type",
+ ))
+ }
+ }
+
+ fn __radd__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ self.__add__(other)
+ }
+
+ fn __sub__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ if let Ok(value) = other.extract::() {
+ Ok(Self {
+ inner: self.inner.clone() + value,
+ })
+ } else if let Ok(other_tensor) = other.extract::>() {
+ Ok(Self {
+ inner: self.inner.clone() - other_tensor.inner.clone(),
+ })
+ } else {
+ Err(PyTypeError::new_err(
+ "unsupported operand type(s) for +: 'delta.Tensor' and given type",
+ ))
+ }
+ }
+
+ fn __rsub__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ self.__sub__(other)
+ }
+
+ fn __mul__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ if let Ok(value) = other.extract::() {
+ Ok(Self {
+ inner: self.inner.clone() + value,
+ })
+ } else if let Ok(other_tensor) = other.extract::>() {
+ Ok(Self {
+ inner: self.inner.clone() * other_tensor.inner.clone(),
+ })
+ } else {
+ Err(PyTypeError::new_err(
+ "unsupported operand type(s) for +: 'delta.Tensor' and given type",
+ ))
+ }
+ }
+
+ fn __rmul__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ self.__mul__(other)
+ }
+
+ fn __truediv__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ if let Ok(value) = other.extract::() {
+ Ok(Self {
+ inner: self.inner.clone() + value,
+ })
+ } else if let Ok(other_tensor) = other.extract::>() {
+ Ok(Self {
+ inner: self.inner.clone() / other_tensor.inner.clone(),
+ })
+ } else {
+ Err(PyTypeError::new_err(
+ "unsupported operand type(s) for +: 'delta.Tensor' and given type",
+ ))
+ }
+ }
+
+ fn __rtruediv__(&self, other: &Bound<'_, PyAny>) -> PyResult {
+ self.__truediv__(other)
+ }
+
+ fn __neg__(&self) -> Self {
+ Self {
+ inner: -self.inner.clone(),
+ }
+ }
+
+ fn __matmul__(&self, other: &PyTensor) -> PyTensor {
+ PyTensor {
+ inner: delta::linalg::matmul(self.inner.clone(), other.inner.clone()),
+ }
+ }
+}
+
+#[pyfunction]
+pub fn tensor(obj: &Bound<'_, PyAny>) -> PyResult {
+ let (data, shape) = extract_nested(obj)?;
+ Ok(PyTensor {
+ inner: Tensor::tensor(&data, &shape),
+ })
+}
+
+#[pyfunction]
+pub fn randn(shape: Vec) -> PyResult {
+ Ok(PyTensor {
+ inner: Tensor::randn(&shape),
+ })
+}
+
+#[pyfunction]
+pub fn zeros(shape: Vec) -> PyResult {
+ Ok(PyTensor {
+ inner: Tensor::zeros(&shape),
+ })
+}
+
+#[pyfunction]
+pub fn zeros_like(tensor: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: Tensor::zeros_like(&tensor.inner),
+ })
+}
+
+#[pyfunction]
+pub fn ones(shape: Vec) -> PyResult {
+ Ok(PyTensor {
+ inner: Tensor::ones(&shape),
+ })
+}
+
+#[pyfunction]
+pub fn ones_like(tensor: PyRef<'_, PyTensor>) -> PyResult {
+ Ok(PyTensor {
+ inner: Tensor::ones_like(&tensor.inner),
+ })
+}
+
+fn extract_nested(obj: &Bound<'_, PyAny>) -> PyResult<(Vec, Vec)> {
+ if let Ok(value) = obj.extract::() {
+ return Ok((vec![value], vec![]));
+ }
+
+ if let Ok(list) = obj.cast::() {
+ return extract_sequence(list.iter().collect());
+ }
+
+ if let Ok(tuple) = obj.cast::() {
+ return extract_sequence(tuple.iter().collect());
+ }
+
+ Err(PyTypeError::new_err(
+ "tensor() expects a number or a nested list/tuple of numbers",
+ ))
+}
+
+fn extract_sequence(items: Vec>) -> PyResult<(Vec, Vec)> {
+ let len = items.len();
+
+ if len == 0 {
+ return Ok((Vec::new(), vec![0]));
+ }
+
+ let mut flat = Vec::new();
+ let mut inner_shape: Option> = None;
+
+ for item in items {
+ let (child_flat, child_shape) = extract_nested(&item)?;
+
+ match &inner_shape {
+ None => inner_shape = Some(child_shape.clone()),
+ Some(expected) if *expected == child_shape => {}
+ Some(expected) => {
+ return Err(PyValueError::new_err(format!(
+ "ragged nested sequence: expected inner shape {:?}, got {:?}",
+ expected, child_shape
+ )));
+ }
+ }
+
+ flat.extend(child_flat);
+ }
+
+ let mut shape = vec![len];
+ if let Some(child_shape) = inner_shape {
+ shape.extend(child_shape);
+ }
+
+ Ok((flat, shape))
+}
+
+fn parse_shape(obj: &Bound<'_, PyAny>, total_len: usize) -> PyResult> {
+ let dims: Vec = if let Ok(v) = obj.extract::() {
+ vec![v]
+ } else if let Ok(v) = obj.extract::>() {
+ v
+ } else if let Ok(tuple) = obj.cast::() {
+ tuple.extract::>()?
+ } else if let Ok(list) = obj.cast::() {
+ list.extract::>()?
+ } else {
+ return Err(PyValueError::new_err(
+ "reshape expects an int, tuple, or list of ints",
+ ));
+ };
+
+ if dims.is_empty() {
+ return Err(PyValueError::new_err("reshape shape cannot be empty"));
+ }
+
+ let mut out = Vec::with_capacity(dims.len());
+ let mut infer_idx: Option = None;
+ let mut known_product = 1usize;
+
+ for (i, &d) in dims.iter().enumerate() {
+ if d == -1 {
+ if infer_idx.is_some() {
+ return Err(PyValueError::new_err("only one dimension can be inferred"));
+ }
+ infer_idx = Some(i);
+ out.push(0);
+ } else if d < 0 {
+ return Err(PyValueError::new_err(
+ "reshape dimensions must be >= 0, except -1",
+ ));
+ } else {
+ let u = d as usize;
+ known_product = known_product
+ .checked_mul(u)
+ .ok_or_else(|| PyValueError::new_err("shape product overflow"))?;
+ out.push(u);
+ }
+ }
+
+ if let Some(i) = infer_idx {
+ if known_product == 0 || !total_len.is_multiple_of(known_product) {
+ return Err(PyValueError::new_err(format!(
+ "cannot infer shape {:?} for tensor of length {}",
+ dims, total_len
+ )));
+ }
+ out[i] = total_len / known_product;
+ } else if known_product != total_len {
+ return Err(PyValueError::new_err(format!(
+ "cannot reshape tensor of length {} into shape {:?}",
+ total_len, dims
+ )));
+ }
+
+ Ok(out)
+}
diff --git a/delta-py/uv.lock b/delta-py/uv.lock
new file mode 100644
index 0000000..3c0f3c4
--- /dev/null
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+ "serde_json",
+ "unicode-xid",
+ "wasmparser",
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diff --git a/delta/Cargo.toml b/delta/Cargo.toml
new file mode 100644
index 0000000..4210a8d
--- /dev/null
+++ b/delta/Cargo.toml
@@ -0,0 +1,22 @@
+[package]
+name = "delta"
+# versions
+version = "0.1.0"
+edition = "2024"
+rust-version = "1.95.0"
+# info
+authors = ["commanderxa"]
+readme = "README.md"
+repository = "https://github.com/CommanderXA/delta"
+license-file = "LICENSE.txt"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+rand = "0.10.1"
+delta-macros = { path = "../delta-macros" }
+
+[lib]
+name = "delta"
+path = "src/lib.rs"
+crate-type = ["rlib"]
diff --git a/delta/examples/linear.rs b/delta/examples/linear.rs
new file mode 100644
index 0000000..a0b669c
--- /dev/null
+++ b/delta/examples/linear.rs
@@ -0,0 +1,16 @@
+use delta::{
+ Tensor, ivalue,
+ nn::{Linear, Module, functional as F},
+};
+
+fn main() {
+ let linear = Linear::new(20, 10, true);
+ let x = Tensor::randn(&[2, 20]);
+ println!("Weights: {}", linear.weights.0);
+ println!("IN:\n{x}");
+ let (args, kwargs) = ivalue![[x]];
+ let out = linear.forward(args, kwargs).unwrap_tensor();
+ println!("OUT:\n{out}");
+ let out = F::sigmoid(out);
+ println!("OUT:\n{out}");
+}
diff --git a/examples/matmul.rs b/delta/examples/matmul.rs
similarity index 87%
rename from examples/matmul.rs
rename to delta/examples/matmul.rs
index 524eea8..e469070 100644
--- a/examples/matmul.rs
+++ b/delta/examples/matmul.rs
@@ -1,4 +1,4 @@
-use athena::{linalg, Tensor};
+use delta::{linalg, Tensor};
fn main() {
let a = Tensor::randn(&[2, 3, 2]);
diff --git a/examples/mlp.rs b/delta/examples/mlp.rs
similarity index 79%
rename from examples/mlp.rs
rename to delta/examples/mlp.rs
index cb92e8d..65893cf 100644
--- a/examples/mlp.rs
+++ b/delta/examples/mlp.rs
@@ -1,7 +1,9 @@
-use athena::{
- Tensor,
- module::{Forward, Module},
- nn::{Linear, MSELoss, functional as F},
+use std::collections::HashMap;
+
+use delta::{
+ Tensor, ivalue,
+ ivalue::IValue,
+ nn::{self, Linear, MSELoss, Module, functional as F},
optim::{Optim, SGD},
};
@@ -51,7 +53,8 @@ fn main() {
let x = x.clone().unsqueeze(0);
let y = y.clone();
- let out = mlp.forward(x).squeeze(&[0]);
+ let (args, kwargs) = ivalue![[x]];
+ let out = mlp.forward(args, kwargs).unwrap_tensor().squeeze(&[0]);
let loss = criterion.measure(out, y);
loss.backward();
@@ -64,7 +67,8 @@ fn main() {
for (x, y) in test_data.iter().zip(test_targets.clone()) {
let x = x.clone().unsqueeze(0);
- let out = mlp.forward(x.clone()).squeeze(&[0]);
+ let (args, kwargs) = ivalue![[x.clone()]];
+ let out = mlp.forward(args, kwargs).unwrap_tensor().squeeze(&[0]);
let loss = criterion.measure(out.clone(), y.clone());
println!(
@@ -95,15 +99,13 @@ impl Module for MLP {
"MLP".to_owned()
}
- fn parameters(&self) -> Vec {
+ fn parameters(&self) -> Vec {
let parameters = self.linear.parameters();
parameters
}
-}
-impl Forward for MLP {
- fn forward(&self, x: Tensor) -> Tensor {
- let x = self.linear.forward(x);
- F::sigmoid(x)
+ fn forward(&self, args: Vec, kwargs: HashMap) -> IValue {
+ let x = self.linear.forward(args, kwargs).unwrap_tensor();
+ IValue::Tensor(F::sigmoid(x))
}
}
diff --git a/examples/mlp2.rs b/delta/examples/mlp2.rs
similarity index 81%
rename from examples/mlp2.rs
rename to delta/examples/mlp2.rs
index 66e5c42..426042e 100644
--- a/examples/mlp2.rs
+++ b/delta/examples/mlp2.rs
@@ -1,8 +1,10 @@
-use athena::{
- module::{Forward, Module},
- nn::{Linear, MSELoss},
+use std::collections::HashMap;
+
+use delta::{
+ Tensor, ivalue,
+ ivalue::IValue,
+ nn::{self, Linear, MSELoss, Module},
optim::{Optim, SGD},
- Tensor,
};
fn main() {
@@ -52,7 +54,8 @@ fn main() {
let x = x.clone();
let y = y.clone();
- let out = mlp.forward(x.clone());
+ let (args, kwargs) = ivalue![[x.clone()]];
+ let out = mlp.forward(args, kwargs).unwrap_tensor();
let loss = criterion.measure(out, y);
loss.backward();
@@ -70,14 +73,16 @@ fn main() {
let mut test_res = vec![];
for (x, y) in data.iter().zip(targets.clone()) {
- let out = mlp.forward(x.clone());
+ let (args, kwargs) = ivalue![[x.clone()]];
+ let out = mlp.forward(args, kwargs).unwrap_tensor();
let loss = criterion.measure(out.clone(), y.clone());
loss.backward();
training_res.push((out.item()[0], y.item()[0]))
}
for (x, y) in test_data.iter().zip(test_targets.clone()) {
- let out = mlp.forward(x.clone());
+ let (args, kwargs) = ivalue![[x.clone()]];
+ let out = mlp.forward(args, kwargs).unwrap_tensor();
let loss = criterion.measure(out.clone(), y.clone());
loss.backward();
test_res.push((out.item()[0], y.item()[0]))
@@ -105,15 +110,12 @@ impl Module for MLP {
"MLP".to_owned()
}
- fn parameters(&self) -> Vec {
+ fn parameters(&self) -> Vec {
let parameters = self.linear1.parameters();
parameters
}
-}
-impl Forward for MLP {
- fn forward(&self, x: Tensor) -> Tensor {
- let x = self.linear1.forward(x);
- x
+ fn forward(&self, args: Vec, kwargs: HashMap) -> IValue {
+ self.linear1.forward(args, kwargs)
}
}
diff --git a/examples/softmax.rs b/delta/examples/softmax.rs
similarity index 80%
rename from examples/softmax.rs
rename to delta/examples/softmax.rs
index 5ba956d..5cf34fc 100644
--- a/examples/softmax.rs
+++ b/delta/examples/softmax.rs
@@ -1,4 +1,4 @@
-use athena::{nn::functional as F, Tensor};
+use delta::{nn::functional as F, Tensor};
fn main() {
let x = Tensor::tensor(&[0.24, 0.1, 0.5, 0.8, 1.2, 2.2], &[1, 2, 3]);
diff --git a/examples/tensor.rs b/delta/examples/tensor.rs
similarity index 92%
rename from examples/tensor.rs
rename to delta/examples/tensor.rs
index 55fc5c4..159cd63 100644
--- a/examples/tensor.rs
+++ b/delta/examples/tensor.rs
@@ -1,4 +1,4 @@
-use athena::{Tensor, tensor};
+use delta::{Tensor, tensor};
fn main() {
let a = tensor!([1., 2., 3.]);
diff --git a/src/backward.rs b/delta/src/backward.rs
similarity index 69%
rename from src/backward.rs
rename to delta/src/backward.rs
index 172976a..5e4695c 100644
--- a/src/backward.rs
+++ b/delta/src/backward.rs
@@ -1,4 +1,4 @@
-use crate::{linalg, op::Op, tensor_data::TensorData, Tensor};
+use crate::{Tensor, linalg, op::Op, tensor_data::TensorData};
/// Backward trait for backpropagation operation.
pub trait Backward {
@@ -47,6 +47,69 @@ impl Backward for Op {
t._prev[1].add_to_grad(l_item.iter().zip(grad).map(|(a, b)| a * b).collect());
}
+ Op::Sum { dim, keepdim } => {
+ let t = tensor.inner.borrow();
+ let grad = t.grad.clone().unwrap();
+ let prev = &t._prev[0];
+ let prev_shape = prev.shape.clone();
+
+ let back = match dim {
+ None => {
+ vec![grad[0]; prev.length()]
+ }
+ Some(dim) => {
+ let mut grad_tensor =
+ Tensor::tensor(&grad, &tensor.shape).requires_grad(false);
+
+ if !*keepdim {
+ let mut shape = prev_shape.clone();
+ shape[*dim] = 1;
+ grad_tensor = grad_tensor.reshape(&shape);
+ }
+
+ grad_tensor.expand(&prev_shape).item()
+ }
+ };
+
+ prev.add_to_grad(back);
+ }
+
+ Op::Mean {
+ dim,
+ keepdim,
+ count,
+ } => {
+ let t = tensor.inner.borrow();
+ let grad = t.grad.clone().unwrap();
+ let prev = &t._prev[0];
+ let prev_shape = prev.shape.clone();
+
+ let mut back = match dim {
+ None => {
+ vec![grad[0]; prev.length()]
+ }
+ Some(dim) => {
+ let mut grad_tensor =
+ Tensor::tensor(&grad, &tensor.shape).requires_grad(false);
+
+ if !*keepdim {
+ let mut shape = prev_shape.clone();
+ shape[*dim] = 1;
+ grad_tensor = grad_tensor.reshape(&shape);
+ }
+
+ grad_tensor.expand(&prev_shape).item()
+ }
+ };
+
+ let scale = 1.0 / *count as f64;
+ for g in back.iter_mut() {
+ *g *= scale;
+ }
+
+ prev.add_to_grad(back);
+ }
+
// Power backward
// d(x^n)/dx * grad = n * x^(n-1) * grad
Op::Pow(n) => {
@@ -126,9 +189,9 @@ impl Backward for Op {
for i in 0..n {
for j in 0..n {
if i == j {
- jacobian[i * j] = s[i] * (1.0 - s[i]);
+ jacobian[i * n + j] = s[i] * (1.0 - s[i]);
} else {
- jacobian[i * j] = -s[i] * s[j];
+ jacobian[i * n + j] = -s[i] * s[j];
}
}
}
diff --git a/src/data.rs b/delta/src/data.rs
similarity index 100%
rename from src/data.rs
rename to delta/src/data.rs
diff --git a/src/data/dataloader.rs b/delta/src/data/dataloader.rs
similarity index 100%
rename from src/data/dataloader.rs
rename to delta/src/data/dataloader.rs
diff --git a/src/data/dataset.rs b/delta/src/data/dataset.rs
similarity index 100%
rename from src/data/dataset.rs
rename to delta/src/data/dataset.rs
diff --git a/src/data/sample.rs b/delta/src/data/sample.rs
similarity index 100%
rename from src/data/sample.rs
rename to delta/src/data/sample.rs
diff --git a/delta/src/ivalue.rs b/delta/src/ivalue.rs
new file mode 100644
index 0000000..66671f5
--- /dev/null
+++ b/delta/src/ivalue.rs
@@ -0,0 +1,223 @@
+use crate::Tensor;
+
+#[derive(Debug)]
+pub enum IValue {
+ Tensor(Tensor),
+ TensorList(Vec),
+ Tuple(Vec),
+ Int(i64),
+ Float(f64),
+ Bool(bool),
+ Str(String),
+ None,
+}
+
+impl IValue {
+ /// Unwraps the IValue as a Tensor, panics if it is not a Tensor variant
+ pub fn unwrap_tensor(self) -> Tensor {
+ match self {
+ IValue::Tensor(t) => t,
+ other => panic!("expected Tensor, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the IValue as a tuple (Vec), panics otherwise
+ pub fn unwrap_tuple(self) -> Vec {
+ match self {
+ IValue::Tuple(v) => v,
+ other => panic!("expected Tuple, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the Tensor as a tensor list (Vec), panics otherwise
+ pub fn unwrap_list(self) -> Vec {
+ match self {
+ IValue::TensorList(v) => v,
+ other => panic!("expected Tuple, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the IValue as i64, panics otherwise
+ pub fn unwrap_int(self) -> i64 {
+ match self {
+ IValue::Int(i) => i,
+ other => panic!("expected Int, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the IValue as f64, panics otherwise
+ pub fn unwrap_float(self) -> f64 {
+ match self {
+ IValue::Float(f) => f,
+ other => panic!("expected Float, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the IValue as bool, panics otherwise
+ pub fn unwrap_bool(self) -> bool {
+ match self {
+ IValue::Bool(b) => b,
+ other => panic!("expected Bool, got {:?}", other),
+ }
+ }
+
+ /// Unwraps the IValue as String, panics otherwise
+ pub fn unwrap_str(self) -> String {
+ match self {
+ IValue::Str(s) => s,
+ other => panic!("expected Str, got {:?}", other),
+ }
+ }
+}
+
+/// Creates a tuple of `(Vec, HashMap)` from positional args and keyword args.
+///
+/// # Syntax
+/// ```ignore
+/// ivalue![[arg1, arg2, ...], { key1: val1, key2: val2, ... }]
+/// ```
+///
+/// - The first bracket `[...]` contains positional arguments.
+/// - The second brace `{...}` contains keyword arguments as `key: value` pairs.
+/// - All values are automatically converted via `IValue::from(...)`,
+/// so any type implementing `From for IValue` works directly.
+///
+/// # Shorthand Forms
+/// - `ivalue![[x, y]]` — args only, empty kwargs
+/// - `ivalue![{ key: val }]` — kwargs only, empty args
+/// - `ivalue![[], {}]` — fully empty
+///
+/// # Examples
+/// ```ignore
+/// # use delta::ivalue;
+/// # use delta::Tensor;
+/// # use delta::nn;
+/// # use delta::nn::Module;
+/// # let x = Tensor::zeros(&[1, 1]);
+/// # let q = Tensor::zeros(&[1, 1]);
+/// # let k = Tensor::zeros(&[1, 1]);
+/// # let v = Tensor::zeros(&[1, 1]);
+///
+/// // args only shorthand
+/// let (args, kwargs) = ivalue![[x]];
+///
+/// // kwargs only shorthand
+/// let (args, kwargs) = ivalue![{ mask: true, scale: 1.0f64 }];
+///
+/// // multiple positional args
+/// let (args, kwargs) = ivalue![[q, k, v]];
+///
+/// // both args and kwargs
+/// let (args, kwargs) = ivalue![[x], { dropout: false }];
+///
+/// // inline directly into forward call
+/// let model = nn::Linear::new(1, 1, true);
+/// let (args, kwargs) = ivalue![[x], { scale: 0.5f64 }];
+/// model.forward(args, kwargs);
+///
+/// // fully empty
+/// let (args, kwargs) = ivalue![[], {}];
+/// ```
+#[macro_export]
+macro_rules! ivalue {
+ // fully empty: ivalue![[], {}]
+ ([], {}) => {
+ (vec![], std::collections::HashMap::new())
+ };
+
+ // args only shorthand: ivalue![[x, y]]
+ // sugar for ivalue![[x, y], {}]
+ ([$($arg:expr),* $(,)?]) => {
+ $crate::ivalue!([$($arg),*], {})
+ };
+
+ // kwargs only shorthand: ivalue![{ key: val }]
+ // sugar for ivalue![[], { key: val }]
+ ({ $($key:ident : $val:expr),* $(,)? }) => {
+ $crate::ivalue!([], { $($key : $val),* })
+ };
+
+ // args only, explicit empty kwargs: ivalue![[x, y], {}]
+ ([$($arg:expr),* $(,)?], {}) => {
+ (
+ // wrap each positional arg into IValue via From trait
+ vec![$($crate::ivalue::IValue::from($arg)),*],
+ std::collections::HashMap::new(),
+ )
+ };
+
+ // kwargs only, explicit empty args: ivalue![[], { key: val }]
+ ([], { $($key:ident : $val:expr),* $(,)? }) => {
+ (
+ vec![],
+ {
+ let mut map = std::collections::HashMap::new();
+ // stringify the identifier key and wrap value into IValue via From trait
+ $(map.insert(
+ stringify!($key).to_string(),
+ $crate::ivalue::IValue::from($val)
+ );)*
+ map
+ }
+ )
+ };
+
+ // both args and kwargs: ivalue![[x], { mask: true }]
+ ([$($arg:expr),* $(,)?], { $($key:ident : $val:expr),* $(,)? }) => {
+ (
+ // positional args → Vec
+ vec![$($crate::ivalue::IValue::from($arg)),*],
+ {
+ // keyword args → HashMap
+ let mut map = std::collections::HashMap::new();
+ $(map.insert(
+ stringify!($key).to_string(),
+ $crate::ivalue::IValue::from($val)
+ );)*
+ map
+ }
+ )
+ };
+}
+
+impl From for IValue {
+ fn from(t: Tensor) -> Self {
+ IValue::Tensor(t)
+ }
+}
+
+impl From> for IValue {
+ fn from(v: Vec) -> Self {
+ IValue::TensorList(v)
+ }
+}
+
+impl From for IValue {
+ fn from(i: i64) -> Self {
+ IValue::Int(i)
+ }
+}
+
+impl From for IValue {
+ fn from(f: f64) -> Self {
+ IValue::Float(f)
+ }
+}
+
+impl From for IValue {
+ fn from(b: bool) -> Self {
+ IValue::Bool(b)
+ }
+}
+
+impl From for IValue {
+ fn from(s: String) -> Self {
+ IValue::Str(s)
+ }
+}
+
+impl From<&str> for IValue {
+ fn from(s: &str) -> Self {
+ IValue::Str(s.to_owned())
+ }
+}
diff --git a/src/lib.rs b/delta/src/lib.rs
similarity index 68%
rename from src/lib.rs
rename to delta/src/lib.rs
index e499076..dda1f13 100644
--- a/src/lib.rs
+++ b/delta/src/lib.rs
@@ -1,12 +1,14 @@
pub mod backward;
pub mod data;
+pub mod ivalue;
pub mod linalg;
-pub mod module;
pub mod nn;
mod op;
+pub mod operations;
pub mod optim;
pub mod tensor;
mod tensor_data;
// define short paths
+pub use operations::{cat, mean, sum};
pub use tensor::Tensor;
diff --git a/src/linalg.rs b/delta/src/linalg.rs
similarity index 100%
rename from src/linalg.rs
rename to delta/src/linalg.rs
diff --git a/src/linalg/product.rs b/delta/src/linalg/product.rs
similarity index 94%
rename from src/linalg/product.rs
rename to delta/src/linalg/product.rs
index a9d27de..c19ad73 100644
--- a/src/linalg/product.rs
+++ b/delta/src/linalg/product.rs
@@ -1,4 +1,4 @@
-use crate::{op::Op, tensor_data::TensorData, Tensor};
+use crate::{Tensor, op::Op, tensor_data::TensorData};
/// Matrix multiplication
///
@@ -23,14 +23,14 @@ pub fn matmul(a: Tensor, b: Tensor) -> Tensor {
assert_eq!(
a_shape.last().unwrap(),
b_shape.first().unwrap(),
- "The shapes of the tensors must have the same inner dimension -> (M x N) @ (N x M), but you have tensors A: {:?} and B: {:?}",
- format!("({a_shape:?})").replace('[', "").replace(']', ""),
- format!("({b_shape:?})").replace('[', "").replace(']', ""),
+ "The shapes of the tensors must have the same inner dimension -> (M x N) @ (N x M), but you have tensors A: {:?} and B: {:?}",
+ format!("({a_shape:?})").replace('[', "").replace(']', ""),
+ format!("({b_shape:?})").replace('[', "").replace(']', ""),
);
// get batch dimensions if they exist
let mut batches: Vec = vec![];
- for i in 2..a_shape.len() {
- batches.push(a_shape[i - 2]);
+ for i in 0..(a_shape.len() - 2) {
+ batches.push(a_shape[i]);
}
// remove batch dimensions from the A tensor shape
a_shape.drain(0..batches.len());
@@ -58,9 +58,7 @@ pub fn matmul(a: Tensor, b: Tensor) -> Tensor {
.iter()
.zip(b_data)
.map(|(&a, &b)| a * b)
- .collect::>()
- .iter()
- .sum();
+ .sum::();
}
}
}
diff --git a/src/nn.rs b/delta/src/nn.rs
similarity index 58%
rename from src/nn.rs
rename to delta/src/nn.rs
index b4091e0..d9075cb 100644
--- a/src/nn.rs
+++ b/delta/src/nn.rs
@@ -1,9 +1,15 @@
pub mod criterions;
pub mod functional;
pub mod linear;
+pub mod module;
+pub mod parameter;
// define short paths
// layers
pub use linear::Linear;
// criterions
pub use criterions::MSELoss;
+// module
+pub use module::Module;
+// parameter
+pub use parameter::Parameter;
diff --git a/src/nn/criterions.rs b/delta/src/nn/criterions.rs
similarity index 100%
rename from src/nn/criterions.rs
rename to delta/src/nn/criterions.rs
diff --git a/src/nn/functional.rs b/delta/src/nn/functional.rs
similarity index 87%
rename from src/nn/functional.rs
rename to delta/src/nn/functional.rs
index 4a2dcc1..8bcc36d 100644
--- a/src/nn/functional.rs
+++ b/delta/src/nn/functional.rs
@@ -1,4 +1,4 @@
-use crate::{op::Op, tensor_data::TensorData, Tensor};
+use crate::{Tensor, op::Op, tensor_data::TensorData};
pub fn relu(x: Tensor) -> Tensor {
let mut data = x.item();
@@ -16,8 +16,17 @@ pub fn sigmoid(x: Tensor) -> Tensor {
Tensor::new(inner, &data.shape)
}
-pub fn softmax(x: Tensor, dim: usize) -> Tensor {
+pub fn softmax(x: Tensor, dim: isize) -> Tensor {
+ assert!(dim >= -1, "cat: `dim` cannot be negative integer");
+
let shape = x.shape();
+
+ let dim: usize = if dim == -1 {
+ shape.len() - 1
+ } else {
+ dim as usize
+ };
+
let mut shape2 = shape.clone();
assert_eq!(
dim,
diff --git a/delta/src/nn/linear.rs b/delta/src/nn/linear.rs
new file mode 100644
index 0000000..5a049c6
--- /dev/null
+++ b/delta/src/nn/linear.rs
@@ -0,0 +1,60 @@
+use std::collections::HashMap;
+
+use crate::{
+ Tensor,
+ ivalue::IValue,
+ linalg,
+ nn::{Module, Parameter},
+};
+
+/// # `Linear` Layer
+///
+/// Contains of:
+/// - weights
+/// - bias
+///
+/// Linear layer performs: `x @ W + b`,
+/// where:
+/// - `x` is input
+/// - `W` is weights
+/// - `b` is bias
+// #[derive(Module, Clone)]
+pub struct Linear {
+ // #[param]
+ pub weights: Parameter,
+}
+
+impl Linear {
+ pub fn new(mut in_features: usize, out_features: usize, bias: bool) -> Self {
+ if bias {
+ in_features += 1;
+ }
+ let _weights = Parameter(Tensor::randn(&[in_features, out_features]));
+ Self { weights: _weights }
+ }
+}
+
+impl Module for Linear {
+ fn module_name(&self) -> String {
+ "Linear".to_owned()
+ }
+
+ fn parameters(&self) -> Vec {
+ let parameters = vec![self.weights.clone()];
+ parameters
+ }
+
+ fn forward(&self, args: Vec, _kwargs: HashMap) -> IValue {
+ let x = match &args[0] {
+ IValue::Tensor(t) => t.clone(),
+ _ => panic!("Linear expects a Tensor as first argument"),
+ };
+ let weights = self.weights.clone();
+ let mut ones_shape = x.shape();
+ let _ = ones_shape.pop();
+ ones_shape.push(1);
+ let x = Tensor::cat(&[x, Tensor::ones(&ones_shape)], 1);
+ let x = linalg::matmul(x, weights.0);
+ IValue::Tensor(x)
+ }
+}
diff --git a/src/module.rs b/delta/src/nn/module.rs
similarity index 51%
rename from src/module.rs
rename to delta/src/nn/module.rs
index 590f3a4..9e8a755 100644
--- a/src/module.rs
+++ b/delta/src/nn/module.rs
@@ -1,4 +1,6 @@
-use crate::Tensor;
+use std::collections::HashMap;
+
+use crate::{ivalue::IValue, nn};
/// # `Module` Trait
///
@@ -12,17 +14,16 @@ pub trait Module {
/// Returns the name of module
fn module_name(&self) -> String;
- /// Returns the parameters of module
- fn parameters(&self) -> Vec;
-}
+ /// `forward` - performs inference in the module (forward propagation)
+ fn forward(&self, args: Vec, kwargs: HashMap) -> IValue;
-/// # `Forward` Trait
-///
-/// Trait that defines specific behavior for modules that work with tensors.
-/// This trait has to be implemented for struct that implement `Module`.
-///
-/// `forward` - performs inference in the module (forward propagation)
-pub trait Forward: Module {
- /// Forward (inference) function for module
- fn forward(&self, x: Tensor) -> Tensor;
+ /// Returns the submodules of module
+ fn submodules(&self) -> Vec<&dyn Module> {
+ vec![]
+ }
+
+ /// Returns the parameters of module
+ fn parameters(&self) -> Vec {
+ vec![]
+ }
}
diff --git a/delta/src/nn/parameter.rs b/delta/src/nn/parameter.rs
new file mode 100644
index 0000000..0b9544b
--- /dev/null
+++ b/delta/src/nn/parameter.rs
@@ -0,0 +1,12 @@
+use crate::Tensor;
+
+#[derive(Clone, Debug)]
+pub struct Parameter(pub Tensor);
+
+impl std::ops::Deref for Parameter {
+ type Target = Tensor;
+
+ fn deref(&self) -> &Tensor {
+ &self.0
+ }
+}
diff --git a/src/op.rs b/delta/src/op.rs
similarity index 73%
rename from src/op.rs
rename to delta/src/op.rs
index 29ef74c..c7f43fc 100644
--- a/src/op.rs
+++ b/delta/src/op.rs
@@ -7,6 +7,15 @@ pub enum Op {
Add,
Sub,
Mul,
+ Sum {
+ dim: Option,
+ keepdim: bool,
+ },
+ Mean {
+ dim: Option,
+ keepdim: bool,
+ count: usize,
+ },
Pow(i32),
Exp(Tensor),
MatMul,
@@ -23,6 +32,12 @@ impl std::fmt::Display for Op {
Op::Add => write!(f, "Add"),
Op::Sub => write!(f, "Sub"),
Op::Mul => write!(f, "Mul"),
+ Op::Sum { dim: _, keepdim: _ } => write!(f, "Sum"),
+ Op::Mean {
+ dim: _,
+ keepdim: _,
+ count: _,
+ } => write!(f, "Mean"),
Op::Pow(n) => write!(f, "Pow({n})"),
Op::Exp(_) => write!(f, "Exp"),
Op::MatMul => write!(f, "MatMul"),
diff --git a/delta/src/operations.rs b/delta/src/operations.rs
new file mode 100644
index 0000000..f9be400
--- /dev/null
+++ b/delta/src/operations.rs
@@ -0,0 +1,13 @@
+use crate::Tensor;
+
+pub fn sum(input: &Tensor, dim: Option, keepdim: bool) -> Tensor {
+ input.sum(dim, keepdim)
+}
+
+pub fn mean(input: &Tensor, dim: Option, keepdim: bool) -> Tensor {
+ input.mean(dim, keepdim)
+}
+
+pub fn cat(tensors: &[Tensor], dim: isize) -> Tensor {
+ Tensor::cat(tensors, dim)
+}
diff --git a/src/optim.rs b/delta/src/optim.rs
similarity index 100%
rename from src/optim.rs
rename to delta/src/optim.rs
diff --git a/src/optim/lr_scheduler.rs b/delta/src/optim/lr_scheduler.rs
similarity index 100%
rename from src/optim/lr_scheduler.rs
rename to delta/src/optim/lr_scheduler.rs
diff --git a/src/optim/lr_scheduler/multistep_lr.rs b/delta/src/optim/lr_scheduler/multistep_lr.rs
similarity index 100%
rename from src/optim/lr_scheduler/multistep_lr.rs
rename to delta/src/optim/lr_scheduler/multistep_lr.rs
diff --git a/src/optim/sgd.rs b/delta/src/optim/sgd.rs
similarity index 79%
rename from src/optim/sgd.rs
rename to delta/src/optim/sgd.rs
index 049a84c..e3c7a61 100644
--- a/src/optim/sgd.rs
+++ b/delta/src/optim/sgd.rs
@@ -1,4 +1,4 @@
-use crate::Tensor;
+use crate::nn::Parameter;
use super::Optim;
@@ -11,13 +11,25 @@ use super::Optim;
/// - learning rate
#[derive(Clone)]
pub struct SGD {
- parameters: Vec,
+ parameters: Vec,
lr: f64,
maximize: bool,
}
impl SGD {
- pub fn new(parameters: Vec, lr: f64) -> Self {
+ pub fn lr(&self) -> f64 {
+ self.lr
+ }
+
+ pub fn is_maximize(&self) -> bool {
+ self.maximize
+ }
+
+ pub fn parameters(&self) -> &[Parameter] {
+ &self.parameters
+ }
+
+ pub fn new(parameters: Vec, lr: f64) -> Self {
Self {
parameters,
lr,
diff --git a/src/tensor.rs b/delta/src/tensor.rs
similarity index 84%
rename from src/tensor.rs
rename to delta/src/tensor.rs
index c32b76e..519fcb8 100644
--- a/src/tensor.rs
+++ b/delta/src/tensor.rs
@@ -192,6 +192,133 @@ impl Tensor {
self
}
+ pub fn sum(&self, dim: Option, keepdim: bool) -> Tensor {
+ match dim {
+ None => {
+ let value: f64 = self.item().iter().sum();
+ let shape = if keepdim {
+ vec![1; self.shape.len().max(1)]
+ } else {
+ vec![1]
+ };
+ let inner = TensorData::from_op(
+ vec![value],
+ vec![self.clone()],
+ Op::Sum { dim: None, keepdim },
+ );
+ Tensor::new(inner, &shape)
+ }
+ Some(dim) => {
+ assert!(dim < self.shape.len(), "sum: dim out of range");
+
+ let input = self.item();
+ let outer: usize = self.shape[..dim].iter().product();
+ let reduce: usize = self.shape[dim];
+ let inner: usize = self.shape[dim + 1..].iter().product();
+
+ let mut out = vec![0.0; outer * inner];
+
+ for o in 0..outer {
+ for i in 0..inner {
+ let mut acc = 0.0;
+ for r in 0..reduce {
+ let idx = o * reduce * inner + r * inner + i;
+ acc += input[idx];
+ }
+ out[o * inner + i] = acc;
+ }
+ }
+
+ let mut out_shape = self.shape.clone();
+ if keepdim {
+ out_shape[dim] = 1;
+ } else {
+ out_shape.remove(dim);
+ if out_shape.is_empty() {
+ out_shape.push(1);
+ }
+ }
+
+ let inner_data = TensorData::from_op(
+ out,
+ vec![self.clone()],
+ Op::Sum {
+ dim: Some(dim),
+ keepdim,
+ },
+ );
+ Tensor::new(inner_data, &out_shape)
+ }
+ }
+ }
+
+ pub fn mean(&self, dim: Option, keepdim: bool) -> Tensor {
+ match dim {
+ None => {
+ let n = self.length();
+ assert!(n > 0, "mean of empty tensor is undefined");
+ let value: f64 = self.item().iter().sum::() / n as f64;
+ let shape = if keepdim {
+ vec![1; self.shape.len().max(1)]
+ } else {
+ vec![1]
+ };
+ let inner = TensorData::from_op(
+ vec![value],
+ vec![self.clone()],
+ Op::Mean {
+ dim: None,
+ keepdim,
+ count: n,
+ },
+ );
+ Tensor::new(inner, &shape)
+ }
+ Some(dim) => {
+ assert!(dim < self.shape.len(), "mean: dim out of range");
+
+ let input = self.item();
+ let outer: usize = self.shape[..dim].iter().product();
+ let reduce: usize = self.shape[dim];
+ let inner: usize = self.shape[dim + 1..].iter().product();
+
+ let mut out = vec![0.0; outer * inner];
+
+ for o in 0..outer {
+ for i in 0..inner {
+ let mut acc = 0.0;
+ for r in 0..reduce {
+ let idx = o * reduce * inner + r * inner + i;
+ acc += input[idx];
+ }
+ out[o * inner + i] = acc / reduce as f64;
+ }
+ }
+
+ let mut out_shape = self.shape.clone();
+ if keepdim {
+ out_shape[dim] = 1;
+ } else {
+ out_shape.remove(dim);
+ if out_shape.is_empty() {
+ out_shape.push(1);
+ }
+ }
+
+ let inner_data = TensorData::from_op(
+ out,
+ vec![self.clone()],
+ Op::Mean {
+ dim: Some(dim),
+ keepdim,
+ count: reduce,
+ },
+ );
+ Tensor::new(inner_data, &out_shape)
+ }
+ }
+ }
+
/// Transpose
///
/// This method transposes the tensor, it changes the shape.
@@ -436,12 +563,12 @@ impl Tensor {
/// Computes the gradients of all the tensors that have been interacting and
/// have `requires_grad` set to `true`.
pub fn backward(&self) {
- let end_grad: Vec = self.inner.borrow()._prev[0]
- .item()
- .iter()
- .map(|a| a.to_owned())
- .collect::>();
- self.add_to_grad(end_grad);
+ assert!(
+ self.length() == 1,
+ "grad can be implicitly created only for scalar outputs"
+ );
+
+ self.add_to_grad(vec![1.0]);
self._backward()
}
@@ -626,10 +753,18 @@ impl Tensor {
/// Concatenates a slice of tensors along the given dimension.
/// All tensors must have the same shape except in the `dim` axis.
- pub fn cat(tensors: &[Tensor], dim: usize) -> Self {
+ pub fn cat(tensors: &[Tensor], dim: isize) -> Self {
assert!(!tensors.is_empty(), "cat: need at least one tensor");
+ assert!(dim >= -1, "cat: `dim` cannot be negative integer");
let ndim = tensors[0].shape.len();
+
+ let dim: usize = if dim == -1 {
+ tensors[0].shape().len() - 1
+ } else {
+ dim as usize
+ };
+
assert!(
dim < ndim,
"cat: dim {} out of range for {}-D tensor",
diff --git a/src/tensor_data.rs b/delta/src/tensor_data.rs
similarity index 100%
rename from src/tensor_data.rs
rename to delta/src/tensor_data.rs
diff --git a/tests/activations_test.rs b/delta/tests/activations_test.rs
similarity index 92%
rename from tests/activations_test.rs
rename to delta/tests/activations_test.rs
index de5bd37..2ffad02 100644
--- a/tests/activations_test.rs
+++ b/delta/tests/activations_test.rs
@@ -1,6 +1,6 @@
#[cfg(test)]
mod tests {
- use athena::{nn::functional as F, Tensor};
+ use delta::{nn::functional as F, Tensor};
#[test]
fn sigmoid() {
diff --git a/tests/data_test.rs b/delta/tests/data_test.rs
similarity index 98%
rename from tests/data_test.rs
rename to delta/tests/data_test.rs
index e308221..6391601 100644
--- a/tests/data_test.rs
+++ b/delta/tests/data_test.rs
@@ -1,6 +1,6 @@
#[cfg(test)]
mod tests {
- use athena::{
+ use delta::{
data::{dataloader::Dataloader, dataset::Dataset, sample::Sample},
Tensor,
};
diff --git a/tests/linalg_test.rs b/delta/tests/linalg_test.rs
similarity index 99%
rename from tests/linalg_test.rs
rename to delta/tests/linalg_test.rs
index 52bfc48..3e6dc7e 100644
--- a/tests/linalg_test.rs
+++ b/delta/tests/linalg_test.rs
@@ -1,6 +1,6 @@
#[cfg(test)]
mod tests {
- use athena::{linalg, Tensor};
+ use delta::{linalg, Tensor};
#[test]
/// Matrix multiplication
@@ -68,7 +68,7 @@ mod tests {
],
&[3, 1, 3, 1, 2, 3],
);
- let c = athena::linalg::cross(a.clone(), b.clone());
+ let c = delta::linalg::cross(a.clone(), b.clone());
let correct = vec![
-0.5866, 1.0262, -0.4396, -1.9130, -0.8710, 2.7839, -0.5866, 1.0262, -0.4396, -1.9130,
-0.8710, 2.7839, 0.8525, 0.7091, -1.5616, -1.0111, 3.0523, -2.0412, 0.8525, 0.7091,
diff --git a/tests/optim_test.rs b/delta/tests/optim_test.rs
similarity index 70%
rename from tests/optim_test.rs
rename to delta/tests/optim_test.rs
index 2b3f99d..35889a6 100644
--- a/tests/optim_test.rs
+++ b/delta/tests/optim_test.rs
@@ -1,19 +1,21 @@
#[cfg(test)]
mod tests {
- use athena::{
- Tensor,
- module::{Forward, Module},
- nn::{self, Linear, functional as F},
+ use std::collections::HashMap;
+
+ use delta::{
+ Tensor, ivalue,
+ ivalue::IValue,
+ nn::{self, Linear, Module, functional as F},
optim::{Optim, SGD},
tensor,
};
#[test]
fn zero_grad() {
- let a = Tensor::tensor(&[1., 2., 3., 4., 5., 6.], &[2, 3]);
- let b = Tensor::ones(&[2, 3]);
+ let a = nn::Parameter(Tensor::tensor(&[1., 2., 3., 4., 5., 6.], &[2, 3]));
+ let b = nn::Parameter(Tensor::ones(&[2, 3]));
let optim = SGD::new(vec![a.clone(), b.clone()], 1e-3);
- let c = a.clone() * b.clone();
+ let c = delta::sum(&(a.0.clone() * b.0.clone()), None, false);
c.backward();
assert_ne!(a.grad().unwrap().iter().sum::(), 0.0);
assert_ne!(b.grad().unwrap().iter().sum::(), 0.0);
@@ -32,7 +34,8 @@ mod tests {
let x = Tensor::randn(&[10, 4]);
let criterion = nn::MSELoss::default();
- let mut out = mlp.forward(x.clone());
+ let (args, kwargs) = ivalue![[x.clone()]];
+ let mut out = mlp.forward(args, kwargs).unwrap_tensor();
out = out.squeeze(&[]);
let loss = criterion.measure(
out.clone(),
@@ -81,16 +84,13 @@ mod tests {
"MLP".to_owned()
}
- fn parameters(&self) -> Vec {
- let parameters = self.linear1.parameters();
- parameters
+ fn parameters(&self) -> Vec {
+ self.linear1.parameters()
}
- }
- impl Forward for MLP {
- fn forward(&self, x: Tensor) -> Tensor {
- let x = self.linear1.forward(x);
- F::sigmoid(x)
+ fn forward(&self, args: Vec, kwargs: HashMap) -> IValue {
+ let x = self.linear1.forward(args, kwargs).unwrap_tensor();
+ IValue::Tensor(F::sigmoid(x))
}
}
}
diff --git a/tests/tensor_test.rs b/delta/tests/tensor_test.rs
similarity index 99%
rename from tests/tensor_test.rs
rename to delta/tests/tensor_test.rs
index 5d7b9be..6050e9a 100644
--- a/tests/tensor_test.rs
+++ b/delta/tests/tensor_test.rs
@@ -1,6 +1,6 @@
#[cfg(test)]
mod tests {
- use athena::{randn, Tensor};
+ use delta::{randn, Tensor};
#[test]
/// Valid shape of the tensor
diff --git a/examples/linear.rs b/examples/linear.rs
deleted file mode 100644
index 7d84e87..0000000
--- a/examples/linear.rs
+++ /dev/null
@@ -1,16 +0,0 @@
-use athena::{
- module::Forward,
- nn::{functional as F, Linear},
- Tensor,
-};
-
-fn main() {
- let linear = Linear::new(20, 10, true);
- let x = Tensor::randn(&[2, 20]);
- println!("Weights: {}", linear.weight);
- println!("IN:\n{x}");
- let out = linear.forward(x);
- println!("OUT:\n{out}");
- let out = F::sigmoid(out);
- println!("OUT:\n{out}");
-}
diff --git a/media/header.png b/media/header.png
index 6035a21..437466c 100644
Binary files a/media/header.png and b/media/header.png differ
diff --git a/media/header_original.png b/media/header_original.png
index 0942abe..302d7c9 100644
Binary files a/media/header_original.png and b/media/header_original.png differ
diff --git a/src/nn/linear.rs b/src/nn/linear.rs
deleted file mode 100644
index 9c321d4..0000000
--- a/src/nn/linear.rs
+++ /dev/null
@@ -1,52 +0,0 @@
-use crate::{
- Tensor, linalg,
- module::{Forward, Module},
-};
-
-/// # `Linear` Layer
-///
-/// Contains of:
-/// - weights
-/// - bias
-///
-/// Linear layer performs: `x @ W + b`,
-/// where:
-/// - `x` is input
-/// - `W` is weights
-/// - `b` is bias
-pub struct Linear {
- pub weight: Tensor,
-}
-
-impl Linear {
- pub fn new(mut in_features: usize, out_features: usize, bias: bool) -> Self {
- if bias {
- in_features += 1;
- }
- let _weight = Tensor::randn(&[in_features, out_features]);
- Self { weight: _weight }
- }
-}
-
-impl Module for Linear {
- fn module_name(&self) -> String {
- "Linear".to_owned()
- }
-
- fn parameters(&self) -> Vec {
- let parameters = vec![self.weight.clone()];
- parameters
- }
-}
-
-impl Forward for Linear {
- fn forward(&self, x: Tensor) -> Tensor {
- let weight = self.weight.clone();
- let mut ones_shape = x.shape();
- let _ = ones_shape.pop();
- ones_shape.push(1);
- let x = Tensor::cat(&[x, Tensor::ones(&ones_shape)], 1);
- let x = linalg::matmul(x, weight);
- x
- }
-}
-
- 
+
+ 
-
- 
+
+ 
-
- 
+
+ 
-
- 
+
+ 