v0.1.0

README.md

@@ -1,67 +1,189 @@
-# Getting Started
+# vkdispatch
 
-Welcome to **vkdispatch**! This guide will help you install the library and run your first GPU-accelerated code.
+`vkdispatch` is a Python GPU computing framework for writing single-source kernels in Python and dispatching them across multiple runtime backends.
 
-**[WARNING: The documentation is still under heavy development, and has many missing sections]**
+It combines runtime code generation, execution helpers, and FFT/reduction utilities in one package. The default PyPI install ships with the Vulkan backend. CUDA and OpenCL backends can be enabled with optional runtime dependencies.
 
-> **Note:** vkdispatch requires a Vulkan-compatible GPU and drivers installed on your system. Please ensure your system meets these requirements before proceeding.
+## Highlights
+
+- Single-source Python shaders via `@vd.shader` and `vkdispatch.codegen`
+- Multiple runtime backends: Vulkan, CUDA, OpenCL, and a dummy codegen-only backend
+- Backend-aware code generation: GLSL for Vulkan, CUDA source for CUDA, and OpenCL C for OpenCL
+- Native FFT workflows through `vd.fft`, including mapping hooks for fusion and custom I/O
+- VkFFT-backed transforms through `vd.vkfft` on the Vulkan backend
+- Reductions through `vd.reduce`
+- Batched submission and deferred execution through `vd.CommandGraph`
+- CUDA interop through `__cuda_array_interface__` and CUDA Graph capture helpers
 
 ## Installation
 
-The default installation method for `vkdispatch` is through PyPI (pip):
+### Default Vulkan Install
+
+To install `vkdispatch` with the Vulkan backend, run:
 
 ```bash
-# Install the package
 pip install vkdispatch
 ```
 
-On mainstream platforms — Windows (x86_64), macOS (x86_64 and Apple Silicon/arm64), and Linux (x86_64) — pip will download a **prebuilt wheel** (built with `cibuildwheel` on GitHub Actions and tagged as *manylinux* where applicable), so no compiler is needed.
+This installs the core library, the code generation system, and the Vulkan runtime backend. The Vulkan backend is designed to run on systems supporting Vulkan 1.2 or higher, including macOS via a statically linked MoltenVK. Alternate backends can be added with optional dependencies as described below.
+
+On mainstream platforms - Windows (`x86_64`), macOS (`x86_64` and Apple Silicon/`arm64`), and Linux (`x86_64`) - `pip` will usually download a prebuilt wheel, so no compiler is needed.
+
+On less common platforms, `pip` may fall back to a source build, which takes a few minutes. See [Building From Source](https://sharhar.github.io/vkdispatch/tutorials/building_from_source.html) for toolchain requirements and developer-oriented instructions.
+
+### Core package
+
+For cases where only the codegen component is needed, or in environments where only the CUDA or OpenCL backends are needed, install the core package:
+
+```bash
+pip install vkdispatch-core
+```
+
+This installs the core library and codegen components, but not the Vulkan runtime backend. To enable runtime features beyond pure codegen, install the optional dependencies below.
+
+### Optional components
+
+- Optional CLI: `pip install vkdispatch-core[cli]`
+- CUDA runtime backend: `pip install vkdispatch-core[cuda]`
+- OpenCL runtime backend: `pip install vkdispatch-core[opencl]`
+
+## Runtime backends
+
+`vkdispatch` currently supports these runtime backends:
+
+- `vulkan`
+- `cuda`
+- `opencl`
+- `dummy`
+
+If you do not explicitly select a backend, ``vkdispatch`` prefers Vulkan. When the Vulkan backend cannot be imported because it is not installed, initialization falls back to CUDA and then OpenCL.
+
+You can select a backend explicitly in Python:
+
+```python
+import vkdispatch as vd
+
+vd.initialize(backend="vulkan")
+# vd.initialize(backend="cuda")
+# vd.initialize(backend="opencl")
+# vd.initialize(backend="dummy")
+```
+
+You can also select the backend with an environment variable:
+
+```bash
+export VKDISPATCH_BACKEND=vulkan
+```
+
+The dummy backend is useful for codegen-only workflows, source inspection, and development environments where no GPU runtime is available.
+
+There are two intended shader-generation modes:
 
-On less common platforms (e.g., non-Apple ARM or other niche architectures), pip may fall back to a **source build**, which takes a few minutes. See **[Building From Source](https://sharhar.github.io/vkdispatch/tutorials/building_from_source.html)** for toolchain requirements and developer-oriented instructions.
+- Default mode: generate for the current machine/runtime. This is the normal path and is how `vkdispatch` picks backend-specific defaults and limits.
+- Custom mode: initialize with `backend="dummy"` and optionally tune the dummy device limits when you want controlled codegen without relying on the current runtime.
 
-> **Tip:** If you see output like `Building wheel for vkdispatch (pyproject.toml)`, you’re compiling from source.
 
-## Verifying Your Installation
+## Verifying your installation
 
-To ensure `vkdispatch` is installed correctly and can detect your GPU, run:
+If you installed the optional CLI, you can list devices with:
 
 ```bash
-# Quick device listing
 vdlist
 
-# If the above command is unavailable, try:
-python3 -m vkdispatch
+# Explicit backend selection can be done with cmdline flags:
+vdlist --vulkan
+vdlist --cuda
+vdlist --opencl
 ```
 
-If the installation was successful, you should see output listing your GPU(s), for example:
-
-```text
-Device 0: Apple M2 Pro
-    Vulkan Version: 1.2.283
-    Device Type: Integrated GPU
-
-    Features:
-        Float32 Atomic Add: True
-
-    Properties:
-        64-bit Float Support: False
-        16-bit Float Support: True
-        64-bit Int Support: True
-        16-bit Int Support: True
-        Max Push Constant Size: 4096 bytes
-        Subgroup Size: 32
-        Max Compute Shared Memory Size: 32768
-
-    Queues:
-        0 (count=1, flags=0x7): Graphics | Compute
-        1 (count=1, flags=0x7): Graphics | Compute
-        2 (count=1, flags=0x7): Graphics | Compute
-        3 (count=1, flags=0x7): Graphics | Compute
+You can always inspect devices from Python:
+
+```python
+import vkdispatch as vd
+
+for device in vd.get_devices():
+    print(device.get_info_string())
 ```
 
-## Next Steps
+The reported version label depends on the active backend:
+
+- Vulkan devices show a Vulkan version
+- CUDA devices show CUDA compute capability
+- OpenCL devices show an OpenCL version
+
+## Quick start
+
+The example below defines a simple in-place compute kernel in Python:
+
+```python
+import numpy as np
+import vkdispatch as vd
+import vkdispatch.codegen as vc
+from vkdispatch.codegen.abbreviations import Buff, Const, f32
+
+# @vd.shader(exec_size=lambda args: args.buff.size)
+@vd.shader("buff.size")
+def add_scalar(buff: Buff[f32], bias: Const[f32]):
+    tid = vc.global_invocation_id().x
+    buff[tid] = buff[tid] + bias
+
+arr = np.arange(8, dtype=np.float32)
+buff = vd.asbuffer(arr)
+
+# If you want a non-default backend, call vd.initialize(backend=...) first.
+add_scalar(buff, 1.5)
+
+print(buff.read(0))
+```
+
+String launch sizing is the shortest form and is kept for convenience. If you want
+the launch rule to be more explicit and deterministic, use the equivalent lambda form
+instead: `@vd.shader(exec_size=lambda args: args.buff.size)`.
+
+In normal usage, `vkdispatch` initializes itself and creates a default context on first runtime use. Call `vd.initialize()` and `vd.make_context()` manually only when you want non-default settings such as backend selection, custom device selection, debug logging, or multi-device Vulkan contexts.
+
+## Codegen-Only Workflows
+
+If you want generated source without compiling or dispatching it on the current machine, use the dummy backend explicitly:
+
+```python
+import vkdispatch as vd
+import vkdispatch.codegen as vc
+from vkdispatch.codegen.abbreviations import Buff, Const, f32
+
+vd.initialize(backend='dummy')
+vd.set_dummy_context_params(
+    subgroup_size=32,
+    max_workgroup_size=(128, 1, 1),
+    max_workgroup_count=(65535, 65535, 65535),
+)
+vc.set_codegen_backend('cuda')
+
+# @vd.shader(exec_size=lambda args: args.buff.size)
+@vd.shader('buff.size')
+def add_scalar(buff: Buff[f32], bias: Const[f32]):
+    tid = vc.global_invocation_id().x
+    buff[tid] = buff[tid] + bias
+
+src = add_scalar.get_src(line_numbers=True)
+print(src)
+```
+
+In this mode, `vkdispatch` uses the dummy device model for launch/layout defaults and emits source for the backend selected with `vc.set_codegen_backend(...)`.
+
+## Documentation
+
+The docs site is still under active development, but the main entry points are here:
+
+- [Getting Started](https://sharhar.github.io/vkdispatch/getting_started.html)
+- [Tutorials](https://sharhar.github.io/vkdispatch/tutorials/index.html)
+- [Python API Reference](https://sharhar.github.io/vkdispatch/python_api.html)
+
+Some especially useful tutorials:
 
-- **[Tutorials](https://sharhar.github.io/vkdispatch/tutorials/index.html)** — our curated guide to common workflows and examples
-- **[Full Python API Reference](https://sharhar.github.io/vkdispatch/python_api.html)** — comprehensive reference for Python-facing components
+- [Shader Authoring and Dispatch](https://sharhar.github.io/vkdispatch/tutorials/shader_tutorial.html)
+- [Initialization and Context Creation](https://sharhar.github.io/vkdispatch/tutorials/context_system.html)
+- [Command Graph Recording](https://sharhar.github.io/vkdispatch/tutorials/command_graph_tutorial.html)
+- [Reductions and FFT Workflows](https://sharhar.github.io/vkdispatch/tutorials/reductions_and_fft.html)
 
 Happy GPU programming!