Update README with installation steps.

Author: nshepperd

.github/workflows/publish.yml

@@ -109,36 +109,36 @@ jobs:
           name: ${{env.wheel_name}}
           path: ./wheelhouse/${{env.wheel_name}}
 
-  publish_package:
-    name: Publish package
-    needs: [build_wheels]
-
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-
-    steps:
-      - uses: actions/checkout@v3
-
-      - uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-
-      - name: Install dependencies
-        run: |
-          pip install setuptools==68.0.0
-          pip install git+https://github.com/nshepperd/setuptools-cuda-cpp
-          pip install ninja packaging wheel pybind11
-
-      - name: Build core package
-        run: |
-          CUDA_HOME=/ python setup.py sdist --dist-dir=dist
-
-      - name: Retrieve release distributions
-        uses: actions/download-artifact@v4
-        with:
-          path: dist/
-          merge-multiple: true
-
-      - name: Publish release distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
+  # publish_package:
+  #   name: Publish package
+  #   needs: [build_wheels]
+
+  #   runs-on: ubuntu-latest
+  #   permissions:
+  #     id-token: write
+
+  #   steps:
+  #     - uses: actions/checkout@v3
+
+  #     - uses: actions/setup-python@v4
+  #       with:
+  #         python-version: '3.10'
+
+  #     - name: Install dependencies
+  #       run: |
+  #         pip install setuptools==68.0.0
+  #         pip install git+https://github.com/nshepperd/setuptools-cuda-cpp
+  #         pip install ninja packaging wheel pybind11
+
+  #     - name: Build core package
+  #       run: |
+  #         CUDA_HOME=/ python setup.py sdist --dist-dir=dist
+
+  #     - name: Retrieve release distributions
+  #       uses: actions/download-artifact@v4
+  #       with:
+  #         path: dist/
+  #         merge-multiple: true
+
+  #     - name: Publish release distributions to PyPI
+  #       uses: pypa/gh-action-pypi-publish@release/v1

README.md

@@ -3,43 +3,60 @@ This repository provides a jax binding to <https://github.com/Dao-AILab/flash-at
 
 Please see [Tri Dao's repo](https://github.com/Dao-AILab/flash-attention) for more information about flash attention.
 
-## Usage
-
 FlashAttention and FlashAttention-2 are free to use and modify (see LICENSE).
 Please cite (see below) and credit FlashAttention if you use it.
 
-## Installation and features
+## Installation
 
 Requirements:
 - CUDA 11.8 and above.
 - Linux. Same story as with the pytorch repo. I haven't tested compilation of the jax bindings on windows.
 - JAX >=`0.4.24`. The custom sharding used for ring attention requires some somewhat advanced features.
 
-To install: For now, download the appropriate release from the releases page and install it with pip.
+To install: `pip install flash-attn-jax` will get the latest release from pypi. This gives you the cuda 12.3 build. If you want to use the cuda 11.8 build, you can install from the releases page (but according to jax's documentation, 11.8 will stop being supported for newer versions of jax).
+
+### Installing from source
+
+Flash attention takes a long time to compile unless you have a powerful machine. But if you want to compile from source, I use `cibuildwheel` to compile the releases. You could do the same. Something like (for python 3.12):
+
+```sh
+git clone https://github.com/nshepperd/flash-attn-jax
+cd flash-attn-jax
+cibuildwheel --only cp312-manylinux_x86_64 # I think cibuildwheel needs superuser privileges on some systems because of docker reasons?
+```
+
+This will create a wheel in the `wheelhouse` directory. You can then install it with `pip install wheelhouse/flash_attn_jax_0.2.0-cp312-cp312-manylinux_x86_64.whl`. Or you could use setup.py to build the wheel and install it. You need cuda toolkit installed in that case.
+
+## Usage
 
 Interface: `src/flash_attn_jax/flash.py`
 
 ```py
 from flash_attn_jax import flash_mha
 
+# flash_mha : [n, l, h, d] x [n, lk, hk, d] x [n, lk, hk, d] -> [n, l, h, d]
 flash_mha(q,k,v,softmax_scale=None, is_causal=False, window_size=(-1,-1))
 ```
 
-Accepts q,k,v with shape `[n, l, h, d]`, and returns `[n, l, h, d]`. `softmax_scale` is the
-multiplier for the softmax, defaulting to `1/sqrt(d)`. Set window_size
-to positive values for sliding window attention.
+This supports multi-query and grouped-query attention (when hk != h). The `softmax_scale` is the multiplier for the softmax, defaulting to `1/sqrt(d)`. Set `window_size` to positive values for sliding window attention.
 
 ### Now Supports Ring Attention
 
-Use jax.Array and shard your tensors along the length dimension, and flash_mha will automatically use the ring attention algorithm:
+Use jax.Array and shard your tensors along the length dimension, and flash_mha will automatically use the ring attention algorithm (forward and backward).
 
 ```py
+os.environ["XLA_FLAGS"] = '--xla_gpu_enable_latency_hiding_scheduler=true --xla_gpu_enable_async_collectives=true'
+#...
 with Mesh(devices, axis_names=('len',)) as mesh:
-        sharding = NamedSharding(mesh, P(None,'len',None)) # n l d
+        sharding = NamedSharding(mesh, P(None,'len')) # n l
         tokens = jax.device_put(tokens, sharding)
         # invoke your jax.jit'd transformer.forward
 ```
 
+It's not entirely reliable at hiding the communication latency though, depending on the whims of the xla optimizer. I'm waiting https://github.com/google/jax/issues/20864 to be fixed, then I can make it better.
+
+### GPU support
+
 FlashAttention-2 currently supports:
 1. Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100). Support for Turing
    GPUs (T4, RTX 2080) is coming soon, please use FlashAttention 1.x for Turing

pyproject.toml

@@ -1,2 +1,5 @@
 [build-system]
-requires = ["setuptools", "wheel", "setuptools-cuda-cpp @ git+https://github.com/nshepperd/setuptools-cuda-cpp", "packaging", "pybind11"]
+requires = ["setuptools", "wheel", "setuptools-cuda-cpp @ git+https://github.com/nshepperd/setuptools-cuda-cpp", "packaging", "pybind11"]
+
+[tool.cibuildwheel]
+manylinux-x86_64-image = "sameli/manylinux_2_28_x86_64_cuda_12.3"