[CI] Only compile for CUDA 11.8 & 12.2, MAX_JOBS=2,add torch-nightly

.github/workflows/publish.yml

@@ -44,8 +44,8 @@ jobs:
           # manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
           os: [ubuntu-20.04]
           python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
-          torch-version: ['1.12.1', '1.13.1', '2.0.1', '2.1.0']
-          cuda-version: ['11.6.2', '11.7.1', '11.8.0', '12.1.0', '12.2.0']
+          torch-version: ['1.12.1', '1.13.1', '2.0.1', '2.1.1', '2.2.0.dev20231127']
+          cuda-version: ['11.8.0', '12.2.0']
           # We need separate wheels that either uses C++11 ABI (-D_GLIBCXX_USE_CXX11_ABI) or not.
           # Pytorch wheels currently don't use it, but nvcr images have Pytorch compiled with C++11 ABI.
           # Without this we get import error (undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs)
@@ -58,31 +58,17 @@ jobs:
             # Pytorch >= 2.0 only supports Python >= 3.8
             - torch-version: '2.0.1'
               python-version: '3.7'
-            - torch-version: '2.1.0'
+            - torch-version: '2.1.1'
+              python-version: '3.7'
+            - torch-version: '2.2.0.dev20231127'
               python-version: '3.7'
             # Pytorch <= 2.0 only supports CUDA <= 11.8
-            - torch-version: '1.12.1'
-              cuda-version: '12.1.0'
             - torch-version: '1.12.1'
               cuda-version: '12.2.0'
-            - torch-version: '1.13.1'
-              cuda-version: '12.1.0'
             - torch-version: '1.13.1'
               cuda-version: '12.2.0'
-            - torch-version: '2.0.1'
-              cuda-version: '12.1.0'
             - torch-version: '2.0.1'
               cuda-version: '12.2.0'
-            # Pytorch >= 2.1 only supports CUDA >= 11.8
-            - torch-version: '2.1.0'
-              cuda-version: '11.6.2'
-            - torch-version: '2.1.0'
-              cuda-version: '11.7.1'
-            # Pytorch >= 2.1 with nvcc 12.1.0 segfaults during compilation, so
-            # we only use CUDA 12.2. setup.py as a special case that will
-            # download the wheel for CUDA 12.2 instead.
-            - torch-version: '2.1.0'
-              cuda-version: '12.1.0'
 
     steps:
       - name: Checkout
@@ -107,6 +93,12 @@ jobs:
           sudo rm -rf /opt/ghc
           sudo rm -rf /opt/hostedtoolcache/CodeQL
 
+      - name: Set up swap space
+        if: runner.os == 'Linux'
+        uses: pierotofy/set-swap-space@v1.0
+        with:
+          swap-size-gb: 10
+
       - name: Install CUDA ${{ matrix.cuda-version }}
         if: ${{ matrix.cuda-version != 'cpu' }}
         uses: Jimver/cuda-toolkit@v0.2.11
@@ -130,7 +122,7 @@ jobs:
           # We want to figure out the CUDA version to download pytorch
           # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
           # This code is ugly, maybe there's a better way to do this.
-          export TORCH_CUDA_VERSION=$(python -c "import os; minv = {'1.12': 113, '1.13': 116, '2.0': 117, '2.1': 118}[os.environ['MATRIX_TORCH_VERSION']]; maxv = {'1.12': 116, '1.13': 117, '2.0': 118, '2.1': 121}[os.environ['MATRIX_TORCH_VERSION']]; print(max(min(int(os.environ['MATRIX_CUDA_VERSION']), maxv), minv))")
+          export TORCH_CUDA_VERSION=$(python -c "import os; minv = {'1.12': 113, '1.13': 116, '2.0': 117, '2.1': 118, '2.2': 118}[os.environ['MATRIX_TORCH_VERSION']]; maxv = {'1.12': 116, '1.13': 117, '2.0': 118, '2.1': 121, '2.2': 121}[os.environ['MATRIX_TORCH_VERSION']]; print(max(min(int(os.environ['MATRIX_CUDA_VERSION']), maxv), minv))")
           if [[ ${{ matrix.torch-version }} == *"dev"* ]]; then
             pip install --no-cache-dir --pre torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/nightly/cu${TORCH_CUDA_VERSION}
           else
@@ -153,12 +145,8 @@ jobs:
           pip install ninja packaging wheel
           export PATH=/usr/local/nvidia/bin:/usr/local/nvidia/lib64:$PATH
           export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
-          # Currently for this setting the runner goes OOM if we pass --threads 4 to nvcc
-          if [[ ( ${MATRIX_CUDA_VERSION} == "121" || ${MATRIX_CUDA_VERSION} == "122" ) && ${MATRIX_TORCH_VERSION} == "2.1" ]]; then
-            export FLASH_ATTENTION_FORCE_SINGLE_THREAD="TRUE"
-          fi
           # Limit MAX_JOBS otherwise the github runner goes OOM
-          MAX_JOBS=1 FLASH_ATTENTION_FORCE_BUILD="TRUE" FLASH_ATTENTION_FORCE_CXX11_ABI=${{ matrix.cxx11_abi}} python setup.py bdist_wheel --dist-dir=dist
+          MAX_JOBS=2 FLASH_ATTENTION_FORCE_BUILD="TRUE" FLASH_ATTENTION_FORCE_CXX11_ABI=${{ matrix.cxx11_abi}} python setup.py bdist_wheel --dist-dir=dist
           tmpname=cu${MATRIX_CUDA_VERSION}torch${MATRIX_TORCH_VERSION}cxx11abi${{ matrix.cxx11_abi }}
           wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
           ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}

flash_attn/flash_attn_interface.py

+# Copyright (c) 2023, Tri Dao.
+
 from typing import Optional, Union
 
 import torch

setup.py

-# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
+# Copyright (c) 2023, Tri Dao.
+
 import sys
 import warnings
 import os
@@ -43,8 +44,6 @@ FORCE_BUILD = os.getenv("FLASH_ATTENTION_FORCE_BUILD", "FALSE") == "TRUE"
 SKIP_CUDA_BUILD = os.getenv("FLASH_ATTENTION_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
 # For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
 FORCE_CXX11_ABI = os.getenv("FLASH_ATTENTION_FORCE_CXX11_ABI", "FALSE") == "TRUE"
-# For CI, we want the option to not add "--threads 4" to nvcc, since the runner can OOM
-FORCE_SINGLE_THREAD = os.getenv("FLASH_ATTENTION_FORCE_SINGLE_THREAD", "FALSE") == "TRUE"
 
 
 def get_platform():
@@ -84,9 +83,7 @@ def check_if_cuda_home_none(global_option: str) -> None:
 
 
 def append_nvcc_threads(nvcc_extra_args):
-    if not FORCE_SINGLE_THREAD:
     return nvcc_extra_args + ["--threads", "4"]
-    return nvcc_extra_args
 
 
 cmdclass = {}
@@ -233,9 +230,9 @@ def get_wheel_url():
     # _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
     torch_cuda_version = parse(torch.version.cuda)
     torch_version_raw = parse(torch.__version__)
-    # Workaround for nvcc 12.1 segfaults when compiling with Pytorch 2.1
-    if torch_version_raw.major == 2 and torch_version_raw.minor == 1 and torch_cuda_version.major == 12:
-        torch_cuda_version = parse("12.2")
+    # For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.2
+    # to save CI time. Minor versions should be compatible.
+    torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.2")
     python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
     platform_name = get_platform()
     flash_version = get_package_version()