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NVIDIA/apex: A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intent of Apex is to make up-to-date utilities available to users as quickly as possible.

Each apex.contrib module requires one or more install options other than --cpp_ext and --cuda_ext. Note that contrib modules do not necessarily support stable PyTorch releases, some of them might only be compatible with nightlies.

NVIDIA PyTorch Containers are available on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch. The containers come with all the custom extensions available at the moment.

See the NGC documentation for details such as:

• how to pull a container
• how to run a pulled container
• release notes

To install Apex from source, we recommend using the nightly Pytorch obtainable from https://github.com/pytorch/pytorch.

The latest stable release obtainable from https://pytorch.org should also work.

We recommend installing Ninja to make compilation faster.

For performance and full functionality, we recommend installing Apex with CUDA and C++ extensions using environment variables:

git clone https://github.com/NVIDIA/apex
cd apex
# Build with core extensions (cpp and cuda)
APEX_CPP_EXT=1 APEX_CUDA_EXT=1 pip install -v --no-build-isolation .

# To build with additional extensions, specify them with environment variables
APEX_CPP_EXT=1 APEX_CUDA_EXT=1 APEX_FAST_MULTIHEAD_ATTN=1 APEX_FUSED_CONV_BIAS_RELU=1 pip install -v --no-build-isolation .

# To build all contrib extensions at once
APEX_CPP_EXT=1 APEX_CUDA_EXT=1 APEX_ALL_CONTRIB_EXT=1 pip install -v --no-build-isolation .

To reduce the build time, parallel building can be enabled:

NVCC_APPEND_FLAGS="--threads 4" APEX_PARALLEL_BUILD=8 APEX_CPP_EXT=1 APEX_CUDA_EXT=1 pip install -v --no-build-isolation .

When CPU cores or memory are limited, the --parallel option is generally preferred over --threads. See pull#1882 for more details.

The traditional command-line flags are still supported:

# Using pip config-settings (pip >= 23.1)
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./

# For older pip versions
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./

# To build with additional extensions
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_multihead_attn" ./

APEX also supports a Python-only build via:

pip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./

A Python-only build omits:

• Fused kernels required to use apex.optimizers.FusedAdam.
• Fused kernels required to use apex.normalization.FusedLayerNorm and apex.normalization.FusedRMSNorm.
• Fused kernels that improve the performance and numerical stability of apex.parallel.SyncBatchNorm.
• Fused kernels that improve the performance of apex.parallel.DistributedDataParallel and apex.amp. DistributedDataParallel, amp, and SyncBatchNorm will still be usable, but they may be slower.

pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . may work if you were able to build Pytorch from source on your system. A Python-only build via pip install -v --no-cache-dir . is more likely to work.
If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.

If a requirement of a module is not met, then it will not be built.

You can also build all contrib extensions at once by setting APEX_ALL_CONTRIB_EXT=1.

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