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Installation Instructions

Note: The most up-to-date installation instructions are embedded in a set of scripts bundled in the FBGEMM repo under setup_env.bash.

The general steps for installing FBGEMM_GPU are as follows:

  1. Set up an isolated build environment.

  2. Set up the toolchain for either a CPU-only, CUDA, or ROCm runtime.

  3. Install PyTorch.

  4. Install the FBGEMM_GPU package.

  5. Run post-installation checks.

FBGEMM Releases Compatibility Table

FBGEMM is released in accordance to the PyTorch release schedule, and is each release has no guarantee to work in conjunction with PyTorch releases that are older than the one that the FBGEMM release corresponds to.

FBGEMM Release

Corresponding PyTorch Release

Supported Python Versions

Supported CUDA Versions

(Experimental) Supported ROCm Versions

1.0.0

2.5.x

3.9, 3.10, 3.11, 3.12

11.8, 12.1, 12.4

6.0, 6.1

0.8.0

2.4.x

3.8, 3.9, 3.10, 3.11, 3.12

11.8, 12.1, 12.4

6.0, 6.1

0.7.0

2.3.x

3.8, 3.9, 3.10, 3.11, 3.12

11.8, 12.1

6.0

0.6.0

2.2.x

3.8, 3.9, 3.10, 3.11, 3.12

11.8, 12.1

5.7

0.5.0

2.1.x

3.8, 3.9, 3.10, 3.11

11.8, 12.1

5.5, 5.6

0.4.0

2.0.x

3.8, 3.9, 3.10

11.7, 11.8

5.3, 5.4

For more information, please visit the FBGEMM Releases Page.

Set Up CPU-Only Environment

Follow the instructions for setting up the Conda environment at Set Up an Isolated Build Environment, followed by Install Python Libraries.

Set Up CUDA Environment

The CUDA variant of FBGEMM_GPU requires an NVIDIA GPU installed to the machine, along with working NVIDIA drivers installed; otherwise or the library will fall back to running the CPU version of the operators.

The FBGEMM_GPU CUDA package is currently only built for the SM70 and SM80 architectures (V100 and A100 GPUs respectively). Support for other architectures can be achieved by building the package from scratch, but is not guaranteed to work (especially for older architectures).

Install NVIDIA Drivers

The NVIDIA display drivers must be installed on the system prior to all other environment setup. The steps provided by NVIDIA and PyTorch are the most authoritative instructions for doing this. Driver setup may be verified with the nvidia-smi command:

nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.76       Driver Version: 515.76       CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A10G         Off  | 00000000:00:1E.0 Off |                    0 |
|  0%   31C    P0    59W / 300W |      0MiB / 23028MiB |      2%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Set Up the CUDA Docker Container and Conda Environment

It is recommended, though not required, to install and run FBGEMM_GPU through a Docker setup for isolation and reproducibility of the CUDA environment.

The NVIDIA-Docker runtime needs to be installed to expose the driver to the container. The install steps provided by PyTorch provide details on how to achieve this.

Once this is done, follow the instructions in CUDA Docker Image for pulling the CUDA Docker image and launching a container.

From there, the rest of the runtime environment may be constructed through Conda. Follow the instructions for setting up the Conda environment at Set Up an Isolated Build Environment, followed by Install Python Libraries.

Install the CUDA Runtime

If the OS / Docker environment does not already contain the full CUDA runtime, follow the instructions in Install CUDA for installing the CUDA toolkit inside a Conda environment.

Set Up ROCm Environment

The ROCm variant of FBGEMM_GPU requires an AMD GPU installed to the machine, along with working AMDGPU drivers installed; otherwise or the library will fall back to running the CPU version of the operators.

Install AMDGPU Drivers

The AMDGPU display drivers must be installed on the system prior to all other environment setup. The steps provided by AMD are the most authoritative instructions for doing this. Driver setup may be verified with the rocm-smi command:

rocm-smi

======================= ROCm System Management Interface =======================
================================= Concise Info =================================
GPU  Temp (DieEdge)  AvgPwr  SCLK    MCLK     Fan  Perf  PwrCap  VRAM%  GPU%
0    33.0c           37.0W   300Mhz  1200Mhz  0%   auto  290.0W    0%   0%
1    32.0c           39.0W   300Mhz  1200Mhz  0%   auto  290.0W    0%   0%
2    33.0c           37.0W   300Mhz  1200Mhz  0%   auto  290.0W    0%   0%
================================================================================
============================= End of ROCm SMI Log ==============================

Set Up the ROCm Docker Container and Conda Environment

It is recommended, though not required, to install and run FBGEMM_GPU through a Docker setup for isolation and reproducibility of the ROCm environment, which can be difficult to set up.

Follow the instructions in ROCm Docker Image for pulling the full ROCm Docker image and launching a container.

From there, the rest of the runtime environment may be constructed through Conda. Follow the instructions for setting up the Conda environment at Install ROCm, followed by Install Python Libraries.

Install Python Libraries

Install the relevant Python libraries for working with FBGEMM_GPU:

conda install -n ${env_name} -y \
    hypothesis \
    numpy \
    scikit-build

Install PyTorch

Follow the instructions in Install PyTorch for installing PyTorch inside a Conda environment.

Install Triton

This section is only applicable to working the experimental FBGEMM_GPU GenAI module. Triton should already come packaged with the PyTOrch installation. This can be verified with:

conda run -n ${env_name} python -c "import triton"

If Triton is not available, it can be installed through PyTorch PIP:

# Most recent version used can be found in the build scripts
TRITON_VERSION=3.0.0+45fff310c8

conda run -n ${env_name} pip install \
  --pre pytorch-triton==${TRITON_VERSION} \
  --index-url https://download.pytorch.org/whl/nightly/

Information about PyTorch-Triton release can be found here.

Install the FBGEMM_GPU Package

Install through PyTorch PIP

PyTorch PIP is the preferred channel for installing FBGEMM_GPU:

# !! Run inside the Conda environment !!

# CPU-only Nightly
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu/
pip install --pre fbgemm-gpu --index-url https://download.pytorch.org/whl/nightly/cpu/

# CPU-only Release
pip install torch --index-url https://download.pytorch.org/whl/cpu/
pip install fbgemm-gpu --index-url https://download.pytorch.org/whl/cpu/

# CUDA Nightly
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121/
pip install --pre fbgemm-gpu --index-url https://download.pytorch.org/whl/nightly/cu121/

# CUDA Release
pip install torch --index-url https://download.pytorch.org/whl/cu121/
pip install fbgemm-gpu --index-url https://download.pytorch.org/whl/cu121/

# ROCm Nightly
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm5.6/
pip install --pre fbgemm-gpu --index-url https://download.pytorch.org/whl/nightly/rocm5.6/

# Test the installation
python -c "import torch; import fbgemm_gpu"

Install through Public PyPI

# !! Run inside the Conda environment !!

# CPU-Only Nightly
pip install fbgemm-gpu-nightly-cpu

# CPU-Only Release
pip install fbgemm-gpu-cpu

# CUDA Nightly
pip install fbgemm-gpu-nightly

# CUDA Release
pip install fbgemm-gpu

As of time of writing, packages for the ROCm variant of FBGEMM_GPU are not released to public PyPI.

Post-Installation Checks

After installation, run an import test to ensure that the library is correctly linked and set up.

# !! Run inside the Conda environment !!

python -c "import torch; import fbgemm_gpu; print(torch.ops.fbgemm.merge_pooled_embeddings)"

Undefined Symbols

A common error that is encountered is the failure to import FBGEMM_GPU in Python, which has the following error signature:

Traceback (most recent call last):
  File "/root/miniconda/envs/mycondaenv/lib/python3.10/site-packages/torch/_ops.py", line 565, in __getattr__
    op, overload_names = torch._C._jit_get_operation(qualified_op_name)
RuntimeError: No such operator fbgemm::jagged_2d_to_dense
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/root/miniconda/envs/mycondaenv/lib/python3.10/site-packages/fbgemm_gpu-0.4.1.post47-py3.10-linux-aarch64.egg/fbgemm_gpu/__init__.py", line 21, in <module>
    from . import _fbgemm_gpu_docs  # noqa: F401, E402
  File "/root/miniconda/envs/mycondaenv/lib/python3.10/site-packages/fbgemm_gpu-0.4.1.post47-py3.10-linux-aarch64.egg/fbgemm_gpu/_fbgemm_gpu_docs.py", line 18, in <module>
    torch.ops.fbgemm.jagged_2d_to_dense,
  File "/root/miniconda/envs/mycondaenv/lib/python3.10/site-packages/torch/_ops.py", line 569, in __getattr__
    raise AttributeError(
AttributeError: '_OpNamespace' 'fbgemm' object has no attribute 'jagged_2d_to_dense'
ERROR conda.cli.main_run:execute(47): `conda run python -c import fbgemm_gpu` failed. (See above for error)
/root/miniconda/envs/mycondaenv/lib/python3.10/site-packages/fbgemm_gpu-0.4.1.post47-py3.10-linux-aarch64.egg/fbgemm_gpu/fbgemm_gpu_py.so: undefined symbol: _ZN6fbgemm48FloatOrHalfToFusedNBitRowwiseQuantizedSBHalfAvx2ItLi2EEEvPKT_miPh

In general, undefined symbols can appear in an FBGEMM_GPU installation for the following reasons:

  1. The runtime libraries that FBGEMM_GPU depends on, such as libnvidia-ml.so or libtorch.so, are either not installed correctly or are not visible in LD_LIBRARY_PATH.

  2. The FBGEMM_GPU package was built incorrectly and contains declarations that were not linked (see PR 1618 for example).

In the former case, this may be resolved by re-installing the relevant packages and/or manually updating LD_LIBRARY_PATH.

In the latter case, this is a serious building and/or packaging issue tha should be reported to the FBGEMM developers.

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