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Performance

Running the Pipeline in Stages

The run_alphafold.py script can be executed in stages to optimise resource utilisation. This can be useful for:

  1. Splitting the CPU-only data pipeline from model inference (which requires a GPU), to optimise cost and resource usage.
  2. Generating the JSON output file from the data pipeline only run and then using it for multiple different inference only runs across seeds or across variations of other features (e.g. a ligand or a partner chain).
  3. Generating the JSON output for multiple individual monomer chains (e.g. for chains A, B, C, D), then running the inference on all possible chain pairs (AB, AC, AD, BC, BD, CD) by creating dimer JSONs by merging the monomer JSONs. By doing this, the MSA and template search need to be run just 4 times (once for each chain), instead of 12 times.

Data Pipeline Only

Launch run_alphafold.py with --run_inference=false to generate Multiple Sequence Alignments (MSAs) and templates, without running featurisation and model inference. This stage can be quite costly in terms of runtime, CPU, and RAM use. The output will be JSON files augmented with MSAs and templates that can then be directly used as input for running inference.

Pre-computing and reusing MSA and templates

When folding multiple candidate chains with a set of fixed chains (i.e. chains that are the same for all the runs), you can optimize the process by computing the MSA and templates for the fixed chains only once. The computations for the changing candidate chains will still be performed for each run:

  1. Run the AlphaFold 3 data pipeline for the fixed chains using the --run_inference=false flag. This step generates a JSON file containing the MSA and template data for these chains.
  2. When constructing your multimer input JSONs, populate the entries for the fixed chains using the data generated in the previous step.
    • For the fixed chains: Specifically, copy the unpairedMsa, pairedMsa, and templates fields from the pre-computed JSON into the multimer input JSON. This prevents these fields from being recomputed.
    • For the candidate chains: Leave these fields unset (or null) in the multimer input JSON. This will signal the pipeline to compute them dynamically for each run.

This technique can also be extended to efficiently process all combinations of n first chains and m second chains. Instead of performing n × m full computations, you can reduce this to n + m data pipeline runs.

In this scenario:

  1. Run the data pipeline (step 1 above, with --run_inference=false) for all n individual first chains and all m individual second chains.
  2. Assemble the dimer input JSONs for each desired pair by combining their respective pre-computed monomer JSONs.
  3. Run only the inference step on these assembled JSONs using the --run_data_pipeline=false flag.

This approach has been discussed in multiple GitHub issues, such as: google-deepmind#171 (which links to other similar issues).

Featurisation and Model Inference Only

Launch run_alphafold.py with --run_data_pipeline=false to skip the data pipeline and run only featurisation and model inference. This stage requires the input JSON file to contain pre-computed MSAs and templates (or they must be explicitly set to empty if you want to run MSA and template free).

Data Pipeline

The runtime of the data pipeline (i.e. genetic sequence search and template search) can vary significantly depending on the size of the input and the number of homologous sequences found, as well as the available hardware – the disk speed can influence genetic search speed in particular.

If you would like to improve performance, it's recommended to increase the disk speed (e.g. by leveraging a RAM-backed filesystem), or increase the available CPU cores and add more parallelisation. This can help because AlphaFold 3 runs genetic search against 4 databases in parallel, so the optimal number of cores is the number of cores used for each Jackhmmer process times 4. Also note that for sequences with deep MSAs, Jackhmmer or Nhmmer may need a substantial amount of RAM beyond the recommended 64 GB of RAM.

Sharded genetic databases

The run time of the genetic database search can be significantly sped up by splitting the genetic databases if a machine with many CPU cores is used and the databases are on very fast SSD or in a RAM-backed filesystem. With this technique you can make Jackhmmer/Nhmmer genetic search fully utilize your hardware and take advantage of multi-core systems.

Each genetic database with n sequences is split into s shards, each containing roughly n / s sequences. We recommend splitting the sequences between shards randomly to make sure each shard has similar sequence length distribution. This could be achieved using standard tools:

  1. Shuffle the sequences in the fasta. This can be done for example by running: seqkit shuffle --two-pass <db.fasta>
  2. Split the shuffled fasta in s shards. This can be done for example by running: seqkit split2 --by-part <s> <db.fasta>

Make sure the shards names follow this pattern: prefix-<shard_index>-of-<total_shards>, both shard_index and total_shards having always 5 digits, with leading zeros as needed. The shard_index goes from 0 to total_shards - 1. A file "path" (spec) for a sharded file is prefix@<total_shards>.

E.g. for a file named uniprot.fasta split into 3 shards, the names of the shards should be:

  • uniprot.fasta-00000-of-00003
  • uniprot.fasta-00001-of-00003
  • uniprot.fasta-00002-of-00003

The file spec for these files is uniprot.fasta@3.

Save the total number of sequences in the protein databases, and the total number of nucleic bases in the RNA databases – these will be needed later as a flag to Jackhmmer/Nhmmer to correctly scale e-values across all shards.

Save the sharded databases on a fast SSD or in a RAM-backed filesystem, then launch AlphaFold with the sharded paths instead of normal paths and set the Z-values.

For instance with each database sharded into 16 shards:

python run_alphafold.py \
    --small_bfd_database_path="bfd-first_non_consensus_sequences.fasta@64" \
    --small_bfd_z_value=65984053 \
    --mgnify_database_path="mgy_clusters_2022_05.fa@512" \
    --mgnify_z_value=623796864 \
    --uniprot_cluster_annot_database_path="uniprot_cluster_annot_2021_04.fasta@256" \
    --uniprot_cluster_annot_z_value=225619586 \
    --uniref90_database_path="uniref90_2022_05.fasta@128" \
    --uniref90_z_value=153742194 \
    --ntrna_database_path="nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta@256" \
    --ntrna_z_value=76752.808514 \
    --rfam_database_path="rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta@16" \
    --rfam_z_value=138.115553 \
    --rna_central_database_path="rnacentral_active_seq_id_90_cov_80_linclust.fasta@64" \
    --rna_central_z_value=13271.415730
    --jackhmmer_n_cpu=2 \
    --jackhmmer_max_parallel_shards=16 \
    --nhmmer_n_cpu=2 \
    --nhmmer_max_parallel_shards=16

This run will utilize (2 CPUs) × (16 max parallel shards) × (4 protein dbs searched in parallel) = 128 cores for each protein chain, and (2 CPUs) × (16 max parallel shards) × (3 RNA dbs searched in parallel) = 96 cores for each RNA chain. Make sure to tune:

  • the Jackhmmer/Nhmmer number of CPUs,
  • the maximum number of shards searched in parallel,
  • and the number of shards for each database

so that the memory bandwidth and CPUs on your machine are optimally utilized. You should aim for consistent shard sizes across all databases (so e.g. if database A is split into 16 shards and is 3× smaller than database B, database B should be split into 3 × 16 = 48 shards).

Model Inference

Table 8 in the Supplementary Information of the AlphaFold 3 paper provides compile-free inference timings for AlphaFold 3 when configured to run on 16 NVIDIA A100s, with 40 GB of memory per device. In contrast, this repository supports running AlphaFold 3 on a single NVIDIA A100 with 80 GB of memory in a configuration optimised to maximise throughput.

We compare compile-free inference timings of these two setups in the table below using GPU seconds (i.e. multiplying by 16 when using 16 A100s). The setup in this repository is more efficient (by at least 2×) across all token sizes, indicating its suitability for high-throughput applications.

Num Tokens 1 A100 80 GB (GPU secs) 16 A100 40 GB (GPU secs) Improvement
1024 62 352 5.7×
2048 275 1136 4.1×
3072 703 2016 2.9×
4096 1434 3648 2.5×
5120 2547 5552 2.2×

Accelerator Hardware Requirements

We officially support the following configurations, and have extensively tested them for numerical accuracy and throughput efficiency:

  • 1 NVIDIA A100 (80 GB)
  • 1 NVIDIA H100 (80 GB)

We compare compile-free inference timings of both configurations in the following table:

Num Tokens 1 A100 80 GB (seconds) 1 H100 80 GB (seconds)
1024 62 34
2048 275 144
3072 703 367
4096 1434 774
5120 2547 1416

Other Hardware Configurations

NVIDIA A100 (40 GB)

AlphaFold 3 can run on inputs of size up to 4,352 tokens on a single NVIDIA A100 (40 GB) with the following configuration changes:

  1. Enabling unified memory.

  2. Adjusting pair_transition_shard_spec in model_config.py:

      pair_transition_shard_spec: Sequence[_Shape2DType] = (
      (2048, None),
      (3072, 1024),
      (None, 512),
  )

The format of entries in pair_transition_shard_spec is (num_tokens_upper_bound, shard_size). Setting shard_size=None means there is no upper bound.

For the example above:

  • (2048, None): for sequences up to 2,048 tokens, do not shard
  • (3072, 1024): for sequences up to 3,072 tokens, shard in chunks of 1,024
  • (None, 512): for all other sequences, shard in chunks of 512

While numerically accurate, this configuration will have lower throughput compared to the set up on the NVIDIA A100 (80 GB), due to less available memory.

NVIDIA V100

There are known numerical issues with CUDA Capability 7.x devices. To work around the issue, set the ENV XLA_FLAGS to include --xla_disable_hlo_passes=custom-kernel-fusion-rewriter.

With the above flag set, AlphaFold 3 can run on inputs of size up to 1,280 tokens on a single NVIDIA V100 using unified memory.

NVIDIA P100

AlphaFold 3 can run on inputs of size up to 1,024 tokens on a single NVIDIA P100 with no configuration changes needed.

Other devices

Large-scale numerical tests have not been performed on any other devices but they are believed to be numerically accurate.

There are known numerical issues with CUDA Capability 7.x devices. To work around the issue, set the environment variable XLA_FLAGS to include --xla_disable_hlo_passes=custom-kernel-fusion-rewriter.

Compilation Buckets

To avoid excessive re-compilation of the model, AlphaFold 3 implements compilation buckets: ranges of input sizes using a single compilation of the model.

When featurising an input, AlphaFold 3 determines the smallest bucket the input fits into, then adds any necessary padding. This may avoid re-compiling the model when running inference on the input if it belongs to the same bucket as a previously processed input.

The configuration of bucket sizes involves a trade-off: more buckets leads to more re-compilations of the model, but less padding.

By default, the largest bucket size is 5,120 tokens. Processing inputs larger than this maximum bucket size triggers the creation of a new bucket for exactly that input size, and a re-compilation of the model. In this case, you may wish to redefine the compilation bucket sizes via the --buckets flag in run_alphafold.py to add additional larger bucket sizes. For example, suppose you are running inference on inputs with token sizes: 5132, 5280, 5342. Using the default bucket sizes configured in run_alphafold.py will trigger three separate model compilations, one for each unique token size. If instead you pass in the following flag to run_alphafold.py

--buckets 256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120,5376

when running inference on the above three input sizes, the model will be compiled only once for the bucket size 5376. Note: for this specific example with input sizes 5132, 5280, 5342, passing in --buckets 5376 is sufficient to achieve the desired compilation behaviour. The provided example with multiple buckets illustrates a more general solution suitable for diverse input sizes.

Additional Flags

Compilation Time Workaround with XLA Flags

To work around a known XLA issue causing the compilation time to greatly increase, the following environment variable must be set (it is set by default in the provided Dockerfile).

ENV XLA_FLAGS="--xla_gpu_enable_triton_gemm=false"

CUDA Capability 7.x GPUs

For all CUDA Capability 7.x GPUs (e.g. V100) the environment variable XLA_FLAGS must be changed to include --xla_disable_hlo_passes=custom-kernel-fusion-rewriter. Disabling the Tritron GEMM kernels is not necessary as they are not supported for such GPUs.

ENV XLA_FLAGS="--xla_disable_hlo_passes=custom-kernel-fusion-rewriter"

GPU Memory

The following environment variables (set by default in the Dockerfile) enable folding a single input of size up to 5,120 tokens on a single A100 (80 GB) or a single H100 (80 GB):

ENV XLA_PYTHON_CLIENT_PREALLOCATE=true
ENV XLA_CLIENT_MEM_FRACTION=0.95

Unified Memory

If you would like to run AlphaFold 3 on inputs larger than 5,120 tokens, or on a GPU with less memory (an A100 with 40 GB of memory, for instance), we recommend enabling unified memory. Enabling unified memory allows the program to spill GPU memory to host memory if there isn't enough space. This prevents an OOM, at the cost of making the program slower by accessing host memory instead of device memory. To learn more, check out the NVIDIA blog post.

You can enable unified memory by setting the following environment variables in your Dockerfile:

ENV XLA_PYTHON_CLIENT_PREALLOCATE=false
ENV TF_FORCE_UNIFIED_MEMORY=true
ENV XLA_CLIENT_MEM_FRACTION=3.2

JAX Persistent Compilation Cache

You may also want to make use of the JAX persistent compilation cache, to avoid unnecessary recompilation of the model between runs. You can enable the compilation cache with the --jax_compilation_cache_dir <YOUR_DIRECTORY> flag in run_alphafold.py.

More detailed instructions are available in the JAX documentation, and more specifically the instructions for use on Google Cloud. In particular, note that if you would like to make use of a non-local filesystem, such as Google Cloud Storage, you will need to install etils (this is not included by default in the AlphaFold 3 Docker container).