This PR makes both clients and servers work on macOS. Specifically, it:
- Follows https://github.com/learning-at-home/hivemind/pull/586 to run a macOS-compatible `p2pd` binary (both x86-64 and ARM64 are supported)
- Fixes forking issues and tests on macOS, Python 3.10+
- Introduces basic support for serving model blocks on Apple M1/M2 GPUs (torch.mps)
- Increases max number of open files by default (it's not enough on Linux and is really small on macOS)
- rpc_inference: server will now accept allocation timeout from user, defaults to no timeout
- bugfix: inference timeout is now measured from the moment the request is received
- previously, you would have to wait for your timeout plus the time it takes to sort through the queue (other users' timeout)
- now, you get AllocationFailed if you had to wait for over (timeout) seconds - regardless of other users
- a request for inference with no timeout will now fail instantly if there is not enough memory available
- dtype number of bytes is now correctly determined for int, bool & other types
---------
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Aleksandr Borzunov <hxrussia@gmail.com>
This PR drops custom generation codes and introduces compatibility with `transformers.GenerationMixin` instead. This includes support for more sampling options (`top_p`, `top_k`, `repetition_penalty` requested in #460) and beam search - all that is now identical to running model with transformers locally.
Most features (excluding beam search and other rarely used stuff) are also compatible with resuming existing sessions.
### Breaking changes
If `.generate()` or forward passes are being run inside an `.inference_session()` context, they now use the opened session by default. So, these snippets are now equivalent:
```python
# Using default session
with model.inference_session(max_length=100):
output_ids = model.generate(input_ids, max_new_tokens=3)
# Explicitly specifying a session
with model.inference_session(max_length=100) as sess:
output_ids = model.generate(input_ids, max_new_tokens=3, session=sess)
```
Earlier, the 1st snippet was creating a new session, which is not what most people expected (= such code was most likely to introduce a bug, which is now fixed).
Right now, long inference requests may occupy Runtime for a few seconds without giving it away to process short (most latency-sensitive requests). This PR fixes it by disallowing the merged pool for long requests and prioritizing the short ones.
This PR extends CI to:
1. Test Llama code using [TinyLlama-v0](https://huggingface.co/Maykeye/TinyLLama-v0).
2. Test rebalancing (sets up a situation where the 1st server needs to change its original position).
3. Check if benchmark scripts run (in case someone breaks its code). Note that the benchmark results are meaningless here (since they're measured on a tiny swarm of CPU servers, with low `--n_steps`).
4. Test `petals.cli.run_dht`.
5. Increase swap space and watch free RAM (a common issue is that actions are cancelled without explanation if there's not enough RAM - so it's a useful reminder + debug tool).
6. Fix flapping tests for bloom-560m by increasing tolerance.
Other minor changes: fix `--help` messages to show defaults, fix docs, tune rebalancing constants.
This PR is designed to avoid OOMs when processing long sequences that happen due to the huge attention logits matrices.
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
* Hide GeneratorExit in _iterate_inference_steps()
* Update README.md about `--public_name`
* Use .from_pretrained(..., use_auth_token=token) instead of token=token
until it's fully supported across HF libs
* Use default network speed 25 Mbit/s
* Apply relay penalty in max-throughput routing
* Replace RPS with "tokens/sec per block" in logs
* Increase default expiration
Inference RPS may be very different from forward RPS. E.g., currently bnb uses a completely different algorithm for NF4 inference. We report detailed RPS info that can be then used for shortest-path routing for inference.
Implement an option to deploy PEFT adapters to a server. Clients can set active_adapter=... to use these adapters.
---------
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: justheuristic <justheuristic@gmail.com>
This PR:
1. **Abolishes the model conversion procedure.** Now, models are downloaded directly from original repositories like https://huggingface.co/bigscience/bloom. Servers download only shards with blocks to be hosted, and clients download only shards with input/output embeddings and layernorms.
- BLOOM is loaded from `bigscience/bloom`, but we use the DHT prefix `bigscience/bloom-petals` for backward compatibility. Same with smaller BLOOMs and BLOOMZ.
- LLaMA can be loaded from any repo like `username/llama-65b-hf`, but we use the DHT prefix `llama-65b-hf` (without the username) to accomodate blocks from different repos (there're a few of them with minor differences, such as `Llama` vs. `LLaMA` in the class name).
2. **Refactors the client to generalize it for multiple models.** Now, we have `petals.models` packages that contain model-specific code (e.g. `petals.models.bloom`, `petals.models.llama`). General code (e.g. CPU-efficient LM head, p-tuning) is kept in `petals.client`.
3. **Introduces** `WrappedLlamaBlock`, `DistributedLlamaConfig`, `DistributedLlamaForCausalLM`, `DistributedLlamaForSequenceClassification`, and `DistributedLlamaModel` compatible with Petals functionality (p-tuning, adapters, etc.).
4. **Introduces** `AutoDistributedConfig` that automatically chooses the correct config class (`DistributedLlamaConfig` or `DistributedBloomConfig`). The refactored configs contain all model-specific info for both clients and servers.
Upgrade instructions:
- Remove disk caches for blocks in old (converted) format to save disk space. That is, remove `~/.cache/petals/model--bigscience--bloom-petals` and `~/.cache/petals/model--bigscience--bloomz-petals` directories (if present).
We need to sample the next server using its throughput as the weight to actually achieve max throughput for fine-tuning.
As an example, imagine a situation where we have 3 servers with throughputs [1000, 500, 1] hosting the same blocks, then compare the uniform and weighted sampling strategies.
This PR:
1. **Extracts `SequenceManagerConfig` and `SequenceManagerState` subclasses.**
The config is provided by caller and never changed from inside `RemoteSequenceManager`. The state is a part of the `RemoteSequenceManager`'s state shared between the main manager and its slices. We fix some slicing bugs along the way.
2. **Removes `dht_prefix` and `p2p` arguments, makes `dht` argument optional.**
`dht_prefix` can always be overridden using `config.dht_prefix`. `p2p` actually needed only under the hood of `RemoteSequenceManager`, so it can extract it by itself without exposing this low-level class to callers. If strictly necessary, a caller can provide `p2p` as a part of `SequenceManagerState`. `dht` is also needed only by `RemoteSequenceManager`, so we can make it optional in the parent classes and create it automatically when it's not provided.
3. **Simplifies retry logic.**
Previously, we could have "nested" retry loops: one in `._update()`, another in inference/forward/backward steps. The loop in `._update()` could introduce issues to concurrent inference/forward/backward calls, since it blocks the entire class if its delay period becomes too high. Now this logic is simplified: `._update()` performs only one attempt to fetch the DHT info, any retries are triggered by the inference/forward/backward steps.
4. **Removes deprecated `RemoteTransformerBlock`.**
`RemoteTransformerBlock` was deprecated a long time ago, before Petals 1.0.0. Its removal is long due.
5. **Removes `dht_utils.get_remote_module()`, `dht_utils.get_remote_sequence()`.**
This functions duplicate the functionality of the `RemoteSequential` constructor.
6. (minor) **Removes `RemoteSequential.is_subsequence` flag.**
This flag worked incorrectly and was never used. I am removing it for the sake of simplicity.
**Why?**
- We'd like to avoid excess threads for the original sequence manager in case if we only use its slices (e.g. when we add adapters or need only a subset of model blocks):
- If we create a sequence manager just before a fork (e.g. in a web app backend or a multi-thread benchmark), we'd like to avoid excess threads in the original process and only use this thread in child processes where we actually call `.make_sequence()`.
This PR makes servers return their free cache (in tokens * layers to make it compression-agnostic)
To be used when calling make_sequence(optimize="inference")
This pull-request implements a simple (1) greedy (2) latency-agnostic routing optimization that should speed up both our use cases.
Why this exists: our effort to merge full routing (ping-aware, throughut-aware, dijkstra) is in a sorry state between several branches; merging it into main would take many days.
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
This pull request adds an option to run Petals server on multiple local GPUs. It uses https://github.com/BlackSamorez/tensor_parallel
- 8bit approximation error same as in main (mean~=2% q0.9~=5%)
- TP=1, 2, 3 (see screenshots above)
- forward, grad w.r.t. input and inference exact match with main with TP=1
- `>=`80% GPU utilization with 3x 1080ti, batch = 8 tokens
- throughput measured with and without TP
- TP on 1080Tis has near-linear speedup comparable to the benchmarks (see first message)
Co-authored-by: Iaroslav Lisniak <yalisnyak@nes.ru>
Co-authored-by: Andrei Panferov <andrei@blacksamorez.ru>
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
1. Added `from petals.client import *` to `petals/__init__.py`, so you can write just that:
```python
from petals import DistributedBloomForCausalLM
```
I didn't do the same with server, since its classes are supposed to by used by `petals.cli.run_server`, not end-users. Though it's still possible to do `from petals.server.smth import smth` if necessary.
2. Fixed one more logging issue: log lines from hivemind were shown twice due to a bug in #156.
3. Removed unused `runtime.py`, since the server actually uses `hivemind.moe.Runtime`, and `runtime.py` has no significant changes comparing to it.
This pullrequest removes custom speed_test code in favour of speedtest-cli module.
This is necessary to ensure that random warnings / print-outs do not mess with our outputs.
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
* Add missing methods for SamplingAlgorithm, fix docstrings
* Add SamplingAlgorithm to _choose_sample_algorithm
* Add test_sampling
* Add a warning if sampling options were passed, but do_sample=False
* Skip the sampling test for now
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
- latest accelerate, transformers, huggingface_hub
- rearrange attention caches to support https://github.com/huggingface/transformers/pull/18344
- remove unused code
- fix edge case where session crashes when receiving seq length 0
- assert transformer version when importing WrappedBloomBlock
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
- Linear8bitLt now supports for pre-turing GPUs by temporarily upcasting quantized weights.
- added a test for linear8bitlt accuracy with the new fallback, the accuracy is similar than the real thing, (slightly better due to non-quantized A)
- performance is roughly halfway between the default mode and memory_efficient_backward
Alternatives considered:
- cupy - slow, casting to float internally
- triton - fast but unstable af. every 3rd attempt to matmul is a segfault
- bnb.functional.igemm (no lt) - "CuBLAS Error 8" on old GPUs
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
A patch to bitsandbytes 0.34.0 that introduces an option to run backward pass in default (fast) matrix layout.
Authors: cxb inversion by @borzunov, original 8bit code by @timdettmers
* optimized layout inversion code by @borzunov ([original code](https://colab.research.google.com/drive/1EJ0MKifajXSSVq7O2_QGwtb0l6gRAGrh?usp=sharing)) to use less forward calls
* implemented CustomLinear8bitLt, a child of Linear8bitLt that can do backward without CB
* added exact match tests for layouts and linear layers: see tests/test_linear8bitlt.py
* switched petals to the new layer type
Core idea: layouts apply the same permutation to every tile in the matrix. We can treat this as (batched) gather ops.
Reshape input tensor so that ij-th gather operation op will apply to ij-th elements in each tile.
Prototype:
Layout info: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#L2130-L2136
Co-authored-by: Alexander Borzunov <hxrussia@gmail.com>
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Tim Dettmers <tim.dettmers@gmail.com>
- [x] made RemoteSequenceManager into a background thread that pre-fetches information instead of running just in time
- [x] moved routing-related stuff to petals.client.routing
- [x] extract remote peer routing information to RemoteSequenceInfo
- [x] made sure that the code survives continued use (e.g. one hour)
- [x] updated every spot where update_ is called manually
- [x] modified get_sequence to check that the thread is alive, warn if not
- [x] removed max_retries, switched rpc_info to exponential backoff
- [x] fixed a bg that causes RemoteSeq* to lose user-defined hyperparameters (e.g. timeout) upon subsequencing (sequential[3:5])
- [x] moved client-side points strategy to client.routing
- [x] ensured that RemoteSequenceManager thread created in get_remote_module properly shuts down when the module is destroyed
- [x] resolved minor affected todos
- [x] modified tests to no longer use PYTHONPATH
- [x] worked around protocol error in rpc_info
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Artem Chumachenko <artek.chumak@gmail.com>
Currently, the schemas use `torch.float32`, so all inputs and outputs converted to float32 before sending and after receiving on both servers and clients. This creates a huge slowdown for the system.
* This PR makes the schemas use the server's `--torch_dtype` argument (default is `torch.bloat16` for BLOOM-176B)
* an option for client to request a specific output compression. Use case 1: client sends quantized inputs and expects quantized inputs in return. Use case 2: client uses quantization for gradients w.r.t. activations, but keeps grads w.r.t. __prompts__ as is for greater precision.
* a comment explaining the purpose of NoSpendingPolicy - since we likely won't have it for the workshop
* a test with custom compression (janky implementation for testing purposes)
Co-authored-by: justheuristic <justheuristic@gmail.com>
1. Petals can be now installed using `pip install git+https://github.com/bigscience-workshop/petals`
- In case if you already cloned the repo, you can do `pip install .` or `pip install .[dev]`
2. Moved `src` => `src/petals`
- Replaced `from src.smth import smth` with `from petals.smth import smth`
3. Moved `cli` => `src/petals/cli`
- Replaced `python -m cli.run_smth` with `python -m petals.cli.run_smth` (all utilities are now available right after pip installation)
4. Moved the `requirements*.txt` contents to `setup.cfg` (`requirements.txt` for packages is not supported well by modern packaging utils)
5. Increased the package version from `0.2` to `1.0alpha1`
* priority in handlers and backend pools
* simple points system on server side
* priortize task in handler before submit task
* fix tests
* s/expert/block/g
Co-authored-by: justheuristic <justheuristic@gmail.com>
- Maximum length is now provided in `.inference_session(max_length=100)`
- previously, we would always assume max length = 2048
- added a generic way to forward **kwargs to inference session
- for compatibility with #47
- Note to @borzunov : it does *not* pass them arbitrarily, but instead checks for kwarg names at the bottom level
- run_server can be started with a custom max_length for inference
- renamed --cache_size_bytes to --attention_cache_bytes (to avoid collision with --cache_dir)
- --attn_cache_bytes can now support humane file sizes (e.g. 300MB instead of 314572800)
- made some server-side errors more human-readable to user (e.g. when max length is exceeded)
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
Co-authored-by: Alexander Borzunov <hxrussia@gmail.com>
This PR reduces this vocabulary size to save memory during conversion, keeping only the first 50k tokens
As a result,
* tests that load client-side embeddings need significantly less RAM
* we can now run CI tests with 4 servers instead of 2 - needed to test routing - see bugs uncovered
* some of the servers now use load balancing
* CI convert_model now takes 4-5 minutes (was 6-7)
* implemented an option to add learnable prompts to intermediate layers
* added support for prompts (as input) in rpc_forward and rpc_backward
* added a test to check that RemoteSequential works correctly with deep prompts
Co-authored-by: justheuristic <justheuristic@gmail.com>
1. __Reduce memory usage in in test_full_model__
- previously, loading the full model would consistently fail IF github is enforcing memory limit [example](https://github.com/bigscience-workshop/distributed-bloom/runs/7473920049?check_suite_focus=true)
- the new version uses accelerate to save 2GB of peak memory, that was previously used when loading both reference model AND its state dict at the same time - only to load that state dict :)
2. __Safer delays when creating servers__
- run-tests will now wait for a few seconds after creating the first server - and before creating the second one, so as to make
sure that the first server creates a DHT instance that subsequent servers can connect to.
- also increased the wait time after creating servers by 30 seconds to make sure we load the model in time even when bumping into slow remotes on HF side
3. __Fix environment variables in CI to avoid build conflicts__
- the previous code was using a wrong environment variable that was always "main". The current one will correctly resolve branch name, both in main and on pull request.
- For reference, below you can find sample environments when running CI in both cases: on pull request and on push to main.
<details>
<summary> Environment variables when building this branch (on pull request) </summary>
SELENIUM_JAR_PATH=/usr/share/java/selenium-server.jar GOROOT_1_17_X64=/opt/hostedtoolcache/go/1.17.12/x64 CONDA=/usr/share/miniconda GITHUB_WORKSPACE=/home/runner/work/distributed-bloom/distributed-bloom JAVA_HOME_11_X64=/usr/lib/jvm/temurin-11-jdk-amd64 GITHUB_PATH=/home/runner/work/_temp/_runner_file_commands/add_path_0aba811a-a04b-40a2-ba42-79efb2723e9e GITHUB_ACTION=__run_2 JAVA_HOME=/usr/lib/jvm/temurin-11-jdk-amd64 GITHUB_RUN_NUMBER=98 RUNNER_NAME=GitHub Actions 3 GRADLE_HOME=/usr/share/gradle-7.5 XDG_CONFIG_HOME=/home/runner/.config DOTNET_SKIP_FIRST_TIME_EXPERIENCE=1 ANT_HOME=/usr/share/ant JAVA_HOME_8_X64=/usr/lib/jvm/temurin-8-jdk-amd64 HOMEBREW_PREFIX=/home/linuxbrew/.linuxbrew pythonLocation=/opt/hostedtoolcache/Python/3.9.13/x64 GITHUB_REF_TYPE=branch HOMEBREW_CLEANUP_PERIODIC_FULL_DAYS=3650 BOOTSTRAP_HASKELL_NONINTERACTIVE=1 *** PIPX_BIN_DIR=/opt/pipx_bin DEPLOYMENT_BASEPATH=/opt/runner GITHUB_ACTIONS=true ANDROID_NDK_LATEST_HOME=/usr/local/lib/android/sdk/ndk/24.0.8215888 GITHUB_SHA=3b457e8a14e5ecb0d65d6e4c0e9161f7756a8861 POWERSHELL_DISTRIBUTION_CHANNEL=GitHub-Actions-ubuntu20 DOTNET_MULTILEVEL_LOOKUP=0 GITHUB_REF=refs/pull/35/merge RUNNER_OS=Linux GITHUB_REF_PROTECTED=false HOME=/home/runner GITHUB_API_URL=https://api.github.com/ LANG=C.UTF-8 BLOOM_TESTING_WRITE_TOKEN=*** RUNNER_TRACKING_ID=github_cc9b46e4-56a1-40c5-ba08-5a91e21f0f95 STATS_KEEPALIVE=false RUNNER_ARCH=X64 RUNNER_TEMP=/home/runner/work/_temp EDGEWEBDRIVER=/usr/local/share/edge_driver GITHUB_ENV=/home/runner/work/_temp/_runner_file_commands/set_env_0aba811a-a04b-40a2-ba42-79efb2723e9e GITHUB_EVENT_PATH=/home/runner/work/_temp/_github_workflow/event.json INVOCATION_ID=8f0072e74f2847c0851e7ff9b5e4af7c GITHUB_EVENT_NAME=pull_request GITHUB_RUN_ID=2720198689 JAVA_HOME_17_X64=/usr/lib/jvm/temurin-17-jdk-amd64 ANDROID_NDK_HOME=/usr/local/lib/android/sdk/ndk-bundle GITHUB_STEP_SUMMARY=/home/runner/work/_temp/_runner_file_commands/step_summary_0aba811a-a04b-40a2-ba42-79efb2723e9e HOMEBREW_NO_AUTO_UPDATE=1 GITHUB_ACTOR=justheuristic NVM_DIR=/home/runner/.nvm SGX_AESM_ADDR=1 GITHUB_RUN_ATTEMPT=1 ANDROID_HOME=/usr/local/lib/android/sdk GITHUB_GRAPHQL_URL=https://api.github.com/graphql ACCEPT_EULA=Y RUNNER_USER=runner USER=runner GITHUB_SERVER_URL=https://github.com/ HOMEBREW_CELLAR=/home/linuxbrew/.linuxbrew/Cellar PIPX_HOME=/opt/pipx GECKOWEBDRIVER=/usr/local/share/gecko_driver CHROMEWEBDRIVER=/usr/local/share/chrome_driver SHLVL=0 ANDROID_SDK_ROOT=/usr/local/lib/android/sdk VCPKG_INSTALLATION_ROOT=/usr/local/share/vcpkg HOMEBREW_REPOSITORY=/home/linuxbrew/.linuxbrew/Homebrew RUNNER_TOOL_CACHE=/opt/hostedtoolcache ImageVersion=20220717.1 DOTNET_NOLOGO=1 GITHUB_REF_NAME=35/merge STATS_PFS=true GRAALVM_11_ROOT=/usr/local/graalvm/graalvm-ce-java11-22.1.0 GITHUB_JOB=convert-model LD_LIBRARY_PATH=/opt/hostedtoolcache/Python/3.9.13/x64/lib XDG_RUNTIME_DIR=/run/user/1001 AZURE_EXTENSION_DIR=/opt/az/azcliextensions PERFLOG_LOCATION_SETTING=RUNNER_PERFLOG GITHUB_REPOSITORY=bigscience-workshop/distributed-bloom ANDROID_NDK_ROOT=/usr/local/lib/android/sdk/ndk-bundle CHROME_BIN=/usr/bin/google-chrome GOROOT_1_18_X64=/opt/hostedtoolcache/go/1.18.4/x64 GITHUB_RETENTION_DAYS=90 JOURNAL_STREAM=8:23653 RUNNER_WORKSPACE=/home/runner/work/distributed-bloom LEIN_HOME=/usr/local/lib/lein LEIN_JAR=/usr/local/lib/lein/self-installs/leiningen-2.9.8-standalone.jar GITHUB_ACTION_REPOSITORY= PATH=/opt/hostedtoolcache/Python/3.9.13/x64/bin:/opt/hostedtoolcache/Python/3.9.13/x64:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/runner/.local/bin:/opt/pipx_bin:/home/runner/.cargo/bin:/home/runner/.config/composer/vendor/bin:/usr/local/.ghcup/bin:/home/runner/.dotnet/tools:/snap/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin RUNNER_PERFLOG=/home/runner/perflog GITHUB_BASE_REF=main CI=true SWIFT_PATH=/usr/share/swift/usr/bin ImageOS=ubuntu20 GITHUB_REPOSITORY_OWNER=bigscience-workshop GITHUB_HEAD_REF=fix-branch-name GITHUB_ACTION_REF= GITHUB_WORKFLOW=Tests DEBIAN_FRONTEND=noninteractive AGENT_TOOLSDIRECTORY=/opt/hostedtoolcache GOROOT_1_16_X64=/opt/hostedtoolcache/go/1.16.15/x64 _=/usr/bin/env
</details>
<details>
<summary> Environment variables when building in main (on push) </summary>
SELENIUM_JAR_PATH=/usr/share/java/selenium-server.jar GOROOT_1_17_X64=/opt/hostedtoolcache/go/1.17.11/x64 CONDA=/usr/share/miniconda GITHUB_WORKSPACE=/home/runner/work/distributed-bloom/distributed-bloom JAVA_HOME_11_X64=/usr/lib/jvm/temurin-11-jdk-amd64 GITHUB_PATH=/home/runner/work/_temp/_runner_file_commands/add_path_cd6c1ed2-0d0f-496d-b7a6-ffa476dcc144 GITHUB_ACTION=__run_2 JAVA_HOME=/usr/lib/jvm/temurin-11-jdk-amd64 GITHUB_RUN_NUMBER=53 RUNNER_NAME=GitHub Actions 3 GRADLE_HOME=/usr/share/gradle-7.4.2 XDG_CONFIG_HOME=/home/runner/.config DOTNET_SKIP_FIRST_TIME_EXPERIENCE=1 ANT_HOME=/usr/share/ant JAVA_HOME_8_X64=/usr/lib/jvm/temurin-8-jdk-amd64 HOMEBREW_PREFIX=/home/linuxbrew/.linuxbrew pythonLocation=/opt/hostedtoolcache/Python/3.9.13/x64 GITHUB_REF_TYPE=branch HOMEBREW_CLEANUP_PERIODIC_FULL_DAYS=3650 BOOTSTRAP_HASKELL_NONINTERACTIVE=1 *** PIPX_BIN_DIR=/opt/pipx_bin DEPLOYMENT_BASEPATH=/opt/runner GITHUB_ACTIONS=true ANDROID_NDK_LATEST_HOME=/usr/local/lib/android/sdk/ndk/24.0.8215888 GITHUB_SHA=49242d81006454d687ff3293c49f6bf234793627 POWERSHELL_DISTRIBUTION_CHANNEL=GitHub-Actions-ubuntu20 DOTNET_MULTILEVEL_LOOKUP=0 GITHUB_REF=refs/heads/main RUNNER_OS=Linux GITHUB_REF_PROTECTED=true HOME=/home/runner GITHUB_API_URL=https://api.github.com/ LANG=C.UTF-8 BLOOM_TESTING_WRITE_TOKEN=*** RUNNER_TRACKING_ID=github_7668f06a-99e1-4ed1-81e9-46d75fab3f33 STATS_KEEPALIVE=false RUNNER_ARCH=X64 RUNNER_TEMP=/home/runner/work/_temp EDGEWEBDRIVER=/usr/local/share/edge_driver GITHUB_ENV=/home/runner/work/_temp/_runner_file_commands/set_env_cd6c1ed2-0d0f-496d-b7a6-ffa476dcc144 GITHUB_EVENT_PATH=/home/runner/work/_temp/_github_workflow/event.json INVOCATION_ID=3dadac48981b4a679a33224db89be1ed GITHUB_EVENT_NAME=push GITHUB_RUN_ID=2680158280 JAVA_HOME_17_X64=/usr/lib/jvm/temurin-17-jdk-amd64 ANDROID_NDK_HOME=/usr/local/lib/android/sdk/ndk-bundle GITHUB_STEP_SUMMARY=/home/runner/work/_temp/_runner_file_commands/step_summary_cd6c1ed2-0d0f-496d-b7a6-ffa476dcc144 HOMEBREW_NO_AUTO_UPDATE=1 GITHUB_ACTOR=justheuristic NVM_DIR=/home/runner/.nvm SGX_AESM_ADDR=1 GITHUB_RUN_ATTEMPT=1 ANDROID_HOME=/usr/local/lib/android/sdk GITHUB_GRAPHQL_URL=https://api.github.com/graphql ACCEPT_EULA=Y RUNNER_USER=runner USER=runner GITHUB_SERVER_URL=https://github.com/ HOMEBREW_CELLAR=/home/linuxbrew/.linuxbrew/Cellar PIPX_HOME=/opt/pipx GECKOWEBDRIVER=/usr/local/share/gecko_driver CHROMEWEBDRIVER=/usr/local/share/chrome_driver SHLVL=0 ANDROID_SDK_ROOT=/usr/local/lib/android/sdk VCPKG_INSTALLATION_ROOT=/usr/local/share/vcpkg HOMEBREW_REPOSITORY=/home/linuxbrew/.linuxbrew/Homebrew RUNNER_TOOL_CACHE=/opt/hostedtoolcache ImageVersion=20220710.1 DOTNET_NOLOGO=1 GITHUB_REF_NAME=main STATS_PFS=true GRAALVM_11_ROOT=/usr/local/graalvm/graalvm-ce-java11-22.1.0 GITHUB_JOB=convert-model LD_LIBRARY_PATH=/opt/hostedtoolcache/Python/3.9.13/x64/lib XDG_RUNTIME_DIR=/run/user/1001 AZURE_EXTENSION_DIR=/opt/az/azcliextensions PERFLOG_LOCATION_SETTING=RUNNER_PERFLOG GITHUB_REPOSITORY=bigscience-workshop/distributed-bloom CHROME_BIN=/usr/bin/google-chrome ANDROID_NDK_ROOT=/usr/local/lib/android/sdk/ndk-bundle GOROOT_1_18_X64=/opt/hostedtoolcache/go/1.18.3/x64 GITHUB_RETENTION_DAYS=90 JOURNAL_STREAM=8:22000 RUNNER_WORKSPACE=/home/runner/work/distributed-bloom LEIN_HOME=/usr/local/lib/lein LEIN_JAR=/usr/local/lib/lein/self-installs/leiningen-2.9.8-standalone.jar GITHUB_ACTION_REPOSITORY= PATH=/opt/hostedtoolcache/Python/3.9.13/x64/bin:/opt/hostedtoolcache/Python/3.9.13/x64:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/runner/.local/bin:/opt/pipx_bin:/home/runner/.cargo/bin:/home/runner/.config/composer/vendor/bin:/usr/local/.ghcup/bin:/home/runner/.dotnet/tools:/snap/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin RUNNER_PERFLOG=/home/runner/perflog GITHUB_BASE_REF= CI=true SWIFT_PATH=/usr/share/swift/usr/bin ImageOS=ubuntu20 GITHUB_REPOSITORY_OWNER=bigscience-workshop GITHUB_HEAD_REF= GITHUB_ACTION_REF= GITHUB_WORKFLOW=Tests DEBIAN_FRONTEND=noninteractive AGENT_TOOLSDIRECTORY=/opt/hostedtoolcache GOROOT_1_16_X64=/opt/hostedtoolcache/go/1.16.15/x64 _=/usr/bin/env
</details>
Co-authored-by: Dmitry Baranchuk <dmitrybaranchuk@gmail.com>
