This PR fixes problems related to #569:
- block initialization
- throughput calculation and cache usage
- mixtral in tests
Beam search is removed for Mixtral and Llama for now. Those models use DynamicCache, which requires special function to change: (see https://github.com/huggingface/transformers/blob/main/src/transformers/cache_utils.py#L161)
---------
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
This PR updates multiple default arguments in clients and servers:
1. **The client defaults to `torch_dtype=torch.float32` instead of `torch_dtype="auto"`.**
The old default was to load weights in the dtype they are saved in (usually bfloat16/float16), which caused issues when the client was run on CPU (the default unless you call `.cuda()`). Specifically, bfloat16 is slow on most CPUs (unless a CPU supports AVX512) and float16 can't be run natively and leads to an exception. This default was a legacy of the earliest Petals versions designed to run BLOOM - its embeddings were so big that they didn't fit into RAM in float32 (e.g., in Colab). The newer models don't have this issue.
In contrast, the new default leads to good speed on all CPUs and is consistent with PyTorch and HF Transformers. Also, the client now shows "bfloat16 on non-AVX512 CPU" in all cases (previously this warning was shown only if the machine has enough RAM to fit float32 weights, which could hide the crucial reason of inference being slow).
**Note:** This change is backward-incompatible, so we have to increase at least the minor package version (2.2.0 -> 2.3.0.dev0).
2. **The server uses 2x smaller `--attn_cache_tokens`.**
The old default led to loading 39 (out of 80) or 78 (out of 80) blocks for popular models on some GPU types, which visibly slowed down inference due to an excess network hop. It was also leaving too much cache, so that inference slowed down much before the cache is used.
The new default leads to more efficient block layouts and makes the inference routing algorithm choose alternative paths through other servers when a particular server already has enough active inference sessions (= its cache is full).
3. **The client's max number of retries can be limited by the `PETALS_MAX_RETRIES` env var.**
This is to limit `ClientConfig.max_retries` in tests, so we see tracebacks instead of retrying indefinitely in case of errors.
This PR adds:
- Support for models based on `transformers.FalconModel` (the in-library format for Falcon). Tested on Falcon-40B.
- CI tests for Falcon-RW-1B.
- `--throughput dry_run` option to evaluate throughput and exit right away (implemented by @mryab).
Limitations:
- Backward pass support is broken for now, will be fixed in #500.
Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>
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)
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).
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>
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).
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>
- [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>
This commit adds a Dockerfile that sets up the environment for Petals, as well as a GitHub Action to build the corresponding image on each push to the main branch.
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`
This PR makes servers and clients use public swarm's bootstrap peers if no other initial peers are specified.
If you'd like a server to start a new swarm, provide the `--new_swarm` CLI argument.
* update dependency versions
* install bitsandbytes cpuonly from pip
* remove deprecated API from task pool
* clearer startup logs
Co-authored-by: Tim Dettmers <dettmers@cs.washington.edu>
- 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)
* integrate mixed-8bit model
* Fix bug with model duplication in RAM
* set throughput=1.0 to fix zero throughput problem
* add revision support
* update hivemind and bitsandbytes
* update deploy scripts
* update installation instructions
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>
- finish renaming RemoteSequenceInfo -> RemoteSequenceManager (why: if it was an *Info, user would expect it to be similar - to a dataclass; whereas in actuality, the class is doing heavy network interactions on its own)
- implement RemoteSequenceManager.make_sequence (from https://pastebin.com/uXgy2U8B )
- make RemoteSequentialInferenceSession use RemoteSequenceManager.make_sequence
- make tests pass again
- make it possible to create inference session without RemoteTransformerBlock
- make a standalone test for RemoteSequential
- rollback convert-model
Co-authored-by: Tim Dettmers <tim.dettmers@gmail.com>
This PR will run basic tests automatically on each subsequent PR
- convert a small model on every PR
- run existing tests on every PR
- enforce black / isort
- require checks on merge
- make sure tests are not flappy
Co-authored-by: Alexander Borzunov <hxrussia@gmail.com>
Co-authored-by: Dmitry Baranchuk <dmitrybaranchuk@gmail.com>
Artem Chumachenko
justheuristic
Denis Mazur
Alexander Borzunov
Max Ryabinin
Dmitry Baranchuk