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>
- 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>
Your Name
Alexander Borzunov
Artem Chumachenko
Max Ryabinin
justheuristic
Pavel Samygin
Dmitry Baranchuk
Dmitry Baranchuk