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>
A handler's RPC code may be cancelled due to a request timeout or a client closing the connection. Before this PR:
- If `.cancel()` happens while waiting for `hivemind.utils.enter_asynchronously()`, the lock will never be released.
- If `.cancel()` happens while doing that before freeing memory, the memory will never be freed.
This PR fixes it by deferring the cancellation with [asyncio.shield()](https://docs.python.org/3/library/asyncio-task.html#asyncio.shield). Now, the cancellation will happen only when all locks are released and alloc/free has completed.
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>
1. If we connect to the **public swarm**, the server now **automatically checks its DHT's reachability** from the outside world using API at http://health.petals.ml This is important to disallow unreachable servers to proceed (they create issues for the clients, such as repetitive retries).
If http://health.petals.ml is down, the server proceeds without the check (so we don't depend on it). However, if health.petals.ml is up and explicitly tells us that we are unrechable, the server shows the reason of that and how to solve it.
The check may be disabled with the `--skip_reachability_check` option (though I can't imagine cases where someone needs to use it).
2. Added `--port` and `--public_ip` as **simplified convenience options** for users not familiar with `--host_maddrs` and `--announce_maddrs`.
* 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>
The cause of OOMs were the cyclic references `TransformerBackend <-> PrioritizedTaskPool` that could not have been garbage collected properly. Still, I've added explicit tensor removal just in case.
- sequence_manager now takes care for its own updated-ness - no need to manually update it
- if a peer fails a request, sequence manager will ban this peer temporarily. Ban times increase with failure streaks
Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>
Summary:
```python
parser.add_argument('--attn_cache_size', type=str, default=None,
help='The size of GPU memory allocated for storing past attention keys/values between inference steps. '
'Examples: 500MB, 1.2GB, 1073741824 (bytes). Note that 1KB != 1KiB here. '
'Default: 0.5GiB * num_blocks * hidden_size / 14336. '
'The latter is the hidden size of the bigscience/bloom-petals model.')
parser.add_argument('--request_timeout', type=float, required=False, default=3 * 60,
help='Timeout (in seconds) for the whole rpc_forward/rpc_backward/rpc_forward_stream/rpc_backward_stream request')
parser.add_argument('--session_timeout', type=float, required=False, default=30 * 60,
help='Timeout (in seconds) for the whole inference session')
parser.add_argument('--step_timeout', type=float, required=False, default=60,
help="Timeout (in seconds) for waiting the next step's inputs inside an inference session")
parser.add_argument('--load_in_8bit', type=bool, default=None,
help="Convert the loaded model into mixed-8bit quantized model. Default: True if GPU is available")
```
Co-authored-by: justheuristic <justheuristic@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>
Fixes:
- An exception while creating a model with `ptune/deep_ptune` and `low_cpu_mem_usage=True` (which is currently default).
- dtype mismatch between the prompts and the rest of the model in `.forward()`.
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`
This PR:
1. Makes inference/forward/backward calls on client remember the dtype and device of source tensors, then move/cast the outputs to the same dtype/device. This way:
- Users don't need to make changes in the code launching `RemoteSequential` to make it run on a different device.
- `model.generate()` also starts to support both CPU and GPU.
2. Sets default `low_cpu_mem_usage=True`, client's request timeout to 20 sec.
3. Removes excess casts to float32 left in Dmitry's code.
4. (minor) Improves error messages.
- Before this PR, `ServerState.JOINING` was announced only once. This announcement quickly expires in case of the full-size BLOOM, since loading blocks takes several minutes. This PR fixes it, so `ServerState.JOINING` is announced periodically in a thread until blocks are loaded.
- This PR also makes the `Server` class a non-thread, so it runs in the main thread and can catch `KeyboardInterrupt`. This is important, since if we are downloading blocks right now, we need to stop it and send the `ServerState.OFFLINE` message. Note that `ModuleContainer` is still a thread.
- (minor) For the sake of readability, I moved the `ModuleContainer.create()` definition, so it is now defined before `Server.__init__()` (this is because `.create()` is invoked first).
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>
* 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>
Previously, attempting to allocate with MemoryCache that does not have enough space would throw AllocationFailed.
PR changes this behavior to the following:
- by default, wait until memory is freed by other tenants (FIFO)
- if could not allocate within timeout, throw AllocationFailed
- if allocated size is too big to fit even in empty cache, throw AllocationFailed
- [x] passes existing tests
- [x] passes manual load tests
p.s. if anyone wondered: using mp.Condition will not make the code simpler, their lock behavior is slightly different to what we need here
Co-authored-by: Alexander Borzunov <hxrussia@gmail.com>
Co-authored-by: Aleksandr Borzunov <borzunov.alexander@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>
* 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>
Add generation abstraction, that's using inference_session.
Added modes:
- Greedy, top-k/top-p sampling
- Multibatch generation
- Constraint abstraction
In the future, will add prefix-tuned generation, beam-search and more hf-like stuff.
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>
justheuristic
Aleksandr Borzunov
Alexander Borzunov
Max Ryabinin
Artem Chumachenko
Pavel Samygin
Dmitry Baranchuk