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v0.1.0
2022-10-18 07:19:03 -06:00
.idea
Starting some routing tests. (#233)
2023-04-25 06:13:14 -06:00
target
router/tokenizer.json
feat(server): Rework model loading (#344) # What does this PR do? Reworked the loading logic. Idea is to use cleaner loading code: - Remove need for `no_init_weights` - Remove all weird `bnb_linear` and `load_weights` and `post_load_weights`. New code layout: - New class `Weights` in charge of handling loading the weights from multiple files into appropiate tensors (potentially sharded) - TP layers now are "shells", they contain the code to know what kind of sharding we need + eventual `all_reduce`. They do not inherit from linear, but they contain some kind of Linear instead - the contained linear can be either FastLinear, BnbLinear or GPTq Linear next. - All modeling code is explictly made for sharding, process group is just no-ops for non sharded code (removes a lot of test cases) ![Screenshot from 2023-05-19 23-19-59](https://github.com/huggingface/text-generation-inference/assets/204321/9a802654-74a3-488c-87a8-073743a6143f) --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net> Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal> Co-authored-by: OlivierDehaene <olivier@huggingface.co> Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
2023-06-08 06:51:52 -06:00
*__pycache__*
GPTQ support on ROCm (#1489) Tested with ``` CUDA_VISIBLE_DEVICES=0 text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq EXLLAMA_VERSION=1 CUDA_VISIBLE_DEVICES=0 text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq CUDA_VISIBLE_DEVICES="0,1" text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq ``` all with good and identical results on MI210. --------- Co-authored-by: Felix Marty <felix@hf.co> Co-authored-by: OlivierDehaene <olivier@huggingface.co> Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
2024-01-26 08:27:44 -07:00
Rebase TRT-llm (#2331) * wip wip refacto refacto Initial setup for CXX binding to TRTLLM Working FFI call for TGI and TRTLLM backend Remove unused parameters annd force tokenizer name to be set Overall build TRTLLM and deps through CMake build system Enable end to end CMake build First version loading engines and making it ready for inference Remembering to check how we can detect support for chunked context Move to latest TensorRT-LLM version Specify which default log level to use depending on CMake build type make leader executor mode working unconditionally call InitializeBackend on the FFI layer bind to CUDA::nvml to retrieve compute capabilities at runtime updated logic and comment to detect cuda compute capabilities implement the Stream method to send new tokens through a callback use spdlog release 1.14.1 moving forward update trtllm to latest version a96cccafcf6365c128f004f779160951f8c0801c correctly tell cmake to build dependent tensorrt-llm required libraries create cmake install target to put everything relevant in installation folder add auth_token CLI argument to provide hf hub authentification token allow converting huggingface::tokenizers error to TensorRtLlmBackendError use correct include for spdlog include guard to build example in cmakelists working setup of the ffi layer remove fmt import use external fmt lib end to end ffi flow working make sure to track include/ffi.h to trigger rebuild from cargo impl the rust backend which currently cannot move the actual computation in background thread expose shutdown function at ffi layer impl RwLock scenario for TensorRtLllmBackend oops missing c++ backend definitions compute the number of maximum new tokens for each request independently make sure the context is not dropped in the middle of the async decoding. remove unnecessary log add all the necessary plumbery to return the generated content update invalid doc in cpp file correctly forward back the log probabilities remove unneeded scope variable for now refactor Stream impl for Generation to factorise code expose the internal missing start/queue timestamp forward tgi parameters rep/freq penalty add some more validation about grammar not supported define a shared struct to hold the result of a decoding step expose information about potential error happening while decoding remove logging add logging in case of decoding error make sure executor_worker is provided add initial Dockerfile for TRTLLM backend add some more information in CMakeLists.txt to correctly install executorWorker add some more information in CMakeLists.txt to correctly find and install nvrtc wrapper simplify prebuilt trtllm libraries name definition do the same name definition stuff for tensorrt_llm_executor_static leverage pkg-config to probe libraries paths and reuse new install structure from cmake fix bad copy/past missing nvinfer linkage direction align all the linker search dependency add missing pkgconfig folder for MPI in Dockerfile correctly setup linking search path for runtime layer fix missing / before tgi lib path adding missing ld_library_path for cuda stubs in Dockerfile update tgi entrypoint commenting out Python part for TensorRT installation refactored docker image move to TensorRT-LLM v0.11.0 make docker linter happy with same capitalization rule fix typo refactor the compute capabilities detection along with num gpus update TensorRT-LLM to latest version update TensorRT install script to latest update build.rs to link to cuda 12.5 add missing dependant libraries for linking clean up a bit install to decoder_attention target add some custom stuff for nccl linkage fix envvar CARGO_CFG_TARGET_ARCH set at runtime vs compile time use std::env::const::ARCH make sure variable live long enough... look for cuda 12.5 add some more basic info in README.md * Rebase. * Fix autodocs. * Let's try to enable trtllm backend. * Ignore backends/v3 by default. * Fixing client. * Fix makefile + autodocs. * Updating the schema thing + redocly. * Fix trtllm lint. * Adding pb files ? * Remove cargo fmt temporarily. * ? * Tmp. * Remove both check + clippy ? * Backporting telemetry. * Backporting 457fb0a1 * Remove PB from git. * Fixing PB with default member backends/client * update TensorRT-LLM to latest version * provided None for api_key * link against libtensorrt_llm and not libtensorrt-llm --------- Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com> Co-authored-by: Morgan Funtowicz <morgan@huggingface.co>
2024-07-31 02:33:10 -06:00
backends/v3/src/client/pb
backends/client/src/v2/pb
backends/client/src/v3/pb
GPTQ support on ROCm (#1489) Tested with ``` CUDA_VISIBLE_DEVICES=0 text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq EXLLAMA_VERSION=1 CUDA_VISIBLE_DEVICES=0 text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq CUDA_VISIBLE_DEVICES="0,1" text-generation-launcher --model-id TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq ``` all with good and identical results on MI210. --------- Co-authored-by: Felix Marty <felix@hf.co> Co-authored-by: OlivierDehaene <olivier@huggingface.co> Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
2024-01-26 08:27:44 -07:00
# ROCm auto-generated files
*.hip
server/exllamav2_kernels/exllamav2_kernels/hip/
server/exllama_kernels/exllama_kernels/hip/
server/exllama_kernels/exllama_kernels/hip_func/
*_hip.cuh
server/exllama_kernels/exllama_kernels/hip_buffers.cuh
server/exllama_kernels/exllama_kernels/exllama_ext_hip.cpp
Fixing frequency penalty (#1811) Thank you so much for the work you are doing, this is my little contribution to this great thing you have built. I hope it is useful and helpful, please don't hesitate to discuss any matters that are not clear! I am basing my implementation of frequency penalty on OpenAI's implementation: https://platform.openai.com/docs/guides/text-generation/parameter-details The problem I see with TGI's current implementation is that is not taking into account the frequency of tokens which have already been sampled in the current generation stream. Also, the scaling is of the adjusted token logits is done differently for positive and negative logits. While in OpenAI's implementation token frequency is taking into account and the scaling is always done with a subtraction (if penalty is positive) or add operation (if penalty is negative). This leads to corrupt generations as I mentioned in issue #1810 . Moreover, after my tests, other issues are also gone like the one about some request's with ``penalty_frequency = 1.0`` overruling other requests (with ``frequency_penalty = 0.0``) in the same batch and therefore corrupting all generations in the batch. Basically, padding does not affect this implementation so I believe this ``score *= input_ids.ne(0)`` is not needed anymore. Frequency penalty | -1.0 | 0.0 | 1.0 -- | -- | -- | -- Before my change | https://paste.mozilla.org/JxqGJkWY | https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw After my change | https://paste.mozilla.org/7gXCi7zo | https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC --------- Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
2024-04-30 04:13:23 -06:00
data/
Adding scripts to prepare load data. (#1841) # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
2024-05-01 13:48:06 -06:00
load_tests/*.json
Using an enum for flash backens (paged/flashdecoding/flashinfer) (#2385) * Using an enum for flash backens (paged/flashdecoding/flashinfer) * Early exit on server too. * Clippy. * Fix clippy and fmt.
2024-08-09 08:41:17 -06:00
server/fbgemmm