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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
# 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/