* Add API_Key for Auth and conditionally add authorisation for non info/health endpoints.
* change name to info routes
* Fix comment
* convert strings to lowercase for case insensitive comparison
* convert header to string
* fixes and update docs
* update docs again
* revert wrong update
---------
Co-authored-by: Kevin Duffy <kevin.duffy94@gmail.com>
* draft of usage stats
* fix wrong link
* launcher doesn't need sysinfo dep
* only tokenizer class instead of hole struct
* unused import
* fix clippy errors
* update openAPI doc
* cargo fmt
* fix error in passing flags to router
* try again to update docs
* run pre-commit locally
* Update router/src/main.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* Update router/src/main.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* on crash use anonymous error event
* delete json_output and ngrok
* more robust way of checking if is in container
* more robust nvidia smi
* parse xpu more robustly
* fix errors
* add nvidia-smi details in docs
* cargo fmt
* fix clippy
* should make docs check pass
* Update router/src/usage_stats.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* error reason can't be in nested json
* cargo fmt
---------
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
Co-authored-by: Erik Kaunismäki <erikkaum@Eriks-MacBook-Pro.local>
* feat: first draft load multiple lora
* feat: load weights within layer and refactor lora pass
* fix: refactor and reduce lora math
* feat: baseline impl single request multi lora support
* feat: prefer lorax implementation and port loading logic
* fix: prefer adapter_data and refactors
* feat: perfer loraxs custom punica kernels and add mlp loras
* fix: adjust batch for bgmv
* fix: adjust adapter_segments logic when in batch
* fix: refactor and move changes to v3 proto
* fix: pass model_id for all flash causal lms
* fix: pass model_id for all causal and seq2seq lms
* fix: add model_id to model test
* feat: add lora support to mistral and refactors
* feat: prefer model id in request
* fix: include rust code for adapter id
* feat: bump launcher and add new lora docs
* feat: support base model generation and refactors
* fix: rename doc to retry ci build
* feat: support if vlm models
* fix: add adapter_data param and avoid missing layers
* fix: add adapter_data param to phi and neox
* fix: update all models forwards to include adapter_data
* fix: add model_id to IdeficsCausalLM
* Update lora.md
Fixed a typo
* Update lora.md
Fixing spam image
* fix: add lora kernel to dockerfile, support running without kernels and refactors
* fix: avoid dockerfile conflict
* fix: refactors and adjust flash llama lora logic
* fix: skip llama test due to CI issue (temp)
* fix: skip llama test CI (temp) 2
* fix: revert skips and prefer updated ci token for tests
* fix: refactors and helpful comments
* fix: add noop in TensorParallelAdapterRowLinear too
* fix: refactor and move shard_lora_weights logic
* fix: exit early if no adapter_data
---------
Co-authored-by: Derek <datavistics@gmail.com>
* use xpu-smi to dump used memory
xpu use "ZE_AFFINITY_MASK" to control card, usage is like CUDA_VISIBLE_DEVICES
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Update server/text_generation_server/utils/import_utils.py
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* update vllm commit & fix models using sliding window
* update
* update commit
* fix bug where tunableop is bound to cuda graph even when cuda graph are disabled
* enable tunableop by default
* fix sliding window
* address review
* dead code
* precise comment
* is it flaky?
This change adds support for Marlin-quantized models. Marlin is an
FP16xINT4 matmul kernel, which provides good speedups decoding batches
of 16-32 tokens. It supports quantized models with symmetric
quantization, groupsize -1 or 128, and 4-bit.
Tested with:
- Llama 2
- Llama 3
- Phi 3
Mostly straightforward, changes to existing code:
* Wrap quantizer parameters in a small wrapper to avoid passing
around untyped tuples and needing to repack them as a dict.
* Move scratch space computation to warmup, because we need the
maximum input sequence length to avoid allocating huge
scratch buffers that OOM.
- Added a debug log for speculated ids (helps seeing in logs quality of
a speculator).
- Remove newlines from child process logs when re-emitting in non JSON
mode.
- Made standard level be closer to what's expected (only our binaries
level).
- Propagate that level correctly to the shard (was forced into INFO).
# What does this PR do?
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---------
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
# What does this PR do?
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# What does this PR do?
Just unifying some branches and making intentions clearer (no cuda graph
when 0 all the way in the launcher)
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# What does this PR do?
This should enable more aggressive by default stacking, meaning better
throughput (in throughput constrained environements).
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
On TPU (and probably inferentia). The model needs to know right off the
bat about BATCH_SIZE and MAX_TOTAL_TOKENS (since the entire cache will
be determined by both).
This PR sends that information to the shards to they can allocate
accordingly. Should be no-op for other backends.
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# What does this PR do?
- Renamed `max_input_length` into `max_input_tokens` for consistency
(backward compatible change, will yell if both are set.)
- Will now use the config for `max_input_tokens` `max_total_token` and
`max_batch_total_tokens`.
- Capping the values to 16k in order to save VRAM on behalf of users
(overriddable by simply setting the values).
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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
wrap text-generation-launcher in docker image
mask ldconfig failures to user (no need in most cases anyway)
---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
# What does this PR do?
```
text-generation-launcher --model-id XXX # Uses cuda graphs by default
text-generation-launcher --model-id XXX --cuda-graphs "1,2" #Restrict the number of cuda graphs which saves VRAM
text-generation-launcher --model-id XXX --cuda-graphs "0" # Disabling it entirely
```
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# What does this PR do?
Fix the following carsh when build the docker on Ubuntu22.04
```
error[E0432]: unresolved import `nix::sys::signal::Signal`
--> launcher/src/main.rs:2:30
|
2 | use nix::sys::signal::{self, Signal};
| ^^^^^^ no `Signal` in `sys::signal`
|
= help: consider importing this type alias instead:
ctrlc::Signal
error[E0432]: unresolved import `nix::unistd::Pid`
--> launcher/src/main.rs:3:5
|
3 | use nix::unistd::Pid;
| ^^^^^^^^^^^^^^^^ no `Pid` in `unistd`
|
note: found an item that was configured out
--> /usr/local/cargo/registry/src/index.crates.io-6f17d22bba15001f/nix-0.27.1/src/unistd.rs:183:12
|
183 | pub struct Pid(pid_t);
| ^^^
= note: the item is gated behind the `process` feature
error[E0425]: cannot find function `kill` in module `signal`
```
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---------
Signed-off-by: yuanwu <yuan.wu@intel.com>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
# What does this PR do?
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---------
Co-authored-by: Andres Restrepo <andres@thelinuxkid.com>
This PR just bumps the latest rust version and makes clippy happy
```bash
cargo clippy --all -- -D warnings
# Finished dev [unoptimized + debuginfo] target(s) in 0.10s
```
# What does this PR do?
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---------
Co-authored-by: zhaosida <zhaosida@corp.netease.com>
# What does this PR do?
Upgrade all relevant versions and dependencies.
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# Add AWQ quantization inference support
Fixes
https://github.com/huggingface/text-generation-inference/issues/781
This PR (partially) adds support for AWQ quantization for inference.
More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
general, AWQ is faster and more accurate than GPTQ, which is currently
supported by TGI.
This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
(in `requirements.txt`, just one line change).
Quick way to test this PR would be bring up TGI as follows:
```
text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq
text-generation-launcher \
--huggingface-hub-cache ~/.cache/huggingface/hub/ \
--model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
--trust-remote-code --port 8080 \
--max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize awq
```
Please note:
* This PR was tested with FlashAttention v2 and vLLM.
* This PR adds support for AWQ inference, not quantizing the models.
That needs to be done outside of TGI, instructions
[here](f084f40bd9).
* This PR only adds support for `FlashLlama` models for now.
* Multi-GPU setup has not been tested.
* No integration tests have been added so far, will add later if
maintainers are interested in this change.
* This PR can be tested on any of the models released
[here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).
Please refer to the linked issue for benchmarks for
[abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
vs
[TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).
Please note, AWQ has released faster (and in case of Llama, fused)
kernels for 4-bit GEMM, currently at the top of the `main` branch at
https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
that has been tested to work. We can switch to latest commit later on.
## Who can review?
@OlivierDehaene OR @Narsil
---------
# What does this PR do?
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---------
Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>
# What does this PR do?
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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
# What does this PR do?
See #626
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---------
Co-authored-by: krzim <zimmerk4@live.com>
Erik Kaunismäki