* Add support for compressed-tensors w8a8 int checkpoints
This change adds a loader for w8a8 int checkpoints. One large benefit of
int8 support is that the corresponding cutlass matmul kernels also work on
compute capability 7.5.
Evaluation on neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8:
| Tasks |Version| Filter |n-shot| Metric | |Value | |Stderr|
|---------------|------:|----------------|-----:|-----------------------|---|-----:|---|------|
|gsm8k_cot_llama| 3|flexible-extract| 8|exact_match |↑ |0.8431|± |0.0100|
| | |strict-match | 8|exact_match |↑ |0.8393|± |0.0101|
|ifeval | 4|none | 0|inst_level_loose_acc |↑ |0.8597|± | N/A|
| | |none | 0|inst_level_strict_acc |↑ |0.8201|± | N/A|
| | |none | 0|prompt_level_loose_acc |↑ |0.7967|± |0.0173|
| | |none | 0|prompt_level_strict_acc|↑ |0.7468|± |0.0187|
Which is the same ballpark as vLLM.
As usual, lots of thanks to Neural Magic/vLLM for the kernels.
* Always use dynamic input quantization for w8a8 int
It's far less flaky and gives better output.
* Use marlin-kernels 0.3.5
* Fix a typo
Co-authored-by: drbh <david.richard.holtz@gmail.com>
* Small fixes
---------
Co-authored-by: drbh <david.richard.holtz@gmail.com>
* Remove vLLM dependency for CUDA
This change adds `attention-kernels` as a dependency for paged
attention and cache reshaping. With that, we don't use vLLM
anywhere for CUDA.
Tested run (since we don't have paged attention in CI):
```
❯ ATTENTION=paged python -m pytest integration-tests -k "llama and awq" --release
[...]
5 snapshots passed.
```
* Fix clippy warning
compressed-tensors is a safetensors extension for sparse, quantized
tensors. The format is more powerful than earlier AWQ/GPTQ/FP8
quantization, because
- Different quantizer configurations can be used for different targets.
- The format can specify input/output quantizers in addition to weight
quantizers.
- Configurable exclusions for quantization.
This change adds a dependency on the `compressed-tensors` package for
its configuration parsing and layer matching functionality.
The following types of quantization are supported in this PR:
- W8A16 and W4A16 INT using GPTQ-Marlin kernels.
- W8A8 and W8A16 FP using FP8-Marlin and cutlass kernels.
Support for other quantization types will be added in subsequent PRs.
* Switch from fbgemm-gpu w8a8 scaled matmul to vLLM/marlin-kernels
Performance and accuracy of these kernels are on par (tested with Llama
70B and 405B). Removes a dependency and resolves some stability issues
we have been seeing.
* Update test snapshots
* Add support for FP8 KV cache scales
Since FP8 only has limited dynamic range, we can scale keys/values
before storing them into the cache (and unscale them in attention). To
avoid rescaling the cache as the absmax values change, good scales are
usually determined per layer using calibration calibration data and stored
in the checkpoint.
This change adds support for for using key-value scales and loading them
from checkpoints in the two most common formats:
- Separate per-layer `k_scale` and `v_scale` scalars.
- Per-layer `kv_scale` scalar (older format).
Currently, scales are only used with an `float8_e4m3fn` cache.
Besides adding support for key/value scales, the `fp8_quantize` function
is also extended to support quantization with a kernel vendored from
vLLM. This is slightly faster than the PyTorch implementation, but also
scales in FP32, potentially improving accuracy.
* Update FP8 KV cache test to use checkpoint with scales
* `can_scale`: check that the attention is flashinfer
* Add `impureWithCuda` dev shell
This shell is handy when developing some kernels jointly with TGI - it
adds nvcc and a bunch of commonly-used CUDA libraries to the environment.
We don't add this to the normal impure shell to keep the development
environment as clean as possible (avoid accidental dependencies, etc.).
* Add cuDNN
* Working loading state.
* Preprocessing.
* Working state ? (Broke idefics1 temporarily).
* Cleaner condition.
* Fix idefics.
* Updating config, removing TODO
* Mllama
* Ugrade transformers 4.45
* Flashing mllama.
* Starting to get there.
* Working state.
* Integrations tests for mllama (cutting to 10 tokens because there seems'
to be instability after (meaning size of the batch matters.
* Updating model link.
* Earlier assert.
* Fix vlm ?
* remove log.
* Force ignore all images but last.
* Default dtype bfloat16.
* Update integration test after switch to bf16.
* Remove dead code.
* Removed dead code.
* Upgrade the flake to latest transformers/tokenizers
* Move to hf tgi-nix
* Upgrade to 0.5.0
* nix: experimental support for building a Docker image
Run using something like:
```
docker run \
--device nvidia.com/gpu=all \
-it --rm -p 8080:80 \
-v $PWD/data:/data \
-v $PWD/tmp:/tmp \
tgi-docker:latest \
--model-id <model_id>
```
* Example of building the Docker image using Nix inside Docker
* Stream to make the builder image smaller
This avoids storing a Docker image tarball in the image. Instead,
stream the layers while doing `docker run`.
* Don't spam journalctl on Linux
* Other dockerfile.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This change add support for MoE models that use GPTQ quantization.
Currently only models with the following properties are supported:
- No `desc_act` with tensor parallelism, unless `group_size=-1`.
- No asymmetric quantization.
- No AWQ.
* Improve support for GPUs with capability < 8
- For models that cannot use flashinfer, use flash-attn v1 + paged
attention for models with a compute capability older than 8.
- Disable prefix caching when using paged attention.
- When using flash-attn v1, pass the key/value, rather than the
cache, since v1 cannot use block tables.
* nix: add flash-attn-v1 to the server environment
* Move disabling prefix caching into the block of exceptions
* Capability as `usize`s
* Stream options.
* Fetch stuff from nix integration test for easier testing.
* Adding the assert.
* Only send the usage when asked for.
* Update the docs.
* Impure test because we need network.
* develop.
* Optional usage.
* Fixes.
* Workflow
* Add nix test.
* Modifying yourself means you need to rerun.
* Fixing the test + adding click (needed for pre-commit hooks).
* Try thuis.
* Our runner + pure test (not written)
* Reemove server.
* Root user.
* Different user ?
* Add the actual test target.
* Forgot this modification.
* Add a formatter.
* Add the secrets.
* Fixed the auth token ?
* Adding the other tests.
* Missing pre-commit.
* Test requires cargo for cargo fmt.
* Update it a bit.
* Up.
* Attempting to use a cache location for the models.
* Ignore the cache for now.
Ideally we wouldn't have the router wrapper that this change adds,
but when I give PyO3 a Python interpreter with packages, it ends
up linking libpython from the Python interpreter rather than the
constructed environment and cannot pick up the Python modules as
a result.
Updates tgi-nix input:
- Move Torch closer to upstream by building against MKL.
- Remove compute capability 8.7 from Torch (Jetson).
- Sync nixpkgs cumpute capabilities with Torch (avoids
compiling too mana capabilities for MAGMA).
- Use nixpkgs configuration passed through by `tgi-nix`.
The default package wraps the launcher and puts the server/router in the
path.
As a result, TGI can be started using something like:
```
nix run .# -- \
--model-id hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
--port 8080
```
* nix: pure server and support both pure and impure devShells
* nix: remove unused poetry2nix input
It is not wired up and we now have a pure server.
* nix: add ipdb to impure devshell
Try to reduce the number of router/launcher rebuilds by filtering
sources. In this way, recompiles should only be triggered by changes
in Cargo or Rust files.
Daniël de Kok