* 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
* 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
* Move to moe-kernels package and switch to common MoE layer
This change introduces the new `moe-kernels` package:
- Add `moe-kernels` as a dependency.
- Introduce a `SparseMoELayer` module that can be used by MoE
models.
- Port over Mixtral and Deepseek.
* Make `cargo check` pass
* Update runner
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`.
* 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
Daniël de Kok