The compressed-tensors configuration can specify the configuration of
the KV cache as well. Use an FP8 KV cache when the configuration tells
us to do so (all other options and types are ignored for now).
* Move JSON grammar -> regex grammar conversion to the router
This change moves the JSON grammar -> regex grammar conversion to the
router by adding a dependency on the `outlines-core` Rust crate. In
contrast to the Python implementation, the conversions are not LRU-cached
since they seem to be fast enough:
simple schema time: [5.8293 µs 5.8307 µs 5.8320 µs]
change: [-13.166% -12.884% -12.641%] (p = 0.00 < 0.05)
Performance has improved.
complex schema time: [14.875 µs 14.881 µs 14.887 µs]
change: [-2.1637% -1.9914% -1.7852%] (p = 0.00 < 0.05)
Performance has improved.
Using the schemas from:
https://github.com/dottxt-ai/outlines-core/blob/main/benchmarks/bench_json_schema.py
* Incomplete generation stream fix (#2754)
entries.len() could > batch.size in prefill, so need to filter as well.
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* entries was wrongly extended for model that did not support chunking
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
* nix: build and cache all devshells
* nix: add poetry to the impure shell
This shouldn't be used to manage dependencies in a Nix devshell, but can
be handy to update `poetry.lock`.
* Fix Nix build, disable pure shell (covered by Nix tests)
* add OpenAI like tool_choice for named choice
* add tests
* fix: run linter and bump api docs
* fix: consolidate changes and remove old tool type
* feat: improve, simplify and rename tool choice struct add required support and refactor
* fix: simplify tool choice logic, improve tests, openapi and rust docs
* fix: refactor away prepare_chat_input and improve tool grammar apply control flow
* feat: update docs and add tool choice configuration section
* fix: simplify naming, tool choice default and improve test
* fix: adjust tool choice none logic, add test and small refactors
* fix: add missing snapshot file
* fix: adjust tool choice type in test
* fix: adjust default when json tool choice is
* fix: remove trailing space lint after rebase
* fix: remove mostly mocked unit test
---------
Co-authored-by: Linus Bierhoff <linus.bierhoff@icloud.com>
* 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>
* add ipex moe implementation to support Mixtral and PhiMoe
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* update to ipex xpu 2.5
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* torch has xpu support in 2.5
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix oneapi basekit version
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Apply suggestions from code review
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>
* 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
* feat: return streaming errors as an event formatted for openai's client
* fix: propagate completions error events to stream
* fix: improve stream api error format and add status code
* fix: improve streamin error to include error_type
* Revert "fix: improve streamin error to include error_type"
This reverts commit 2b1a360b1511d94ea9a24e5432e498e67939506a.
* Reworked the implementation.
* Revert "Reworked the implementation."
This reverts commit 7c3f29777f17411ae4ade57e2f88e73cde704ee5.
* Small lifting.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Upgrade outlines to 0.1.1
* Update for new API
* Check if allowed tokens is None
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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.
fix incorrect output of Qwen2-7B-Instruct-GPTQ-Int4 and Qwen2-7B-Instruct-AWQ
ipex kernel provide func like add_bias, so no need add it outside
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* feat: support multidimensional position ids on batch to enable cuda graphs on qwen2-vl
* fix: only check model type if config exists
* fix: adjust sharding and lm head logic
* fix qwen2 failure in intel cpu
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix: return correct shape logits and add streaming test
* fix: remove unused import and refactor test
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* feat: add support for qwen2 vl model
* feat: fix token padding, enable warmup and process basic request
* fix: improve get_position_ids, add lift embed_tokens
* fix: remove get_cos_sin_hack dev function
* feat: add simple test chat with meesage and text
* fix: lint test
* fix: adjust positional embeddings for multi dimensional position ids
* fix: update docs and lint unused vars
* fix: include linted file
* fix: add norm after text output
* fix: format model file
* fix: adjust for ruff lints
* fix: remove unused rotate_half
* feat: refactors and calc num features
* fix: prefer position_ids passed from vlm causal lm and reset ids on batch
* fix: adjust get_position_ids if not available and add required args to signatures
* fix: adjust resize case for qwen2_vl warmup
* fix: avoid qwen2 vl specific paths with qwen2
add xpu triton in dockerfile, or will show "Could not import Flash Attention enabled models: No module named 'triton'"
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
