* 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 2b1a360b15.
* 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>
* We can have a tokenizer anywhere.
* Handling potential lack of offsets (python tokenizer)
* Remove redundancy.
* Fixing the tests.
* Flake.lock update ?
* Fixing the GIL locking.
* Fixing mamba by using the transformers version.
* Adding the legacy handle.
* Ellide lifetime.
* Lint.
* Deprecation message.
* Fixing bad rebase.
* 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
* feat(trtllm): rewrite health to not account for current state
* chore(looper): cleanup a bit more
* feat(post_processing): max_new_tokens is const evaluated now
* chore(ffi):formatting
* feat(trtllm): add stop words handling
# Conflicts:
# backends/trtllm/lib/backend.cpp
* chore(trtllm): create specific parallelconfig factory and logging init methods
* chore(trtllm): define a macro for SizeType cast
* chore(trtllm): use GetParallelConfig
* chore(trtllm): minor refactoring
* chore(trtllm): validate there are enough GPus on the system for the desired model
* chore(trtllm): ensure max throughput scheduling policy is selected
* chore(trtllm): minor fix
* chore(router): minor refactorings
* feat(docker): build with-slurm ompi
* feat(docker): add python3.10 dev to runtime deps
* chore(docker): add mpi to ld_library_path
* chore(docker): install transformers
* feat(trtllm): detect stop_words from generation_config.json
* (backend) use parking_lot crate for RwLock fairness
# Conflicts:
# backends/trtllm/src/backend.rs
* (launcher) default new server::run parameters to false for now
* (chore) fmt ... why?
* (ffi) use const for GetSamplingConfig
* (server) expose new SchedulingError
* (trt)
* (build) setup ccache if available
* (ffi) add max_new_tokens parameters
* (backend) cleanup a bit
* (backend) expose PullNewTokens
* (ffi) cleanup again
* (ffi) add missing headers imports
* (ffi) add template specialization to catch and convert to Rust Result<T, tensorrt_llm::common::TllmException>
* (looper) new looper initial implementation
* (ffi) remove narrowing type warning
* (ffi) encode the provided user prompt within each request thread
* (misc) change scope identifiers
* (backend) implement the post_processor background thread
* (misc) missing Result types for Rust
* use blocking_recv in looper to consume awaiting_requests at max before pulling in a single step
* (server) forward auth_token to server::run
* (build) fetchcontent use archives instead of git
* (ffi) fix usage of wrong vector constructor making a capacity fill call
* (ffi) missing namespace for tle::Response
* (ffi) do not use reference capture in lambda as we are not capturing anything
* (backend) refactor & cleanup
* (Dockerfile.trtllm) delete for now
* (misc) simplify [make_]move_iterator by using c++20 type inference
* (misc) no need to move for uint32_t items
* (scheduler) rework submit/pull logic
* (post) impl postprocessing
* (misc) delete backend.rs
* (misc) rerun-if-changed all the cmake modules
* (misc) move to latest trtllm
* (fix): HOPPER_SM_MAJOR is 9 not 8
* (misc: build for sm_{75,80,86,89,90} by default
* (misc): build with trtllm 0.13.0
* (misc): increase verbosity of spdlog
* (fix): do not recreate the stateful hashmap at every it
* (misc): update dependency in trtllm dockerfile
* (misc): update dependency in trtllm dockerfile
* (misc): disable logging in release mode
* (misc): improve trtllm download script robustness
* (fix): ore fixes for Dockerfile
* misc(cuda): require 12.6
* chore(cmake): use correct policy for download_timestamp
* feat(looper): check engine and executorWorker paths exist before creating the backend
* chore(cmake): download timestamp should be before URL
* feat(looper): minor optimizations to avoid growing too much the containers
* chore(trtllm): move dockerfile to right place
* chore(trtllm): disable tokenizer parallelism by default
* chore(trtllm): fmt
* chore(trtllm): post-rebase commit
* chore(trtllm): remove unused method
* feat(trtllm): cache maxNumTokens to avoid calling JSON everytime
* misc(router): remove SchedulingError
* feat(trtllm): do not tokenize twice
* Revert "chore(trtllm): remove unused method"
This reverts commit 31747163
* chore(rebase): fix invalid references
* chore(router): add python dependency
* Lint.
* Fix bad rebase
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* 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
Miquel Farre