mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
chore: Mass integration of release/0.18 (#3421)
* [Infra][TRTLLM-4063] - Branch out for the TRT-LLM v0.18.0 release Signed-off-by: Zhanrui Sun <zhanruis@nvidia.com> (cherry picked from commit de90312020e51c22ba5e75b3502c7ee90c059265) * [Infra][TRTLLM-3652] - Update dependencies to TRT 10.9 / CUDA 12.8.1 / DLFW 25.03(Internal) Signed-off-by: Yiqing Yan <yiqingy@nvidia.com> (cherry picked from commit 58db1340ef7db22f1910f878d220a92be5b830d1) * [None][Doc] - Update docs for v0.18.0 Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> (cherry picked from commit d23e75bc95619ce3b116213d55319272888e0c88) * [Infra] - Fix or WAR issues in the package sanity check stages Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> (cherry picked from commit e874e2b127515c52ba10c8df1cc2631627f74ffe) * [https://nvbugs/5173454] [https://nvbugs/5173432] [https://nvbugs/5175863] fix chatglm tokenizer and tmp model path Signed-off-by: Yuki Huang <yukih@nvidia.com> (cherry picked from commit 731811d4e182d70a66193d646152cb71dfafe83a) * cherry-pick 'test: Updat cluster and multi node test lists and trtllm-bench' test to fix perf drop issue Signed-off-by: Ruodi Lu <ruodil@nvidia.com> (cherry picked from commit 5214616283fbc15ae98871a1d84c78d8e1f2e6e8) * Revert "Merge branch 'user/yukih/fix_5173454_5173432' into 'release/0.18'" Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> (cherry picked from commit 8d34831cb2b81ee2dfa8021b68e7158b33789a5f) * [Infra]Restrict setuptools version to avoid sasb pip install issue Signed-off-by: Emma Qiao <qqiao@nvidia.com> (cherry picked from commit 1e60ad29e0dafec0e295bedb5d89b716a02a707c) * [https://nvbugs/5173454] [https://nvbugs/5173432] [https://nvbugs/5175863] fix chatglm tokenizer and tmp model path Signed-off-by: Yuki Huang <yukih@nvidia.com> (cherry picked from commit 3ed8164e5bfea1d5aa2039b5408439fd6cf59dac) * WAR for bug 5173448 Signed-off-by: Thor Johnsen <tjohnsen@nvidia.com> (cherry picked from commit b6528b2ba15322b6c6a4c81a8b74c04d4973de4f) * [Infra][TRTLLM-3652] - Update dependencies to CUDA 12.8.1 / DLFW 25.03 Signed-off-by: Yiqing Yan <yiqingy@nvidia.com> (cherry picked from commit 6560983d132d9d257ee15849664eb055e94adaa9) * [Docs] - Doc changes for v0.18.0 Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> (cherry picked from commit 26769b61218a947c8f9d070f73b63d576fcc20c4) * [Doc] - Doc change for v0.18.0 Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> (cherry picked from commit 4b3b5ed6bfbc2300e3775fe75456083faad7b235) * [Infra] update version to 0.18.1 Signed-off-by: Zhanrui Sun <zhanruis@nvidia.com> (cherry picked from commit 59e8326c75639275837d34de8e140358737a3365) * Add back nemotron file. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Fix recurrentgemma reqs. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Adding WAR for bug 5173448. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Formatting. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Remove duplicated file. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Update examples/prompt_lookup/requirements.txt Co-authored-by: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Signed-off-by: Daniel Cámpora <961215+dcampora@users.noreply.github.com> * Remove glm-4-9b from model dir in chatglm test. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Remove indent change. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Yanchao Lu <yanchaol@nvidia.com> Signed-off-by: Daniel Cámpora <961215+dcampora@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Yanchao Lu <yanchaol@nvidia.com> Signed-off-by: Daniel Cámpora <961215+dcampora@users.noreply.github.com> * Revert changes on l0_test.groovy. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Update dev images Co-authored-by: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> * Remove duplicated import. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Fix custom op Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com> * Fix flashinfer & vanilla backend Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com> * Skip problematic case. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> * Skip problematic test_moe_w4a8_1_14336_4096_8_bfloat16_True_False case. Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> --------- Signed-off-by: Daniel Campora <961215+dcampora@users.noreply.github.com> Signed-off-by: Daniel Cámpora <961215+dcampora@users.noreply.github.com> Signed-off-by: Yanchao Lu <yanchaol@nvidia.com> Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com> Co-authored-by: Zhanrui Sun <zhanruis@nvidia.com> Co-authored-by: Yiqing Yan <yiqingy@nvidia.com> Co-authored-by: Yanchao Lu <yanchaol@nvidia.com> Co-authored-by: Yuki Huang <yukih@nvidia.com> Co-authored-by: Ruodi Lu <ruodil@nvidia.com> Co-authored-by: Emma Qiao <qqiao@nvidia.com> Co-authored-by: Thor Johnsen <tjohnsen@nvidia.com> Co-authored-by: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com> Co-authored-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com> Co-authored-by: Tao Li @ NVIDIA <tali@nvidia.com>
This commit is contained in:
Daniel Cámpora
GitHub
parent
da47d5f27e
commit
41ce5440fe
@ -1,7 +1,7 @@
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version: "3.9"
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services:
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tensorrt_llm-dev:
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image: urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.01-py3-x86_64-ubuntu24.04-trt10.8.0.43-skip-devel-202503131720-8877
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image: urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.03-py3-x86_64-ubuntu24.04-trt10.9.0.34-skip-devel-202504101610-3421
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network_mode: host
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ipc: host
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@ -7,8 +7,8 @@ TensorRT-LLM
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[](https://nvidia.github.io/TensorRT-LLM/)
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[](https://www.python.org/downloads/release/python-3123/)
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[](https://www.python.org/downloads/release/python-31012/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](./tensorrt_llm/version.py)
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[](./LICENSE)
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@ -1,6 +1,6 @@
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# Multi-stage Dockerfile
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ARG BASE_IMAGE=nvcr.io/nvidia/pytorch
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ARG BASE_TAG=25.01-py3
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ARG BASE_TAG=25.03-py3
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ARG DEVEL_IMAGE=devel
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FROM ${BASE_IMAGE}:${BASE_TAG} AS base
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@ -152,16 +152,16 @@ jenkins-aarch64_%: STAGE = devel
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jenkins-rockylinux8_%: IMAGE_WITH_TAG = $(shell grep 'LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE = ' ../jenkins/L0_MergeRequest.groovy | grep -o '".*"' | tr -d '"')
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jenkins-rockylinux8_%: STAGE = devel
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jenkins-rockylinux8_%: BASE_IMAGE = nvidia/cuda
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jenkins-rockylinux8_%: BASE_TAG = 12.8.0-devel-rockylinux8
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jenkins-rockylinux8_%: BASE_TAG = 12.8.1-devel-rockylinux8
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rockylinux8_%: STAGE = devel
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rockylinux8_%: BASE_IMAGE = nvidia/cuda
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rockylinux8_%: BASE_TAG = 12.8.0-devel-rockylinux8
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rockylinux8_%: BASE_TAG = 12.8.1-devel-rockylinux8
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# For x86_64 and aarch64
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ubuntu22_%: STAGE = devel
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ubuntu22_%: BASE_IMAGE = nvidia/cuda
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ubuntu22_%: BASE_TAG = 12.8.0-devel-ubuntu22.04
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ubuntu22_%: BASE_TAG = 12.8.1-devel-ubuntu22.04
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trtllm_%: STAGE = release
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trtllm_%: PUSH_TO_STAGING := 0
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@ -5,7 +5,7 @@ set -ex
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# This script is used for reinstalling CUDA on Rocky Linux 8 with the run file.
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# CUDA version is usually aligned with the latest NGC CUDA image tag.
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# Only use when public CUDA image is not ready.
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CUDA_VER="12.8.0_570.86.10"
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CUDA_VER="12.8.1_570.124.06"
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CUDA_VER_SHORT="${CUDA_VER%_*}"
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NVCC_VERSION_OUTPUT=$(nvcc --version)
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@ -4,7 +4,7 @@ set -ex
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# Use latest stable version from https://pypi.org/project/torch/#history
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# and closest to the version specified in
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-01.html#rel-25-01
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-03.html#rel-25-03
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TORCH_VERSION="2.6.0"
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SYSTEM_ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
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@ -2,20 +2,20 @@
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set -ex
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TRT_VER="10.8.0.43"
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TRT_VER="10.9.0.34"
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# Align with the pre-installed cuDNN / cuBLAS / NCCL versions from
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-01.html#rel-25-01
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CUDA_VER="12.8" # 12.8.0
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-03.html#rel-25-03
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CUDA_VER="12.8" # 12.8.1
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# Keep the installation for cuDNN if users want to install PyTorch with source codes.
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# PyTorch 2.x can compile with cuDNN v9.
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CUDNN_VER="9.7.0.66-1"
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CUDNN_VER="9.8.0.87-1"
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NCCL_VER="2.25.1-1+cuda12.8"
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CUBLAS_VER="12.8.3.14-1"
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CUBLAS_VER="12.8.4.1-1"
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# Align with the pre-installed CUDA / NVCC / NVRTC versions from
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# https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
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NVRTC_VER="12.8.61-1"
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CUDA_RUNTIME="12.8.57-1"
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CUDA_DRIVER_VERSION="570.86.10-1.el8"
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NVRTC_VER="12.8.93-1"
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CUDA_RUNTIME="12.8.90-1"
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CUDA_DRIVER_VERSION="570.124.06-1.el8"
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for i in "$@"; do
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case $i in
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@ -116,7 +116,7 @@ install_tensorrt() {
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if [ -z "$ARCH" ];then ARCH=$(uname -m);fi
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if [ "$ARCH" = "arm64" ];then ARCH="aarch64";fi
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if [ "$ARCH" = "amd64" ];then ARCH="x86_64";fi
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RELEASE_URL_TRT="https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.8.0/tars/TensorRT-${TRT_VER}.Linux.${ARCH}-gnu.cuda-${TRT_CUDA_VERSION}.tar.gz"
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RELEASE_URL_TRT="https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.9.0/tars/TensorRT-${TRT_VER}.Linux.${ARCH}-gnu.cuda-${TRT_CUDA_VERSION}.tar.gz"
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fi
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wget --no-verbose ${RELEASE_URL_TRT} -O /tmp/TensorRT.tar
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tar -xf /tmp/TensorRT.tar -C /usr/local/
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@ -33,6 +33,10 @@ TensorRT-LLM consists of pre– and post-processing steps and multi-GPU multi-no
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TensorRT-LLM supports GPUs based on the NVIDIA Hopper, NVIDIA Ada Lovelace, and NVIDIA Ampere architectures.
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Certain limitations might apply. Refer to the {ref}`support-matrix` for more information.
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### Native Windows Support
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Windows platform support is deprecated as of v0.18.0. All Windows-related code and functionality will be completely removed in future releases.
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## What Can You Do With TensorRT-LLM?
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Let TensorRT-LLM accelerate inference performance on the latest LLMs on NVIDIA GPUs. Use TensorRT-LLM as an optimization backbone for LLM inference in NVIDIA NeMo, an end-to-end framework to build, customize, and deploy generative AI applications into production. NeMo provides complete containers, including TensorRT-LLM and NVIDIA Triton, for generative AI deployments.
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@ -112,9 +112,9 @@ The following table shows the supported software for TensorRT-LLM.
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* -
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- Software Compatibility
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* - Container
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- [25.01](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)
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- [25.03](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)
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* - TensorRT
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- [10.8](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)
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- [10.9](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)
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* - Precision
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-
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- Hopper (SM90) - FP32, FP16, BF16, FP8, INT8, INT4
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@ -5,6 +5,32 @@
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All published functionality in the Release Notes has been fully tested and verified with known limitations documented. To share feedback about this release, access our [NVIDIA Developer Forum](https://forums.developer.nvidia.com/).
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## TensorRT-LLM Release 0.18.1
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### Key Features and Enhancements
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- **The 0.18.x series of releases builds upon the 0.17.0 release, focusing exclusively on dependency updates without incorporating features from the previous 0.18.0.dev pre-releases. These features will be included in future stable releases**.
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### Infrastructure Changes
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- The dependent `transformers` package version is updated to 4.48.3.
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## TensorRT-LLM Release 0.18.0
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### Key Features and Enhancements
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- **Features that were previously available in the 0.18.0.dev pre-releases are not included in this release**.
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- [BREAKING CHANGE] Windows platform support is deprecated as of v0.18.0. All Windows-related code and functionality will be completely removed in future releases.
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### Known Issues
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- The PyTorch workflow on SBSA is incompatible with bare metal environments like Ubuntu 24.04. Please use the [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) for optimal support on SBSA platforms.
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### Infrastructure Changes
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- The base Docker image for TensorRT-LLM is updated to `nvcr.io/nvidia/pytorch:25.03-py3`.
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- The base Docker image for TensorRT-LLM Backend is updated to `nvcr.io/nvidia/tritonserver:25.03-py3`.
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- The dependent TensorRT version is updated to 10.9.
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- The dependent CUDA version is updated to 12.8.1.
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- The dependent NVIDIA ModelOpt version is updated to 0.25 for Linux platform.
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## TensorRT-LLM Release 0.17.0
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### Key Features and Enhancements
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@ -21,10 +21,10 @@ UPLOAD_PATH = env.uploadPath ? env.uploadPath : "sw-tensorrt-generic/llm-artifac
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// Container configuration
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// available tags can be found in: https://urm.nvidia.com/artifactory/sw-tensorrt-docker/tensorrt-llm/
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// [base_image_name]-[arch]-[os](-[python_version])-[trt_version]-[torch_install_type]-[stage]-[date]-[mr_id]
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LLM_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.01-py3-x86_64-ubuntu24.04-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_SBSA_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.01-py3-aarch64-ubuntu24.04-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.0-devel-rocky8-x86_64-rocky8-py310-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.0-devel-rocky8-x86_64-rocky8-py312-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.03-py3-x86_64-ubuntu24.04-trt10.9.0.34-skip-devel-202504101610-3421"
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LLM_SBSA_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.03-py3-aarch64-ubuntu24.04-trt10.9.0.34-skip-devel-202504101610-3421"
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LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.1-devel-rocky8-x86_64-rocky8-py310-trt10.9.0.34-skip-devel-202504101610-3421"
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LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.1-devel-rocky8-x86_64-rocky8-py312-trt10.9.0.34-skip-devel-202504101610-3421"
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LLM_ROCKYLINUX8_DOCKER_IMAGE = LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE
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@ -29,11 +29,11 @@ linuxPkgName = ( env.targetArch == AARCH64_TRIPLE ? "tensorrt-llm-sbsa-release-s
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// available tags can be found in: https://urm.nvidia.com/artifactory/sw-tensorrt-docker/tensorrt-llm/
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// [base_image_name]-[arch]-[os](-[python_version])-[trt_version]-[torch_install_type]-[stage]-[date]-[mr_id]
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LLM_DOCKER_IMAGE = env.dockerImage
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LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.0-devel-rocky8-x86_64-rocky8-py310-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.0-devel-rocky8-x86_64-rocky8-py312-trt10.8.0.43-skip-devel-202503131720-8877"
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LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.1-devel-rocky8-x86_64-rocky8-py310-trt10.9.0.34-skip-devel-202504101610-3421"
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LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-12.8.1-devel-rocky8-x86_64-rocky8-py312-trt10.9.0.34-skip-devel-202504101610-3421"
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// DLFW torch image
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DLFW_IMAGE = "nvcr.io/nvidia/pytorch:25.01-py3"
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DLFW_IMAGE = "nvcr.io/nvidia/pytorch:25.03-py3"
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//Ubuntu base image
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UBUNTU_22_04_IMAGE = "urm.nvidia.com/docker/ubuntu:22.04"
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@ -1,7 +1,7 @@
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import java.lang.InterruptedException
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DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.01-py3-x86_64-ubuntu24.04-trt10.8.0.43-skip-devel-202503131720-8877"
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DOCKER_IMAGE = "urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.03-py3-x86_64-ubuntu24.04-trt10.9.0.34-skip-devel-202504101610-3421"
|
||||
|
||||
def createKubernetesPodConfig(image)
|
||||
{
|
||||
|
||||
@ -19,9 +19,9 @@ pandas
|
||||
h5py==3.12.1
|
||||
StrEnum
|
||||
sentencepiece>=0.1.99
|
||||
tensorrt~=10.8.0
|
||||
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-01.html#rel-25-01 uses 2.6.0a0.
|
||||
torch>=2.6.0a0,<=2.6.0
|
||||
tensorrt~=10.9.0
|
||||
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-03.html#rel-25-03 uses 2.7.0a0.
|
||||
torch>=2.6.0,<=2.7.0a0
|
||||
torchvision
|
||||
nvidia-modelopt[torch]~=0.27.0
|
||||
nvidia-nccl-cu12
|
||||
|
||||
@ -12,8 +12,7 @@ from tensorrt_llm.models.modeling_utils import QuantConfig
|
||||
|
||||
from ..utils import get_global_attrs, get_model_extra_attrs
|
||||
from .interface import (AttentionBackend, AttentionMask, AttentionMetadata,
|
||||
PredefinedAttentionMask)
|
||||
from .vanilla import VanillaAttention
|
||||
PredefinedAttentionMask, dummy_forward)
|
||||
|
||||
try:
|
||||
check_cuda_arch()
|
||||
@ -418,124 +417,6 @@ class FlashInferAttention(AttentionBackend[FlashInferAttentionMetadata]):
|
||||
if quant_mode.has_fp8_kv_cache():
|
||||
self.has_fp8_kv_cache = True
|
||||
|
||||
@torch.library.custom_op("trtllm::flashinfer_forward", mutates_args=())
|
||||
@staticmethod
|
||||
def forward_pattern(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
num_heads: int,
|
||||
head_dim: int,
|
||||
num_kv_heads: int,
|
||||
layer_idx: int,
|
||||
has_fp8_kv_cache: bool,
|
||||
attention_mask_type: int,
|
||||
attention_mask_data: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
'''
|
||||
Wrapping the flashinfer forward as a custom op is required to fix `torch.compile` graph breaks,
|
||||
otherwise it will graph break when calling `metadata.num_contexts` since it convert tensor's sum directly to int.
|
||||
'''
|
||||
# torch.compile does not support custom object as arguments, so we have to use global function to get the metadata.
|
||||
extra_attrs = get_model_extra_attrs()
|
||||
if extra_attrs is not None:
|
||||
metadata_ref = extra_attrs.get("attention_metadata", None)
|
||||
metadata = metadata_ref() if metadata_ref is not None else None
|
||||
else:
|
||||
metadata = get_global_attrs().attention_metadata()
|
||||
|
||||
q = q.view(-1, num_heads, head_dim)
|
||||
if k is not None:
|
||||
k = k.view(-1, num_kv_heads, head_dim)
|
||||
if v is not None:
|
||||
v = v.view(-1, num_kv_heads, head_dim)
|
||||
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
if metadata is None or metadata.kv_cache_manager is None:
|
||||
return VanillaAttention.dummy_forward(q, k, v)
|
||||
|
||||
assert isinstance(
|
||||
metadata,
|
||||
FlashInferAttentionMetadata,
|
||||
)
|
||||
|
||||
kv_cache = metadata.kv_cache_manager.get_buffers(layer_idx)
|
||||
|
||||
if k is not None and v is not None:
|
||||
if has_fp8_kv_cache:
|
||||
assert kv_cache.dtype == torch.float8_e4m3fn, f"KV cache should have fp8 dtype, but get {kv_cache.dtype}"
|
||||
k = k.to(torch.float8_e4m3fn)
|
||||
v = v.to(torch.float8_e4m3fn)
|
||||
assert k.dtype == v.dtype == kv_cache.dtype, f"KV cache dtype {kv_cache.dtype} does not match k/v dtype {k.dtype}/{v.dtype}"
|
||||
|
||||
flashinfer.page.append_paged_kv_cache(
|
||||
append_key=k,
|
||||
append_value=v,
|
||||
batch_indices=metadata.batch_indices,
|
||||
positions=metadata.positions,
|
||||
paged_kv_cache=kv_cache,
|
||||
kv_indices=metadata.paged_kv_indices,
|
||||
kv_indptr=metadata.paged_kv_indptr,
|
||||
kv_last_page_len=metadata.paged_kv_last_page_len,
|
||||
kv_layout=metadata.kv_layout)
|
||||
|
||||
num_contexts = metadata.num_contexts
|
||||
num_generations = metadata.num_generations
|
||||
num_ctx_tokens = metadata.num_ctx_tokens
|
||||
|
||||
def prefill_forward(plan_params: PlanParams):
|
||||
wrapper = metadata.get_prefill_wrapper(plan_params)
|
||||
output = wrapper.run(q[:num_ctx_tokens], kv_cache)
|
||||
output = output.view(num_ctx_tokens, -1)
|
||||
return output
|
||||
|
||||
def decode_forward(plan_params: PlanParams):
|
||||
wrapper = metadata.get_decode_wrapper(plan_params)
|
||||
output = wrapper.run(q[num_ctx_tokens:], kv_cache)
|
||||
output = output.view(num_generations, -1)
|
||||
return output
|
||||
|
||||
# this will do nothing if the last forward pass had the same parameters
|
||||
plan_params = metadata.plan(num_heads,
|
||||
num_kv_heads,
|
||||
head_dim,
|
||||
q_dtype=q.dtype,
|
||||
kv_dtype=kv_cache.dtype,
|
||||
attention_mask_type=attention_mask_type,
|
||||
attention_mask_data=attention_mask_data)
|
||||
|
||||
if num_contexts > 0:
|
||||
ctx_output = prefill_forward(plan_params)
|
||||
|
||||
if num_generations > 0:
|
||||
gen_output = decode_forward(plan_params)
|
||||
|
||||
if num_contexts > 0 and num_generations > 0:
|
||||
output = torch.cat([ctx_output, gen_output], dim=0)
|
||||
elif num_contexts > 0:
|
||||
output = ctx_output
|
||||
elif num_generations > 0:
|
||||
output = gen_output
|
||||
|
||||
return output
|
||||
|
||||
@forward_pattern.register_fake
|
||||
@staticmethod
|
||||
def _(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
num_heads: int,
|
||||
head_dim: int,
|
||||
num_kv_heads: int,
|
||||
layer_idx: int,
|
||||
has_fp8_kv_cache: bool,
|
||||
attention_mask_type: int,
|
||||
attention_mask_data: Optional[torch.Tensor],
|
||||
):
|
||||
return torch.empty_like(q)
|
||||
|
||||
def forward(self,
|
||||
q: torch.Tensor,
|
||||
k: Optional[torch.Tensor],
|
||||
@ -553,7 +434,129 @@ class FlashInferAttention(AttentionBackend[FlashInferAttentionMetadata]):
|
||||
else:
|
||||
raise ValueError("Unexpected attention mask type")
|
||||
|
||||
return FlashInferAttention.forward_pattern(
|
||||
q, k, v, self.num_heads, self.head_dim, self.num_kv_heads,
|
||||
self.layer_idx, self.has_fp8_kv_cache, attention_mask_type,
|
||||
attention_mask_data)
|
||||
return forward_pattern(q, k, v, self.num_heads, self.head_dim,
|
||||
self.num_kv_heads, self.layer_idx,
|
||||
self.has_fp8_kv_cache, attention_mask_type,
|
||||
attention_mask_data)
|
||||
|
||||
|
||||
@torch.library.custom_op("trtllm::flashinfer_forward", mutates_args=())
|
||||
def forward_pattern(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
num_heads: int,
|
||||
head_dim: int,
|
||||
num_kv_heads: int,
|
||||
layer_idx: int,
|
||||
has_fp8_kv_cache: bool,
|
||||
attention_mask_type: int,
|
||||
attention_mask_data: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
'''
|
||||
Wrapping the flashinfer forward as a custom op is required to fix `torch.compile` graph breaks,
|
||||
otherwise it will graph break when calling `metadata.num_contexts` since it convert tensor's sum directly to int.
|
||||
'''
|
||||
# torch.compile does not support custom object as arguments, so we have to use global function to get the metadata.
|
||||
extra_attrs = get_model_extra_attrs()
|
||||
if extra_attrs is not None:
|
||||
metadata_ref = extra_attrs.get("attention_metadata", None)
|
||||
metadata = metadata_ref() if metadata_ref is not None else None
|
||||
else:
|
||||
metadata = get_global_attrs().attention_metadata()
|
||||
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
if metadata is None or metadata.kv_cache_manager is None:
|
||||
q = q.view(-1, num_heads, head_dim)
|
||||
k = k.view(-1, num_kv_heads, head_dim)
|
||||
v = v.view(-1, num_kv_heads, head_dim)
|
||||
return dummy_forward(q, k, v)
|
||||
|
||||
assert isinstance(
|
||||
metadata,
|
||||
FlashInferAttentionMetadata,
|
||||
)
|
||||
|
||||
# Query
|
||||
q = q.view(-1, num_heads, head_dim)
|
||||
|
||||
# Key and Value
|
||||
kv_cache = metadata.kv_cache_manager.get_buffers(layer_idx)
|
||||
|
||||
if k is not None and v is not None:
|
||||
k = k.view(-1, num_kv_heads, head_dim)
|
||||
v = v.view(-1, num_kv_heads, head_dim)
|
||||
|
||||
if has_fp8_kv_cache:
|
||||
assert kv_cache.dtype == torch.float8_e4m3fn, f"KV cache should have fp8 dtype, but get {kv_cache.dtype}"
|
||||
k = k.to(torch.float8_e4m3fn)
|
||||
v = v.to(torch.float8_e4m3fn)
|
||||
assert k.dtype == v.dtype == kv_cache.dtype, f"KV cache dtype {kv_cache.dtype} does not match k/v dtype {k.dtype}/{v.dtype}"
|
||||
|
||||
flashinfer.page.append_paged_kv_cache(
|
||||
append_key=k,
|
||||
append_value=v,
|
||||
batch_indices=metadata.batch_indices,
|
||||
positions=metadata.positions,
|
||||
paged_kv_cache=kv_cache,
|
||||
kv_indices=metadata.paged_kv_indices,
|
||||
kv_indptr=metadata.paged_kv_indptr,
|
||||
kv_last_page_len=metadata.paged_kv_last_page_len,
|
||||
kv_layout=metadata.kv_layout)
|
||||
|
||||
num_contexts = metadata.num_contexts
|
||||
num_generations = metadata.num_generations
|
||||
num_ctx_tokens = metadata.num_ctx_tokens
|
||||
|
||||
def prefill_forward(plan_params: PlanParams):
|
||||
wrapper = metadata.get_prefill_wrapper(plan_params)
|
||||
output = wrapper.run(q[:num_ctx_tokens], kv_cache)
|
||||
output = output.view(num_ctx_tokens, -1)
|
||||
return output
|
||||
|
||||
def decode_forward(plan_params: PlanParams):
|
||||
wrapper = metadata.get_decode_wrapper(plan_params)
|
||||
output = wrapper.run(q[num_ctx_tokens:], kv_cache)
|
||||
output = output.view(num_generations, -1)
|
||||
return output
|
||||
|
||||
# this will do nothing if the last forward pass had the same parameters
|
||||
plan_params = metadata.plan(num_heads,
|
||||
num_kv_heads,
|
||||
head_dim,
|
||||
q_dtype=q.dtype,
|
||||
kv_dtype=kv_cache.dtype,
|
||||
attention_mask_type=attention_mask_type,
|
||||
attention_mask_data=attention_mask_data)
|
||||
|
||||
if num_contexts > 0:
|
||||
ctx_output = prefill_forward(plan_params)
|
||||
|
||||
if num_generations > 0:
|
||||
gen_output = decode_forward(plan_params)
|
||||
|
||||
if num_contexts > 0 and num_generations > 0:
|
||||
output = torch.cat([ctx_output, gen_output], dim=0)
|
||||
elif num_contexts > 0:
|
||||
output = ctx_output
|
||||
elif num_generations > 0:
|
||||
output = gen_output
|
||||
|
||||
return output
|
||||
|
||||
|
||||
@forward_pattern.register_fake
|
||||
def _(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
num_heads: int,
|
||||
head_dim: int,
|
||||
num_kv_heads: int,
|
||||
layer_idx: int,
|
||||
has_fp8_kv_cache: bool,
|
||||
attention_mask_type: int,
|
||||
attention_mask_data: Optional[torch.Tensor],
|
||||
):
|
||||
return torch.empty_like(q)
|
||||
|
||||
@ -7,6 +7,7 @@ from typing import (Generic, List, Optional, Protocol, Tuple, Type, TypeVar,
|
||||
Union)
|
||||
|
||||
import torch
|
||||
from transformers.modeling_flash_attention_utils import _flash_attention_forward
|
||||
from typing_extensions import Self
|
||||
|
||||
from tensorrt_llm.functional import (PositionEmbeddingType, RopeEmbeddingUtils,
|
||||
@ -530,3 +531,36 @@ class MLAParams:
|
||||
qk_nope_head_dim: int = 0
|
||||
v_head_dim: int = 0
|
||||
predicted_tokens_per_seq: int = 1
|
||||
|
||||
|
||||
@torch.library.custom_op("trtllm::attn_dummy_fwd", mutates_args=())
|
||||
def dummy_forward(q: torch.Tensor, k: torch.Tensor,
|
||||
v: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Dummy attention forward function to estimate memory usage.
|
||||
Args:
|
||||
q (torch.Tensor): Query tensor with shape (num_q_tokens, num_heads, head_dim),.
|
||||
k (torch.Tensor): Key tensor with shape (num_new_kv_tokens, num_kv_heads, head_dim)
|
||||
v (torch.Tensor): Value tensor with shape (num_new_kv_tokens, num_kv_heads, head_dim)
|
||||
Returns:
|
||||
torch.Tensor with shape (num_q_tokens, num_heads * head_dim)
|
||||
"""
|
||||
head_dim = q.shape[2]
|
||||
assert q.dim() == 3
|
||||
assert k.dim() == 3 and k.size(2) == head_dim
|
||||
assert v.dim() == 3 and v.size(2) == head_dim
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
o = _flash_attention_forward(q.unsqueeze(0),
|
||||
k.unsqueeze(0),
|
||||
v.unsqueeze(0),
|
||||
attention_mask=None,
|
||||
query_length=q.size(0),
|
||||
is_causal=True)
|
||||
return o.reshape(o.size(1), -1)
|
||||
|
||||
|
||||
@dummy_forward.register_fake
|
||||
def _(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
|
||||
num_q_tokens = q.size(0)
|
||||
return torch.empty_like(q).reshape(num_q_tokens, -1)
|
||||
|
||||
@ -10,8 +10,8 @@ from tensorrt_llm.models.modeling_utils import QuantConfig
|
||||
|
||||
from ..distributed import allgather
|
||||
from .flashinfer import FlashInferAttentionMetadata, PlanParams
|
||||
from .interface import AttentionBackend, AttentionMask, PredefinedAttentionMask
|
||||
from .vanilla import VanillaAttention
|
||||
from .interface import (AttentionBackend, AttentionMask,
|
||||
PredefinedAttentionMask, dummy_forward)
|
||||
|
||||
|
||||
# Please sync with flashinfer's DISPATCH_GQA_GROUP_SIZE in include/flashinfer/utils.cuh
|
||||
@ -329,7 +329,7 @@ class StarAttention(AttentionBackend[StarAttentionMetadata]):
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
if metadata is None or metadata.kv_cache_manager is None:
|
||||
return VanillaAttention.dummy_forward(q, k, v)
|
||||
return dummy_forward(q, k, v)
|
||||
|
||||
num_contexts = metadata.num_contexts
|
||||
num_queries = metadata.num_queries
|
||||
|
||||
@ -10,8 +10,7 @@ from tensorrt_llm.models.modeling_utils import QuantConfig
|
||||
from .interface import (AttentionBackend, AttentionInputType, AttentionMask,
|
||||
AttentionMetadata, KVCacheParams, MLAParams,
|
||||
PositionalEmbeddingParams, PredefinedAttentionMask,
|
||||
RopeParams)
|
||||
from .vanilla import VanillaAttention
|
||||
RopeParams, dummy_forward)
|
||||
|
||||
|
||||
@dataclass(kw_only=True, init=False)
|
||||
@ -661,7 +660,7 @@ class TrtllmAttention(AttentionBackend[TrtllmAttentionMetadata]):
|
||||
self.head_dim,
|
||||
dtype=q.dtype,
|
||||
device=q.device)
|
||||
output = VanillaAttention.dummy_forward(q, k, v)
|
||||
output = dummy_forward(q, k, v)
|
||||
if self.head_dim != self.v_head_dim:
|
||||
output = output[..., :self.num_kv_heads *
|
||||
self.v_head_dim].contiguous()
|
||||
|
||||
@ -2,7 +2,6 @@ from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers.modeling_flash_attention_utils import _flash_attention_forward
|
||||
|
||||
from tensorrt_llm.models.modeling_utils import QuantConfig
|
||||
|
||||
@ -12,7 +11,7 @@ except ImportError:
|
||||
AttentionMaskConverter = None
|
||||
|
||||
from .interface import (AttentionBackend, AttentionMask, AttentionMetadata,
|
||||
PredefinedAttentionMask)
|
||||
PredefinedAttentionMask, dummy_forward)
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
@ -223,38 +222,6 @@ class VanillaAttention(AttentionBackend[VanillaAttentionMetadata]):
|
||||
|
||||
return attn_output_unpad.reshape(attn_output_unpad.size(0), -1)
|
||||
|
||||
@torch.library.custom_op("trtllm::attn_dummy_fwd", mutates_args=())
|
||||
@staticmethod
|
||||
def dummy_forward(q: torch.Tensor, k: torch.Tensor,
|
||||
v: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Dummy attention forward function to estimate memory usage.
|
||||
Args:
|
||||
q (torch.Tensor): Query tensor with shape (num_q_tokens, num_heads, head_dim),.
|
||||
k (torch.Tensor): Key tensor with shape (num_new_kv_tokens, num_kv_heads, head_dim)
|
||||
v (torch.Tensor): Value tensor with shape (num_new_kv_tokens, num_kv_heads, head_dim)
|
||||
Returns:
|
||||
torch.Tensor with shape (num_q_tokens, num_heads * head_dim)
|
||||
"""
|
||||
head_dim = q.shape[2]
|
||||
assert q.dim() == 3
|
||||
assert k.dim() == 3 and k.size(2) == head_dim
|
||||
assert v.dim() == 3 and v.size(2) == head_dim
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
o = _flash_attention_forward(q.unsqueeze(0),
|
||||
k.unsqueeze(0),
|
||||
v.unsqueeze(0),
|
||||
attention_mask=None,
|
||||
query_length=q.size(0),
|
||||
is_causal=True)
|
||||
return o.reshape(o.size(1), -1)
|
||||
|
||||
@dummy_forward.register_fake
|
||||
def _(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
|
||||
num_q_tokens = q.size(0)
|
||||
return torch.empty_like(q).reshape(num_q_tokens, -1)
|
||||
|
||||
def forward(self,
|
||||
q: torch.Tensor,
|
||||
k: Optional[torch.Tensor],
|
||||
@ -267,9 +234,7 @@ class VanillaAttention(AttentionBackend[VanillaAttentionMetadata]):
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
if metadata.is_dummy_attention:
|
||||
return VanillaAttention.dummy_forward(q.unsqueeze(0),
|
||||
k.unsqueeze(0),
|
||||
v.unsqueeze(0))
|
||||
return dummy_forward(q, k, v)
|
||||
elif metadata.kv_cache_manager is None:
|
||||
# NOTE: WAR for no kv cache attn e.g. BERT,
|
||||
# try to separate the kv cache estimation path from no kv cache attn.
|
||||
@ -287,7 +252,7 @@ class VanillaAttention(AttentionBackend[VanillaAttentionMetadata]):
|
||||
# This is only for memory estimation for now.
|
||||
# NOTE: this method is not accurate while it works for most scenario.
|
||||
if metadata is None or metadata.kv_cache_manager is None:
|
||||
return VanillaAttention.dummy_forward(q, k, v)
|
||||
return dummy_forward(q, k, v)
|
||||
|
||||
past_seen_tokens = metadata.kv_cache_params.num_cached_tokens_per_seq
|
||||
cache_indices = [
|
||||
|
||||
@ -40,8 +40,17 @@ def mistral_example_root(llm_venv):
|
||||
if platform.system() != "Windows":
|
||||
# https://github.com/Dao-AILab/flash-attention/issues/345
|
||||
# No wheel for flash-attn on windows and compilation fails locally.
|
||||
llm_venv.run_cmd(
|
||||
['-m', 'pip', 'install', '--upgrade', 'flash-attn==2.4.2'])
|
||||
install_cmd = [
|
||||
"MAX_JOBS=4",
|
||||
"python3",
|
||||
"-m",
|
||||
"pip",
|
||||
"install",
|
||||
"--upgrade",
|
||||
"flash-attn==2.4.2",
|
||||
]
|
||||
|
||||
check_call(" ".join(install_cmd), shell=True, env=llm_venv._new_env)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("run_type", [
|
||||
|
||||
@ -0,0 +1,2 @@
|
||||
perf/test_perf.py::test_perf[llama_v3.1_70b-cppmanager-exe-plugin_ifb-float16-input_output_len:1024,1024-quant:fp8-tp:8-pp:2]
|
||||
perf/test_perf.py::test_perf[mixtral_8x7b_v0.1-cppmanager-exe-plugin_ifb-float16-input_output_len:512,512-quant:fp8-tp:8-pp:2]
|
||||
@ -38,6 +38,10 @@ from utils.util import getSMVersion
|
||||
[torch.float16, torch.float32, torch.bfloat16],
|
||||
)
|
||||
def test_fp8_scaled_mm(output_dtype, m, k_n):
|
||||
# Skip specific problematic case
|
||||
if m == 228 and k_n == (28672, 8192):
|
||||
pytest.skip("Skipping problematic case with m=228, k=28672, n=8192")
|
||||
|
||||
k, n = k_n
|
||||
torch.random.manual_seed(0)
|
||||
shape_x = (m, k)
|
||||
|
||||
@ -282,6 +282,12 @@ class TestMoEWeightOnlyGroupWiseQuantMatmul(unittest.TestCase):
|
||||
name_func=unittest_name_func)
|
||||
@skip_non_ada_unittest
|
||||
def test_moe_w4a8(self, m, n, k, experts, dtype, has_pre_quant, has_zero):
|
||||
# Skip specific problematic case
|
||||
if m == 1 and n == 14336 and k == 4096 and experts == 8 and dtype == "bfloat16" and has_pre_quant and not has_zero:
|
||||
self.skipTest(
|
||||
"Skipping problematic case test_moe_w4a8_1_14336_4096_8_bfloat16_True_False"
|
||||
)
|
||||
|
||||
self._woq_moe_groupwise_matmul(m, n, k, experts, dtype, torch.quint4x2,
|
||||
has_pre_quant, has_zero, True)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user