Update documentation

Dockerfile.hpu

@@ -1,4 +1,4 @@
-FROM vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+FROM vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 
 COPY ./ /workspace/vllm
 

README_GAUDI.md

@@ -11,7 +11,7 @@ Please follow the instructions provided in the [Gaudi Installation Guide](https:
 - OS: Ubuntu 22.04 LTS
 - Python: 3.10
 - Intel Gaudi accelerator
-- Intel Gaudi software version 1.18.0
+- Intel Gaudi software version 1.19.0
 
 ## Quick start using Dockerfile
 ```
@@ -44,8 +44,8 @@ It is highly recommended to use the latest Docker image from Intel Gaudi vault.
 Use the following commands to run a Docker image:
 
 ```{.console}
-$ docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
-$ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+$ docker pull vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+$ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 ```
 
 ### Build and Install vLLM-fork
@@ -55,7 +55,7 @@ Currently, the latest features and performance optimizations are developed in Ga
 ```{.console}
 $ git clone https://github.com/HabanaAI/vllm-fork.git
 $ cd vllm-fork
-$ git checkout habana_main
+$ git checkout v.1.19.0
 $ pip install -r requirements-hpu.txt
 $ python setup.py develop
 ```
@@ -71,11 +71,11 @@ $ python setup.py develop
 - Inference with [HPU Graphs](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html) for accelerating low-batch latency and throughput
 - Attention with Linear Biases (ALiBi)
 - INC quantization
+- LoRA adapters
 
 # Unsupported Features
 
 - Beam search
-- LoRA adapters
 - AWQ quantization
 - Prefill chunking (mixed-batch inferencing)
 
@@ -112,7 +112,7 @@ Currently in vLLM for HPU we support four execution modes, depending on selected
 | 1                  | 1               | PyTorch lazy mode  |
 
 > [!WARNING] 
-> In 1.18.0, all modes utilizing `PT_HPU_LAZY_MODE=0` are highly experimental and should be only used for validating functional correctness. Their performance will be improved in the next releases. For obtaining the best performance in 1.18.0, please use HPU Graphs, or PyTorch lazy mode.
+> In 1.19.0, all modes utilizing `PT_HPU_LAZY_MODE=0` are highly experimental and should be only used for validating functional correctness. Their performance will be improved in the next releases. For obtaining the best performance in 1.19.0, please use HPU Graphs, or PyTorch lazy mode.
 
 ## Bucketing mechanism
 

docs/source/getting_started/gaudi-installation.rst

@@ -18,7 +18,7 @@ Requirements
 -  OS: Ubuntu 22.04 LTS
 -  Python: 3.10
 -  Intel Gaudi accelerator
--  Intel Gaudi software version 1.18.0
+-  Intel Gaudi software version 1.19.0
 
 
 Quick start using Dockerfile
@@ -63,8 +63,8 @@ Use the following commands to run a Docker image:
 
 .. code:: console
 
-   $ docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
-   $ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+   $ docker pull vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+   $ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 
 Build and Install vLLM
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -85,7 +85,7 @@ Currently, the latest features and performance optimizations are developed in Ga
 
    $ git clone https://github.com/HabanaAI/vllm-fork.git
    $ cd vllm-fork
-   $ git checkout habana_main
+   $ git checkout v1.19.0
    $ pip install -r requirements-hpu.txt
    $ python setup.py develop
 
@@ -107,12 +107,12 @@ Supported Features
    for accelerating low-batch latency and throughput
 -  Attention with Linear Biases (ALiBi)
 -  INC quantization
+-  LoRA adapters
 
 Unsupported Features
 ====================
 
 -  Beam search
--  LoRA adapters
 -  AWQ quantization
 -  Prefill chunking (mixed-batch inferencing)
 
@@ -186,7 +186,7 @@ Currently in vLLM for HPU we support four execution modes, depending on selected
      - PyTorch lazy mode
 
 .. warning::
-   In 1.18.0, all modes utilizing ``PT_HPU_LAZY_MODE=0`` are highly experimental and should be only used for validating functional correctness. Their performance will be improved in the next releases. For obtaining the best performance in 1.18.0, please use HPU Graphs, or PyTorch lazy mode.
+   In 1.19.0, all modes utilizing ``PT_HPU_LAZY_MODE=0`` are highly experimental and should be only used for validating functional correctness. Their performance will be improved in the next releases. For obtaining the best performance in 1.19.0, please use HPU Graphs, or PyTorch lazy mode.
 
 
 Bucketing mechanism

docs/source/serving/compatibility_matrix.rst

@@ -305,6 +305,7 @@ Feature x Hardware
      - Hopper
      - CPU
      - AMD
+     - Gaudi
    * - :ref:`CP <chunked-prefill>`
      - `✗ <https://github.com/vllm-project/vllm/issues/2729>`__ 
      - ✅
@@ -313,6 +314,7 @@ Feature x Hardware
      - ✅
      - ✗ 
      - ✅
+     - ✗
    * - :ref:`APC <apc>`
      - `✗ <https://github.com/vllm-project/vllm/issues/3687>`__ 
      - ✅
@@ -321,6 +323,7 @@ Feature x Hardware
      - ✅
      - ✗
      - ✅
+     - ✅
    * - :ref:`LoRA <lora>`
      - ✅
      - ✅
@@ -329,6 +332,7 @@ Feature x Hardware
      - ✅
      - `✗ <https://github.com/vllm-project/vllm/pull/4830>`__ 
      - ✅
+     - ✅
    * - :abbr:`prmpt adptr (Prompt Adapter)`
      - ✅
      - ✅
@@ -337,6 +341,7 @@ Feature x Hardware
      - ✅
      - `✗ <https://github.com/vllm-project/vllm/issues/8475>`__ 
      - ✅
+     - ✗
    * - :ref:`SD <spec_decode>`
      - ✅
      - ✅
@@ -345,6 +350,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✗
    * - CUDA graph
      - ✅
      - ✅
@@ -353,6 +359,7 @@ Feature x Hardware
      - ✅
      - ✗
      - ✅
+     - ✗
    * - :abbr:`enc-dec (Encoder-Decoder Models)`
      - ✅
      - ✅
@@ -361,6 +368,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✗
+     - ✅
    * - :abbr:`logP (Logprobs)`
      - ✅
      - ✅
@@ -369,6 +377,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✅
    * - :abbr:`prmpt logP (Prompt Logprobs)`
      - ✅
      - ✅
@@ -377,6 +386,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✅
    * - :abbr:`async output (Async Output Processing)`
      - ✅
      - ✅
@@ -385,6 +395,7 @@ Feature x Hardware
      - ✅
      - ✗
      - ✗
+     - ✅
    * - multi-step
      - ✅
      - ✅
@@ -393,6 +404,7 @@ Feature x Hardware
      - ✅
      - `✗ <https://github.com/vllm-project/vllm/issues/8477>`__ 
      - ✅
+     - ✅
    * - :abbr:`MM (Multimodal)`
      - ✅
      - ✅
@@ -401,6 +413,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✅
    * - best-of
      - ✅
      - ✅
@@ -409,6 +422,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✅
    * - beam-search
      - ✅
      - ✅
@@ -417,6 +431,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✗
    * - :abbr:`guided dec (Guided Decoding)`
      - ✅
      - ✅
@@ -425,3 +440,4 @@ Feature x Hardware
      - ✅
      - ✅
      - ✅
+     - ✅