Optimize DeepSeekOCR2 RelPosAttention and CustomQwen2Decoder and add doc for DeepSeekOCR2.md

docs/source/tutorials/models/DeepSeekOCR2.md

@@ -0,0 +1,171 @@
+# DeepSeek-OCR-2
+
+## Introduction
+
+DeepSeekOCR2 is a model to investigate the role of vision encoders from an LLM-centric viewpoint.
+
+The `DeepSeek-OCR-2` model is first supported in `vllm-ascend:v0.16.0`.
+
+This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node deployment, accuracy and performance evaluation.
+
+## Supported Features
+
+Refer to [supported features](../../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
+
+Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the feature's configuration.
+
+## Environment Preparation
+
+### Model Weight
+
+- `DeepSeek-OCR-2`: [Download model weight](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2).
+
+It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`.
+
+### Verify Multi-node Communication(Optional)
+
+If you want to deploy multi-node environment, you need to verify multi-node communication according to [verify multi-node communication environment](../../installation.md#verify-multi-node-communication).
+
+### Installation
+
+You can use our official docker image to run `DeepSeek-OCR-2` directly.
+
+Select an image based on your machine type and start the docker image on your node, refer to [using docker](../../installation.md#set-up-using-docker).
+
+```{code-block} bash
+   :substitutions:
+# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
+# Update the vllm-ascend image according to your environment.
+# Note you should download the weight to /root/.cache in advance.
+# Update the vllm-ascend image
+export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+export NAME=vllm-ascend
+
+# Run the container using the defined variables
+# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance.
+docker run --rm \
+--name $NAME \
+--net=host \
+--shm-size=1g \
+--device /dev/davinci0 \
+--device /dev/davinci1 \
+--device /dev/davinci2 \
+--device /dev/davinci3 \
+--device /dev/davinci4 \
+--device /dev/davinci5 \
+--device /dev/davinci6 \
+--device /dev/davinci7 \
+--device /dev/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
+-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /root/.cache:/root/.cache \
+-it $IMAGE bash
+```
+
+If you want to deploy multi-node environment, you need to set up environment on each node.
+
+## Deployment
+
+### Single-node Deployment
+
+- `DeepSeek-OCR-2` can be deployed on 1 Atlas 800 A2.
+
+Run the following script to execute online inference.
+
+```shell
+#!/bin/sh
+
+export VLLM_USE_V1=1
+export VLLM_ASCEND_ENABLE_NZ=0
+export TOKENIZERS_PARALLELISM=false
+export PYTORCH_NPU_ALLOC_CONF="expandable_segments:True"
+export TASK_QUEUE_ENABLE=1
+export TOKENIZERS_PARALLELISM=false
+
+vllm serve /weights/DeepSeek-OCR-2 \
+    --served-model-name deepseekocr2 \
+    --trust-remote-code \
+    -tp 1  \
+    --port 1055 \
+    --max_model_len 8192 \
+    --no-enable-prefix-caching \
+    --gpu-memory-utilization 0.8 \
+    --allowed-local-media-path / \
+    --async-scheduling \
+    --additional-config '{
+      "enable_cpu_binding": true,
+      "multistream_overlap_shared_expert": true,
+      "ascend_compilation_config": {"fuse_qknorm_rope": false}
+    }' \
+    --mm-processor-cache-gb 0
+```
+
+### Multi-node Deployment
+
+Single-node deployment is recommended.
+
+### Prefill-Decode Disaggregation
+
+We don't neel to Prefill-Decode disaggregation
+
+## Functional Verification
+
+If your service start successfully, you can see the info shown below:
+
+```bash
+INFO:     Started server process [87471]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+```
+
+Once your server is started, you can query the model with input prompts:
+
+```shell
+curl http://<node0_ip>:<port>/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "deepseekocr2",
+        "prompt": "The future of AI is",
+        "max_completion_tokens": 50,
+        "temperature": 0
+    }'
+```
+
+## Accuracy Evaluation
+
+Here are two accuracy evaluation methods.
+
+### Using AISBench
+
+1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details.
+
+2. After execution, you can get the result, here is the result of `DeepSeek-OCR-2` for reference only.
+
+| dataset | version | metric | mode | vllm-api-general-chat | note |
+|----- | ----- | ----- | ----- | -----| ----- |
+| textvqa | - | accuracy | gen | 50.28 | 1 Atlas 800 A2 |
+| ominidocbench | - | accuracy | gen | 66.86 | 1 Atlas 800 A2 |
+
+### Using Language Model Evaluation Harness
+
+Not test yet.
+
+## Performance
+
+### Using AISBench
+
+Refer to [Using AISBench for performance evaluation](../../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
+
+The performance result is:  
+
+**Hardware**: A2-313T, 1 node
+
+**Input/Output**: 1080P/256
+
+**Performance**: TTFT = 2s, TPOT = 200ms, Average performance of each card is 864 TPS (Token Per Second).

docs/source/tutorials/models/index.md

@@ -27,6 +27,7 @@ Qwen3.5-397B-A17B.md
 DeepSeek-V3.1.md
 DeepSeek-V3.2.md
 DeepSeek-R1.md
+DeepSeekOCR2.md
 GLM4.x.md
 GLM5.md
 Kimi-K2-Thinking.md

docs/source/user_guide/support_matrix/supported_models.md

@@ -27,6 +27,7 @@ Get the latest info here: <https://github.com/vllm-project/vllm-ascend/issues/16
 | Qwen2.5                       | ✅        |                                                                      | ✅ | A2/A3 | ✅ | ✅ | ✅ |||| ✅ ||| ✅ |||||| [Qwen2.5-7B](../../tutorials/models/Qwen2.5-7B.md) |
 | GLM-4.x                       | 🔵        |                                                                      || A2/A3 |✅|✅|✅||✅|✅|✅||✅|✅|✅|✅|✅|198k||[GLM-4.x](../../tutorials/models/GLM4.x.md)|
 | Kimi-K2-Thinking              | 🔵        |                                                                      || A2/A3 |||||||||||||||| [Kimi-K2-Thinking](../../tutorials/models/Kimi-K2-Thinking.md) |
+| DeepseekOCR2                  | ✅        |                                                                      | ✅ | A2/A3 ||✅||||✅|||||||||| [DeepSeekOCR2](../../tutorials/models/DeepSeekOCR2.md) |
 
 #### Extended Compatible Models