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bump version to v0.5.0 (#1852)
* bump version to v0.5.0 * update news * update news * update supported models * update * fix lint * set LMDEPLOY_VERSION 0.5.0
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‎README.md‎

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@@ -26,6 +26,7 @@ ______________________________________________________________________
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<details open>
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<summary><b>2024</b></summary>
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- \[2024/06\] PyTorch engine support DeepSeek-V2 and several VLMs, such as CogVLM2, Mini-InternVL, LlaVA-Next
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- \[2024/05\] Balance vision model when deploying VLMs with multiple GPUs
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- \[2024/05\] Support 4-bits weight-only quantization and inference on VMLs, such as InternVL v1.5, LLaVa, InternLMXComposer2
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- \[2024/04\] Support Llama3 and more VLMs, such as InternVL v1.1, v1.2, MiniGemini, InternLMXComposer2.
@@ -112,6 +113,7 @@ For detailed inference benchmarks in more devices and more settings, please refe
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<li>QWen (1.8B - 72B)</li>
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<li>QWen1.5 (0.5B - 110B)</li>
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<li>QWen1.5 - MoE (0.5B - 72B)</li>
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<li>QWen2 (0.5B - 72B)</li>
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<li>Baichuan (7B)</li>
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<li>Baichuan2 (7B-13B)</li>
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<li>Code Llama (7B - 34B)</li>
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<li>YI (6B-34B)</li>
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<li>Mistral (7B)</li>
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<li>DeepSeek-MoE (16B)</li>
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<li>DeepSeek-V2 (16B, 236B)</li>
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<li>Mixtral (8x7B, 8x22B)</li>
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<li>Gemma (2B - 7B)</li>
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<li>Dbrx (132B)</li>
@@ -162,7 +165,7 @@ pip install lmdeploy
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Since v0.3.0, The default prebuilt package is compiled on **CUDA 12**. However, if CUDA 11+ is required, you can install lmdeploy by:
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```shell
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export LMDEPLOY_VERSION=0.3.0
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```

‎README_zh-CN.md‎

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@@ -26,6 +26,7 @@ ______________________________________________________________________
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<details open>
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<summary><b>2024</b></summary>
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- \[2024/06\] PyTorch engine 支持了 DeepSeek-V2 和若干 VLM 模型推理, 比如 CogVLM2，Mini-InternVL，LlaVA-Next
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- \[2024/05\] 在多 GPU 上部署 VLM 模型时，支持把视觉部分的模型均分到多卡上
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- \[2024/05\] 支持InternVL v1.5, LLaVa, InternLMXComposer2 等 VLMs 模型的 4bit 权重量化和推理
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- \[2024/04\] 支持 Llama3 和 InternVL v1.1, v1.2，MiniGemini，InternLM-XComposer2 等 VLM 模型
@@ -113,6 +114,7 @@ LMDeploy TurboMind 引擎拥有卓越的推理能力，在各种规模的模型
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<li>QWen (1.8B - 72B)</li>
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<li>QWen1.5 (0.5B - 110B)</li>
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<li>QWen1.5 - MoE (0.5B - 72B)</li>
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<li>QWen2 (0.5B - 72B)</li>
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<li>Baichuan (7B)</li>
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<li>Baichuan2 (7B-13B)</li>
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<li>Code Llama (7B - 34B)</li>
@@ -122,6 +124,7 @@ LMDeploy TurboMind 引擎拥有卓越的推理能力，在各种规模的模型
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<li>YI (6B-34B)</li>
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<li>Mistral (7B)</li>
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<li>DeepSeek-MoE (16B)</li>
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<li>DeepSeek-V2 (16B, 236B)</li>
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<li>Mixtral (8x7B, 8x22B)</li>
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<li>Gemma (2B - 7B)</li>
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<li>Dbrx (132B)</li>
@@ -163,7 +166,7 @@ pip install lmdeploy
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自 v0.3.0 起，LMDeploy 预编译包默认基于 CUDA 12 编译。如果需要在 CUDA 11+ 下安装 LMDeploy，请执行以下命令：
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```shell
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export LMDEPLOY_VERSION=0.3.0
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```

‎docs/en/get_started.md‎

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@@ -13,7 +13,7 @@ pip install lmdeploy
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The default prebuilt package is compiled on **CUDA 12**. However, if CUDA 11+ is required, you can install lmdeploy by:
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```shell
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export LMDEPLOY_VERSION=0.4.2
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```

‎docs/en/multi_modal/cogvlm.md‎

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@@ -22,7 +22,7 @@ Install LMDeploy with pip (Python 3.8+). Refer to [Installation](https://lmdeplo
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```shell
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# cuda 11.8
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# to get the latest version, run: pip index versions lmdeploy
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export LMDEPLOY_VERSION=0.4.2
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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# cuda 12.1

‎docs/en/supported_models/supported_models.md‎

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| InternLM-XComposer | 7B | Yes | Yes | Yes | Yes |
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| InternLM-XComposer2 | 7B, 4khd-7B | Yes | Yes | Yes | Yes |
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| QWen | 1.8B - 72B | Yes | Yes | Yes | Yes |
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| QWen1.5 | 1.8B - 72B | Yes | Yes | Yes | Yes |
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| QWen1.5 | 1.8B - 110B | Yes | Yes | Yes | Yes |
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| QWen2 | 1.5B - 72B | Yes | Yes | Yes | Yes |
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| Mistral | 7B | Yes | Yes | Yes | No |
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| QWen-VL | 7B | Yes | Yes | Yes | Yes |
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| DeepSeek-VL | 7B | Yes | Yes | Yes | Yes |
@@ -35,29 +36,31 @@ The TurboMind engine doesn't support window attention. Therefore, for models tha
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## Models supported by PyTorch
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| Model | Size | FP16/BF16 | KV INT8 | W8A8 |
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| :-----------------: | :--------: | :-------: | :-----: | :--: |
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| Llama | 7B - 65B | Yes | No | Yes |
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| Llama2 | 7B - 70B | Yes | No | Yes |
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| Llama3 | 8B, 70B | Yes | No | Yes |
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| InternLM | 7B - 20B | Yes | No | Yes |
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| InternLM2 | 7B - 20B | Yes | No | - |
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| InternLM2.5 | 7B | Yes | No | - |
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| Baichuan2 | 7B - 13B | Yes | No | Yes |
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| ChatGLM2 | 6B | Yes | No | No |
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| Falcon | 7B - 180B | Yes | No | No |
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| YI | 6B - 34B | Yes | No | No |
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| Mistral | 7B | Yes | No | No |
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| Mixtral | 8x7B | Yes | No | No |
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| QWen | 1.8B - 72B | Yes | No | No |
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| QWen1.5 | 0.5B - 72B | Yes | No | No |
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| QWen1.5-MoE | A2.7B | Yes | No | No |
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| DeepSeek-MoE | 16B | Yes | No | No |
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| Gemma | 2B-7B | Yes | No | No |
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| Dbrx | 132B | Yes | No | No |
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| StarCoder2 | 3B-15B | Yes | No | No |
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| Phi-3-mini | 3.8B | Yes | No | No |
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| CogVLM-Chat | 17B | Yes | No | No |
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| CogVLM2-Chat | 19B | Yes | No | No |
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| LLaVA(1.5,1.6) | 7B-34B | Yes | No | No |
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| InternVL-Chat(v1.5) | 2B-26B | Yes | No | No |
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| Model | Size | FP16/BF16 | KV INT8 | W8A8 |
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| :-----------------: | :---------: | :-------: | :-----: | :--: |
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| Llama | 7B - 65B | Yes | No | Yes |
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| Llama2 | 7B - 70B | Yes | No | Yes |
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| Llama3 | 8B, 70B | Yes | No | Yes |
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| InternLM | 7B - 20B | Yes | No | Yes |
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| InternLM2 | 7B - 20B | Yes | No | - |
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| InternLM2.5 | 7B | Yes | No | - |
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| Baichuan2 | 7B - 13B | Yes | No | Yes |
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| ChatGLM2 | 6B | Yes | No | No |
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| Falcon | 7B - 180B | Yes | No | No |
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| YI | 6B - 34B | Yes | No | No |
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| Mistral | 7B | Yes | No | No |
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| Mixtral | 8x7B | Yes | No | No |
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| QWen | 1.8B - 72B | Yes | No | No |
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| QWen1.5 | 0.5B - 110B | Yes | No | No |
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| QWen1.5-MoE | A2.7B | Yes | No | No |
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| QWen2 | 0.5B - 72B | Yes | No | No |
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| DeepSeek-MoE | 16B | Yes | No | No |
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| DeepSeek-V2 | 16B, 236B | Yes | No | No |
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| Gemma | 2B-7B | Yes | No | No |
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| Dbrx | 132B | Yes | No | No |
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| StarCoder2 | 3B-15B | Yes | No | No |
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| Phi-3-mini | 3.8B | Yes | No | No |
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| CogVLM-Chat | 17B | Yes | No | No |
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| CogVLM2-Chat | 19B | Yes | No | No |
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| LLaVA(1.5,1.6) | 7B-34B | Yes | No | No |
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| InternVL-Chat(v1.5) | 2B-26B | Yes | No | No |

‎docs/zh_cn/get_started.md‎

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@@ -13,7 +13,7 @@ pip install lmdeploy
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LMDeploy的预编译包默认是基于 CUDA 12 编译的。如果需要在 CUDA 11+ 下安装 LMDeploy，请执行以下命令：
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```shell
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export LMDEPLOY_VERSION=0.4.2
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```

‎docs/zh_cn/multi_modal/cogvlm.md‎

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```shell
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# cuda 11.8
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export LMDEPLOY_VERSION=0.4.2
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export LMDEPLOY_VERSION=0.5.0
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export PYTHON_VERSION=38
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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# cuda 12.1

‎docs/zh_cn/supported_models/supported_models.md‎

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| InternLM-XComposer | 7B | Yes | Yes | Yes | Yes |
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| InternLM-XComposer2 | 7B, 4khd-7B | Yes | Yes | Yes | Yes |
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| QWen | 1.8B - 72B | Yes | Yes | Yes | Yes |
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| QWen1.5 | 1.8B - 72B | Yes | Yes | Yes | Yes |
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| QWen1.5 | 1.8B - 110B | Yes | Yes | Yes | Yes |
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| QWen2 | 1.5B - 72B | Yes | Yes | Yes | Yes |
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| Mistral | 7B | Yes | Yes | Yes | No |
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| QWen-VL | 7B | Yes | Yes | Yes | Yes |
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| DeepSeek-VL | 7B | Yes | Yes | Yes | Yes |
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### PyTorch 支持的模型
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| 模型 | 模型规模 | FP16/BF16 | KV INT8 | W8A8 |
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| :-----------------: | :--------: | :-------: | :-----: | :--: |
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| Llama | 7B - 65B | Yes | No | Yes |
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| Llama2 | 7B - 70B | Yes | No | Yes |
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| Llama3 | 8B, 70B | Yes | No | Yes |
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| InternLM | 7B - 20B | Yes | No | Yes |
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| InternLM2 | 7B - 20B | Yes | No | - |
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| InternLM2.5 | 7B | Yes | No | - |
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| Baichuan2 | 7B - 13B | Yes | No | Yes |
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| ChatGLM2 | 6B | Yes | No | No |
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| Falcon | 7B - 180B | Yes | No | No |
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| YI | 6B - 34B | Yes | No | No |
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| Mistral | 7B | Yes | No | No |
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| Mixtral | 8x7B | Yes | No | No |
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| QWen | 1.8B - 72B | Yes | No | No |
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| QWen1.5 | 0.5B - 72B | Yes | No | No |
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| QWen1.5-MoE | A2.7B | Yes | No | No |
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| DeepSeek-MoE | 16B | Yes | No | No |
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| Gemma | 2B-7B | Yes | No | No |
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| Dbrx | 132B | Yes | No | No |
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| StarCoder2 | 3B-15B | Yes | No | No |
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| Phi-3-mini | 3.8B | Yes | No | No |
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| CogVLM-Chat | 17B | Yes | No | No |
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| CogVLM2-Chat | 19B | Yes | No | No |
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| LLaVA(1.5,1.6) | 7B-34B | Yes | No | No |
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| InternVL-Chat(v1.5) | 2B-26B | Yes | No | No |
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| 模型 | 模型规模 | FP16/BF16 | KV INT8 | W8A8 |
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| :-----------------: | :---------: | :-------: | :-----: | :--: |
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| Llama | 7B - 65B | Yes | No | Yes |
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| Llama2 | 7B - 70B | Yes | No | Yes |
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| Llama3 | 8B, 70B | Yes | No | Yes |
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| InternLM | 7B - 20B | Yes | No | Yes |
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| InternLM2 | 7B - 20B | Yes | No | - |
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| InternLM2.5 | 7B | Yes | No | - |
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| Baichuan2 | 7B - 13B | Yes | No | Yes |
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| ChatGLM2 | 6B | Yes | No | No |
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| Falcon | 7B - 180B | Yes | No | No |
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| YI | 6B - 34B | Yes | No | No |
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| Mistral | 7B | Yes | No | No |
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| Mixtral | 8x7B | Yes | No | No |
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| QWen | 1.8B - 72B | Yes | No | No |
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| QWen1.5 | 0.5B - 110B | Yes | No | No |
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| QWen2 | 0.5B - 72B | Yes | No | No |
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| QWen1.5-MoE | A2.7B | Yes | No | No |
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| DeepSeek-MoE | 16B | Yes | No | No |
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| DeepSeek-V2 | 16B, 236B | Yes | No | No |
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| Gemma | 2B-7B | Yes | No | No |
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| Dbrx | 132B | Yes | No | No |
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| StarCoder2 | 3B-15B | Yes | No | No |
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| Phi-3-mini | 3.8B | Yes | No | No |
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| CogVLM-Chat | 17B | Yes | No | No |
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| CogVLM2-Chat | 19B | Yes | No | No |
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| LLaVA(1.5,1.6) | 7B-34B | Yes | No | No |
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| InternVL-Chat(v1.5) | 2B-26B | Yes | No | No |

‎lmdeploy/cli/utils.py‎

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'--cache-max-entry-count',
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type=float,
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default=0.8,
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help=
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'The percentage of free gpu memory occupied by the k/v cache, excluding weights'
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)
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help='The percentage of free gpu memory occupied by the k/v '
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'cache, excluding weights ')
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@staticmethod
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def adapters(parser):

‎lmdeploy/version.py‎

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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Tuple
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__version__ = '0.4.2'
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__version__ = '0.5.0'
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short_version = __version__
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