diff --git a/.cspell/compound.txt b/.cspell/compound.txt index efddde2..599fc9f 100644 --- a/.cspell/compound.txt +++ b/.cspell/compound.txt @@ -4,3 +4,4 @@ knative kserve xinference servicemeshv1 +ipynb diff --git a/.gitignore b/.gitignore index 9ed7ef1..fe07d48 100644 --- a/.gitignore +++ b/.gitignore @@ -9,4 +9,7 @@ **/public/_remotes .idea -.DS_Store \ No newline at end of file +.DS_Store + +.claude +CLAUDE.md diff --git a/docs/en/llm-compressor/how_to/compressor_by_workbench.mdx b/docs/en/llm-compressor/how_to/compressor_by_workbench.mdx new file mode 100644 index 0000000..72eb79f --- /dev/null +++ b/docs/en/llm-compressor/how_to/compressor_by_workbench.mdx @@ -0,0 +1,199 @@ +--- +weight: 30 +--- + +# LLM Compressor with Alauda AI + +This document describes how to use the LLM Compressor integration with the Alauda AI platform to perform model compression workflows. The Alauda AI integration of LLM Compressor provides two example workflows: + +- A workbench image and the [data-free-compressor.ipynb](/data-free-compressor.ipynb) that demonstrate how to compress a model. +- A workbench image and the [calibration-compressor.ipynb](/calibration-compressor.ipynb) that demonstrate how to compress a model using a calibration dataset. + +notebook + +## Supported Model Compression Workflows + +On the Alauda AI platform, you can use the Workbench feature to run LLM Compressor on models stored in your model repository. The following workflow outlines the typical steps for compressing a model. + +### Create a Workbench + +Follow the instructions in [Create Workbench](../../workbench/how_to/create_workbench.mdx) to create a new Workbench instance. When creating the Workbench, make sure to select the image `odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-ubi9`, which includes the required LLM Compressor environment. Model compression is currently supported only within **JupyterLab**. + +If this image is not available in your environment, refer to the linked article for instructions on how to synchronize the image and patch the WorkspaceKind resource so that the image becomes available in the Workbench creation options. + +### Create a Model Repository and Upload Models + +Refer to [Upload Models Using Notebook](../../model_inference/model_management/how_to/upload_models_using_notebook.mdx) for detailed steps on creating a model repository and uploading your model files. The example notebooks in this guide use the [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model. + +```python title=data-free-compressor.ipynb +from llmcompressor.modifiers.quantization import QuantizationModifier + +model_id = "./TinyLlama-1.1B-Chat-v1.0" #[!code callout] +recipe = QuantizationModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"]) #[!code callout] +``` + + + 1. Model to compress. **You can modify this line if you want to use your own model**. + 2. This recipe will quantize all Linear layers except those in the `lm_head`, + which is often sensitive to quantization. The `W4A16` scheme compresses + weights to `4-bit` integers while retaining `16-bit` activations. + + + +### (Optional) Prepare and Upload a Dataset + +:::note +If you plan to use the **data-free compressor notebook**, you can skip this step. +::: + +To use the **calibration compressor notebook**, you must prepare and upload a calibration dataset. Prepare your dataset using the same process described in *Upload Models Using Notebook*. The example calibration notebook uses the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset. + +```python title=calibration-compressor.ipynb +from datasets import load_dataset + +dataset_id = "./ultrachat_200k" #[!code callout] + +num_calibration_samples = 512 if use_gpu else 4 #[!code callout] +max_sequence_length = 2048 if use_gpu else 16 + +ds = load_dataset(dataset_id, split="train_sft") #[!code callout] +ds = ds.shuffle(seed=42).select(range(num_calibration_samples)) #[!code callout] + +def preprocess(example): #[!code callout] + text = tokenizer.apply_chat_template( + example["messages"], + tokenize=False, + ) + return tokenizer( + text, + padding=False, + max_length=max_sequence_length, + truncation=True, + add_special_tokens=False, + ) + +ds = ds.map(preprocess, remove_columns=ds.column_names) +``` + + + 1. Create the calibration dataset, using Huggingface datasets API. **You can modify this line if you want to use your own dataset**. + 2. Select number of samples. 512 samples is a good place to start. Increasing the number of samples can improve accuracy. + 3. Load dataset. + 4. Shuffle and grab only the number of samples we need. + 5. Preprocess and tokenize into format the model uses. + + +### (Optional) Upload Dataset into S3 Storage + +If you wish to upload datasets into S3, you can run those codes in `JupyterLab`. + +```python +import os +from boto3.s3.transfer import TransferConfig +import boto3 + +local_folder = "./ultrachat_200k" #[!code callout] +bucket_name = "datasets" + +config = TransferConfig( + multipart_threshold=100*1024*1024, + max_concurrency=10, + multipart_chunksize=100*1024*1024, + use_threads=True +) #[!code callout] + +for root, dirs, files in os.walk(local_folder): + for filename in files: + local_path = os path.join(root, filename) + relative_path = os.path.relpath(local_path, local_folder) + s3_key = f"ultrachat_200k/{relative_path.replace(os.sep, '/')}" + s3.upload_file(local_path, bucket_name, s3_key, Config=config) + print(f"Uploaded {local_path} -> {s3_key}") +``` + + + 1. **You can modify this line if you want to use your own dataset**. + 2. Configure multipart upload with 100 MB chunks and a maximum of 10 concurrent threads. + + +### (Optional) Use Dataset in S3 Storage + +If you wish to use datasets from S3, you can first install the `s3fs` tool and then modify the dataset loading section in the example by following the code below. + +```bash +pip install s3fs -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +```python title=calibration-compressor.ipynb +import os +from datasets import load_dataset + +os.environ["AWS_ACCESS_KEY_ID"] = "@7Apples@" #[!code callout] +os.environ["AWS_SECRET_ACCESS_KEY"] = "07Apples@" + +storage_options = { + "key": "07Apples@", + "secret": "O7Apples@", + "client_kwargs": { + "endpoint_url": "http://minio.minio-system.svc.cluster.local:80" #[!code callout] + } +} + +ds = load_dataset( + 'parquet', + data_files='s3://datasets/ultrachat_200k/data/train_sft-*.parquet', #[!code callout] + storage_options=storage_options, #[!code callout] + split="train" +) +``` + + + 1. Set environment variables (as a backup, some underlying components will use them). + 2. Define storage configuration; you must explicitly specify the endpoint_url to connect to MinIO. + 3. If the dataset is split, this is equivalent to `split="train_sft"` in the example. + + +### Clone Models and Datasets in JupyterLab + +In the JupyterLab terminal, use `git clone` to download the model repository (and dataset, if applicable) to your workspace. The data-free compressor notebook does not require a dataset. + +:::info +We recommend using the Hugging Face (hf) command-line tool to download models and datasets directly when network access allows it. This approach is usually faster and simpler than cloning repositories manually. +::: + +If you are in a restricted network environment, you can use the Hugging Face mirror for accelerated access: +```bash +export HF_ENDPOINT=https://hf-mirror.com +``` +You can use the `hf` command-line tool to download models and datasets directly. For example, to download the TinyLlama model: +```bash +hf download TinyLlama/TinyLlama-1.1B-Chat-v1.0 --local-dir TinyLlama-1.1B-Chat-v1.0 +``` +For calibration datasets, download them similarly: +```bash +hf download --repo-type dataset HuggingFaceH4/ultrachat_200k --local-dir ultrachat_200k +``` + +### Create and Run Compression Notebooks + +Download the appropriate example notebook for your use case: the **calibration compressor notebook** if you are using a dataset, or the **data-free compressor notebook** otherwise. Click the upward arrow button on the JupyterLab page to upload the downloaded notebook file. + +### Upload the Compressed Model to the Repository + +Once compression is complete, upload the compressed model back to the model repository. See [Upload Models Using Notebook](../../model_inference/model_management/how_to/upload_models_using_notebook.mdx) for detailed steps on uploading model files to the model repository. + +```python +model_dir = "./" + model_id.split("/")[-1] + "-W4A16" #[!code callout] +model.save_pretrained(model_dir) +tokenizer.save_pretrained(model_dir); +``` + + + 1. Save model and tokenizer. **You can modify this line if you want to change the name of output**. + + +### Deploy and Use the Compressed Model for Inference + +Quantized and sparse models that you create with LLM Compressor are saved using the `compressed-tensors` library (an extension of [Safetensors](https://huggingface.co/docs/safetensors/en/index)). +The compression format matches the model's quantization or sparsity type. These formats are natively supported in vLLM, enabling fast inference through optimized deployment kernels by using Alauda AI Inference Server. +Follow the instructions in [create inference service](../../model_inference/inference_service/functions/inference_service.mdx#create-inference-service) to complete this step. diff --git a/docs/en/llm-compressor/how_to/index.mdx b/docs/en/llm-compressor/how_to/index.mdx new file mode 100644 index 0000000..2c72779 --- /dev/null +++ b/docs/en/llm-compressor/how_to/index.mdx @@ -0,0 +1,7 @@ +--- +weight: 60 +--- + +# How To + + diff --git a/docs/en/llm-compressor/index.mdx b/docs/en/llm-compressor/index.mdx new file mode 100644 index 0000000..850aab7 --- /dev/null +++ b/docs/en/llm-compressor/index.mdx @@ -0,0 +1,7 @@ +--- +weight: 82 +--- + +# LLM Compressor + + diff --git a/docs/en/llm-compressor/intro.mdx b/docs/en/llm-compressor/intro.mdx new file mode 100644 index 0000000..70cbdf7 --- /dev/null +++ b/docs/en/llm-compressor/intro.mdx @@ -0,0 +1,35 @@ +--- +weight: 10 +--- + +# Introduction + +## Preface + +[LLM Compressor](https://github.com/vllm-project/llm-compressor), part of [the vLLM project](https://docs.vllm.ai/en/latest/) for efficient serving of LLMs, integrates the latest model compression research into a single open-source library enabling the generation of efficient, compressed models with minimal effort. + +The framework allows users to apply some of the most recent research on model compression techniques to improve generative AI (gen AI) models' efficiency, scalability and performance while maintaining accuracy. With native support for Hugging Face and vLLM, the compressed models can be integrated into deployment pipelines, delivering faster and more cost-effective inference at scale. + +LLM Compressor allows you to perform model optimization techniques such as quantization, sparsity, and compression to reduce memory use, model size, and improve inference without affecting the accuracy of model responses. The following compression methodologies are supported by LLM Compressor: + +- **Quantization**: Converts model weights and activations to lower-bit formats such as int8, reducing memory usage. +- **Sparsity**: Sets a portion of model weights to zero, often in fixed patterns, allowing for more efficient computation. +- **Compression**: Shrinks the saved model file size, ideally with minimal impact on performance. + +Use these methods together to deploy models more efficiently on resource-limited hardware. + +## LLM Compressor supports a wide variety of compression techniques: + +- Weight-only quantization (W4A16) compresses model weights to 4-bit precision, valuable for AI applications with limited hardware resources or high sensitivity to latency. +- Weight and activation quantization (W8A8) compresses both weights and activations to 8-bit precision, targeting general server scenarios for integer and floating-point formats. + +## LLM Compressor supports several compression algorithms: + +- AWQ: Weight only `INT4` quantization +- GPTQ: Weight-only `INT4` quantization +- FP8: Dynamic per-token quantization +- SparseGPT: Post-training sparsity +- SmoothQuant: Activation quantization + +For more information about compression algorithms and formats, please refer to the [documentation](https://docs.vllm.ai/projects/llm-compressor/en/latest/guides/compression_schemes/) and examples in the [llmcompressor](https://github.com/vllm-project/llm-compressor) repository. +Each of these compression methods computes optimal scales and zero-points for weights and activations. Optimized scales can be per tensor, channel, group, or token. The final result is a compressed model saved with all its applied quantization parameters. diff --git a/docs/en/workbench/how_to/create_workbench.mdx b/docs/en/workbench/how_to/create_workbench.mdx index d96273d..6b316eb 100644 --- a/docs/en/workbench/how_to/create_workbench.mdx +++ b/docs/en/workbench/how_to/create_workbench.mdx @@ -35,10 +35,159 @@ After creating a workbench instance, click `Workbench` in the left navigation ba :::info We have built-in WorkspaceKind resources that are ready to use out of the box; you can see the two options we provide in the dropdown menu. -- [jupyter-lab](https://hub.docker.com/r/alaudadockerhub/base-notebook) -- [codeserver](https://hub.docker.com/r/alaudadockerhub/base-codeserver) +- [jupyter-lab](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-datascience-cpu-py312-ubi9) +- [codeserver](https://hub.docker.com/r/alaudadockerhub/odh-workbench-codeserver-datascience-cpu-py312-ubi9) ::: +The following additional workbench images are available but are **not built into the platform by default**: + +- [alaudadockerhub/odh-workbench-jupyter-tensorflow-cuda-py312-ubi9](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-tensorflow-cuda-py312-ubi9) +- [alaudadockerhub/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-ubi9](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-ubi9) +- [alaudadockerhub/odh-workbench-jupyter-pytorch-cuda-py312-ubi9](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-pytorch-cuda-py312-ubi9) +- [alaudadockerhub/odh-workbench-jupyter-minimal-cpu-py312-ubi9](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-minimal-cpu-py312-ubi9) +- [alaudadockerhub/odh-workbench-jupyter-minimal-cuda-py312-ubi9](https://hub.docker.com/r/alaudadockerhub/odh-workbench-jupyter-minimal-cuda-py312-ubi9) + +If you want to use these images, you must first **manually synchronize them to your own image registry** (for example, by using a tool such as `skopeo`). + +## DockerHub Image Synchronization Script Guide + +[sync-from-dockerhub.sh](/sync-from-dockerhub.sh) is an automated tool designed to synchronize specific DockerHub images (especially those with ultra-large capacities, such as a single layer exceeding 7GB) to a private image registry (like Harbor). +Because large-capacity images are highly susceptible to Out-Of-Memory (OOM) errors or timeout failures during direct transfer (via pipelines or memory) due to network fluctuations, this script adopts a relay strategy of **Pull to local -> Export as tar archive -> Push from tar archive to target registry**. This ensures stable synchronization even for files in the tens of gigabytes range. Additionally, it features an **automatic temporary file cleanup** mechanism that triggers upon task completion or unexpected errors, protecting your disk space. + +### Script Prerequisites +Before running this script, ensure the following tools are installed and accessible on your execution machine: +- `bash` (Execution environment) +- `nerdctl` (For pulling images and exporting layers as tar archives) +- `skopeo` (For pushing the tar image archives to the target private registry) + +## Environment Variables Configuration + +The script executes synchronization by reading environment variables, providing flexible usage without the need to modify the code. + +### Required Parameters (Target Private Registry Configuration) +| Environment Variable | Description | Example Value | +|:---------------------|:----------------------------------------------------------------------|:-------------------------| +| `TARGET_REGISTRY` | Address of the target private image registry | `build-harbor.alauda.cn` | +| `TARGET_PROJECT` | Specific project/namespace in the target registry to store the images | `mlops/workbench-images` | +| `TARGET_USER` | Username for logging into the target registry | `admin` | +| `TARGET_PASSWORD` | Password for logging into the target registry | `YourSecretPassword` | + +### Optional Parameters (Source DockerHub Configuration) +To prevent triggering DockerHub's Rate Limit when pulling a large volume of images, you can provide your DockerHub credentials to log in prior to pulling. If unnecessary, leave these blank. +| Environment Variable | Description | Example Value | +| :--- | :--- | :--- | +| `DOCKERHUB_USER` | DockerHub account username | `your_dockerhub_account` | +| `DOCKERHUB_PASSWORD` | DockerHub password or Access Token | `dckr_pat_xxxxxx...` | + +--- + +### Example 1: Basic Usage (Most Common) +If you only need to synchronize the images defined within the script to your private Harbor: + +```bash +# 1. Export environment variables for the target registry +export TARGET_REGISTRY="build-harbor.alauda.cn" +export TARGET_PROJECT="mlops/workbench-images" +export TARGET_USER="admin" +export TARGET_PASSWORD="YourHarborPassword" + +# 2. Grant execution permissions to the script (if not already done) +chmod +x ./sync-from-dockerhub.sh + +# 3. Execute the synchronization +./sync-from-dockerhub.sh +``` + +### Example 2: Single-Line Command Execution (Suitable for CI Environments) +You can declare environment variables and run the script on the same line. This approach avoids polluting the current Shell environment variables: + +```bash +TARGET_REGISTRY="build-harbor.alauda.cn" \ +TARGET_PROJECT="mlops/workbench-images" \ +TARGET_USER="admin" \ +TARGET_PASSWORD="YourHarborPassword" \ +./sync-from-dockerhub.sh +``` + +### Example 3: Full Execution with DockerHub Authentication (Rate-Limit Prevention) +When pulling images frequently from the same machine, DockerHub might reject your requests. In this case, include your DockerHub credentials: + +```bash +export TARGET_REGISTRY="build-harbor.alauda.cn" +export TARGET_PROJECT="mlops/workbench-images" +export TARGET_USER="admin" +export TARGET_PASSWORD="YourHarborPassword" + +export DOCKERHUB_USER="alaudadockerhub" +export DOCKERHUB_PASSWORD="dckr_pat_xxx_your_token_xxx" + +./sync-from-dockerhub.sh +``` + +--- + +### Troubleshooting and Notes +1. **Disk Space**: Since the script needs to temporarily store ultra-large images (e.g., 13GB) as `tar` archives, ensure that your system's `/tmp` directory (or its underlying root partition) has ample free space (at least 30GB recommended). The script's default staging directory is `/tmp/workbench-images-export-from-hub`. +2. **Transfer Timeouts**: The current script sets a timeout of 120 minutes (`SKOPEO_TIMEOUT="120m"`) for pushing large files. If the process fails due to extremely slow network speeds, you can adjust this parameter value at the top of the script using any text editor. +3. **Modifying the Image List**: If there are images you no longer wish to synchronize, simply open `sync-from-dockerhub.sh` and use a `#` to comment out those specific lines within the `WORKBENCH_IMAGES` array (similar to how the minimal images were filtered out in `sync.sh`). + +After the image is available in your registry, you also need to **add the corresponding configuration to the `imageConfig` field of the WorkspaceKind resource** that you plan to use. Below is an example patch YAML that adds a new image configuration to an existing WorkspaceKind: + +```json title=add-llmcompressor-image-patch.json +[ + { + "op": "add", + "path": "/spec/podTemplate/options/imageConfig/values/-", + "value": { + "id": "jupyter-pytorch-llmcompressor-cuda-py312", + "spawner": { + "displayName": "Jupyter | PyTorch LLM Compressor | CUDA | Python 3.12", + "description": "JupyterLab with PyTorch and LLM Compressor for CUDA", + "labels": [ + { + "key": "python_version", + "value": "3.12" + }, + { + "key": "framework", + "value": "pytorch" + }, + { + "key": "accelerator", + "value": "cuda" + } + ] + }, + "spec": { + "image": "build-harbor.alauda.cn/mlops/workbench-images/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-ubi9:3.4_ea1-v1.41", + "imagePullPolicy": "IfNotPresent", + "ports": [ + { + "id": "jupyterlab", + "displayName": "JupyterLab", + "port": 8888, + "protocol": "HTTP" + } + ] + } + } + } +] +``` + +You can apply the patch to the WorkspaceKind you are using with a command similar to the following: + +```bash +kubectl patch workspacekind jupyterlab-internal-3-4-ea1-v1-41 \ + --type=json \ + --patch-file add-llmcompressor-image-patch.json \ + -o yaml +``` + +This command applies the JSON patch file to the specified WorkspaceKind and updates its `imageConfig` so the new workbench image becomes available in the workbench creation UI. + +In practice, you can adapt the `name`, `image`, and `description` fields according to the image you synchronized and the naming conventions used in your cluster. + :::info We have also built in some resource options, which you can see in the dropdown menu. ::: diff --git a/docs/en/workbench/how_to/fine_tunning_using_notebooks.mdx b/docs/en/workbench/how_to/fine_tunning_using_notebooks.mdx index 99f683b..754740d 100644 --- a/docs/en/workbench/how_to/fine_tunning_using_notebooks.mdx +++ b/docs/en/workbench/how_to/fine_tunning_using_notebooks.mdx @@ -240,10 +240,10 @@ spec: # Download dataset DATASET_NAME=$(basename ${DATASET_URL}) DATASET_URL_NO_HTTPS="${DATASET_URL//https:\/\/}" - + rm -rf ${DATASET_NAME} rm -rf data - + if [ -d ${DATASET_NAME} ]; then echo "dataset ${DATASET_NAME} already exists skipping download" else @@ -316,14 +316,14 @@ spec: export NNODES=1 export MASTER_HOST="" fi - + cd /mnt/workspace BASE_MODEL_NAME=$(basename ${BASE_MODEL_URL}) DATASET_NAME=$(basename ${DATASET_URL}) - + cat >lf-sft.yaml <> lf-sft.yaml @@ -381,14 +381,14 @@ spec: adapter_name_or_path: output_models template: qwen finetuning_type: lora - + ### export export_dir: output_models_merged export_size: 4 export_device: cpu export_legacy_format: false EOL - + llamafactory-cli export lf-merge-config.yaml else # move output adapter for push @@ -401,7 +401,7 @@ spec: OUTPUT_MODEL_NO_HTTPS="${OUTPUT_MODEL_URL//https:\/\/}" PUSH_URL="https://${gitauth}@${OUTPUT_MODEL_NO_HTTPS}" push_branch=$(date +'%Y%m%d-%H%M%S') - + git init git checkout -b sft-${push_branch} git lfs track *.safetensors @@ -446,12 +446,12 @@ In the YAML file above, modify the following settings before submitting the task 2. The example script uses the `LLaMA-Factory` to launch the fine-tuning task, which can handle most LLM fine-tuning training scenarios. 5. **Hyperparameters**: In the example above, the hyperparameters are defined directly in the startup script. You can also use environment variables to read hyperparameters that may be adjusted repeatedly, making it easier to run and configure multiple times. -### Note about using **NFS** PVC +### Note about using **NFS** PVC When using **NFS** as workspace or model cache PVC, you should make sure below operations are performed to ensure the mounted NFS volume have correct filesystem permissions: - All K8s nodes that can use the **NFS** PVC must install `nfs-utils`, e.g. `yum install -y nfs-utils`. -- Add `mountPermissions: "0757"` settings when creating the **NFS** storage class, like: +- Add `mountPermissions: "0757"` settings when creating the **NFS** storage class, like: ```yaml apiVersion: storage.k8s.io/v1 @@ -478,6 +478,10 @@ When using **NFS** as workspace or model cache PVC, you should make sure below o driverName: "" ``` +:::info +The Workbench image does not come with `kubectl` pre-installed. You can use the upload button in the Jupyter interface to upload the `kubectl` binary downloaded from your local machine to the Jupyter environment. +::: + Once you have completed above settings, open a terminal in Notebook and execute: `kubectl create -f vcjob_sft.yaml` to submit the `VolcanoJob` to the cluster. ### Manage the Task diff --git a/docs/public/calibration-compressor.ipynb b/docs/public/calibration-compressor.ipynb new file mode 100644 index 0000000..548477f --- /dev/null +++ b/docs/public/calibration-compressor.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LLM Compressor Workbench -- Getting Started\n", + "\n", + "This notebook will demonstrate how common [LLM Compressor](https://github.com/vllm-project/llm-compressor) flows can be run on the Alauda AI.\n", + "\n", + "We will show how a user can compress a Large Language Model, with a calibration dataset.\n", + "\n", + "The notebook will detect if a GPU is available. If one is not available, it will demonstrate an abbreviated run, so users without GPU access can still get a feel for `llm-compressor`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibrated Compression with a Dataset\n", + "\n", + "Some more advanced compression algorithms require a small dataset of calibration samples that are meant to be a representative random subset of the language the model will see at inference.\n", + "\n", + "We will show how the previous section can be augmented with a calibration dataset and GPTQ, one of the first published LLM compression algorithms.\n", + "\n", + "
\n", + "Note: This will take several minutes if no GPU is available\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "use_gpu = torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We will use a new recipe running GPTQ (https://arxiv.org/abs/2210.17323)\n", + "# to reduce error caused by quantization. GPTQ requires a calibration dataset.\n", + "from llmcompressor.modifiers.quantization import GPTQModifier\n", + "\n", + "# model to compress\n", + "model_id = \"./TinyLlama-1.1B-Chat-v1.0\"\n", + "recipe = GPTQModifier(targets=\"Linear\", scheme=\"W4A16\", ignore=[\"lm_head\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load up model using huggingface API\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_id, device_map=\"auto\", torch_dtype=\"auto\"\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "# Create the calibration dataset, using Huggingface datasets API\n", + "dataset_id = \"./ultrachat_200k\"\n", + "\n", + "# Select number of samples. 512 samples is a good place to start.\n", + "# Increasing the number of samples can improve accuracy.\n", + "num_calibration_samples = 512 if use_gpu else 4\n", + "max_sequence_length = 2048 if use_gpu else 16\n", + "\n", + "# Load dataset\n", + "ds = load_dataset(dataset_id, split=\"train_sft\")\n", + "# Shuffle and grab only the number of samples we need\n", + "ds = ds.shuffle(seed=42).select(range(num_calibration_samples))\n", + "\n", + "\n", + "# Preprocess and tokenize into format the model uses\n", + "def preprocess(example):\n", + " text = tokenizer.apply_chat_template(\n", + " example[\"messages\"],\n", + " tokenize=False,\n", + " )\n", + " return tokenizer(\n", + " text,\n", + " padding=False,\n", + " max_length=max_sequence_length,\n", + " truncation=True,\n", + " add_special_tokens=False,\n", + " )\n", + "\n", + "\n", + "ds = ds.map(preprocess, remove_columns=ds.column_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run oneshot, with dataset\n", + "from llmcompressor import oneshot\n", + "\n", + "model = oneshot(\n", + " model=model,\n", + " dataset=ds,\n", + " recipe=recipe,\n", + " max_seq_length=max_sequence_length,\n", + " num_calibration_samples=num_calibration_samples,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save model and tokenizer\n", + "model_dir = \"./\" + model_id.split(\"/\")[-1] + \"-GPTQ-W4A16\"\n", + "model.save_pretrained(model_dir)\n", + "tokenizer.save_pretrained(model_dir);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/public/data-free-compressor.ipynb b/docs/public/data-free-compressor.ipynb new file mode 100644 index 0000000..dfddb43 --- /dev/null +++ b/docs/public/data-free-compressor.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LLM Compressor Workbench -- Getting Started\n", + "\n", + "This notebook will demonstrate how common [LLM Compressor](https://github.com/vllm-project/llm-compressor) flows can be run on the Alauda AI.\n", + "\n", + "We will show how a user can compress a Large Language Model, without data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data-Free Model Compression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llmcompressor.modifiers.quantization import QuantizationModifier\n", + "\n", + "# model to compress\n", + "model_id = \"./TinyLlama-1.1B-Chat-v1.0\"\n", + "\n", + "# This recipe will quantize all Linear layers except those in the `lm_head`,\n", + "# which is often sensitive to quantization. The W4A16 scheme compresses\n", + "# weights to 4-bit integers while retaining 16-bit activations.\n", + "recipe = QuantizationModifier(targets=\"Linear\", scheme=\"W4A16\", ignore=[\"lm_head\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load up model using huggingface API\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_id, device_map=\"auto\", torch_dtype=\"auto\"\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run compression using `oneshot`\n", + "from llmcompressor import oneshot\n", + "\n", + "model = oneshot(model=model, recipe=recipe, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save model and tokenizer\n", + "model_dir = \"./\" + model_id.split(\"/\")[-1] + \"-W4A16\"\n", + "model.save_pretrained(model_dir)\n", + "tokenizer.save_pretrained(model_dir);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/public/sync-from-dockerhub.sh b/docs/public/sync-from-dockerhub.sh new file mode 100755 index 0000000..046b04d --- /dev/null +++ b/docs/public/sync-from-dockerhub.sh @@ -0,0 +1,187 @@ +#!/usr/bin/env bash +set -euo pipefail + +BLUE='\033[34m' +GREEN='\033[32m' +YELLOW='\033[33m' +RED='\033[31m' +NC='\033[0m' + +# ========================================== +# 环境变量配置 +# ========================================== +TARGET_REGISTRY="${TARGET_REGISTRY:-}" +TARGET_PROJECT="${TARGET_PROJECT:-}" +TARGET_USER="${TARGET_USER:-}" +TARGET_PASSWORD="${TARGET_PASSWORD:-}" + +# 可选：配置 Docker Hub 的凭证以避免 pull 被限流，非强需求但可以预留 +DOCKERHUB_USER="${DOCKERHUB_USER:-}" +DOCKERHUB_PASSWORD="${DOCKERHUB_PASSWORD:-}" + +RETRY_MAX=5 +RETRY_DELAY=30 +SKOPEO_TIMEOUT="120m" + +WORK_DIR="/tmp/workbench-images-export-from-hub" + +# 全局清理机制 (Trap) +trap 'log "${YELLOW}正在清理临时目录释放空间: ${WORK_DIR}${NC}"; rm -rf "${WORK_DIR}"' EXIT + +declare -a WORKBENCH_IMAGES=( + "docker.io/alaudadockerhub/odh-workbench-codeserver-datascience-cpu-py312-ubi9:3.4_ea1-v1.41" + "docker.io/alaudadockerhub/odh-workbench-jupyter-datascience-cpu-py312-ubi9:3.4_ea1-v1.41" + "docker.io/alaudadockerhub/odh-workbench-jupyter-minimal-cpu-py312-ubi9:3.4_ea2-v1.42" + "docker.io/alaudadockerhub/odh-workbench-jupyter-minimal-cuda-py312-ubi9:3.4_ea2-v1.42" + "docker.io/alaudadockerhub/odh-workbench-jupyter-pytorch-cuda-py312-ubi9:3.4_ea1-v1.41" + "docker.io/alaudadockerhub/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-ubi9:3.4_ea1-v1.41" + "docker.io/alaudadockerhub/odh-workbench-jupyter-tensorflow-cuda-py312-ubi9:3.4_ea2-v1.42" +) + +# ========================================== +# 通用工具函数 +# ========================================== +log() { + printf "%b\n" "$1" +} + +retry() { + local max=${RETRY_MAX} + local delay=${RETRY_DELAY} + local attempt=1 + local -a cmd=("$@") + while true; do + if "${cmd[@]}"; then + return 0 + else + if [ "$attempt" -ge "$max" ]; then + log "${RED}Command failed after ${max} attempts: ${cmd[*]}${NC}" + return 1 + fi + log "${YELLOW}Attempt ${attempt} failed. Retrying in ${delay}s...${NC}" + sleep "$delay" + delay=$((delay * 2)) + attempt=$((attempt + 1)) + fi + done +} + +extract_image_name() { + local full_image="$1" + local without_registry="${full_image#docker.io/alaudadockerhub/}" + echo "${without_registry//[:\/]/_}" +} + +transform_target_dest() { + local src="$1" + local path="${src##*/}" + echo "${TARGET_REGISTRY}/${TARGET_PROJECT}/${path}" +} + +check_local_image() { + local src="$1" + if nerdctl image inspect "$src" >/dev/null 2>&1; then + return 0 + else + return 1 + fi +} + +check_target_image_exists() { + local dest="$1" + if skopeo inspect --tls-verify=false "docker://${dest}" >/dev/null 2>&1; then + return 0 + else + return 1 + fi +} + +login_target() { + log "${BLUE}登录私有镜像仓库: ${TARGET_REGISTRY}...${NC}" + # 大部分私有 Harbor 不需要代理，如果需要可以类似原脚本注入 HTTP_PROXY 或者让用户全局声明 + skopeo login -u "$TARGET_USER" -p "$TARGET_PASSWORD" --tls-verify=false "$TARGET_REGISTRY" || { + log "${RED}私有镜像仓库 ${TARGET_REGISTRY} 登录失败，请检查凭据。${NC}" + exit 1 + } +} + +sync_image() { + local SRC="$1" + local DEST + DEST=$(transform_target_dest "$SRC") + + log "${BLUE}Syncing: ${SRC} -> ${DEST}${NC}" + + # 1. 检查目标仓库是否已有该镜像 + if check_target_image_exists "$DEST"; then + log "${GREEN}Image exists on target registry. Skipping.${NC}" + return 0 + fi + + # 2. 如果本地不存在该镜像先进行拉取 + if ! check_local_image "$SRC"; then + log "${BLUE}Pulling original image from DockerHub (nerdctl pull)...${NC}" + retry nerdctl pull "$SRC" + else + log "${GREEN}Image already pulled locally.${NC}" + fi + + local image_name + image_name=$(extract_image_name "$SRC") + local TAR_FILE="${WORK_DIR}/${image_name}.tar" + + mkdir -p "$WORK_DIR" + + # 3. 将单层极大（如 7G）的镜像保存到本地成为 tar 包，避免直接通过内存/管道传输引起的崩溃或超时 + log "${BLUE}Exporting to tarball via nerdctl save...${NC}" + nerdctl save -o "$TAR_FILE" "$SRC" + + # 4. 从本地 tar 包推送到目标仓库（兼容大容量镜像） + log "${BLUE}Pushing tarball to target registry via Skopeo...${NC}" + retry skopeo copy \ + --command-timeout "$SKOPEO_TIMEOUT" \ + --retry-times 3 \ + --dest-tls-verify=false \ + "docker-archive:${TAR_FILE}" "docker://${DEST}" + + log "${GREEN}Successfully synced ${SRC} to ${DEST}${NC}" + + # 5. 推送成功后即刻清理以释放大容量的临时磁盘占用 + rm -f "$TAR_FILE" + return 0 +} + +main() { + if [ -z "$TARGET_REGISTRY" ] || [ -z "$TARGET_PROJECT" ] || [ -z "$TARGET_USER" ] || [ -z "$TARGET_PASSWORD" ]; then + log "${RED}错误: 缺少必要的环境变量。${NC}" + log "请通过 export 的方式提供相关环境参数:" + log " export TARGET_REGISTRY=build-harbor.alauda.cn" + log " export TARGET_PROJECT=mlops/workbench-images" + log " export TARGET_USER=admin" + log " export TARGET_PASSWORD=your_password" + log "执行示例: ./$0" + exit 1 + fi + + if [ -n "$DOCKERHUB_USER" ] && [ -n "$DOCKERHUB_PASSWORD" ]; then + log "${BLUE}登录 DockerHub 以避免拉取限流...${NC}" + nerdctl login -u "$DOCKERHUB_USER" -p "$DOCKERHUB_PASSWORD" docker.io || true + fi + + login_target + + log "${BLUE}=== 开始同步 Workbench 镜像至 ${TARGET_REGISTRY}/${TARGET_PROJECT} ===${NC}" + local TOTAL=${#WORKBENCH_IMAGES[@]} + local i=0 SRC + for SRC in "${WORKBENCH_IMAGES[@]}"; do + i=$((i + 1)) + echo -en "\n[${i}/${TOTAL}] " + sync_image "$SRC" || { + log "${RED}同步失败：${SRC}${NC}" + exit 1 + } + done + log "${GREEN}=== 所有镜像同步完成 ===${NC}" +} + +main "$@" diff --git a/package.json b/package.json index 3fa6dba..6e943cd 100644 --- a/package.json +++ b/package.json @@ -1,20 +1,18 @@ { "name": "aml-docs", "type": "module", - "dependencies": { - "@alauda/doom": "^1.13.0" - }, + "packageManager": "yarn@4.9.4", "scripts": { - "dev": "doom dev", "build": "doom build", + "dev": "doom dev", + "export": "doom export", "lint": "doom lint", + "prepare": "simple-git-hooks", "serve": "doom serve", - "translate": "doom translate", - "export": "doom export", - "prepare": "simple-git-hooks" + "translate": "doom translate" }, - "packageManager": "yarn@4.9.4", "devDependencies": { + "@alauda/doom": "^1.18.3", "prettier": "^3.6.2", "prettier-plugin-pkg": "^0.21.2", "simple-git-hooks": "^2.13.1" diff --git a/theme/index.ts b/theme/index.ts index d92ab5e..a64583e 100644 --- a/theme/index.ts +++ b/theme/index.ts @@ -1 +1,5 @@ -export * from '@alauda/doom/theme' +import Layout from './layout/index.tsx' + +export { Layout } + +export * from '@rspress/core/theme-original' diff --git a/theme/layout/index.tsx b/theme/layout/index.tsx new file mode 100644 index 0000000..42ff00a --- /dev/null +++ b/theme/layout/index.tsx @@ -0,0 +1,40 @@ +import { withBase } from "@rspress/core/runtime"; +import { + Layout, + getCustomMDXComponent, +} from "@rspress/core/theme-original"; +import { useEffect } from "react"; +import type { AnchorHTMLAttributes } from 'react' + +import { getPathname, shouldDownload } from "../utils/download.ts"; + +export default () => { + useEffect(() => { + window.parent.postMessage(window.location.href, "*"); + }, []); + + return ( + ) => { + const CustomMDXComponent = getCustomMDXComponent() + const pathname = getPathname(props.href ?? '') + if (!shouldDownload(pathname)) { + return + } + + const href = props.href ? withBase(props.href) : props.href + + return ( + + ) + }, + }} + > + ) +}; diff --git a/theme/utils/download.ts b/theme/utils/download.ts new file mode 100644 index 0000000..d2cae92 --- /dev/null +++ b/theme/utils/download.ts @@ -0,0 +1,22 @@ +const DOWNLOAD_EXTENSIONS = [ + ".ipynb", + ".zip", + ".tgz", + ".tar.gz", + ".sh", + ".py", + ".sql", +]; + +export const shouldDownload = (pathname: string): boolean => { + const lowerPath = pathname.toLowerCase(); + return DOWNLOAD_EXTENSIONS.some((ext) => lowerPath.endsWith(ext)); +}; + +export const getPathname = (href: string): string => { + try { + return new URL(href, window.location.origin).pathname; + } catch { + return href.split("?")[0].split("#")[0]; + } +}; diff --git a/tsconfig.json b/tsconfig.json index 5e68b5d..8e8d0ed 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -1,17 +1,19 @@ { - "compilerOptions": { - "jsx": "react-jsx", - "module": "NodeNext", - "moduleResolution": "NodeNext", - "noUnusedLocals": true, - "noUnusedParameters": true, - "resolveJsonModule": true, - "skipLibCheck": true, - "strict": true, - "target": "ESNext", - }, - "mdx": { - "checkMdx": true, - }, - } - \ No newline at end of file + "compilerOptions": { + "allowImportingTsExtensions": true, + "jsx": "react-jsx", + "module": "NodeNext", + "moduleResolution": "NodeNext", + "noUnusedLocals": true, + "noUnusedParameters": true, + "resolveJsonModule": true, + "skipLibCheck": true, + "strict": true, + "target": "ESNext", + "baseUrl": ".", + "paths": { + "i18n": ["./docs/i18n.json"] + } + }, + "include": ["plugins/**/*", "theme/**/*", "*.d.ts", "virtual-modules.d.ts"] +} diff --git a/yarn.lock b/yarn.lock index 082124b..1983de9 100644 --- a/yarn.lock +++ b/yarn.lock @@ -5,66 +5,66 @@ __metadata: version: 8 cacheKey: 10c0 -"@alauda/doom-export@npm:^0.1.0": - version: 0.1.0 - resolution: "@alauda/doom-export@npm:0.1.0" +"@alauda/doom-export@npm:^0.3.1": + version: 0.3.1 + resolution: "@alauda/doom-export@npm:0.3.1" dependencies: + "@playwright/browser-chromium": "npm:1.57.0" cli-progress: "npm:^3.12.0" html-entities: "npm:^2.6.0" pdf-lib: "npm:^1.17.1" pdf-merger-js: "npm:^5.1.2" - playwright: "npm:^1.55.0" + playwright: "npm:1.57.0" yoctocolors: "npm:^2.1.2" - checksum: 10c0/a3a78a73bcf7b12e8fc91c15348727efea451ab9ce574d7c498ffeb1b1de4020eaec22c9d13be8643bbbc2d4c174f3e8d725aca03e92a8cf82e437603e7ca60c + checksum: 10c0/1934e55f4737917dfbf02f8680e5e1a5c01a90e8bddf279cf3246dbb7df5f026e4ab71e8a9a4a5c19b16b459c2f712ea686c24efda1c24bb89adb296c9e1d587 languageName: node linkType: hard -"@alauda/doom@npm:^1.13.0": - version: 1.13.0 - resolution: "@alauda/doom@npm:1.13.0" +"@alauda/doom@npm:^1.18.3": + version: 1.18.3 + resolution: "@alauda/doom@npm:1.18.3" dependencies: - "@alauda/doom-export": "npm:^0.1.0" - "@cspell/eslint-plugin": "npm:^8.19.4 || ^9.2.1" - "@eslint-react/eslint-plugin": "npm:^1.53.1" - "@inquirer/prompts": "npm:^7.8.6" + "@alauda/doom-export": "npm:^0.3.1" + "@cspell/eslint-plugin": "npm:^9.4.0" + "@eslint-react/eslint-plugin": "npm:^2.5.5" + "@inquirer/prompts": "npm:^8.2.0" "@openapi-contrib/openapi-schema-to-json-schema": "npm:^5.1.0" - "@playwright/browser-chromium": "npm:^1.55.0" - "@rsbuild/plugin-react": "npm:^1.4.0" + "@rsbuild/plugin-react": "npm:^1.4.2" "@rsbuild/plugin-sass": "npm:^1.4.0" - "@rsbuild/plugin-svgr": "npm:^1.2.2" + "@rsbuild/plugin-svgr": "npm:^1.2.3" "@rsbuild/plugin-yaml": "npm:^1.0.3" - "@rspress/core": "npm:2.0.0-beta.32" - "@rspress/plugin-algolia": "npm:2.0.0-beta.32" - "@rspress/plugin-llms": "npm:2.0.0-beta.32" - "@rspress/plugin-sitemap": "npm:2.0.0-beta.32" - "@rspress/shared": "npm:2.0.0-beta.32" - "@shikijs/transformers": "npm:^3.12.2" + "@rspress/core": "npm:2.0.0-rc.5" + "@rspress/plugin-algolia": "npm:2.0.0-rc.5" + "@rspress/plugin-sitemap": "npm:2.0.0-rc.5" + "@rspress/shared": "npm:2.0.0-rc.5" + "@shikijs/transformers": "npm:^3.21.0" 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