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To create a new 4-bit quantized model, we can leverage AutoGPTQ. Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%. The main benefits are lower latency and memory usage.\n", + "\n", + "Developed in collaboration with IST-Austria, [GPTQ](https://arxiv.org/abs/2210.17323) is the leading quantization algorithm for LLMs, which enables compressing the model weights from 16 bits to 4 bits with limited impact on accuracy. [nm-vllm](https://github.com/neuralmagic/nm-vllm) includes support for the recently-developed Marlin kernels for accelerating GPTQ models. Prior to Marlin, the existing kernels for INT4 inference failed to scale in scenarios with multiple concurrent users.\n", + "\n", + "This notebook requires an NVIDIA GPU with compute capability >= 8.0 (>=Ampere) because of Marlin kernel restrictions. This will not run on T4 or V100 currently. This was tested on an A100 on Colab.\n" + ], + "metadata": { + "id": "lmEnoVL3BnMk" + } + }, + { + "cell_type": "code", + "source": [ + "!nvidia-smi" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oEZelODU8nrR", + "outputId": "28e0e585-08a9-4946-b569-45857132e3e0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tue Mar 5 20:22:47 2024 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 35C P0 49W / 400W | 2MiB / 40960MiB | 0% Default |\n", + "| | | Disabled |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "| No running processes found |\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Install AutoGPTQ\n", + "\n", + "AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on the GPTQ algorithm (weight-only quantization).\n", + "\n" + ], + "metadata": { + "id": "3r9t1dZNSPoO" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install auto-gptq==0.7.1 torch==2.2.1" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mv3DoC7_x7D2", + "outputId": "a4db630a-3a72-46c3-8212-7e5b70c687b0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting auto-gptq==0.7.1\n", + " Downloading auto_gptq-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.5 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.5/23.5 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting torch==2.2.1\n", + " Downloading torch-2.2.1-cp310-cp310-manylinux1_x86_64.whl (755.5 MB)\n", + "\u001b[2K 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nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, gekko, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, torch, datasets, accelerate, peft, auto-gptq\n", + " Attempting uninstall: triton\n", + " Found existing installation: triton 2.1.0\n", + " Uninstalling triton-2.1.0:\n", + " Successfully uninstalled triton-2.1.0\n", + " Attempting uninstall: torch\n", + " Found existing installation: torch 2.1.0+cu121\n", + " Uninstalling torch-2.1.0+cu121:\n", + " Successfully uninstalled torch-2.1.0+cu121\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n", + "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n", + "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n", + "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed accelerate-0.27.2 auto-gptq-0.7.1 datasets-2.18.0 dill-0.3.8 gekko-1.0.6 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 peft-0.9.0 rouge-1.0.1 torch-2.2.1 triton-2.2.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Quantizing an LLM\n", + "\n", + "After installing AutoGPTQ, you are ready to quantize a model.\n", + "\n", + "Below is an example of how to quantize [TinyLlama 1B Chat v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using GPTQ.\n", + "\n", + "We will be using 512 samples from the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset so we load and tokenize those samples first." + ], + "metadata": { + "id": "7Cu5Zx9uC4zQ" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer\n", + "from datasets import load_dataset\n", + "\n", + "MAX_SEQ_LEN = 512\n", + "NUM_EXAMPLES = 512\n", + "\n", + "MODEL_ID = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n", + "DATASET = \"HuggingFaceH4/ultrachat_200k\"\n", + "\n", + "def preprocess(example):\n", + " return {\"text\": tokenizer.apply_chat_template(example[\"messages\"], tokenize=False)}\n", + "\n", + "dataset = load_dataset(DATASET, split=\"train_sft\")\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n", + "ds = dataset.shuffle().select(range(NUM_EXAMPLES))\n", + "ds = ds.map(preprocess)\n", + "\n", + "examples = [\n", + " tokenizer(\n", + " example[\"text\"], padding=False, max_length=MAX_SEQ_LEN, truncation=True,\n", + " ) for example in ds\n", + "]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 717, + "referenced_widgets": [ + "257b3e8698124d308c58f8d46318b6eb", + "da3d8d2cae1a4d299dd5cb11a3c25bc8", + "64b3eef796f546f6bb6157dde7fe411e", + "26d9f66ea31e46e8b61984f529563005", + "95d88a0e484d4bd7b89c6add7ee4e8b2", + "cd693cc3561d4ead80426830942bdf77", + "e11f0a81ec98495d8337333823a964ac", + "8133496823ae421092f0201537ca4083", + "ccc0e6831b1c4001b8ece804826a895a", + "d5837864984a46c89b671f70d447cc64", + "efca32a32cb344029a27691d677364ec", + "8e10b44649a94ff39cc75031e4d698d1", + 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This behaviour is the source of the following dependency conflicts.\n", + "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n", + "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n", + "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n", + "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed aioprometheus-23.12.0 cupy-cuda12x-12.1.0 fastapi-0.110.0 h11-0.14.0 httptools-0.6.1 ninja-1.11.1.1 nm-vllm-0.1.0 nvidia-nccl-cu12-2.18.1 orjson-3.9.15 pynvml-11.5.0 python-dotenv-1.0.1 quantile-python-1.1 ray-2.9.3 starlette-0.36.3 torch-2.1.2 triton-2.1.0 uvicorn-0.27.1 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0 xformers-0.0.23.post1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "torch", + "torchgen", + "triton" + ] + }, + "id": "b47701ba2d2a4519b06472a5fbded2e1" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we can simply pass in the quantized Marlin model we just made to use directly within the engine." + ], + "metadata": { + "id": "fNMhHIRBUMjZ" + } + }, + { + "cell_type": "code", + "source": [ + "from vllm import LLM, SamplingParams\n", + "\n", + "prompts = [\n", + " \"Hello, my name is\",\n", + " \"The president of the United States is\",\n", + " \"The capital of France is\",\n", + " \"The future of AI is\",\n", + "]\n", + "sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=100)\n", + "\n", + "# Create an LLM.\n", + "llm = LLM(\"nm-testing/TinyLlama-1.1B-Chat-v1.0-marlin\")\n", + "\n", + "# Generate texts from the prompts. The output is a list of RequestOutput objects\n", + "# that contain the prompt, generated text, and other information.\n", + "outputs = llm.generate(prompts, sampling_params)\n", + "\n", + "# Print the outputs.\n", + "for output in outputs:\n", + " prompt = output.prompt\n", + " generated_text = output.outputs[0].text\n", + " print(f\"\\nGenerated text: {prompt}{generated_text}\\n\")" + ], + "metadata": { + "id": "B2kLIrUbDdWE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0b032cb3-32e5-41c3-bcc4-9a9b89e4d6f8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "INFO 03-05 19:29:18 llm_engine.py:81] Initializing an LLM engine with config: model='nm-testing/TinyLlama-1.1B-Chat-v1.0-marlin', tokenizer='nm-testing/TinyLlama-1.1B-Chat-v1.0-marlin', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=marlin, sparsity=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)\n", + "INFO 03-05 19:29:22 weight_utils.py:177] Using model weights format ['*.safetensors']\n", + "INFO 03-05 19:29:23 llm_engine.py:340] # GPU blocks: 102209, # CPU blocks: 11915\n", + "INFO 03-05 19:29:26 model_runner.py:676] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n", + "INFO 03-05 19:29:26 model_runner.py:680] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n", + "INFO 03-05 19:29:32 model_runner.py:748] Graph capturing finished in 5 secs.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Processed prompts: 100%|██████████| 4/4 [00:00<00:00, 6.51it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Generated text: Hello, my name is John Smith. And I’m a senior at your high school. I'm interested in learning more about your school and how it compares to other schools in our area. I've heard good things about your school, so I would appreciate it if you could give me some information on the facilities, the teachers, and the overall student body. Please provide me with specific examples of the academic programs and extracurricular activities that you offer, as well as any notable sports teams or clubs\n", + "\n", + "\n", + "Generated text: The president of the United States is a member of the presidential Cabinet.\n", + "\n", + "2. You may also be interested in learning about the role of the vice president, as well as their responsibilities and duties.\n", + "\n", + "3. To learn more about the office of the president, you may want to explore our collection of articles, podcasts, and videos.\n", + "\n", + "4. If you're looking for a guide to the US presidential elections, I would recommend checking out The Guardian's guide, which\n", + "\n", + "\n", + "Generated text: The capital of France is Paris. The capital city of China is Beijing. The capital of Turkey is Ankara. The capital of India is New Delhi. The capital of Brazil is Brasilia. The capital of Singapore is Singapore. The capital of Malaysia is Kuala Lumpur. The capital of the United States is Washington, D.C. The capital of Canada is Ottawa. The capital of Switzerland is Zurich. The capital of Italy is Rome. The capital of Spain is Madrid\n", + "\n", + "\n", + "Generated text: The future of AI is likely to bring about a proliferation of new technologies and techniques. Some of these advancements could have significant impacts on industries like healthcare, manufacturing, and education. However, there are also concerns about the potential negative consequences of AI technology, such as its impact on job opportunities and its ability to create artificial intelligence bubbles. Despite these concerns, there is no denying that AI has the potential to revolutionize the way we live, work,\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "For more details on how to deploy, go to the [nm-vllm Github repo](https://github.com/neuralmagic/nm-vllm).\n", + "\n", + "For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)" + ], + "metadata": { + "id": "GedhVbjxUY_i" + } + } + ] +} \ No newline at end of file diff --git a/notebooks/marlin-nm-vllm/readme.md b/notebooks/marlin-nm-vllm/readme.md new file mode 100644 index 0000000..c17e232 --- /dev/null +++ b/notebooks/marlin-nm-vllm/readme.md @@ -0,0 +1,56 @@ +## Performantly Quantize LLMs to 4-bits with Marlin and nm-vllm +[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuralmagic/notebooks/blob/main/notebooks/marlin-nm-vllm/Performantly_Quantize_LLMs_to_4_bits_with_Marlin_and_nm_vllm.ipynb) + + +This notebook walks through how to compress a pretrained LLM and deploy it with `nm-vllm`. + +## Installation + +To install the necessary libraries, run the following command: + +```bash +pip install auto-gptq==0.7.1 torch==2.2.1 +``` +## Perform Quantization +Follow the steps in the notebook to perform quantization. The process involves quantizing the model using GPTQ and then converting it to Marlin format. + +## Deploy With nm-vllm +You can save the resulting model to Hugging Face and use it for inference using `nm-vllm`. +```python +from transformers import AutoTokenizer +from vllm import LLM, SamplingParams + +model_id = "neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin" +model = LLM(model_id) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +messages = [ + {"role": "user", "content": "How to make banana bread?"}, +] +formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) +sampling_params = SamplingParams(max_tokens=200) +outputs = model.generate(formatted_prompt, sampling_params=sampling_params) +print(outputs[0].outputs[0].text) +""" +Sure! Here's a simple recipe for banana bread: + +Ingredients: +- 3-4 ripe bananas,mashed +- 1 large egg +- 2 Tbsp. Flour +- 2 tsp. Baking powder +- 1 tsp. Baking soda +- 1/2 tsp. Ground cinnamon +- 1/4 tsp. Salt +- 1/2 cup butter, melted +- 3 Cups All-purpose flour +- 1/2 tsp. Ground cinnamon + +Instructions: + +1. Preheat your oven to 350 F (175 C). +""" +``` +For more details on how to deploy, go to the [nm-vllm Github repo](https://github.com/neuralmagic/nm-vllm). + +For further support, and discussions on these models join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) \ No newline at end of file