20cf910d8f
Co-authored-by: zhaochenyang20 <zhaochenyang20@gmail.com> Co-authored-by: jamessand <shazhizhou0@gmail.com>
107 lines
5.1 KiB
Markdown
107 lines
5.1 KiB
Markdown
# Quantization
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SGLang supports various quantization methods, including offline quantization and online dynamic quantization.
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Offline quantization loads pre-quantized model weights directly during inference. This is useful for methods requiring pre-computed stats such as AWQ, which collects activation stats from the pre-training set.
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Online quantization dynamically computes scaling parameters—such as the maximum/minimum values of model weights—during runtime. Like NVIDIA FP8 training's [delayed scaling](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/examples/fp8_primer.html#Mixed-precision-training-with-FP8) mechanism, online quantization calculates the appropriate scaling factors on-the-fly to convert high-precision weights into a lower-precision format.
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**Note that, for better performance, usability and convenience, offline quantization is recommended over online quantization.** And if you use a pre-quantized model, do not add `--quantization` to enable online quantization at the same time. For popular pre-quantized models, please visit [neuralmagic collection](https://huggingface.co/collections/neuralmagic) for some popular quantized LLMs on huggingface.
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## Offline Quantization
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To load already quantized models, simply load the model weights and config. **Again, if the model has been quantized offline, there's no need to add "--quantization" argument when starting the engine. The quantization method will be parsed from the downloaded Hugging Face config. For example, DeepSeek V3/R1 models are already in FP8, so do not add redundant parameters.**
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```bash
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python3 -m sglang.launch_server \
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--model-path hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
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--port 30000 --host 0.0.0.0
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```
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To do offline quantization for your model, firstly you need to install [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
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```bash
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pip install llmcompressor
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```
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Here, we take quantize `meta-llama/Meta-Llama-3-8B-Instruct` to `FP8` as an example to elaborate on how to do offline quantization.
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```python
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from transformers import AutoTokenizer
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from llmcompressor.transformers import SparseAutoModelForCausalLM
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from llmcompressor.transformers import oneshot
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from llmcompressor.modifiers.quantization import QuantizationModifier
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# Step 1: Load the original model.
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MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
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model = SparseAutoModelForCausalLM.from_pretrained(
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MODEL_ID, device_map="auto", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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# Step 2: Perform offline quantization.
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# Step 2.1: Configure the simple PTQ quantization.
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recipe = QuantizationModifier(
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
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# Step 2.2: Apply the quantization algorithm.
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oneshot(model=model, recipe=recipe)
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# Step 3: Save the model.
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SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
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model.save_pretrained(SAVE_DIR)
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tokenizer.save_pretrained(SAVE_DIR)
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```
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Then, you can directly use the quantized model with `SGLang`, by using the following command:
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```bash
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python3 -m sglang.launch_server \
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--model-path $PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic \
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--port 30000 --host 0.0.0.0
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```
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## Online Quantization
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To enable online quantization, you can simply specify `--quantization` in the command line. For example, you can launch the server with the following command to enable `FP8` quantization for model `meta-llama/Meta-Llama-3.1-8B-Instruct`:
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```bash
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python3 -m sglang.launch_server \
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--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
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--quantization fp8 \
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--port 30000 --host 0.0.0.0
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```
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Our team is working on supporting more online quantization methods. We will soon support methods including but not limited to `["awq", "gptq", "marlin", "gptq_marlin", "awq_marlin", "bitsandbytes", "gguf"]`
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We also support quantization methods based on [torchao](https://github.com/pytorch/ao). You can simply specify `--torchao-config` in the command line to support this feature. For example, if you want to enable `int4wo-128` for model `meta-llama/Meta-Llama-3.1-8B-Instruct`, you can launch the server with the following command:
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```bash
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python3 -m sglang.launch_server \
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--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
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--torchao-config int4wo-128 \
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--port 30000 --host 0.0.0.0
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```
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We support the following quantization methods based on torchao `["int8dq", "int8wo", "fp8wo", "fp8dq-per_tensor", "fp8dq-per_row", "int4wo-32", "int4wo-64", "int4wo-128", "int4wo-256"]`.
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Note: According to [this issue](https://github.com/sgl-project/sglang/issues/2219#issuecomment-2561890230), `"int8dq"` method currently has some bugs when using together with cuda graph capture. So we suggest to disable cuda graph capture when using `"int8dq"` method. Namely, please use the following command:
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```bash
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python3 -m sglang.launch_server \
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--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
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--torchao-config int8dq \
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--disable-cuda-graph \
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--port 30000 --host 0.0.0.0
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```
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## Reference
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- [quantization document of vllm](https://docs.vllm.ai/en/latest/quantization/fp8.html)
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- [torchao](https://github.com/pytorch/ao)
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- [llm-compressor](https://github.com/vllm-project/llm-compressor/)
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