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59 lines
2.9 KiB
Markdown
59 lines
2.9 KiB
Markdown
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# FBGEMM FP8
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With FBGEMM FP8 quantization method, you can quantize your model in FP8 (W8A8):
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- the weights will be quantized in 8bit (FP8) per channel
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- the activation will be quantized in 8bit (FP8) per token
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It relies on the [FBGEMM](https://github.com/pytorch/FBGEMM) library which provides efficient low-precision general matrix multiplication for small batch sizes and support for accuracy-loss minimizing techniques such as row-wise quantization and outlier-aware quantization.
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> [!TIP]
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> You need a GPU with compute capability>=9 (e.g. H100)
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Before you begin, make sure the following libraries are installed with their latest version:
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```bash
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pip install --upgrade accelerate fbgemm-gpu torch
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```
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If you are having issues with fbgemm-gpu and torch library, you might need to install the nightly release. You can follow the instruction [here](https://pytorch.org/FBGEMM/fbgemm_gpu-development/InstallationInstructions.html#fbgemm-gpu-install-libraries:~:text=found%20here.-,Install%20the%20FBGEMM_GPU%20Package,-Install%20through%20PyTorch)
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By default, the weights are loaded in full precision (torch.float32) regardless of the actual data type the weights are stored in such as torch.float16. Set `torch_dtype="auto"` to load the weights in the data type defined in a model's `config.json` file to automatically load the most memory-optimal data type.
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```py
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from transformers import FbgemmFp8Config, AutoModelForCausalLM, AutoTokenizer
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model_name = "meta-llama/Meta-Llama-3-8B"
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quantization_config = FbgemmFp8Config()
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quantized_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", quantization_config=quantization_config)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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input_text = "What are we having for dinner?"
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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output = quantized_model.generate(**input_ids, max_new_tokens=10)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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A quantized model can be saved via "saved_pretrained" and be reused again via the "from_pretrained".
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```py
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quant_path = "/path/to/save/quantized/model"
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model.save_pretrained(quant_path)
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model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
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``` |