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62 lines
3.0 KiB
Markdown
62 lines
3.0 KiB
Markdown
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# Fine-grained FP8
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With FP8 quantization method, you can quantize your model in FP8 (W8A8):
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- the weights will be quantized in 8bit (FP8) per 2D block (e.g. weight_block_size=(128, 128)) which is inspired from the deepseek implementation
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- Activations are quantized to 8 bits (FP8) per group per token, with the group value matching that of the weights in the input channels (128 by default)
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It's implemented to add support for DeepSeek-V3 and DeepSeek-R1 models, you can see the paper [here](https://arxiv.org/pdf/2412.19437), and the image below explains the quantization scheme :
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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 torch
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```
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> [!TIP]
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> You need to install a torch version compatible with the cuda version of your GPU.
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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 FineGrainedFP8Config, AutoModelForCausalLM, AutoTokenizer
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model_name = "meta-llama/Meta-Llama-3-8B"
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quantization_config = FineGrainedFP8Config()
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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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``` |