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* fix example with dataset Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * update torchao doc Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * update torchao doc Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * fix device type Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * revert torchao change Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * fix torchao doc Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * revert torchao change Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * update xpu torchao doc Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * update chat_templating_multimodal.md Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * use full name for int8 Signed-off-by: jiqing-feng <jiqing.feng@intel.com> * revert int8 title Signed-off-by: jiqing-feng <jiqing.feng@intel.com> --------- Signed-off-by: jiqing-feng <jiqing.feng@intel.com> Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
62 lines
2.9 KiB
Markdown
62 lines
2.9 KiB
Markdown
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rendered properly in your Markdown viewer.
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# Fine-grained FP8
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Fine-grained FP8 quantization quantizes the weights and activations to fp8.
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- The weights are quantized to 8-bits for each 2D block (`weight_block_size=(128, 128)`).
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- The activations are quantized to 8-bits for each group per token. The group value matches the weights in the input channel (128 by default).
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FP8 quantization enables support for [DeepSeek-V3](https://hf.co/papers/2412.19437) and DeepSeek-R1.
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/b7b3b34bf826a6423ea82ffc57ecac80c46c3c76/transformers/quantization/quantization_deepseek.png">
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</div>
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> [!TIP]
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> You need a GPU with Compute Capability>=9 (H100), and install a PyTorch version compatible with the CUDA version of your GPU.
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Install Accelerate and upgrade to the latest version of PyTorch.
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```bash
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pip install --upgrade accelerate torch
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```
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Create a [`FineGrainedFP8Config`] class and pass it to [`~PreTrainedModel.from_pretrained`] to quantize it. The weights are loaded in full precision (`torch.float32`) by default regardless of the actual data type the weights are stored in. Set `torch_dtype="auto"` to load the weights in the data type defined in a models `config.json` file to automatically load the most memory-optiomal 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(quantized_model.device.type)
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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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Use [`~PreTrainedModel.save_pretrained`] to save the quantized model and reload it with [`~PreTrainedModel.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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``` |