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* fix mess * better documentation * typo * fix doc * update * add test * fix test * more tests * Update src/transformers/modeling_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * move to utils * Apply suggestions from code review Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com> * nit --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
126 lines
6.8 KiB
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126 lines
6.8 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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-->
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# Efficient Inference on a Single GPU
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In addition to this guide, relevant information can be found as well in [the guide for training on a single GPU](perf_train_gpu_one) and [the guide for inference on CPUs](perf_infer_cpu).
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## Better Transformer: PyTorch-native transformer fastpath
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PyTorch-native [`nn.MultiHeadAttention`](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/) attention fastpath, called BetterTransformer, can be used with Transformers through the integration in the [🤗 Optimum library](https://huggingface.co/docs/optimum/bettertransformer/overview).
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PyTorch's attention fastpath allows to speed up inference through kernel fusions and the use of [nested tensors](https://pytorch.org/docs/stable/nested.html). Detailed benchmarks can be found in [this blog post](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2).
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After installing the [`optimum`](https://github.com/huggingface/optimum) package, to use Better Transformer during inference, the relevant internal modules are replaced by calling [`~PreTrainedModel.to_bettertransformer`]:
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```python
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model = model.to_bettertransformer()
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```
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The method [`~PreTrainedModel.reverse_bettertransformer`] allows to go back to the original modeling, which should be used before saving the model in order to use the canonical transformers modeling:
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```python
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model = model.reverse_bettertransformer()
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model.save_pretrained("saved_model")
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```
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As of PyTorch 2.0, the attention fastpath is supported for both encoders and decoders. The list of supported architectures can be found [here](https://huggingface.co/docs/optimum/bettertransformer/overview#supported-models).
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## `bitsandbytes` integration for Int8 mixed-precision matrix decomposition
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<Tip>
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Note that this feature can also be used in a multi GPU setup.
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</Tip>
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From the paper [`LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale`](https://arxiv.org/abs/2208.07339), we support Hugging Face integration for all models in the Hub with a few lines of code.
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The method reduces `nn.Linear` size by 2 for `float16` and `bfloat16` weights and by 4 for `float32` weights, with close to no impact to the quality by operating on the outliers in half-precision.
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Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models.
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For more details regarding the method, check out the [paper](https://arxiv.org/abs/2208.07339) or our [blogpost about the integration](https://huggingface.co/blog/hf-bitsandbytes-integration).
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Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature.
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Below are some notes to help you use this module, or follow the demos on [Google colab](#colab-demos).
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### Requirements
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- If you have `bitsandbytes<0.37.0`, make sure you run on NVIDIA GPUs that support 8-bit tensor cores (Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100). For `bitsandbytes>=0.37.0`, all GPUs should be supported.
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- Install the correct version of `bitsandbytes` by running:
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`pip install bitsandbytes>=0.31.5`
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- Install `accelerate`
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`pip install accelerate>=0.12.0`
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### Running mixed-Int8 models - single GPU setup
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After installing the required libraries, the way to load your mixed 8-bit model is as follows:
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```py
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from transformers import AutoModelForCausalLM
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model_name = "bigscience/bloom-2b5"
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model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
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```
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For text generation, we recommend:
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* using the model's `generate()` method instead of the `pipeline()` function. Although inference is possible with the `pipeline()` function, it is not optimized for mixed-8bit models, and will be slower than using the `generate()` method. Moreover, some sampling strategies are like nucleaus sampling are not supported by the `pipeline()` function for mixed-8bit models.
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* placing all inputs on the same device as the model.
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Here is a simple example:
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "bigscience/bloom-2b5"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
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prompt = "Hello, my llama is cute"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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generated_ids = model.generate(**inputs)
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outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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```
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### Running mixed-int8 models - multi GPU setup
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The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup):
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```py
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model_name = "bigscience/bloom-2b5"
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model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
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```
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But you can control the GPU RAM you want to allocate on each GPU using `accelerate`. Use the `max_memory` argument as follows:
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```py
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max_memory_mapping = {0: "1GB", 1: "2GB"}
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model_name = "bigscience/bloom-3b"
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model_8bit = AutoModelForCausalLM.from_pretrained(
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model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
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)
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```
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In this example, the first GPU will use 1GB of memory and the second 2GB.
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### Colab demos
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With this method you can infer on models that were not possible to infer on a Google Colab before.
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Check out the demo for running T5-11b (42GB in fp32)! Using 8-bit quantization on Google Colab:
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[](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing)
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Or this demo for BLOOM-3B:
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[](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing)
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