In the past year, Large Language Models (LLMs) have seen rapid advancements. In this article, we will explore several ways to (quantize) as well as shard and utilize different storage and compression strategies. Note: It is recommended to clear cache after loading each LLM example to avoid OutOfMemory errors. delmodel,tokenizer,pipe import torch torch.cuda.empty_cache() If the GPU memory cannot be released in Jupyter, restart the Jupyter notebook.

Model Loading

The most straightforward and common way of loading an LLM is through:

from transformers import AutoModelForCausalLM
  model = AutoModelForCausalLM.from_pretrained("model_name")
  

This will download the model weights and configuration from the Hugging Face Hub and load them into the model. However, this approach can be memory-intensive, especially for large models. To mitigate this issue, we can use:

from transformers import AutoModelForCausalLM
  model = AutoModelForCausalLM.from_pretrained("model_name", low_cpu_mem_usage=True)
  

This will load the modelweights and configuration in a memory-efficient manner. However, it is important to note that this may result in a slight decrease in performance.

Quantization

Quantization is a technique used to reduce the size of a model by reducing the precision of its weights and activations. This can be done without significantly affecting the model's performance.

There are several different quantization methods available. One common method is post-training quantization, which involves quantizing a pre-trained model. This can be done using the following code:

from transformers import AutoModelForCausalLM, quantize
  model = AutoModelForCausalLM.from_pretrained("model_name")
  quantized_model = quantize(model)
  

This will quantize the model's weights and activations to 8-bit precision. The quantized model will be significantly smaller than the original model, while maintaining comparable performance.

Sharding

Sharding is a technique used to split a model across multiple GPUs. This can be done to improve performance and scalability. There are several different sharding methods available. One common method is data parallelism, which involves splitting the model's data across multiple GPUs.

To shard a model using data parallelism, we can use the following code:

from transformers import AutoModelForCausalLM, DataParallel
  model = AutoModelForCausalLM.from_pretrained("model_name")
  model = DataParallel(model)
  

This will split the model's data across the available GPUs. The sharded model will have the same performance as the original model, but it will be able to train on larger datasets.

Storage and Compression Strategies

There are several different storage and compression strategies that can be used to reduce the size of a model. One common strategy is to use a compressed file format, such as ZIP or GZ. Another strategy is to use a sparse storage format, which stores only the non-zero values of a model's weights and activations.

To use a compressed file format, we can use the following code:

importzipfile
  with zipfile.ZipFile("model.zip", "w") as f:
      f.write("model.pt")
  

This will compress the model into a ZIP file. The compressed file will be significantly smaller than the original model.

To use a sparse storage format, we can use the following code:

import torch.sparse
  model = AutoModelForCausalLM.from_pretrained("model_name")
  sparse_model = torch.sparse.coo_tensor(model.state_dict())
  

This will convert the model's weights and activations into a sparse format. The sparse model will be significantly smaller than the original model, while maintaining comparable performance.

Conclusion

In this article, we have explored several ways to quantize, shard, and store LLMs. These techniques can be used to reduce the size of LLMs, which can make them more efficient and scalable. As LLMs continue to grow in size and complexity, these techniques will become increasingly important for managing their resource requirements.

大模型量化之 AWQ 方法

大模型量化新突破：AWQ方法引领性能与效率的双丰收

由MIT、SJTU和清华大学联合研发的AWQ（Activation-aware Weight Quantization）方法，是大模型领域的一项革新性探索。AWQ的独到之处在于其基于对模型权重重要性的深入理解，只保护1%的显著权重，显著降低了量化误差，保持了大模型在不同任务和模式上的卓越泛化能力。相较于传统方法，AWQ无需依赖反向传播或数据布局调整，从而避免了过拟合和额外的硬件开销。

AWQ的核心策略在于，它认识到并非所有权重都具有同等影响力。通过保留1%具有高激活值的权重，保持在FP16精度下的精确性，模型性能得到了显著保护。不同于单纯基于权重的量化策略，AWQ发现根据权重激活值选择权重能带来显著的性能提升。

尽管保留显著权重的FP16量化策略提高了模型效果，但其对硬件效率的友好性却有所牺牲。这与8方法类似，但AWQ通过激活感知的缩放策略，巧妙地平衡了量化损失与性能提升。通过启发式规则和自动搜索最佳缩放比例，AWQ确保了重要权重得到充分表示，同时限制了非显著权重的量化影响。

在实现上，AWQ摒弃了GPTQ的矩阵-向量（MV）乘法，转而利用A100和H100的高效张量核心内核，实现了1.45倍于GPTQ的在线反量化速度，进一步优化了计算效率。在实际应用中，AWQ在MMLU和Common Sense数据集上，显著优于RTN和GPTQ，展示了其在不同模型尺寸和精度下的通用性。

在指令微调的Vicuna 7B和13B模型上，AWQ同样表现出色，超越了RTN和GPTQ。在量化速度上，4-bit的AWQ方法比3-bit的GPTQ快1.45倍，是Triton实现的GPTQ的2.4倍，展现了其在模型推理性能上的显著提升。

AWQ的成功证明了，通过激活感知的量化策略，能够在保持性能的同时，兼顾硬件效率，为大模型的量化带来新的可能。它不仅在理论和实验上超越了现有的大模型量化方法，如8、SmoothQuant和GPTQ，更是为大模型的未来优化开辟了新的道路。

尽管本文提供了AWQ方法的深入剖析，我们期待更多研究者和开发者探索更先进的量化技术，共同推动大模型时代的性能和效率边界。如果你对这个领域感兴趣，不妨进一步探索江峰NLP的其他量化方法，如8、SmoothQuant和GPTQ，以期在实践中找到最适合你需求的解决方案。