git clone https://github.com/SqueezeAILab/SqueezeLLM cd SqueezeLLM pip install -e . cd squeezellm python setup_cuda.py install From-scratch Quantization To quantize your own models, follow the procedure in thislink. Currently, we supportLLaMA7B, 13B, 30B and 65B,LLaMA-27B and 13B, instruct...
To Reproduce mapping = ME.utils.sparse_quantize(coords, return_index=True, device=['cuda:1']) Desktop (please complete the following information): OS: Ubuntu 18.04 Python version: 3.8.10 Pytorch version: 1.8.1 CUDA version: 10.2 Minkowski Engine version: 0.5.4chris...
This YOLOv5 blog post was edited in September 2022 to reflect more-recent sparsification research, software updates, better performance numbers, and easier benchmarking and transfer learning flows. Prune and Quantize YOLOv5 for a 12x Increase in Performance and a 12x Decrease in Model Files Neural...
然后我们就可以得到一个quantize的模型: 在这里面,我们展示的是一个实例分割模型,这里面包含了非常多的复杂操作,例如各种shape的组合,以及各种concat,各种interpolate, 其中很多算子是没有办法去量化的,至少很多前推引擎并不支持。 但是我们不管那么多,一顿梭哈,无脑梭哈。 然后我们就可以得到这么一个int8的模型: 模型...
clone() q[mask1[:, i]] = 0 if hasattr(self, 'quantizer'): q = quantize(q.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq).flatten() Q1[:, i] = q Losses1[:, i] = (w - q) ** 2 / d ** 2 # (w - q) / d它的shape其实是一维的,就是...
To use the Minkowski Engine, you first would need to import the engine. Then, you would need to define the network. If the data you have is not quantized, you would need to voxelize or quantize the (spatial) data into a sparse tensor. Fortunately, the Minkowski Engine provides the quant...
For a new given fingerprint images, represent its patches according to the dictionary by computing l0-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The ...
apply(compress_quantized_weights) output_dir = "./ex_llama1.1b_w4a16_packed_quantize" compressor = ModelCompressor(quantization_config=config) compressed_state_dict = compressor.compress(model) model.save_pretrained(output_dir, state_dict=compressed_state_dict) For more in-depth tutorial on ...
Sparse Quantize and Sparse Collate The way to convert a point cloud toSparseTensorso that it can be consumed by networks built with Sparse Convolution or Sparse Point-Voxel Convolution is to use the functiontorchsparse.utils.sparse_quantize. An example is given here: ...
However, in scenarios where pre-splitting is inappropriate, the system has to quantize the input vector as a whole. Such scenarios lead to Type V SVQ. In contrast to Type I SVQ, Type V SVQ performs post-splitting of an input vector, which breaks the input vector into several separate ...