frompytorch_quantizationimportnnasquant_nnfrompytorch_quantizationimportquant_modulesquant_nn.TensorQuantizer.use_fb_fake_quant=Truequant_modules.initialize()model=nn.Linear(512,2048)torch.onnx.export(model.to(dtype=torch.float32,device='cuda'),torch.rand(1024,512).to(dtype=torch.float32,device='c...
🚀 tl;dr Attached is a proposal for graph mode quantization in pytorch (model_quantizer) that provides end to end post training quantization support for both mobile and server backends. Model quantization supports fp32 and int8 precisions...
Simply model., fuse usingtorch.quantizationthe result not same: def model_equivalence(model_1, model_2, device, rtol=1e-05, atol=1e-08, num_tests=100, input_size=(1, 3, 32, 32)): model_1.to(device) model_2.to(device) for _ in range(num_tests): x = torch.rand(size=input...
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime - intel/neural-compressor
[EMNLP 2024 Industry Track] This is the official PyTorch implementation of "LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit". - ModelTC/llmc
File:torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py,Entity:quantization_perchannel_hook,Line: 122,Description: First line should be in imperative mood (perhaps 'Apply', not 'Applies') File:torch/distributed/algorithms/model_averaging/averagers.py,Entity:average_parameters,Line: 106,...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - torch._export.aot_compile reports an error when compiling the model after int8 quantization · pytorch/pytorch@5a90ed3
This repository contains the PyTorch implementation of IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact.IntactKV is a simple and orthogonal method to enhance the quantized LLMs. It can be feasibly combined with various existing quantization approaches (e.g., AWQ,...
model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net);2、 pruning: normal、regular and group convol
[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models" - ModelTC/QLLM