# See the License for the specific language governing permissions and # limitations under the License. r """ Quantization ^^^^^ """ import copy import functools from typing import Any, Callable, Dict, Optional, Sequence, Union import torch from torch import Tensor from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_8 if …
Jul 22, 2021 · Tell PyTorch about the details of how to quantize including the quantization strategy, quantized dtype, which statistics to base the calibration on, by assigning a QConfig structure to our model as a member qconfig. PyTorch provides reasonable defaults, and PyTorch Lightning will set these for use when we let it know which backend we want. Fuse ...
This post covers how to improve model inference efficiency (compute, memory, time) through model quantization with PyTorch Lightning for edge inference and ...
Bases: pytorch_lightning.callbacks.base.Callback. Quantization allows speeding up inference and decreasing memory requirements by performing computations and storing tensors at lower bitwidths (such as INT8 or FLOAT16) than floating point precision. We use native PyTorch API so for more information see PyTorch Quantization.
pytorch-lightning / pytorch_lightning / callbacks / quantization.py / Jump to Code definitions wrap_qat_forward_context Function wrapper Function wrap_quantize_forward_context Function wrapper Function _recursive_hasattr Function QuantizationAwareTraining Class __init__ Function _check_feasible_fuse Function on_fit_start Function on_fit_end ...
Read writing about Quantization in PyTorch Lightning Developer Blog. PyTorch Lightning is a lightweight machine learning framework that handles most of the engineering work, leaving you to focus on the science. Check it out: pytorchlightning.ai
Model quantization is another performance optimization technique that allows speeding up inference and decreasing memory requirements by performing computations ...