- `"per_channel_quantization"`:一个布尔值,用于指示是否对权重参数进行逐通道量化。默认值为False。 - `"reduce_range"`:一个布尔值,用于指示是否缩放激活函数的范围。默认值为True。 - `"quant_mode"`:一个字符串,用于指定量化的模式。可选值有:"weights"(仅对权重参数量化)和"full"(同时
model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'relu']]) # Prepare the model for static quantization. This inserts observers in # the model that will observe activation tensors during calibration. model_fp32_prepared = torch.quantization.prepare(model_fp32_fused) ...
对模型进行量化:使用torch.quantization.quantize_dynamic函数对模型进行量化。 importtorch.quantizationasquant quantized_model=quant.quantize_dynamic(model,{torch.nn.Linear},dtype=torch.qint8) 1. 2. 3. 量化后的微调:在将模型量化后,可以使用一些数据对其进行微调,以优化量化后模型的性能。 quantized_model.fu...
2 超参优化 3 轻量化(Quantization) 3.1 方法一:模型的精度全部降级 3.2 方法二:部分NN组件精度降级 4.1.2 全局剪枝 4.1.3 剪枝相关的PyTorch函数 4.2 自定义剪枝 5 部署(Deploy) 5.1 部署的工具 5.1.1 Python API 5.1.2 TorchScript 5.1.2.1 tracing(大多数场景) 5.1.2.2 scripting 5.1.2.2.1 转换代码 ...
Post training quantization Y Y Y Y Better Good Quantization aware training N Y Y Y Better Better Existing models can be converted to 8 bit integer after training (Post Training Quantization) or trained specifically to be executed in quantized form (Quantization Aware Training) — which often resu...
import torch.quantization #加载模型 model = torch.load('model.pth') #创建一个将模型量化的配置 quantization_config = torch.quantization.get_default_qconfig('fbgemm') #使用动态量化方法量化模型 quantized_model = torch.quantization.quantize_dynamic(model, qconfig_spec=quantization_config) #打印量化前后...
from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): # The training configurations were not carefully selected. ...
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...
>>>低比特量化(Quantization) 量化将模型参数从32位浮点数(FP32)转换为8位整数(INT8)或更低精度的数据类型,以减少计算量。 使用PyTorch进行量化 from torch.quantization import quantize_dynamic model = LongformerModel.from_pretrained("allenai/longformer-base-4096") ...
小模型好说,大模型的话,除非模型简单都可以直接量化,否则需要在torch.nn.Module中添加很多torch.quantization.QuantStub()的标记精细化整个模型的量化策略,这个其实和之前在量化番外篇——TensorRT-8的量化细节介绍的QDQ挺像,这篇中的TensorRT处理的QDQ模型就是通过FX导出来的,只不过QDQ是FX自动生成插入的,不像Eager ...