weight=ao.quantization.observer.default_per_channel_weight_observer ) 然后单独对模型中的某一类型算子操作torch.nn.ConvTranspose2d进行设置,这个qconfig会优先匹配,优先级比整体qconfig高,具体细节可以参考_propagate_qconfig_helper这个函数。 为啥要单独配置torch.nn.ConvTranspose2d,因为torch.fx中默认对torch.nn.Con...
然后,使用torch.quantization.get_default_qconfig函数选择合适的qconfig。最后,使用torch.quantization.quantize_dynamic函数将模型量化,并将量化后的模型保存为.pth文件。 注意:在进行量化之前,需要先安装pytorch_quantization库,可以使用以下命令进行安装: ``` pip install pytorch_quantization ```...
model_fp32.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') 3、模型融合 model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'relu']]) 4、量化模型准备 model_fp32_prepared = torch.quantization.prepare_qat(model_fp32_fused) 5、喂数据,进行训练 # run t...
from torch.quantization.quantize_fx import prepare_fx, convert_fx 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): ...
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. ...
When applying quantization to a CNN - ReLu - Batch Norm model, and converting it to Torchscript format, the inference fails. Reproductible code: importtorch.nnasnnimporttorchfromtorch.quantizationimportget_default_qconfigfromtorch.quantization.quantize_fximportprepare_fx,convert_fxclassMyModel(nn.Modul...
Specify quantization configuration: This can be done either as a dictionary (qConfigDict) or as a field .qconfig for each module. Modify forward function to use modules as needed. Call the conversion functions in torch.quantization, three flows are supported with more being planned: Post trainin...
get('version', None) if (version is None or version < 2) and self.track_running_stats: # at version 2: added num_batches_tracked buffer # this should have a default value of 0 num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key not in state_dict:...
convert_fxfromtorch.ao.quantization.fx.graph_moduleimportObservedGraphModulefromtorch.quantizationimportget_default_qconfigfromtorchimportoptimimportosimportonnximportonnxruntimeimportnumpyasnpfromonnxsimimportsimplifyimporttimedefprepare_dataloader(num_workers=8,train_batch_size=128,eval_batch_size=256):train...
quantization_config import AwqConfig, BitsAndBytesConfig, GPTQConfig, QuantizationMethod from .utils.versions import require_version_core XLA_USE_BF16 = os.environ.get("XLA_USE_BF16", "0").upper() @@ -2898,10 +2897,6 @@ def from_pretrained( raise ValueError("Passing along a `device_...