torch.quantize_per_tensor()是按照tensor来进行转化的,每个tensor中所有数据进行一样的操作 torch.quantize_per_channel()是对每个channel进行不同的变化。 import torch print(torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 1
quantize_per_tensor(weight, weight_scale, weight_zp, torch.qint8) # 生成量化 conv节点 ctor = torch.nn.intrinsic.quantized.ConvReLU2d if self.relu_node is not None else torch.ao.nn.quantized.Conv2d qconv = ctor(mod.in_channels, mod.out_channels, mod.kernel_size, mod.stride, mod....
tensor() sparse_coo_tensor() as_tensor() as_strided() from_numpy() zeros() zeros_like() ones() ones_like() arange() range() linspace() logspace() eye() empty() empty_like() empty_strided() full() full_like() quantize_per_tensor() quantize_per_channel() dequantize() complex()...
🐛 Describe the bug torch.Tensor.rot90 causes heap buffer overflow with specific input. Test code: import torch t_base = torch.randn(2,2) t = torch.quantize_per_tensor(t_base, 0.1, 10, torch.quint4x2) t.rot90(-3,(1,0)) Error log: ===...
from torch.quantization.quantize_fximportprepare_fx,convert_fx float_model.eval()# 因为是PTQ,所以就推理模式就够了 qconfig=get_default_qconfig("fbgemm")# 指定量化细节配置 qconfig_dict={"":qconfig}# 指定量化选项 defcalibrate(model,data_loader):# 校准功能函数 ...
quantize_tensor_per_channel_float_qparams_stub>::operator()<at::Tensor const&, at::Tensor&, at::Tensor&, at::Tensor&, long&>(c10::DeviceType, at::Tensor const&, at::Tensor&, at::Tensor&, at::Tensor&, long&) /home/yonghyeon/pytorch/pytorch-asan/aten/src/ATen/native/DispatchStub...
为啥要单独配置torch.nn.ConvTranspose2d,因为torch.fx中默认对torch.nn.ConvTranspose2d是per-tensor的量化,精度会受影响,我这里修改为per-channel量化,同时指定量化维度ch_axis=1。 完整的config如下: prepared = prepare_fx(fx_model, {"": qconfig, ...
q_per_channel_axis q_per_channel_scales q_per_channel_zero_points q_scale q_zero_point qint32 qint8 qr qscheme quantile quantization quantize_per_channel quantize_per_tensor quantized_batch_norm quantized_gru quantized_gru_cell quantized_lstm quantized_lstm_cell quantized_max_pool1d quantized_...
') @parse_args('v', 't', 'i', 'i', 'i') def fake_quantize_per_tensor_affine(g, inputs, scale, zero_point, quant_min=-128, quant_max=127): if quant_min not in [0, -128] or quant_max not in [127, 255]: raise RuntimeError( "ONNX defines [0, 255] for quint8 and...
For QAT, TensorRT introduced new APIs:QuantizeLayerandDequantizeLayer, which map the quantization-related ops in PyTorch to TensorRT. Operations likeaten::fake_quantize_per_*_affineis converted intoQuantizeLayer + DequantizeLayerby Torch-TensorRT internally. For more information about optimizing models...