调用torch.nn.functional.conv2d函数时,input与weight的shape在一定条件下会导致npu不能正常shutdown或报错“Filter range is too large”。 具体报错: npu不能正常shutdown情况: RuntimeError: npuSynchronizeDevice:/usr1/workspace/FPTA_Daily_open_2.0.3.tr5/CODE/pytorch/c10/npu/NPUStream.cpp:407 NPU error...
🐛 Describe the bug Under specific inputs, torch.nn.functional.conv3d triggered a crash. code import torch input_data = torch.randn(2, 3, 10, 10, 10) weight = torch.randn(4, 3, 3, 3, 3) bias = torch.randn(4) output = torch.nn.functional.c...
torch.nn.functional.conv2d(CPU) is very slow on a specific trained weight: 50x slower than using a random weight. Same shapes, same input, but: Fast conv2d (random weights) Time elapsed: 0.08310413360595703 Slow conv2d (trained weights) Time elapsed: 3.3756301403045654 To Reproduce Use the gis...
占坑,详见pytorch.org/tutorials/a及pytorch.org/cppdocs/ PyG对TorchScripy的支持 将PyG模型转换为TorchScript模型 以一个简单的GNN模型为例: import torch import torch.nn.functional as F from torch_geometric.nn import GCNConv class GNN(torch.nn.Module): def __init__(self, in_channels, out_channel...
我们在构建网络的时候,通常使用 pytorch 所提供的nn.Module(例如nn.Conv2d,nn.ReLU等)作为基本单元。而这些 Module 通常是包裹 autograd function,以其作为真正实现的部分。例如nn.ReLU实际使用torch.nn.functional.relu(F.relu): fromtorch.nnimportfunctionalasFclassReLU(Module):__constants__=['inplace']inplac...
六,torch.nn.functional函数模块 该模块定义了一些与神经网络相关的函数,包括卷积函数和池化函数等,torch.nn中定义的模块一般会调用torch.nn.functional里的函数,比如,nn.ConvNd会调用torch.nn.functional.convNd函数。另外,torch.nn.functional里面还定义了一些不常用的激活函数,包括torch.nn.functional.relu6和torch....
conv1d conv2d conv3d conv_tbc conv_transpose1d conv_transpose2d conv_transpose3d convolution copysign cos cos_ cosh cosh_ cosine_embedding_loss cosine_similarity count_nonzero cpp cross ctc_loss ctypes cuda cuda_path cuda_version cudnn_affine_grid_generator cudnn_batch_norm cudnn_convolution cu...
feature_conv.bias.view(1, -1, 1, 1).expand_as(output) else: output_bias = torch.zeros_like(output) with torch.no_grad(): output_mask = self.mask_conv(mask) mask_sum = output_mask output = (output - output_bias) / mask_sum + output_bias new_mask = torch.ones_like(output) ...
(algoPerf->memory)); } } // 选择卷积 forward 算法的函数 // 具体位置的网址: https://github.com/pytorch/pytorch/blob/b5fa9a340a0d174131ad0a452c395860d571b5b0/aten/src/ATen/native/cudnn/Conv.cpp#L504 template<> struct algorithm_search<cudnnConvolutionFwdAlgoPerf_t> { using perf_t ...
hidden_states = hidden_states.unsqueeze(1) # print(hidden_states.shape) # print(self.kernel_size, self.in_conv_dim, self.dilation) # 5 512 1 hidden_states = nn.functional.unfold( hidden_states, (5, 512), # stride=(1, 512), # dilation=(1, 1), )...