若API未标明支持情况,则代表该API的支持情况待验证。
API名称 |
是否支持 |
限制与说明 |
---|---|---|
torch.nn.functional.conv1d |
是 |
支持fp32,fp16 |
torch.nn.functional.conv2d |
是 |
支持fp16,fp32 |
torch.nn.functional.conv3d |
否 |
|
torch.nn.functional.conv_transpose1d |
是 |
支持fp32 |
torch.nn.functional.conv_transpose2d |
是 |
支持fp32,fp16 |
torch.nn.functional.conv_transpose3d |
否 |
|
torch.nn.functional.unfold |
||
torch.nn.functional.fold |
||
torch.nn.functional.avg_pool1d |
否 |
|
torch.nn.functional.avg_pool2d |
否 |
|
torch.nn.functional.avg_pool3d |
否 |
|
torch.nn.functional.max_pool1d |
否 |
|
torch.nn.functional.max_pool2d |
||
torch.nn.functional.max_pool3d |
否 |
|
torch.nn.functional.max_unpool1d |
是 |
支持fp16,fp32,fp64,int8,uint8,int32,int64 |
torch.nn.functional.max_unpool2d |
是 |
支持fp16,fp32,fp64,int8,uint8,int32,int64 |
torch.nn.functional.max_unpool3d |
||
torch.nn.functional.lp_pool1d |
否 |
|
torch.nn.functional.lp_pool2d |
否 |
|
torch.nn.functional.adaptive_max_pool1d |
||
torch.nn.functional.adaptive_max_pool2d |
||
torch.nn.functional.adaptive_max_pool3d |
否 |
|
torch.nn.functional.adaptive_avg_pool1d |
是 |
支持fp32,fp16 |
torch.nn.functional.adaptive_avg_pool2d |
是 |
支持fp32,fp16 |
torch.nn.functional.adaptive_avg_pool3d |
||
torch.nn.functional.fractional_max_pool2d |
否 |
|
torch.nn.functional.fractional_max_pool3d |
否 |
|
torch.nn.functional.scaled_dot_product_attention |
否 |
|
torch.nn.functional.threshold |
是 |
支持fp16,fp32,int8,int16,uint8,int32,int64 |
torch.nn.functional.threshold_ |
是 |
支持fp16,fp32,int8,int16,uint8,int32,int64 |
torch.nn.functional.relu |
是 |
支持fp16,fp32,int8,uint8,int32,int64 |
torch.nn.functional.relu_ |
是 |
支持fp16,fp32,int8,uint8,int32,int64 |
torch.nn.functional.hardtanh |
是 |
支持fp32,fp16,int8,int16,int32,int64 |
torch.nn.functional.hardtanh_ |
是 |
支持fp32,fp16,int8,int16,int32,int64 |
torch.nn.functional.hardswish |
是 |
支持fp16,fp32 |
torch.nn.functional.relu6 |
是 |
支持fp32,fp16,uint8,int8,int16,int32,int64 |
torch.nn.functional.elu |
是 |
支持fp32,fp16 |
torch.nn.functional.elu_ |
是 |
支持fp32,fp16 |
torch.nn.functional.selu |
是 |
支持fp32,fp16 |
torch.nn.functional.celu |
是 |
支持fp32,fp16 |
torch.nn.functional.leaky_relu |
是 |
支持fp16,fp32,fp64 |
torch.nn.functional.leaky_relu_ |
是 |
支持fp16,fp32,fp64 |
torch.nn.functional.prelu |
是 |
支持fp32,fp16 |
torch.nn.functional.rrelu |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.rrelu_ |
||
torch.nn.functional.glu |
是 |
支持fp32,fp16 |
torch.nn.functional.gelu |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.logsigmoid |
是 |
支持fp32,fp16 |
torch.nn.functional.hardshrink |
是 |
支持fp32,fp16 |
torch.nn.functional.tanhshrink |
||
torch.nn.functional.softsign |
是 |
支持bf16,fp32,fp16,uint8,int8,int16,int32,int64 |
torch.nn.functional.softplus |
是 |
支持fp16,fp32 |
torch.nn.functional.softmin |
||
torch.nn.functional.softmax |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.softshrink |
是 |
支持fp32,fp16 |
torch.nn.functional.gumbel_softmax |
||
torch.nn.functional.log_softmax |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.tanh |
是 |
支持fp16,fp32,int8,int16,uint8,int32,int64,bool |
torch.nn.functional.sigmoid |
是 |
支持fp32,fp16,uint8,int8,int16,int32,int64,bool,complex64,complex128 |
torch.nn.functional.hardsigmoid |
是 |
支持fp16,fp32 |
torch.nn.functional.silu |
是 |
支持fp16,fp32,int64,bool |
torch.nn.functional.mish |
否 |
|
torch.nn.functional.batch_norm |
是 |
支持fp32,fp16 |
torch.nn.functional.group_norm |
否 |
|
torch.nn.functional.instance_norm |
||
torch.nn.functional.layer_norm |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.local_response_norm |
否 |
|
torch.nn.functional.normalize |
否 |
|
torch.nn.functional.linear |
是 |
支持fp32,fp16 |
torch.nn.functional.bilinear |
是 |
支持fp32,fp16 |
torch.nn.functional.dropout |
||
torch.nn.functional.alpha_dropout |
是 |
支持bf16,fp32,fp16,uint8,int8,int16,int32,int64,bool,complex64,complex128 |
torch.nn.functional.feature_alpha_dropout |
是 |
支持bf16,fp32,fp16,uint8,int8,int16,int32,int64,bool,complex64,complex128 |
torch.nn.functional.dropout1d |
否 |
|
torch.nn.functional.dropout2d |
||
torch.nn.functional.dropout3d |
||
torch.nn.functional.embedding |
是 |
支持int32,int64 |
torch.nn.functional.embedding_bag |
||
torch.nn.functional.one_hot |
是 |
支持int32,int64 |
torch.nn.functional.pairwise_distance |
||
torch.nn.functional.cosine_similarity |
是 |
支持fp32,fp16 |
torch.nn.functional.pdist |
||
torch.nn.functional.binary_cross_entropy |
是 |
支持fp32,fp16 |
torch.nn.functional.binary_cross_entropy_with_logits |
是 |
支持fp32,fp16 |
torch.nn.functional.poisson_nll_loss |
是 |
支持bf16,fp32,fp16,int64 |
torch.nn.functional.cosine_embedding_loss |
||
torch.nn.functional.cross_entropy |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.ctc_loss |
是 |
支持fp32 |
torch.nn.functional.gaussian_nll_loss |
是 |
支持bf16,fp32,fp16,uint8,int8,int16,int32,int64 |
torch.nn.functional.hinge_embedding_loss |
||
torch.nn.functional.kl_div |
是 |
支持fp16,fp32 |
torch.nn.functional.l1_loss |
是 |
支持fp16,fp32,int64 |
torch.nn.functional.mse_loss |
是 |
支持fp32,fp16 |
torch.nn.functional.margin_ranking_loss |
是 |
支持bf16,fp32,fp16 |
torch.nn.functional.multilabel_margin_loss |
||
torch.nn.functional.multilabel_soft_margin_loss |
否 |
|
torch.nn.functional.multi_margin_loss |
否 |
可以走CPU实现 |
torch.nn.functional.nll_loss |
是 |
支持fp32 |
torch.nn.functional.huber_loss |
否 |
|
torch.nn.functional.smooth_l1_loss |
是 |
支持fp32,fp16 |
torch.nn.functional.soft_margin_loss |
否 |
|
torch.nn.functional.triplet_margin_loss |
否 |
|
torch.nn.functional.triplet_margin_with_distance_loss |
否 |
|
torch.nn.functional.pixel_shuffle |
是 |
支持bf16,fp16,fp32,fp64,int8,uint8,int16,int32,int64,bool |
torch.nn.functional.pixel_unshuffle |
是 |
支持bf16,fp16,fp32,fp64,int8,uint8,int16,int32,int64,bool |
torch.nn.functional.pad |
是 |
支持bf16,fp32,fp16,uint8,int16,int32,int64,bool 在输入x为六维以上时可能会出现性能下降问题 |
torch.nn.functional.interpolate |
是 |
支持fp16,fp32,fp64 只支持mode = nearest |
torch.nn.functional.upsample |
是 |
支持fp16,fp32,fp64 只支持mode = nearest |
torch.nn.functional.upsample_nearest |
是 |
支持fp16,fp32,fp64 只支持3-5维 |
torch.nn.functional.upsample_bilinear |
是 |
支持fp32,fp16 |
torch.nn.functional.grid_sample |
||
torch.nn.functional.affine_grid |
是 |
支持fp16,fp32 |
torch.nn.parallel.data_parallel |
否 |