This page contains the list of core tensor operator primitives pre-defined in tvm.relay. The core tensor operator primitives cover typical workloads in deep learning. They can represent workloads in front-end frameworks and provide basic building blocks for optimization. Since deep learning is a fast evolving field, it is possible to have operators that are not in here.
Note
This document will directly list the function signature of these operators in the python frontend.
Level 1: Basic Operators
This level enables fully connected multi-layer perceptron.
.. autosummary:: :nosignatures: tvm.relay.log tvm.relay.sqrt tvm.relay.rsqrt tvm.relay.exp tvm.relay.sigmoid tvm.relay.add tvm.relay.subtract tvm.relay.multiply tvm.relay.divide tvm.relay.mod tvm.relay.tanh tvm.relay.concatenate tvm.relay.expand_dims tvm.relay.nn.softmax tvm.relay.nn.log_softmax tvm.relay.nn.relu tvm.relay.nn.dropout tvm.relay.nn.batch_norm tvm.relay.nn.bias_add
Level 2: Convolutions
This level enables typical convnet models.
.. autosummary:: :nosignatures: tvm.relay.nn.conv2d tvm.relay.nn.conv2d_transpose tvm.relay.nn.conv3d tvm.relay.nn.conv3d_transpose tvm.relay.nn.dense tvm.relay.nn.max_pool2d tvm.relay.nn.max_pool3d tvm.relay.nn.avg_pool2d tvm.relay.nn.avg_pool3d tvm.relay.nn.global_max_pool2d tvm.relay.nn.global_avg_pool2d tvm.relay.nn.upsampling tvm.relay.nn.upsampling3d tvm.relay.nn.batch_flatten tvm.relay.nn.pad tvm.relay.nn.lrn tvm.relay.nn.l2_normalize tvm.relay.nn.bitpack tvm.relay.nn.bitserial_dense tvm.relay.nn.bitserial_conv2d tvm.relay.nn.contrib_conv2d_winograd_without_weight_transform tvm.relay.nn.contrib_conv2d_winograd_weight_transform tvm.relay.nn.contrib_conv3d_winograd_without_weight_transform tvm.relay.nn.contrib_conv3d_winograd_weight_transform
Level 3: Additional Math And Transform Operators
This level enables additional math and transform operators.
.. autosummary:: :nosignatures: tvm.relay.nn.leaky_relu tvm.relay.nn.prelu tvm.relay.reshape tvm.relay.reshape_like tvm.relay.copy tvm.relay.transpose tvm.relay.squeeze tvm.relay.floor tvm.relay.ceil tvm.relay.sign tvm.relay.trunc tvm.relay.clip tvm.relay.round tvm.relay.abs tvm.relay.negative tvm.relay.take tvm.relay.zeros tvm.relay.zeros_like tvm.relay.ones tvm.relay.ones_like tvm.relay.gather tvm.relay.gather_nd tvm.relay.full tvm.relay.full_like tvm.relay.cast tvm.relay.reinterpret tvm.relay.split tvm.relay.arange tvm.relay.stack tvm.relay.repeat tvm.relay.tile tvm.relay.reverse tvm.relay.reverse_sequence tvm.relay.unravel_index tvm.relay.sparse_to_dense
Level 4: Broadcast and Reductions
.. autosummary:: :nosignatures: tvm.relay.right_shift tvm.relay.left_shift tvm.relay.equal tvm.relay.not_equal tvm.relay.greater tvm.relay.greater_equal tvm.relay.less tvm.relay.less_equal tvm.relay.all tvm.relay.any tvm.relay.logical_and tvm.relay.logical_or tvm.relay.logical_not tvm.relay.logical_xor tvm.relay.maximum tvm.relay.minimum tvm.relay.power tvm.relay.where tvm.relay.argmax tvm.relay.argmin tvm.relay.sum tvm.relay.max tvm.relay.min tvm.relay.mean tvm.relay.variance tvm.relay.std tvm.relay.mean_variance tvm.relay.mean_std tvm.relay.prod tvm.relay.strided_slice tvm.relay.broadcast_to
Level 5: Vision/Image Operators
.. autosummary:: :nosignatures: tvm.relay.image.resize tvm.relay.image.crop_and_resize tvm.relay.image.dilation2d tvm.relay.vision.multibox_prior tvm.relay.vision.multibox_transform_loc tvm.relay.vision.nms tvm.relay.vision.yolo_reorg
Level 6: Algorithm Operators
.. autosummary:: :nosignatures: tvm.relay.argsort tvm.relay.topk
Level 10: Temporary Operators
This level support backpropagation of broadcast operators. It is temporary.
.. autosummary:: :nosignatures: tvm.relay.broadcast_to_like tvm.relay.collapse_sum_like tvm.relay.slice_like tvm.relay.shape_of tvm.relay.ndarray_size tvm.relay.layout_transform tvm.relay.device_copy tvm.relay.annotation.on_device tvm.relay.reverse_reshape tvm.relay.sequence_mask tvm.relay.nn.batch_matmul tvm.relay.nn.adaptive_max_pool2d tvm.relay.nn.adaptive_avg_pool2d tvm.relay.one_hot
Level 11: Dialect Operators
This level supports dialect operators.
.. autosummary:: :nosignatures: tvm.relay.qnn.op.requantize tvm.relay.qnn.op.conv2d