Relay Core Tensor Operators

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 ::: title Note :::

This document will directly list the function signature of these operators in the python frontend. :::

Overview of Operators

Level 1: Basic Operators

This level enables fully connected multi-layer perceptron.

::: 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.

::: 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.

::: 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.meshgrid 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

::: 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

::: tvm.relay.image.resize1d tvm.relay.image.resize2d tvm.relay.image.resize3d 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

::: tvm.relay.argsort tvm.relay.topk :::

Level 10: Temporary Operators

This level support backpropagation of broadcast operators. It is temporary.

::: 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.

::: tvm.relay.qnn.op.add tvm.relay.qnn.op.batch_matmul tvm.relay.qnn.op.concatenate tvm.relay.qnn.op.conv2d tvm.relay.qnn.op.conv2d_transpose tvm.relay.qnn.op.dense tvm.relay.qnn.op.dequantize tvm.relay.qnn.op.mul tvm.relay.qnn.op.quantize tvm.relay.qnn.op.requantize tvm.relay.qnn.op.rsqrt tvm.relay.qnn.op.simulated_dequantize tvm.relay.qnn.op.simulated_quantize tvm.relay.qnn.op.subtract :::