[RELAY][OP] Relay Op Sprint (Part 2)

This is follow-up work to #1799. Now that we have merged an initial version of the Relay evaluator and runtime system in #1954 it is possible to use Relay for end-to-end inference, and optimization.

In order to do so we need to add attributes to the existing operators so that they may be correctly lowered to TVM.

General Steps of Porting

In order to enable lowering of an operator we need to add both a scheduling and compute primitive.

#2050 shows an example for all of the ops in tensor.py

The List

The List

Level 1: Common Basic Ops

• nn.dense
• nn.relu
• tanh
• sigmoid
• exp
• log
• sqrt
• add
• subtract
• multiply
• divide
• mod
• nn.batch_flatten
• concatenate
• nn.softmax
• nn.log_softmax
• nn.batch_norm
• nn.dropout
• expand_dims

Level 2: Convolutions

• nn.conv2d
• nn.conv2d_transpose
• nn.max_pool2d
• nn.avg_pool2d
• nn.global_max_pool2d
• nn.global_avg_pool2d
• nn.pad
• nn.lrn

Level 3: Additional Math And Transform Operators

• reshape
• copy
• negative
• floor
• ceil
• round
• trunc
• clip
• abs
• leaky_relu
• tranpose
• split
• squeeze
• take
• full
• zeros
• ones
• transpose
• zeros_like
• ones_like

Level 4: All broadcast and reduction functions that are not in previous level

• pow
• less
• greater
• less_than
• greater_than
• right_shift
• left_shift
• maximum
• minimum
• sum
• max
• prod
• argmax, argmin
• strided_slice
• broadcast_to
• where

Level 5: Vision Operators

• image.resize
• vision.multibox_prior
• vision.nms

[yzhliu]

@kevinthesun @masahi Yes I’m working on it. ref https://discuss.tvm.ai/t/datalayout-structure/80

BTW sorry for mess up the edit history, my edition has been reverted. @jroesch

[tqchen]

Thanks to everyone’s effort, most of the ops are now supported. Let us open new threads to catch the remaining outliers when necessary