[RELAY][OP] Relay Op Sprint (Part 2) #2051
Description
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