On my way to support NLP models in TVM and Relay, I encountered some problems that probably requires to some fundamental change or redesign in Relay. So I want to discuss them on the forum.
The problem is how to represent the following examples in Relay.
# Suppose data is a Tensor of shape L x N, where L is sequence length, and N is hidden size length = data.shape_array() x = arange(length)
Another example which I found online
inputs_ = tf.placeholder(tf.float32, shape=(None, None, None, None)) depth = tf.shape(inputs_)[-1] with tf.control_dependencies([ tf.Assert( tf.logical_or(tf.equal(depth, 3), tf.equal(depth, 1)), [depth]) ]): inputs = tf.cond( tf.equal(tf.shape(inputs_)[-1], 3), lambda: inputs_, lambda: tf.image.grayscale_to_rgb(inputs_))
We can find that both examples need to extract the shape value from a tensor and use it in further computation. It might be trivial when input type is constant, since we can use type inference and constant folding to solve this. But a more interesting and common case is when the input shape is unknown during the compilation time. In order to represent these examples, certain things are missing in Relay:
- Convert relay type node to value node, and potentially from value node to type node again
- Be able to use relay expr in the attribute (for the first example)
- (minor) Extract one element from a tensor into a scalar
I think these changes are necessary as we want to support more general RNN models and dynamic shapes in TVM and Relay. I’d like to hear what community thinks about this.