…pass (#252)

During the graph rewrite, we still keep constant tensors which could be
deleted. For example, in
[TwoMatmulFusion](https://github.com/CentML/hidet/blob/main/python/hidet/graph/transforms/graph_patterns/matmul_patterns.py#L36):
Initially we have:
```
out1 = Matmul(x, c1)
out2 = Matmul(x, c2)
```
After graph rewrite optimizations:
```
c = concat([c1, c2])
m = Matmul(x, c)
out1, out2 = split(m)
```
We can safely remove `c1` and `c2` after computing `c` and set its trace
to None (as if it is a terminal node). However `m` cannot me removed,
thus compilation process with ***hidet*** inevitably consumes some
additional memory. UPD: `m` is a symbolic tensor (because `x` is
symbolic), it does not occupy any memory.

Currently testing this approach, but for some reason, after removing
those constant tensors `resolve_variant_pass` optimization causes all
outputs to be `Nan`. If I exclude `resolve_variant_pass` optimization,
it works

---------

Co-authored-by: Zhumakhan <nazirzhumakhan@gmail,.com>

…pass (#252)

During the graph rewrite, we still keep constant tensors which could be
deleted. For example, in
[TwoMatmulFusion](https://github.com/CentML/hidet/blob/main/python/hidet/graph/transforms/graph_patterns/matmul_patterns.py#L36):
Initially we have:
```
out1 = Matmul(x, c1)
out2 = Matmul(x, c2)
```
After graph rewrite optimizations:
```
c = concat([c1, c2])
m = Matmul(x, c)
out1, out2 = split(m)
```
We can safely remove `c1` and `c2` after computing `c` and set its trace
to None (as if it is a terminal node). However `m` cannot me removed,
thus compilation process with ***hidet*** inevitably consumes some
additional memory. UPD: `m` is a symbolic tensor (because `x` is
symbolic), it does not occupy any memory.

Currently testing this approach, but for some reason, after removing
those constant tensors `resolve_variant_pass` optimization causes all
outputs to be `Nan`. If I exclude `resolve_variant_pass` optimization,
it works

---------

Co-authored-by: Zhumakhan <nazirzhumakhan@gmail,.com>

…pass (#252)

During the graph rewrite, we still keep constant tensors which could be
deleted. For example, in
[TwoMatmulFusion](https://github.com/CentML/hidet/blob/main/python/hidet/graph/transforms/graph_patterns/matmul_patterns.py#L36):
Initially we have:
```
out1 = Matmul(x, c1)
out2 = Matmul(x, c2)
```
After graph rewrite optimizations:
```
c = concat([c1, c2])
m = Matmul(x, c)
out1, out2 = split(m)
```
We can safely remove `c1` and `c2` after computing `c` and set its trace
to None (as if it is a terminal node). However `m` cannot me removed,
thus compilation process with ***hidet*** inevitably consumes some
additional memory. UPD: `m` is a symbolic tensor (because `x` is
symbolic), it does not occupy any memory.

Currently testing this approach, but for some reason, after removing
those constant tensors `resolve_variant_pass` optimization causes all
outputs to be `Nan`. If I exclude `resolve_variant_pass` optimization,
it works

---------

Co-authored-by: Zhumakhan <nazirzhumakhan@gmail,.com>