Improve concat like comms

see:

```

%21 = stablehlo.slice %15 [8:12, 6:6134, 12271:12272] : (tensor<20x6144x12272xf32>) -> tensor<4x6128x1xf32> loc(#loc2576)
%31 = stablehlo.slice %15 [8:12, 6:6134, 0:12272] : (tensor<20x6144x12272xf32>) -> tensor<4x6128x12272xf32> loc(#loc4550)
%723 = stablehlo.concatenate %21, %31, dim = 2 : (tensor<4x6128x1xf32>, tensor<4x6128x12272xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

%42 = stablehlo.slice %36 [8:12, 11:6139, 12271:12272] : (tensor<20x6144x12272xf32>) -> tensor<4x6128x1xf32> loc(#loc2576)
%47 = stablehlo.slice %36 [8:12, 11:6139, 0:12272] : (tensor<20x6144x12272xf32>) -> tensor<4x6128x12272xf32> loc(#loc4550)
%744 = stablehlo.concatenate %42, %47, dim = 2 : (tensor<4x6128x1xf32>, tensor<4x6128x12272xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

%895 = stablehlo.slice %827 [0:4, 0:1, 0:12272] : (tensor<4x6128x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%1190 = stablehlo.slice %893 [0:4, 0:1, 0:12272] : (tensor<4x6126x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%1189 = stablehlo.add %1149, %1188 : tensor<4x6124x12272xf32> loc(#loc2658)
%894 = stablehlo.slice %893 [0:4, 6125:6126, 0:12272] : (tensor<4x6126x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%896 = stablehlo.slice %827 [0:4, 6127:6128, 0:12272] : (tensor<4x6128x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%1191 = stablehlo.concatenate %895, %1190, %1189, %894, %896, dim = 1 : (tensor<4x1x12272xf32>, tensor<4x1x12272xf32>, tensor<4x6124x12272xf32>, tensor<4x1x12272xf32>, tensor<4x1x12272xf32>) -> tensor<4x6128x12272xf32> loc(#loc2576)

%1587 = stablehlo.add %1582, %1586 : tensor<4x6126x12272xf32> loc(#loc2432)
%1588 = stablehlo.slice %1572 [0:4, 0:1, 0:12272] : (tensor<4x6128x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%1589 = stablehlo.slice %1572 [0:4, 6127:6128, 0:12272] : (tensor<4x6128x12272xf32>) -> tensor<4x1x12272xf32> loc(#loc2576)
%1590 = stablehlo.concatenate %1588, %1587, %1589, dim = 1 : (tensor<4x1x12272xf32>, tensor<4x6126x12272xf32>, tensor<4x1x12272xf32>) -> tensor<4x6128x12272xf32> loc(#loc2576)

%1609 = stablehlo.slice %arg20 [8:12, 8:6136, 8:12278] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x12270xf32> loc(#loc2576)
%1610 = stablehlo.slice %arg20 [8:12, 8:6136, 12277:12280] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x3xf32> loc(#loc2576)
%1611 = stablehlo.concatenate %1610, %1609, dim = 2 : (tensor<4x6128x3xf32>, tensor<4x6128x12270xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

%1613 = stablehlo.slice %arg20 [8:12, 8:6136, 12278:12280] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x2xf32> loc(#loc2576)
%1614 = stablehlo.slice %arg20 [8:12, 8:6136, 8:12279] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x12271xf32> loc(#loc2576)
%1615 = stablehlo.concatenate %1613, %1614, dim = 2 : (tensor<4x6128x2xf32>, tensor<4x6128x12271xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

%1608 = stablehlo.slice %arg20 [8:12, 8:6136, 12279:12280] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x1xf32> loc(#loc2576)
...
%1617 = stablehlo.concatenate %1608, %695, dim = 2 : (tensor<4x6128x1xf32>, tensor<4x6128x12272xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

...
%1619 = stablehlo.concatenate %695, %1241, dim = 2 : (tensor<4x6128x12272xf32>, tensor<4x6128x1xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

%1621 = stablehlo.slice %arg20 [8:12, 8:6136, 9:12280] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x12271xf32> loc(#loc2576)
%1622 = stablehlo.slice %arg20 [8:12, 8:6136, 8:10] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x2xf32> loc(#loc2576)
%1623 = stablehlo.concatenate %1621, %1622, dim = 2 : (tensor<4x6128x12271xf32>, tensor<4x6128x2xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

...
%1653 = stablehlo.slice %arg20 [8:12, 8:6136, 10:11] : (tensor<20x6144x12288xf32>) -> tensor<4x6128x1xf32> loc(#loc2576)
%1654 = stablehlo.concatenate %1246, %1653, dim = 2 : (tensor<4x6128x12272xf32>, tensor<4x6128x1xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

...
%1795 = stablehlo.concatenate %26, %24, dim = 2 : (tensor<4x6128x1xf32>, tensor<4x6128x12272xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

...
%1798 = stablehlo.concatenate %46, %45, dim = 2 : (tensor<4x6128x1xf32>, tensor<4x6128x12272xf32>) -> tensor<4x6128x12273xf32> loc(#loc2576)

...
%1825 = stablehlo.concatenate %95, %94, dim = 2 : (tensor<4x6128x12272xf32>, tensor<4x6128x1xf32>) -> tensor<4x6128x12273xf32> loc(#loc2100)
```

two clear optimizations from here:

1) if we have a concat of two things, and one is a slice of the other (on the opposite side), we should pad the bigger one to the end size, rotate the pad to place the corresponding slice at the right location, the dus it it

2) if we have a concat we should identify the operand with he biggest size along the concat dimension. we should pad it to the result operand. We then will dus in the other operands.

all of these should be added to https://github.com/EnzymeAD/Enzyme-JAX/blob/main/src/enzyme_ad/jax/Passes/OptimizeCommunication.cpp

cc @avik-pal