Store tile shapes in row-major order

[maleadt]

Tile IR is natively row-major (Python cuTile passes shapes through verbatim). We were passing Julia's column-major shapes as-is, then compensating per-operation: reshape emitted a permute-reshape-permute sandwich, and batched matmul emitted 4 permute ops to convert trailing batch dims to MmaFOp's leading-batch convention.

This PR proposes to reverse all shapes once at the Julia-Tile IR boundary: Julia (M, K, B) becomes Tile IR (B, K, M).

What changes

Reshape — the double-permute hack is gone. Was:

permute [1, 0]  →  reshape  →  permute [1, 0]

Now:

reshape

Batched matmul — 4 permute ops per MmaFOp eliminated. Operands are swapped instead (mmaf(b, a, acc)), which produces the correct result because Julia (M,K,B) → Tile IR (B,K,M) and (B,N,K) @ (B,K,M) = (B,N,M) → Julia (M,N,B). Was:

permute [2, 0, 1]   # a: trailing → leading batch
permute [2, 0, 1]   # b: trailing → leading batch
permute [2, 0, 1]   # acc: trailing → leading batch
mmaf a, b, acc
permute [1, 2, 0]   # result: leading → trailing batch

Now:

mmaf b, a, acc

Axes for reduce/scan/cat are flipped (tileir_axis = ndim - 1 - julia_axis). Permutation indices are transformed. Load/store indices and tensor view sizes/strides are reversed.

Tile IR comparison: 3D batched matmul (32,16,4) * (16,32,4)

Before (7 ops between load and store):

%cst      = constant <f32: 0> : tile<32x32x4xf32>
%4        = permute %tile [2, 0, 1]     : tile<32x16x4xf32> -> tile<4x32x16xf32>
%5        = permute %tile_25 [2, 0, 1]  : tile<16x32x4xf32> -> tile<4x16x32xf32>
%6        = permute %cst [2, 0, 1]      : tile<32x32x4xf32> -> tile<4x32x32xf32>
%7        = mmaf %4, %5, %6             : tile<4x32x16xf32>, tile<4x16x32xf32>, tile<4x32x32xf32>
%8        = permute %7 [1, 2, 0]        : tile<4x32x32xf32> -> tile<32x32x4xf32>

After (2 ops):

%cst      = constant <f32: 0> : tile<4x32x32xf32>
%4        = mmaf %tile_25, %tile, %cst   : tile<4x32x16xf32>, tile<4x16x32xf32>, tile<4x32x32xf32>

Benchmarks (RTX 5080, min of 10 runs)

Benchmark	Before (ms)	After (ms)	Change
batchmatmul	0.696	0.602	-13.5%
matmul	4.177	3.744	-10.4%
layernorm fwd	0.986	0.967	-1.9%
layernorm bwd	2.000	2.015	+0.8%
FFT	0.537	0.535	-0.4%
transpose	0.669	0.669	0%
vadd	1.917	1.918	0%

[AntonOresten]

Operands are swapped instead (mmaf(b, a, acc))

This is a great solution. I was previously confused by the operand ordering and shapes and why it was that they could be passed directly to mmaf, but this resolves the confusion.