Do not set block load attribute for transposed A matrices

[alexbaden]

We cannot lower a transposed A matrix to a transposed 2D block load. Instead, the load is lowered via the LLVM path introduced in #2181 . There appears to be a performance regression in this path which is slower than materializing the block in SLM and then reading into registers and computing the dot product from there. Using the work in #2420 I am able to drop the block load attribute for this case and go down the non block ptr path.

Performance on main:

Compute A x B
✅ Triton and Torch match
Time for torch: 0.32444801926612854 ms
Time for triton: 0.44371041655540466 ms
Compute A x B.T
✅ Triton and Torch match
Time for torch: 0.32708799839019775 ms
Time for triton: 0.634996771812439 ms
Compute A.T x B
✅ Triton and Torch match
Time for torch: 0.31204161047935486 ms
Time for triton: 3.4140689373016357 ms
Compute A.T x B.T
✅ Triton and Torch match
Time for torch: 0.45701122283935547 ms
Time for triton: 3.7463345527648926 ms

Performance on this PR:

Compute A x B
✅ Triton and Torch match
Time for torch: 0.3081200122833252 ms
Time for triton: 0.44333598017692566 ms
Compute A x B.T
✅ Triton and Torch match
Time for torch: 0.33799198269844055 ms
Time for triton: 0.6391856074333191 ms
Compute A.T x B
✅ Triton and Torch match
Time for torch: 0.31700319051742554 ms
Time for triton: 1.5733630657196045 ms
Compute A.T x B.T
✅ Triton and Torch match
Time for torch: 0.45083683729171753 ms
Time for triton: 1.8271965980529785 ms

Note that the important commit is 31386ef1132c3f6cf9cb5f1063ecfab705f4c2a1. Once #2420 is merged I will rebase this.

Depends on #2420. Links to #1795.

[whitneywhtsang]

This change can be used to workaround the performance regression in the short term, let's improve the block pointer to tensor of pointer lowering in the long term.

[alexbaden]

Sounds good to me!