[Triton-MLIR][BACKEND] Make mmav1 works on basic cases (#944)

TODO:

- Add more cases
- Currently, we just set vec to 4 to make the basic cases pass

Issue:

- the vec in shared layout is different compared to master branch
- when vec=1, it encounters CUDA misalignment error, it doesn't work in
master branch as well
- when setting vec to the value identical to master branch, the MMA
works

python/tests/test_gemm.py

@@ -172,7 +172,7 @@ def get_proper_err(a, b, golden):
     [128, 64, 128, 4, 128, 64, 128, False, False],
     [16, 16, 16, 16, 16, 16, 16, False, False],  # wpt overflow issue
     # K-Forloop
-    #[16, 16, 64, 4, 8, 8, 8, False, False],  # Wrap threads
+    # [16, 16, 64, 4, 8, 8, 8, False, False],  # Wrap threads
     [32, 32, 64, 4, 32, 32, 32, False, False],  # Single shared encoding
     [16, 16, 128, 4, 16, 16, 16, False, False],  # Single shared encoding and small k
     [64, 32, 128, 4, 64, 32, 64, False, False],
@@ -292,3 +292,21 @@ def test_gemm_fp32(M, N, K, num_warps, block_M, block_N, block_K, allow_tf32):
         torch.testing.assert_close(c, golden, rtol=max(1e-2, 1.5 * golden_rel_err), atol=max(1e-2, 1.5 * golden_abs_err))
     else:
         torch.testing.assert_close(c, golden, rtol=max(1e-4, 1.5 * golden_rel_err), atol=max(1e-4, 1.5 * golden_abs_err))
+
+
+# NOTE this is useful only on Volta GPU.
+@pytest.mark.parametrize('SHAPE,NUM_WARPS,TRANS_A,TRANS_B', [
+    (shape, num_warps, trans_a, trans_b)
+    for shape in [
+        [16, 16, 16],
+        [16, 16, 32],
+        [32, 16, 16],
+        [32, 32, 32],
+        [128, 16, 16],
+    ]
+    for num_warps in [1]
+    for trans_a in [False]
+    for trans_b in [False]
+])
+def test_gemm_no_scf_for_mmav1(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
+    test_gemm_no_scf(SHAPE, NUM_WARPS, TRANS_A, TRANS_B)