diff --git a/python/test/unit/language/test_core_amd.py b/python/test/unit/language/test_core_amd.py
index e63ec27397b7..67218c5bc588 100644
--- a/python/test/unit/language/test_core_amd.py
+++ b/python/test/unit/language/test_core_amd.py
@@ -1588,10 +1588,11 @@ def get_variant_golden(a, b):
     return c_padded[:SIZE_M, :SIZE_N]
 
 
-@pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS,BLOCK_SIZE_M,BLOCK_SIZE_N,BLOCK_SIZE_K', [
-    [64, 32, 128, 4, 64, 32, 64],
+@pytest.mark.parametrize('SIZE_M,SIZE_N,SIZE_K,NUM_WARPS,BLOCK_SIZE_M,BLOCK_SIZE_N,BLOCK_SIZE_K,NUM_STAGES', [
+    [64, 32, 128, 4, 64, 32, 64, 0],
+    [64, 32, 128, 4, 64, 32, 64, 2]
 ])
-def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K):
+def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, NUM_STAGES):
     a = torch.randn((SIZE_M, SIZE_K), device='cuda', dtype=torch.float16)
     b = torch.randn((SIZE_K, SIZE_N), device='cuda', dtype=torch.float16)
     c = torch.empty((SIZE_M, SIZE_N), device=a.device, dtype=torch.float32)
@@ -1603,7 +1604,7 @@ def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLO
                         M=a.shape[0], N=b.shape[1], K=a.shape[1],
                         BLOCK_SIZE_M=BLOCK_SIZE_M, BLOCK_SIZE_N=BLOCK_SIZE_N, BLOCK_SIZE_K=BLOCK_SIZE_K,
                         num_warps=NUM_WARPS,
-                        num_stages=2)
+                        num_stages=NUM_STAGES)
     golden = torch.matmul(a, b)
 
     # It's not easy to get a proper error threshold in different size
diff --git a/python/triton/compiler/compiler.py b/python/triton/compiler/compiler.py
index e03d030680c0..ab7832258341 100644
--- a/python/triton/compiler/compiler.py
+++ b/python/triton/compiler/compiler.py
@@ -91,7 +91,7 @@ def optimize_ttgir(mod, num_stages, arch):
         pm.add_tritongpu_accelerate_matmul_pass(matrix_core_version)
     pm.add_tritongpu_remove_layout_conversions_pass()
     pm.add_tritongpu_optimize_dot_operands_pass()
-    if num_stages == 0 and is_hip() and gpu_has_mfma():
+    if num_stages == 0 and is_hip() and gpu_matrix_core_version() != 0:
         pm.add_tritongpu_stream_pipeline_pass()
         pm.add_canonicalizer_pass()
     else: