diff --git a/include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td b/include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td
index f588a471b..bdce88ba8 100644
--- a/include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td
+++ b/include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td
@@ -764,8 +764,7 @@ V [ 0,4,8...60   1,5...61     2,6...62     3,7...63    ]   [ 128,132...188  129,
     ins
     "unsigned":$nonKDim,
     ArrayRefParameter<"unsigned">:$warpsPerCTA,
-    "bool":$isTransposed,
-    "CTALayoutAttr":$CTALayout
+    "bool":$isTransposed
   );
 
   let hasCustomAssemblyFormat = 1;
diff --git a/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp b/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp
index 104401e64..a395c2180 100644
--- a/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp
+++ b/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp
@@ -772,7 +772,7 @@ private:
         srcType.getEncoding().dyn_cast<triton::gpu::MfmaEncodingAttr>();
     auto newMfmaEnc = triton::gpu::MfmaEncodingAttr::get(
         mod.getContext(), srcMfma.getNonKDim(), {warpsPerCtaX, warpsPerCtaY},
-        srcMfma.getIsTransposed(), srcMfma.getCTALayout());
+        srcMfma.getIsTransposed());
 
     auto newDstType = RankedTensorType::get(
         dstType.getShape(), dstType.getElementType(), dstType.getEncoding());
diff --git a/lib/Dialect/TritonGPU/IR/Dialect.cpp b/lib/Dialect/TritonGPU/IR/Dialect.cpp
index f284e2f97..bd0e6e36a 100644
--- a/lib/Dialect/TritonGPU/IR/Dialect.cpp
+++ b/lib/Dialect/TritonGPU/IR/Dialect.cpp
@@ -543,7 +543,7 @@ SmallVector<unsigned> getCTAsPerCGA(Attribute layout) {
     ref = mmaLayout.getCTALayout().getCTAsPerCGA();
 #ifdef USE_ROCM
   else if (auto mfmaLayout = layout.dyn_cast<MfmaEncodingAttr>())
-    ref = mfmaLayout.getCTALayout().getCTAsPerCGA();
+    return {1, 1};
 #endif
   else if (auto dotLayout = layout.dyn_cast<DotOperandEncodingAttr>())
     return getCTAsPerCGA(dotLayout.getParent());
@@ -568,8 +568,8 @@ SmallVector<unsigned> getCTASplitNum(Attribute layout) {
                mmaLayout.getCTALayout().getCTASplitNum().end());
 #ifdef USE_ROCM
   } else if (auto mfmaLayout = layout.dyn_cast<MfmaEncodingAttr>()) {
-    res.assign(mfmaLayout.getCTALayout().getCTASplitNum().begin(),
-               mfmaLayout.getCTALayout().getCTASplitNum().end());
+    res.resize(2);
+    res[0] = res[1] = 1;
 #endif
   } else if (auto dotLayout = layout.dyn_cast<DotOperandEncodingAttr>()) {
     res = getCTASplitNum(dotLayout.getParent());
@@ -598,7 +598,7 @@ SmallVector<unsigned> getCTAOrder(Attribute layout) {
     ref = mmaLayout.getCTALayout().getCTAOrder();
 #ifdef USE_ROCM
   } else if (auto mfmaLayout = layout.dyn_cast<MfmaEncodingAttr>()) {
-    ref = mfmaLayout.getCTALayout().getCTAOrder();
+    return {0, 1};
 #endif
   } else if (auto dotLayout = layout.dyn_cast<DotOperandEncodingAttr>()) {
     return getCTAOrder(dotLayout.getParent());
@@ -673,8 +673,9 @@ unsigned getNumCTAs(Attribute layout) {
   else if (auto mmaLayout = layout.dyn_cast<MmaEncodingAttr>())
     CTAsPerCGA = mmaLayout.getCTALayout().getCTAsPerCGA();
 #ifdef USE_ROCM
-  else if (auto mfmaLayout = layout.dyn_cast<MfmaEncodingAttr>())
-    CTAsPerCGA = mfmaLayout.getCTALayout().getCTAsPerCGA();
+  else if (auto mfmaLayout = layout.dyn_cast<MfmaEncodingAttr>()) {
+    return 1;
+  }
 #endif
   else if (auto dotLayout = layout.dyn_cast<DotOperandEncodingAttr>())
     return getNumCTAs(dotLayout.getParent());
@@ -1316,9 +1317,6 @@ Attribute MfmaEncodingAttr::parse(AsmParser &parser, Type type) {
   unsigned nonKDim = 0;
   SmallVector<unsigned> warpsPerCTA;
   bool isTransposed;
-  SmallVector<unsigned> CTAsPerCGA;
-  SmallVector<unsigned> CTASplitNum;
-  SmallVector<unsigned> CTAOrder;
 
   for (const NamedAttribute &attr : dict) {
     if (attr.getName() == "nonKDim") {
@@ -1332,35 +1330,17 @@ Attribute MfmaEncodingAttr::parse(AsmParser &parser, Type type) {
       if (parseBool(parser, attr, isTransposed, "isTransposed").failed())
         return {};
     }
-    if (attr.getName() == "CTAsPerCGA") {
-      if (parseIntArrayAttr(parser, attr, CTAsPerCGA, "CTAsPerCGA").failed())
-        return {};
-    }
-    if (attr.getName() == "CTASplitNum") {
-      if (parseIntArrayAttr(parser, attr, CTASplitNum, "CTASplitNum").failed())
-        return {};
-    }
-    if (attr.getName() == "CTAOrder") {
-      if (parseIntArrayAttr(parser, attr, CTAOrder, "CTAOrder").failed())
-        return {};
-    }
   }
 
-  auto CTALayout = CTALayoutAttr::get(parser.getContext(), CTAsPerCGA,
-                                      CTASplitNum, CTAOrder);
-
-  return parser.getChecked<MfmaEncodingAttr>(
-      parser.getContext(), nonKDim, warpsPerCTA, isTransposed, CTALayout);
+  return parser.getChecked<MfmaEncodingAttr>(parser.getContext(), nonKDim,
+                                             warpsPerCTA, isTransposed);
 }
 
 void MfmaEncodingAttr::print(AsmPrinter &printer) const {
   printer << "<{"
           << "nonKDim = " << getNonKDim() << ", "
           << "warpsPerCTA = [" << getWarpsPerCTA() << "], "
-          << "isTransposed = " << getIsTransposed() << ", "
-          << "CTAsPerCGA = [" << getCTALayout().getCTAsPerCGA() << "], "
-          << "CTASplitNum = [" << getCTALayout().getCTASplitNum() << "], "
-          << "CTAOrder = [" << getCTALayout().getCTAOrder() << "]}>";
+          << "isTransposed = " << getIsTransposed() << "}>";
 }
 
 //===----------------------------------------------------------------------===//
diff --git a/lib/Dialect/TritonGPU/Transforms/AccelerateAMDMatmul.cpp b/lib/Dialect/TritonGPU/Transforms/AccelerateAMDMatmul.cpp
index 91280d608..b4554a73a 100644
--- a/lib/Dialect/TritonGPU/Transforms/AccelerateAMDMatmul.cpp
+++ b/lib/Dialect/TritonGPU/Transforms/AccelerateAMDMatmul.cpp
@@ -243,6 +243,9 @@ public:
       return failure();
 
     auto CTALayout = ttg::getCTALayout(oldRetType.getEncoding());
+    assert(CTALayout.getCTAsPerCGA().size() == 2);
+    assert(CTALayout.getCTAsPerCGA()[0] == 1);
+    assert(CTALayout.getCTAsPerCGA()[1] == 1);
 
     // get MFMA encoding for the given number of warps
     auto retShape = oldRetType.getShape();
@@ -264,7 +267,7 @@ public:
 
     bool isTransposed = isChainDot(dotOp);
     mfmaEnc = ttg::MfmaEncodingAttr::get(oldRetType.getContext(), nonKDim,
-                                         warpsPerTile, isTransposed, CTALayout);
+                                         warpsPerTile, isTransposed);
 
     auto newRetType =
         RankedTensorType::get(retShape, oldRetType.getElementType(), mfmaEnc);
diff --git a/python/test/unit/language/test_core_amd.py b/python/test/unit/language/test_core_amd.py
index 1bae88783..314021c9a 100644
--- a/python/test/unit/language/test_core_amd.py
+++ b/python/test/unit/language/test_core_amd.py
@@ -2700,16 +2700,13 @@ class MmaLayout:
 
 
 class MfmaLayout:
-    def __init__(self, non_k_dim, warps_per_cta, is_transposed, ctas_per_cga, cta_split_num, cta_order):
+    def __init__(self, non_k_dim, warps_per_cta, is_transposed):
         self.non_k_dim = str(non_k_dim)
         self.warps_per_cta = str(warps_per_cta)
         self.is_transposed = str(is_transposed).lower()
-        self.ctas_per_cga = str(ctas_per_cga)
-        self.cta_split_num = str(cta_split_num)
-        self.cta_order = str(cta_order)
 
     def __str__(self):
-        return f"#{GPU_DIALECT}.mfma<{{nonKDim = {self.non_k_dim}, warpsPerCTA = {self.warps_per_cta}, isTransposed = {self.is_transposed}, CTAsPerCGA={self.ctas_per_cga}, CTASplitNum={self.cta_split_num}, CTAOrder={self.cta_order}}}>"
+        return f"#{GPU_DIALECT}.mfma<{{nonKDim = {self.non_k_dim}, warpsPerCTA = {self.warps_per_cta}, isTransposed = {self.is_transposed}}}>"
 
 
 class BlockedLayout:
@@ -2776,7 +2773,7 @@ def test_dot_mfma_vector_load(vec_size, swizzle, transposeA, transposeB):
     blocked = BlockedLayout(size_per_thread=[1, 4], threads_per_warp=[8, 8], warps_per_cta=[4, 1], order=[1, 0], ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[0, 1])
     shared_a = SharedLayout(vec=vec_size, per_phase=1, max_phase=max_phase, order=[0, 1] if transposeA else [1, 0], ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[0, 1])
     shared_b = SharedLayout(vec=vec_size, per_phase=1, max_phase=max_phase, order=[0, 1] if transposeB else [1, 0], ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[0, 1])
-    mfma = MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=False, ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[0, 1])
+    mfma = MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=False)
 
     ir = f"""
 #blocked = {blocked}
@@ -2938,8 +2935,8 @@ module attributes {"triton_gpu.num-ctas" = 1, "triton_gpu.num-warps" = 4 : i32,
 
 if torch.version.hip is not None and _get_warp_size() == 64:
     layouts = [
-        MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=True, ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[1, 0]),
-        MfmaLayout(non_k_dim=32, warps_per_cta=[2, 2], is_transposed=False, ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[1, 0]),
+        MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=True),
+        MfmaLayout(non_k_dim=32, warps_per_cta=[2, 2], is_transposed=False),
     ]
     shapes = [[128, 32], [128, 128], [32, 128], [64, 64]]
 else:
@@ -3097,8 +3094,8 @@ def test_scan_layouts(M, N, src_layout, axis, device):
 
 @pytest.mark.parametrize("shape", [(64, 64)])
 @pytest.mark.parametrize("dtype", ['float16'])
-@pytest.mark.parametrize("src_layout", [MfmaLayout(non_k_dim=32, warps_per_cta=[2, 1], is_transposed=False, ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[1, 0]),
-                                        MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=True, ctas_per_cga=[1, 1], cta_split_num=[1, 1], cta_order=[1, 0])])
+@pytest.mark.parametrize("src_layout", [MfmaLayout(non_k_dim=32, warps_per_cta=[2, 1], is_transposed=False),
+                                        MfmaLayout(non_k_dim=32, warps_per_cta=[4, 1], is_transposed=True)])
 @pytest.mark.parametrize("dst_layout", [BlockedLayout([1, 4], [4, 16], [1, 1], [1, 0], [1, 1], [1, 1], [0, 1])])
 def test_make_range(dtype, shape, src_layout, dst_layout, device='cuda'):
     ir = f"""
diff --git a/test/Conversion/AMDGPU/mfma_variants.mlir b/test/Conversion/AMDGPU/mfma_variants.mlir
index a9362a9e5..d5891b494 100644
--- a/test/Conversion/AMDGPU/mfma_variants.mlir
+++ b/test/Conversion/AMDGPU/mfma_variants.mlir
@@ -5,7 +5,7 @@
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -25,7 +25,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -45,7 +45,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -65,7 +65,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -85,7 +85,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -105,7 +105,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 2
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -125,7 +125,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -145,7 +145,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 1
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -165,7 +165,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = i32
 #k_width = 4
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -185,7 +185,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = i32
 #k_width = 8
 #non_k_dim = 32
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -205,7 +205,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -225,7 +225,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -245,7 +245,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -265,7 +265,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 8
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -285,7 +285,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -305,7 +305,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 2
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -325,7 +325,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -345,7 +345,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 1
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -365,7 +365,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = i32
 #k_width = 4
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -385,7 +385,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = i32
 #k_width = 8
 #non_k_dim = 16
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -405,7 +405,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 4
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -425,7 +425,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 2
 #non_k_dim = 4
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -445,7 +445,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 4
 #non_k_dim = 4
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -465,7 +465,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = f32
 #k_width = 1
 #non_k_dim = 4
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -485,7 +485,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 !c_ty = i32
 #k_width = 4
 #non_k_dim = 4
-#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = #non_k_dim, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = #k_width}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = #k_width}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
diff --git a/test/Conversion/minimize_alloc.mlir b/test/Conversion/minimize_alloc.mlir
index b08c474fb..8ec4bd6c4 100644
--- a/test/Conversion/minimize_alloc.mlir
+++ b/test/Conversion/minimize_alloc.mlir
@@ -5,7 +5,7 @@
 #blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [4, 1], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
 #shared = #triton_gpu.shared<{vec = 4, perPhase = 2, maxPhase = 8, order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
 #shared1 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
-#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTransposed=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTransposed=false}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 64 : i32} {
   tt.func public @matmul_kernel_0d1d2d3d4d5d6d7c8d9c10d11c(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32 {tt.divisibility = 16 : i32}, %arg5: i32 {tt.divisibility = 16 : i32}, %arg6: i32 {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
     %cst = arith.constant dense<0.000000e+00> : tensor<64x64xf32, #mfma>
diff --git a/test/Conversion/tritongpu_to_llvm.mlir b/test/Conversion/tritongpu_to_llvm.mlir
index 8e2fd68c2..df23a4f61 100644
--- a/test/Conversion/tritongpu_to_llvm.mlir
+++ b/test/Conversion/tritongpu_to_llvm.mlir
@@ -1355,7 +1355,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 
 #blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [16, 4], warpsPerCTA = [1, 1], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
 #shared0 = #triton_gpu.shared<{vec = 1, perPhase=1, maxPhase=1, order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
-#mfma0 = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA=[1,1], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma0 = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA=[1,1], isTranspose=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma0, kWidth = 4}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma0, kWidth = 4}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
@@ -1379,7 +1379,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
 // -----
 
 #blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [64, 1], warpsPerCTA = [1, 4], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
-#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTranspose=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTranspose=false}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32} {
   // CHECK: llvm.mlir.global external @global_smem() {addr_space = 3 : i32} : !llvm.array<0 x i8>
   // CHECK-LABEL: convert_layout_mfma_block
@@ -1529,7 +1529,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
 
 #blocked = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 32], warpsPerCTA = [1, 4], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
 #shared = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
-#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTransposed=false, CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [1, 0]}>
+#mfma = #triton_gpu.mfma<{nonKDim = 32, warpsPerCTA = [2, 2], isTransposed=false}>
 #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mfma, kWidth = 4}>
 #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mfma, kWidth = 4}>
 module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32} {