[mlir][GPUtoNVVM] Relax restriction on wmma op lowering

[ThomasRaoux]

Summary

Allow lowering of wmma ops with 64bits indexes

[mravishankar]

Could you give me a pointer as to what this is for?

[ThomasRaoux]

In D112479#3088930, @mravishankar wrote:

Could you give me a pointer as to what this is for?

It's to support lowering to nvvm with 64bits indexes

[bondhugula]

This looks good to me - thanks.

mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNvvm.cpp

187–189

I assume this explanation wasn't a valid one. Could you clarify?

mlir/test/Conversion/GPUToNVVM/wmma-ops-to-nvvm.mlir

13–23

The default has changed to 64-bit here: could you please capture this in the commit summary?

mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir

3

This can be run with a 32-bit index bitwidth. Do we need this change?

[bondhugula]

mlir/test/Conversion/GPUToNVVM/wmma-ops-to-nvvm.mlir

13–23

I now realize that the default now just works as opposed to failing: it's just derived from the data layout and would happen to be 64-bit by default.

[bondhugula]

LGTM!

mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNvvm.cpp

87

auto -> IntegerAttr here.

[ThomasRaoux]

Thanks for the review!

mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNvvm.cpp

187–189

Yes, even though the intrinsic only accept 32bits stride those ops can be used with 64bits indexes.

mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir

3

Correct it can run with 32bits index but most tests use the default 64bit mode. Overall I don't believe that the 32bit mode is as relevant for CUDA. Let me know if you want me to keep an integration test in 32bit mode, note that I'm still testing both mode in the lit tests.

[bondhugula]

mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir

3

Reg. 32-bit mode's relevance for CUDA, my understanding is exactly the opposite. The 32-bit indexing is quite important and common: it's often sufficient and is expected to only perform better. I recall @herhut or others from his team reporting on the MLIR forum that they saw better performance in many cases when using 32-bit indexing. So I think it's completely fine to use 32-bit indexing for cases where it's guaranteed to be sufficient.

[ThomasRaoux]

Address review comment

mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNvvm.cpp

struct WmmaLoadOpToNVVMLowering
   LogicalResult
   matchAndRewrite(gpu::SubgroupMmaLoadMatrixOp subgroupMmaLoadMatrixOp,
                   OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Operation *op = subgroupMmaLoadMatrixOp.getOperation();
     if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
       return failure();
 
-    unsigned indexTypeBitwidth =
-        this->getTypeConverter()->getIndexTypeBitwidth();
-
-    // The corresponding intrinsics expects leadDimension to be a 32-bit
-    // integer, so all the calculations of linearizing the load address
-    // must also follow this restriction.
-    if (indexTypeBitwidth != 32)
-      return rewriter.notifyMatchFailure(
-          op, "Expected indices to the memref to be 32-bit wide.");
     Location loc = op->getLoc();
 
-    auto leadDimension = subgroupMmaLoadMatrixOp.leadDimensionAttr();
-
     // MemRefDescriptor to extract alignedPtr and offset.
     MemRefDescriptor promotedSrcOp(adaptor.srcMemref());
 
     // Emit ops which compute the load offset using `srcOffsetI`,
     // `srcOffsetJ`. The actualOffset is (memrefOffset + (alignedPtr +
     // ((leadDimension * srcOffsetI) + srcOffsetJ)). The memrefs here are
     // assumed to be normalized and hence the simple conversion works.
+    IntegerAttr leadDimension = subgroupMmaLoadMatrixOp.leadDimensionAttr();
     SmallVector<Value> indices(adaptor.indices());
     Value srcOffsetIVal = indices[0];
     Value srcOffsetJVal = indices[1];
-    Type i32Ty = rewriter.getI32Type();
-    Value leadingDim32 =
-        rewriter.create<LLVM::ConstantOp>(loc, i32Ty, leadDimension);
+    Value leadingDim = rewriter.create<LLVM::ConstantOp>(
+        loc, srcOffsetIVal.getType(), leadDimension);
     Value numElemsLeadDim =
-        rewriter.create<LLVM::MulOp>(loc, i32Ty, leadingDim32, srcOffsetIVal);
-    Value loadOffset = rewriter.create<LLVM::AddOp>(loc, i32Ty, numElemsLeadDim,
-                                                    srcOffsetJVal);
+        rewriter.create<LLVM::MulOp>(loc, leadingDim, srcOffsetIVal);
+    Value loadOffset =
+        rewriter.create<LLVM::AddOp>(loc, numElemsLeadDim, srcOffsetJVal);
 
     Value promotedSrcOpToUse;
     promotedSrcOpToUse = promotedSrcOp.offset(rewriter, loc);
-    Value actualOffset = rewriter.create<LLVM::AddOp>(loc, i32Ty, loadOffset,
-                                                      promotedSrcOpToUse);
+    Value actualOffset =
+        rewriter.create<LLVM::AddOp>(loc, loadOffset, promotedSrcOpToUse);
     Value loadAddress = rewriter.create<LLVM::GEPOp>(
         loc, promotedSrcOp.getElementPtrType(),
         promotedSrcOp.alignedPtr(rewriter, loc), ArrayRef<Value>{actualOffset});
 
     // Bitcast the base address pointer of the destination memref, So that
     // values can be stored in chunks of 32-bits and semantics match with the
     // intrinsic exposed by NVPTX backend.
     Value loadAddressCasted = rewriter.create<LLVM::BitcastOp>(
         loc,
         LLVM::LLVMPointerType::get(
-            i32Ty, promotedSrcOp.getElementPtrType().getAddressSpace()),
+            rewriter.getI32Type(),
+            promotedSrcOp.getElementPtrType().getAddressSpace()),
         loadAddress);
 
     // Get the shape of the MMAMatrix type being returned. The shape will
     // choose which intrinsic this op will be lowered to.
     gpu::MMAMatrixType retType =
         subgroupMmaLoadMatrixOp.res().getType().cast<gpu::MMAMatrixType>();
     ArrayRef<int64_t> retTypeShape = retType.getShape();
 
     Type resType = convertMMAToLLVMType(retType);
     StringRef operandStr = retType.getOperand();
 
     // Create nvvm.mma_load op according to the operand types.
+    Value leadingDim32 = rewriter.create<LLVM::ConstantOp>(
+        loc, rewriter.getI32Type(), leadDimension);
     SmallVector<Value, 2> loadOpOperands({loadAddressCasted, leadingDim32});
     if (operandStr.equals("AOp")) {
       if (retTypeShape[0] == 16 && retTypeShape[1] == 16) {
         rewriter.replaceOpWithNewOp<NVVM::WMMALoadAM16N16K16Op>(op, resType,
                                                                 loadOpOperands);
       } else {
         return rewriter.notifyMatchFailure(op, kInvalidCaseStr);
       }
struct WmmaStoreOpToNVVMLowering
   LogicalResult
   matchAndRewrite(gpu::SubgroupMmaStoreMatrixOp subgroupMmaStoreMatrixOp,
                   OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Operation *op = subgroupMmaStoreMatrixOp.getOperation();
     if (failed(areAllLLVMTypes(op, adaptor.getOperands(), rewriter)))
       return failure();
 
-    unsigned indexTypeBitwidth =
-        this->getTypeConverter()->getIndexTypeBitwidth();
-    // The corresponding intrinsics expects leadDimension to be a 32-bit
-    // integer, so all the calculations of linearizing the store address
-    // must also follow this restriction.
-    if (indexTypeBitwidth != 32)
-      return rewriter.notifyMatchFailure(
-          op, "expected indices to the memref to be 32-bit wide.");
-
     Location loc = op->getLoc();
 
     // MemRefDescriptor to extract alignedPtr and offset.
     MemRefDescriptor promotedDstOp(adaptor.dstMemref());
 
-    auto leadDimension = subgroupMmaStoreMatrixOp.leadDimensionAttr();
-
     // Emit ops which compute the store offset using `dstOffsetI`,
     // `dstOffsetJ`. The actualOffset is (memrefOffset + (alignedPtr +
     // ((leadDimension * dstOffsetI) + dstOffsetJ)).
+    auto leadDimension = subgroupMmaStoreMatrixOp.leadDimensionAttr();
     SmallVector<Value> indices(adaptor.indices());
     Value dstOffsetIVal = indices[0];
     Value dstOffsetJVal = indices[1];
-    Type i32Ty = rewriter.getI32Type();
-    Value leadingDim32 =
-        rewriter.create<LLVM::ConstantOp>(loc, i32Ty, leadDimension);
+    Value leadingDim = rewriter.create<LLVM::ConstantOp>(
+        loc, dstOffsetIVal.getType(), leadDimension);
     Value numElemsLeadDim =
-        rewriter.create<LLVM::MulOp>(loc, i32Ty, leadingDim32, dstOffsetIVal);
-    Value loadOffset = rewriter.create<LLVM::AddOp>(loc, i32Ty, numElemsLeadDim,
-                                                    dstOffsetJVal);
+        rewriter.create<LLVM::MulOp>(loc, leadingDim, dstOffsetIVal);
+    Value loadOffset =
+        rewriter.create<LLVM::AddOp>(loc, numElemsLeadDim, dstOffsetJVal);
 
     Value promotedDstOpToUse;
     promotedDstOpToUse = promotedDstOp.offset(rewriter, loc);
-    Value actualOffset = rewriter.create<LLVM::AddOp>(loc, i32Ty, loadOffset,
-                                                      promotedDstOpToUse);
+    Value actualOffset =
+        rewriter.create<LLVM::AddOp>(loc, loadOffset, promotedDstOpToUse);
     Value storeAddress = rewriter.create<LLVM::GEPOp>(
         loc, promotedDstOp.getElementPtrType(),
         promotedDstOp.alignedPtr(rewriter, loc), ArrayRef<Value>{actualOffset});
 
     // Bitcast the base address pointer of the destination memref, So that
     // values can be stored in chunks of 32-bits and semantics match with the
     // intrinsic exposed by NVPTX backend.
     Value storeAddressCasted = rewriter.create<LLVM::BitcastOp>(
         loc,
         LLVM::LLVMPointerType::get(
-            i32Ty, promotedDstOp.getElementPtrType().getAddressSpace()),
+            rewriter.getI32Type(),
+            promotedDstOp.getElementPtrType().getAddressSpace()),
         storeAddress);
 
     SmallVector<Value, 4> storeOpOperands;
     storeOpOperands.push_back(storeAddressCasted);
 
     // Get the shape of the MMAMatrix type being stored. The shape will
     // choose which intrinsic this op will be lowered to.
     gpu::MMAMatrixType srcType =
         subgroupMmaStoreMatrixOp.src().getType().cast<gpu::MMAMatrixType>();
     ArrayRef<int64_t> srcTypeShape = srcType.getShape();
 
     auto matrixType = adaptor.src().getType().cast<LLVM::LLVMStructType>();
     for (unsigned i = 0, e = matrixType.getBody().size(); i < e; ++i) {
       Value toUse = rewriter.create<LLVM::ExtractValueOp>(
           loc, matrixType.getBody()[i], adaptor.src(),
           rewriter.getI32ArrayAttr(i));
       storeOpOperands.push_back(toUse);
     }
+    Value leadingDim32 = rewriter.create<LLVM::ConstantOp>(
+        loc, rewriter.getI32Type(), leadDimension);
     storeOpOperands.push_back(leadingDim32);
     // Unpack the results from the source.
     if (srcType.getElementType().isF16()) {
       // Create nvvm.mma_store op.
       if (srcTypeShape[0] == 16 && srcTypeShape[1] == 16) {
         rewriter.create<NVVM::WMMAStoreF16M16N16K16Op>(loc, storeOpOperands);
       } else {
         return rewriter.notifyMatchFailure(op, kInvalidCaseStr);

mlir/test/Conversion/GPUToNVVM/wmma-ops-to-nvvm.mlir

-// RUN: mlir-opt --convert-gpu-to-nvvm="index-bitwidth=32" --split-input-file %s | FileCheck %s
+// RUN: mlir-opt --convert-gpu-to-nvvm --split-input-file %s | FileCheck %s
+// RUN: mlir-opt --convert-gpu-to-nvvm="index-bitwidth=32" --split-input-file %s | FileCheck --check-prefix=CHECK32 %s
 
 gpu.module @test_module {
 
   // CHECK-LABEL: func @gpu_wmma_load_op() ->
   // CHECK-SAME: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)> {
+  // CHECK32-LABEL: func @gpu_wmma_load_op() ->
   builtin.func @gpu_wmma_load_op() -> (!gpu.mma_matrix<16x16xf16, "AOp">) {
     %wg = memref.alloca() {alignment = 32} : memref<32x32xf16, 3>
     %i = arith.constant 16 : index
     %j = arith.constant 16 : index
     %0 = gpu.subgroup_mma_load_matrix %wg[%i, %j] {leadDimension = 32 : index} : memref<32x32xf16, 3> -> !gpu.mma_matrix<16x16xf16, "AOp">
-    // CHECK:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i32
+    // CHECK:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i64
     // CHECK: %{{.*}} = llvm.insertvalue %{{.*}}, %{{.*}}[{{.*}}, {{.*}}]
-    // CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
-    // CHECK:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
-    // CHECK:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
-    // CHECK:  %[[OFFSET:.*]] = llvm.extractvalue %{{.*}}[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
-    // CHECK:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
-    // CHECK:  %[[BASE:.*]] = llvm.extractvalue %{{.*}}[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
-    // CHECK:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    // CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i64
+    // CHECK:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i64
+    // CHECK:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i64
+    // CHECK:  %[[OFFSET:.*]] = llvm.extractvalue %{{.*}}[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i64, array<2 x i64>, array<2 x i64>)>
+    // CHECK:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i64
+    // CHECK:  %[[BASE:.*]] = llvm.extractvalue %{{.*}}[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i64, array<2 x i64>, array<2 x i64>)>
+    // CHECK:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i64) -> !llvm.ptr<f16, 3>
     // CHECK:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
-    // CHECK:  %[[FRAG:.*]] = nvvm.wmma.m16n16k16.load.a.f16.row.stride %[[CADDRESS]], %[[LDM]] : (!llvm.ptr<i32, 3>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK:  %[[LDM32:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK:  %[[FRAG:.*]] = nvvm.wmma.m16n16k16.load.a.f16.row.stride %[[CADDRESS]], %[[LDM32]] : (!llvm.ptr<i32, 3>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
     // CHECK:  llvm.return %[[FRAG]] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+
+    // CHECK32:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i32
+    // CHECK32: %{{.*}} = llvm.insertvalue %{{.*}}, %{{.*}}[{{.*}}, {{.*}}]
+    // CHECK32:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK32:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
+    // CHECK32:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
+    // CHECK32:  %[[OFFSET:.*]] = llvm.extractvalue %{{.*}}[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    // CHECK32:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
+    // CHECK32:  %[[BASE:.*]] = llvm.extractvalue %{{.*}}[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    // CHECK32:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    // CHECK32:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
+    // CHECK32:  %[[LDM32:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK32:  %[[FRAG:.*]] = nvvm.wmma.m16n16k16.load.a.f16.row.stride %[[CADDRESS]], %[[LDM32]] : (!llvm.ptr<i32, 3>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK32:  llvm.return %[[FRAG]] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
     return %0 : !gpu.mma_matrix<16x16xf16, "AOp">
   }
 }
 
 // -----
 
 gpu.module @test_module {
 
   // CHECK-LABEL: func @gpu_wmma_store_op
   // CHECK-SAME: (%[[D:.*]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>) {
+  // CHECK32-LABEL: func @gpu_wmma_store_op
+  // CHECK32-SAME: (%[[D:.*]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>) {
   builtin.func @gpu_wmma_store_op(%arg0 : !gpu.mma_matrix<16x16xf16, "COp">) -> () {
     %sg = memref.alloca(){alignment = 32} : memref<32x32xf16, 3>
     %i = arith.constant 16 : index
     %j = arith.constant 16 : index
     gpu.subgroup_mma_store_matrix %arg0, %sg[%i,%j] {leadDimension= 32 : index} : !gpu.mma_matrix<16x16xf16, "COp">, memref<32x32xf16, 3>
-    // CHECK:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i32
+    // CHECK:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i64
     // CHECK: %{{.*}} = llvm.insertvalue %{{.*}}, %{{.*}}[{{.*}}, {{.*}}]
-    // CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
-    // CHECK:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
-    // CHECK:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
-    // CHECK:  %[[OFFSET:.*]] = llvm.extractvalue %17[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
-    // CHECK:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
-    // CHECK:  %[[BASE:.*]] = llvm.extractvalue %17[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
-    // CHECK:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    // CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i64
+    // CHECK:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i64
+    // CHECK:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i64
+    // CHECK:  %[[OFFSET:.*]] = llvm.extractvalue %17[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i64, array<2 x i64>, array<2 x i64>)>
+    // CHECK:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i64
+    // CHECK:  %[[BASE:.*]] = llvm.extractvalue %17[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i64, array<2 x i64>, array<2 x i64>)>
+    // CHECK:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i64) -> !llvm.ptr<f16, 3>
     // CHECK:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
     // CHECK:  %[[EL1:.*]] = llvm.extractvalue %[[D]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
     // CHECK:  %[[EL2:.*]] = llvm.extractvalue %[[D]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
     // CHECK:  %[[EL3:.*]] = llvm.extractvalue %[[D]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
     // CHECK:  %[[EL4:.*]] = llvm.extractvalue %[[D]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-    // CHECK:  nvvm.wmma.m16n16k16.store.d.f16.row.stride %[[CADDRESS]], %[[EL1]], %[[EL2]], %[[EL3]], %[[EL4]], %[[LDM]] : !llvm.ptr<i32, 3>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, i32
+    // CHECK:  %[[LDM32:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK:  nvvm.wmma.m16n16k16.store.d.f16.row.stride %[[CADDRESS]], %[[EL1]], %[[EL2]], %[[EL3]], %[[EL4]], %[[LDM32]] : !llvm.ptr<i32, 3>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, i32
     // CHECK:  llvm.return
+
+    // CHECK32:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : i32
+    // CHECK32: %{{.*}} = llvm.insertvalue %{{.*}}, %{{.*}}[{{.*}}, {{.*}}]
+    // CHECK32:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK32:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
+    // CHECK32:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
+    // CHECK32:  %[[OFFSET:.*]] = llvm.extractvalue %17[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    // CHECK32:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
+    // CHECK32:  %[[BASE:.*]] = llvm.extractvalue %17[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    // CHECK32:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    // CHECK32:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
+    // CHECK32:  %[[EL1:.*]] = llvm.extractvalue %[[D]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK32:  %[[EL2:.*]] = llvm.extractvalue %[[D]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK32:  %[[EL3:.*]] = llvm.extractvalue %[[D]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK32:  %[[EL4:.*]] = llvm.extractvalue %[[D]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    // CHECK32:  %[[LDM32:.*]] = llvm.mlir.constant(32 : index) : i32
+    // CHECK32:  nvvm.wmma.m16n16k16.store.d.f16.row.stride %[[CADDRESS]], %[[EL1]], %[[EL2]], %[[EL3]], %[[EL4]], %[[LDM32]] : !llvm.ptr<i32, 3>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, i32
+    // CHECK32:  llvm.return
     return
   }
 }
 
 // -----
 
 gpu.module @test_module {
 

 }
 
 // -----
 
 gpu.module @test_module {
 
 // CHECK-LABEL: func @gpu_wmma_mma_loop_op
 //       CHECK:   %[[C:.+]] = nvvm.wmma.m16n16k16.load.c.f16.row.stride %{{.*}}, %{{.*}} : (!llvm.ptr<i32>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   llvm.br ^bb1(%{{.*}}, %[[C]] : i32, !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>)
-//       CHECK:  ^bb1(%{{.*}}: i32, %[[ACC:.+]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>):  // 2 preds: ^bb0, ^bb2
-//       CHECK:   llvm.cond_br %38, ^bb2, ^bb3
+//       CHECK:   llvm.br ^bb1(%{{.*}}, %[[C]] : i64, !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>)
+//       CHECK:  ^bb1(%{{.*}}: i64, %[[ACC:.+]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>):  // 2 preds: ^bb0, ^bb2
+//       CHECK:   llvm.cond_br %{{.*}}, ^bb2, ^bb3
 //       CHECK:  ^bb2:  // pred: ^bb1
 //       CHECK:   %[[A:.+]] = nvvm.wmma.m16n16k16.load.a.f16.row.stride %{{.*}}, %{{.*}} : (!llvm.ptr<i32>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B:.+]] = nvvm.wmma.m16n16k16.load.b.f16.row.stride %{{.*}}, %{{.*}} : (!llvm.ptr<i32>, i32) -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A0:.+]] = llvm.extractvalue %[[A]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A1:.+]] = llvm.extractvalue %[[A]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A2:.+]] = llvm.extractvalue %[[A]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A3:.+]] = llvm.extractvalue %[[A]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A4:.+]] = llvm.extractvalue %[[A]][4 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A5:.+]] = llvm.extractvalue %[[A]][5 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A6:.+]] = llvm.extractvalue %[[A]][6 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[A7:.+]] = llvm.extractvalue %[[A]][7 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B0:.+]] = llvm.extractvalue %[[B]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B1:.+]] = llvm.extractvalue %[[B]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B2:.+]] = llvm.extractvalue %[[B]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B3:.+]] = llvm.extractvalue %[[B]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B4:.+]] = llvm.extractvalue %[[B]][4 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B5:.+]] = llvm.extractvalue %[[B]][5 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B6:.+]] = llvm.extractvalue %[[B]][6 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[B7:.+]] = llvm.extractvalue %[[B]][7 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[ACC0:.+]] = llvm.extractvalue %[[ACC]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[ACC1:.+]] = llvm.extractvalue %[[ACC]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[ACC2:.+]] = llvm.extractvalue %[[ACC]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[ACC3:.+]] = llvm.extractvalue %[[ACC]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
 //       CHECK:   %[[ACC_MUL:.+]] = nvvm.wmma.m16n16k16.mma.row.row.f16.f16 %[[A0]], %[[A1]], %[[A2]], %[[A3]], %[[A4]], %[[A5]], %[[A6]], %[[A7]], %[[B0]], %[[B1]], %[[B2]], %[[B3]], %[[B4]], %[[B5]], %[[B6]], %[[B7]], %[[ACC0]], %[[ACC1]], %[[ACC2]], %[[ACC3]] : vector<2xf16> -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   llvm.br ^bb1(%{{.*}}, %[[ACC_MUL]] : i32, !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>)
+//       CHECK:   llvm.br ^bb1(%{{.*}}, %[[ACC_MUL]] : i64, !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>)
 //       CHECK:  ^bb3:  // pred: ^bb1
-//       CHECK:   %87 = llvm.extractvalue %[[ACC]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   %88 = llvm.extractvalue %[[ACC]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   %89 = llvm.extractvalue %[[ACC]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   %90 = llvm.extractvalue %[[ACC]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
-//       CHECK:   nvvm.wmma.m16n16k16.store.d.f16.row.stride %86, %87, %88, %89, %90, %79 : !llvm.ptr<i32>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, i32
+//       CHECK:   %[[E0:.+]] = llvm.extractvalue %[[ACC]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+//       CHECK:   %[[E1:.+]] = llvm.extractvalue %[[ACC]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+//       CHECK:   %[[E2:.+]] = llvm.extractvalue %[[ACC]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+//       CHECK:   %[[E3:.+]] = llvm.extractvalue %[[ACC]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+//       CHECK:   nvvm.wmma.m16n16k16.store.d.f16.row.stride %{{.*}}, %[[E0]], %[[E1]], %[[E2]], %[[E3]], %{{.*}} : !llvm.ptr<i32>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, i32
 
   builtin.func @gpu_wmma_mma_loop_op(%arg0: memref<128x128xf16>, %arg1: memref<128x128xf16>, %arg2: memref<128x128xf16>) {
       %c0 = arith.constant 0 : index
       %c128 = arith.constant 128 : index
       %c32 = arith.constant 32 : index
       %0 = gpu.subgroup_mma_load_matrix %arg2[%c0, %c0] {leadDimension = 128 : index} : memref<128x128xf16> -> !gpu.mma_matrix<16x16xf16, "COp">
       br ^bb1(%c0, %0 : index, !gpu.mma_matrix<16x16xf16, "COp">)
     ^bb1(%1: index, %2: !gpu.mma_matrix<16x16xf16, "COp">):  // 2 preds: ^bb0, ^bb2

mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir

 // RUN: mlir-opt %s \
 // RUN: -gpu-kernel-outlining \
-// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm{index-bitwidth=32},gpu-to-cubin{chip=sm_70})' \
+// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \
 // RUN: --convert-scf-to-std -gpu-to-llvm \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \
 // RUN:   --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \
 // RUN:   --entry-point-result=void \
 // RUN: | FileCheck %s
 // Test case to check the working of Tensor cores on Nvidia GPUs. The kernel has already
 // been outlined to prevent crashing due to introduction of an empty basic block by --gpu-

mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir

 // RUN: mlir-opt %s \
 // RUN: -gpu-kernel-outlining \
-// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm{index-bitwidth=32},gpu-to-cubin{chip=sm_70})' \
+// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \
 // RUN: --convert-scf-to-std -gpu-to-llvm \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \
 // RUN:   --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \
 // RUN:   --entry-point-result=void \
 // RUN: | FileCheck %s
 
 func @main() {