[MLIR][GPU][NVVM] Add conversion of warp synchronous matrix-multiply accumulate GPU ops

[navdeepkk]

Summary

[MLIR][GPU][NVVM] Add conversion of warp synchronous matrix-multiply
accumulate GPU ops
Add conversion of warp synchronous matrix-multiply accumulate GPU ops to
NVVM ops. The following conversions are added :-

1.) subgroup_mma_load_matrix -> wmma.m16n16k16.load.[a,b,c]..row.stride
2.) subgroup_mma_store_matrix -> wmma.m16n16k16.store.d.[f16,f32].row.stride
3.) subgroup_mma_compute -> wmma.m16n16k16.mma.row.row.[f16,f32].[f16,f32]

[ftynse]

This needs to be rebased on master, I have removed LLVMType several weeks ago.

[navdeepkk]

Changes in this diff :-

1.) Rebase on master to drop the use of LLVMType.
2.) Make changes to use the !gpu.mmafragment type introduced in parent
  revision D95330.

[ftynse]

I haven't done a detailed review since this commit may change if its parent changes.

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

2

Headers need to have -*- C++ -*- at the end of the first line

mlir/lib/Conversion/GPUToNVVM/WmmaLoadOptoNvvmLowering.h

13 ↗

(On Diff #321330)

I don't see the point of putting _implementations_ of conversion patterns into separate _header_ files.

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

16–37

I don't think we care if these operations are on the next line or not. Furthermore, they seem to be testing the conversion of allocations, which isn't anyhow relevant to what this test is about. Such tests lead to excessive churn and spurious breakages. Please only test what the new code does.

[navdeepkk]

Changes in this diff :-

1.) Address comments on diff 321330.

[navdeepkk]

Changes in this diff :-

1.) Rebase on upstream/main.
3.) Make changes to operate with the newly intoduced gpu.mma_matrix type.

[bondhugula]

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

148

C++ style comment here. Terminate with full stop.

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

31

Cannot -> cannot

36

Typo: implements

64

Expected -> expected

73

Remove commented out code please.

302–307

Nit: In such cases, consider using braces for the then and else blocks.

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

41–56

Nit: Leave a space after //, i.e., CHECK -> CHECK

[bondhugula]

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

25

Many of these are now built-in MLIR types. common LLVM -> 'common LLVM and builtin MLIR`

62–63

The context here isn't clear as to operands to which op. Mention about mma/wmma when referring to fragments?

74–75

Doc comments for these two.

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

119

Drop commented out code.

[navdeepkk]

Changes in this diff :-

1.) Address comments on previous diff(342267).

[bondhugula]

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

16

clang-tidy warning if this is a meaningful one.

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

83–91

All of this code in header files instead of a source file? Is this an exception for some reason?

[ftynse]

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

26

Add a documentation comment please.

48

How about having this class (privately) derive CommonLLVMAndBuiltInMLIRTypes instead of containing it? This will let it use the fields directly without the annoying prefix.

Also, I'm not entirely certain why this is even needed. Most patterns don't need all of the types, yet this will create the types inside each pattern, taking locks in the context. Yes, we won't need to do it every time the pattern is applied, but it feels like the number of spuriously created types compensates this. Another alternative would be to have a single instance of CommonLLVMAndBuiltInMLIRTypes and pass a reference to this instance to each pattern.

65

Typo: meref -> memref ?

76

Please expand auto here.

76

Also, I suppose you need promoteOneMemRefDescriptor instead. Then you can wrap the result into a MemRefDescriptor class and get much more readable code below.

77

Could this use OpAdaptor to get named operand accessors instead of magic constants?

83–91

I made the same comment on the first version of this patch, there is no point for this code to be in a header, even if the header lives under lib/.

94

Please avoid magic constants.

187

Same comments as above in this class.

302–307

This doesn't look done, did you forget to upload?

mlir/lib/Conversion/GPUToNVVM/WmmaMmaOptoNvvmLowering.h

36–45

A similar function is present in another file this patch is adding. Consider refactoring to only have one definition.

61

SmallVector now has a default number of stack elements. Drop the manually specified number unless there is a specific reason to choose the value. Here, I don't see why 24 is special.

88

Please use OpAdaptors here and below to avoid operands[magic-constant]

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

16–37

I still see a lot of CHECK-NEXT, and I am not convinced that we should care about these operations being on subsequent strings. If tomorrow we decide to change the syntax of llvm.extractvalue to print the type a separate line, these tests would break for no good reason.

[navdeepkk]

Changes in this diff :-

1.)  Address comments on previous diff(343284).

[navdeepkk]

Changes in this diff :-

1.) Fix formatting in WmmaOpsToNvvmLowering.cpp.

[ftynse]

This is okay with me except the splitting between files. I really don't understand the motivation behind adding header files to lib/. It is justified if there are some declarations or template private implementations that must be shared between several .cpp files and not visible to the external users, but here is is not the case.

This can be organized as follows:

• the file mlir/include/mlir/Conversion/GPUToNVVM/GPUToNVVMPass.h contains a populateGPUWMMAPAtterns(...) next to the populateGPUToNVVM that it already has;
• there's one file, mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNVVM.cpp (also, drop the "lowering" from the name while we are here), that contains everything from CommonTypes.h, and the current header/source pair, all in an anonymous namespace to avoid exporting the names and slowing down the linker;
• whoever needs this patterns just includes the header and calls the populate function.

This is simple to navigate, reason about and is the pattern that all other conversions adopt.

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

2

Looks like this comment wasn't addressed..

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

148

Doesn't look addressed. C++-style comments are line comments starting with //.

mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNvvmLowering.cpp

31 ↗

(On Diff #345382)

Nit: add braces here, this isn't a trivial condition anymore.

50 ↗

(On Diff #345382)

I would have just used unsigned here.

140–142 ↗

(On Diff #345382)

Nit: can you put this comment inside return rewriter.notifyMatchFailure("") instead? Here and below.

[navdeepkk]

In D95331#2770496, @ftynse wrote:

This is okay with me except the splitting between files. I really don't understand the motivation behind adding header files to lib/. It is justified if there are some declarations or template private implementations that must be shared between several .cpp files and not visible to the external users, but here is is not the case.

This can be organized as follows:

• the file mlir/include/mlir/Conversion/GPUToNVVM/GPUToNVVMPass.h contains a populateGPUWMMAPAtterns(...) next to the populateGPUToNVVM that it already has;
• there's one file, mlir/lib/Conversion/GPUToNVVM/WmmaOpsToNVVM.cpp (also, drop the "lowering" from the name while we are here), that contains everything from CommonTypes.h, and the current header/source pair, all in an anonymous namespace to avoid exporting the names and slowing down the linker;
• whoever needs this patterns just includes the header and calls the populate function.

This is simple to navigate, reason about and is the pattern that all other conversions adopt.

Hi @ftynse,
Thanks for the insightful comments. I have a question regarding this new structure. Once I put all the code in WmmaOpsToNVVM.cpp the lowering patterns are no longer exposed. So I cannot directly use them in LowerGpuOpsToNVVMOps.cpp where they are actually added into RewritePatternSet. The way I could find is to include WmmaOpsToNVVm.cpp in LowerGpuOpsToNVVMOps.cpp. Is that okay to do in this context? Or can we just expose the lowerings in a header file?

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

16

The style given here already followed. https://llvm.org/docs/CodingStandards.html#header-guard

mlir/lib/Conversion/GPUToNVVM/WmmaLoadOptoNvvmLowering.h

13 ↗

(On Diff #321330)

We may add more versions of NVVM ops in the future, so each lowering file may grow large. To save hassle later, I kept them in separate files. Let me know if you want me to merge them.

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

48

Took the first approach and removed all the spurious types present. Now the types are minimal and the overheads would be less. Let me know what you think.

[ftynse]

In D95331#2773383, @navdeepkk wrote:

Hi @ftynse,
Thanks for the insightful comments. I have a question regarding this new structure. Once I put all the code in WmmaOpsToNVVM.cpp the lowering patterns are no longer exposed. So I cannot directly use them in LowerGpuOpsToNVVMOps.cpp where they are actually added into RewritePatternSet.

You shouldn't even need to use them directly. Just declare a function populateWmmaToNVVMPatterns(RewritePatternSet &set) in GPUToNVVMPass.h, put its declaration in the header and implementation in WmmaOpsToNVVM.cpp. Then call this function from LowerGpuOpsToNVVMOps.cpp. You understand how function declaration works in C++, right? It's enough for the caller to see the declaration (not definition) to be able to call the function.

The way I could find is to include WmmaOpsToNVVm.cpp in LowerGpuOpsToNVVMOps.cpp. Is that okay to do in this context?

Absolutely not! It's almost never okay to include a .cpp into another .cpp.

Or can we just expose the lowerings in a header file?

You don't need to expose the classes, the function that adds instances of these classes into the set will amply suffice, as I mentioned repeatedly. If you look around a bit in the code base, LowerGpuOpsToNVVMOps.cpp defines the pattern classes in an anonymous namespace and they are not at all visible in the headers. The users of this lowering just call populateGpuToNVVMConversionPatterns and never ever need to see the classes. I see no reason why you can't just do the same.

[navdeepkk]

Changes in this diff :-

1.) Address comments on diff 345382.

[navdeepkk]

Changes in this diff :-

1.) Fix spelling in file title.

[ftynse]

Thanks!

[navdeepkk]

In D95331#2773568, @ftynse wrote:

Thanks!

Thanks for the long series of great insightful comments. And apologies for taking too long in the re-structuring part.

[bondhugula]

In D95331#2773585, @navdeepkk wrote:

In D95331#2773568, @ftynse wrote:

Thanks!

Thanks for the long series of great insightful comments. And apologies for taking too long in the re-structuring part.

There appears to be something weird with this patch - doing an arc patch D95331 yields an exception and then shows zero changes. Looks like an arc bug:

...
Applied patch mlir/include/mlir/Target/LLVMIR/ModuleTranslation.h cleanly.
Applied patch mlir/include/mlir/Dialect/LLVMIR/NVVMOps.td cleanly.
Applied patch mlir/include/mlir/Dialect/GPU/GPUOps.td cleanly.
Applied patch mlir/include/mlir/Dialect/GPU/GPUDialect.h cleanly.
Applied patch mlir/include/mlir/Dialect/GPU/GPUBase.td cleanly.
 COMMITTED  Successfully committed patch.

 Cherry Pick Failed!
 Exception 
Command failed with error #1!
COMMAND
git cherry-pick -- arcpatch-D95330

STDOUT
Auto-merging mlir/test/Dialect/LLVMIR/invalid.mlir
Auto-merging mlir/lib/Target/LLVMIR/ModuleTranslation.cpp
Auto-merging mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
Auto-merging mlir/include/mlir/Target/LLVMIR/ModuleTranslation.h
Auto-merging mlir/include/mlir/Dialect/GPU/GPUOps.td
On branch arcpatch-D95331_1
You are currently cherry-picking commit 372dcf47bd93.
  (all conflicts fixed: run "git cherry-pick --continue")
  (use "git cherry-pick --skip" to skip this patch)
  (use "git cherry-pick --abort" to cancel the cherry-pick operation)

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	mlir/0001-MLIR-Update-affine.for-unroll-for-iter_args-support.patch
	mlir/compile_commands.json
	mlir/tags
	mlir/tf.mlir

nothing added to commit but untracked files present (use "git add" to track)


STDERR
The previous cherry-pick is now empty, possibly due to conflict resolution.
If you wish to commit it anyway, use:

    git commit --allow-empty

Otherwise, please use 'git cherry-pick --skip'

(Run with `--trace` for a full exception trace.)

Using --trace shows:

Otherwise, please use 'git cherry-pick --skip'
 at [<arcanist>/src/future/exec/ExecFuture.php:421]
arcanist(head=master, ref.master=239ad5c55d8d)
  #0 ExecFuture::raiseResultError(array) called at [<arcanist>/src/future/exec/ExecFuture.php:325]
  #1 ExecFuture::resolvex() called at [<arcanist>/src/repository/api/ArcanistRepositoryAPI.php:399]
  #2 ArcanistRepositoryAPI::execxLocal(string, string) called at [<arcanist>/src/workflow/ArcanistPatchWorkflow.php:778]
  #3 ArcanistPatchWorkflow::run() called at [<arcanist>/src/workflow/ArcanistPatchWorkflow.php:983]
  #4 ArcanistPatchWorkflow::applyDependencies(ArcanistBundle) called at [<arcanist>/src/workflow/ArcanistPatchWorkflow.php:469]
  #5 ArcanistPatchWorkflow::run() called at [<arcanist>/src/workflow/ArcanistPatchWorkflow.php:398]
  #6 ArcanistPatchWorkflow::run() called at [<arcanist>/scripts/arcanist.php:419]
<<< [1] (+15,126) <exec> 15,126,163 us

[navdeepkk]

Rebase on upstream/main.

mlir/lib/Conversion/GPUToNVVM/CommonTypes.h

+//===----- CommonTypes.h - Contains LLVM Types common to all Lowerings. ---===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains the common LLVM types that are used by the lowerings of
+// GPU MMA Ops to NVVM ops.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_LIB_CONVERSION_GPUTONVVM_COMMONTYPES_H
+#define MLIR_LIB_CONVERSION_GPUTONVVM_COMMONTYPES_H
+
+#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
+#include "mlir/IR/Builders.h"
+#include "llvm/IR/DerivedTypes.h"
+
+namespace mlir {
+
+/// Contains all the common LLVM types which are used across the lowerings of
+/// GPU subgroup ops to NVVM dialect.
+struct CommonLLVMTypes {
+public:
+  CommonLLVMTypes(MLIRContext *context) {
+    numHalfsInOpFrags.resize(4);
+    numHalfsInOpFrags[A] = 8;
+    numHalfsInOpFrags[B] = 8;
+    numHalfsInOpFrags[C] = 4;
+    numHalfsInOpFrags[D] = 4;
+    int8Type = IntegerType::get(context, 8);
+    int64Type = IntegerType::get(context, 64);
+    int32Type = IntegerType::get(context, 32);
+    int32PtrTy = LLVM::LLVMPointerType::get(int32Type);
+    f16Ty = FloatType::getF16(context);
+    f32Ty = FloatType::getF32(context);
+    f16PtrTy = LLVM::LLVMPointerType::get(f16Ty);
+    f16x8Ty = VectorType::get(8, f16Ty);
+    f16x16Ty = VectorType::get(16, f16Ty);
+    f16x2Ty = VectorType::get(2, f16Ty);
+    fragArrayABTy = LLVM::LLVMStructType::getLiteral(
+        context, SmallVector<Type>(8, f16x2Ty));
+    fragArrayABPtrTy = LLVM::LLVMPointerType::get(fragArrayABTy);
+    fragArrayCDTy = LLVM::LLVMStructType::getLiteral(
+        context, SmallVector<Type>(4, f16x2Ty));
+    fragArrayCDF32Ty =
+        LLVM::LLVMStructType::getLiteral(context, SmallVector<Type>(8, f32Ty));
+  };
+
+  Type int8Type;
+  Type int32Type;
+  Type int64Type;
+  Type int32PtrTy;
+  Type f16Ty;
+  Type f32Ty;
+  Type f16PtrTy;
+  Type f16x2Ty;
+  Type f16x8Ty;
+  Type f16x16Ty;
+  /// Type for the fragment of A and B operands that a single thread holds for
+  /// fp16 data type.
+  Type fragArrayABTy;
+  /// Type for a pointer to the fragment of `AB` operands that a single thread
+  /// holds for fp16 data type.
+  Type fragArrayABPtrTy;
+  /// Type for the fragment of C and D operands that a single thread holds for
+  /// fp16 data type.
+  Type fragArrayCDTy;
+  /// Type for the fragment of C and D operands that a single thread holds for
+  /// fp32 data type.
+  Type fragArrayCDF32Ty;
+  SmallVector<unsigned, 4> numHalfsInOpFrags;
+  enum OperandMap { A, B, C, D };
+};
+
+} // namespace mlir
+#endif

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

 #include "mlir/Transforms/DialectConversion.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "llvm/Support/FormatVariadic.h"
 
 #include "../GPUCommon/GPUOpsLowering.h"
 #include "../GPUCommon/IndexIntrinsicsOpLowering.h"
 #include "../GPUCommon/OpToFuncCallLowering.h"
 #include "../PassDetail.h"
+#include "WmmaLoadStoreToNvvmLowering.h"
+#include "WmmaMmaOptoNvvmLowering.h"
 
 using namespace mlir;
 
 namespace {
 
 struct GPUShuffleOpLowering : public ConvertOpToLLVMPattern<gpu::ShuffleOp> {
   using ConvertOpToLLVMPattern<gpu::ShuffleOp>::ConvertOpToLLVMPattern;
 
void runOnOperation() override {
     LLVMTypeConverter converter(m.getContext(), options);
     converter.addConversion([&](MemRefType type) -> Optional<Type> {
       if (type.getMemorySpaceAsInt() !=
           gpu::GPUDialect::getPrivateAddressSpace())
         return llvm::None;
       return converter.convertType(MemRefType::Builder(type).setMemorySpace(0));
     });
 
+    // Lowering for MMAMatrixType.
+    converter.addConversion([&](gpu::MMAMatrixType type) -> Type {
+      // The number of items in structToReturn are dependent on the the dataType
+      // and the MMA operand that this operation is associated with.
+      llvm::DenseMap<StringRef, int64_t> numElemsPerThreadF16,
+          numElemsPerThreadF32;
+      numElemsPerThreadF16["AOp"] = 8;
+      numElemsPerThreadF16["BOp"] = 8;
+      numElemsPerThreadF16["COp"] = 4;
+      numElemsPerThreadF16["DOp"] = 4;
+      numElemsPerThreadF32["AOp"] = 8;
+      numElemsPerThreadF32["BOp"] = 8;
+      numElemsPerThreadF32["COp"] = 8;
+      numElemsPerThreadF32["DOp"] = 8;
+      Type structToReturn;
+      if (type.getElementType().isF16()) {
+        unsigned vecSize = 2 /*number of f16's in 32-bit*/;
+        Type vec = VectorType::get(vecSize, FloatType::getF16(&getContext()));
+        unsigned size = numElemsPerThreadF16[type.getOperand()];
+        SmallVector<Type> elements(size, vec);
+        structToReturn =
+            LLVM::LLVMStructType::getLiteral(&getContext(), elements);
+      } else if (type.getElementType().isF32()) {
+        unsigned size = numElemsPerThreadF32[type.getOperand()];
+        SmallVector<Type> elements(size, FloatType::getF32(&getContext()));
+        structToReturn =
+            LLVM::LLVMStructType::getLiteral(&getContext(), elements);
+      }
+      return structToReturn;
+    });
+
     RewritePatternSet patterns(m.getContext());
     RewritePatternSet llvmPatterns(m.getContext());
 
     // Apply in-dialect lowering first. In-dialect lowering will replace ops
     // which need to be lowered further, which is not supported by a single
     // conversion pass.
     populateGpuRewritePatterns(patterns);
     (void)applyPatternsAndFoldGreedily(m, std::move(patterns));
void mlir::populateGpuToNVVMConversionPatterns(LLVMTypeConverter &converter,
   // Explicitly drop memory space when lowering private memory
   // attributions since NVVM models it as `alloca`s in the default
   // memory space and does not support `alloca`s with addrspace(5).
   patterns.add<GPUFuncOpLowering>(
       converter, /*allocaAddrSpace=*/0,
       Identifier::get(NVVM::NVVMDialect::getKernelFuncAttrName(),
                       &converter.getContext()));
 
+  patterns.insert<WmmaLoadOpToNVVMLowering>(converter);
+  patterns.insert<WmmaMmaOpToNVVMLowering>(converter);
+  patterns.insert<WmmaStoreOpToNVVMLowering>(converter);
   patterns.add<OpToFuncCallLowering<AbsFOp>>(converter, "__nv_fabsf",
                                              "__nv_fabs");
   patterns.add<OpToFuncCallLowering<math::AtanOp>>(converter, "__nv_atanf",
                                                    "__nv_atan");
   patterns.add<OpToFuncCallLowering<math::Atan2Op>>(converter, "__nv_atan2f",
                                                     "__nv_atan2");
   patterns.add<OpToFuncCallLowering<CeilFOp>>(converter, "__nv_ceilf",
                                               "__nv_ceil");

mlir/lib/Conversion/GPUToNVVM/WmmaLoadStoreToNvvmLowering.h

+//===- WmmaLoadStoreOpToNVVMLowering.h - LD/ST to NVVM lowering -*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains patterns to lower the GPU subgroup MMA_loadOp and
+// MMA_storeop to NVVM Dialect.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_LIB_CONVERSION_GPUTONVVM_WMMALOADOPTONVVMLOWERING_H
+#define MLIR_LIB_CONVERSION_GPUTONVVM_WMMALOADOPTONVVMLOWERING_H
+
+#include "CommonTypes.h"
+#include "mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h"
+#include "mlir/Dialect/GPU/GPUDialect.h"
+#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
+#include "mlir/Dialect/LLVMIR/NVVMDialect.h"
+
+namespace mlir {
+
+static LogicalResult areAllLLVMTypes(Operation *op, ValueRange operands,
+                                     ConversionPatternRewriter &rewriter) {
+  if (!llvm::all_of(operands, [](Value value) {
+        return LLVM::isCompatibleType(value.getType());
+      }))
+    return rewriter.notifyMatchFailure(
+        op, "Cannot convert if operands aren't of LLVM type.");
+
+  return success();
+}
+
+/// This class implemtents the conversion of GPU MMA loadOp to wmma.load op
+/// in the NVVM dialect. The conversion not only emits the NVVM op but also
+/// emits code that is necessary to store the data in the destination memref
+/// after it has been loaded.
+struct WmmaLoadOpToNVVMLowering
+    : public ConvertOpToLLVMPattern<gpu::SubgroupMmaLoadMatrixOp> {
+public:
+  MLIRContext *context = &this->getTypeConverter()->getContext();
+
+  explicit WmmaLoadOpToNVVMLowering(LLVMTypeConverter &typeConverter)
+      : ConvertOpToLLVMPattern<gpu::SubgroupMmaLoadMatrixOp>(typeConverter),
+        llvmTypes(context) {}
+
+  LogicalResult
+  matchAndRewrite(gpu::SubgroupMmaLoadMatrixOp subgroupMmaLoadMatrixOp,
+                  ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    Operation *op = subgroupMmaLoadMatrixOp.getOperation();
+    if (failed(areAllLLVMTypes(op, operands, rewriter)))
+      return failure();
+
+    int8_t 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 meref to be 32-bit wide.");
+
+    // Source memref of the original op.
+    MemRefType srcMemrefType =
+        subgroupMmaLoadMatrixOp.srcMemref().getType().cast<MemRefType>();
+    Location loc = op->getLoc();
+
+    auto beginInx = subgroupMmaLoadMatrixOp.indices().getBeginOperandIndex();
+    auto leadDimension = subgroupMmaLoadMatrixOp.leadDimensionAttr();
+    // auto operand = subgroupMmaLoadMatrixOp.operandAttr();
+
+    // Emit information for the memref operands.
+    auto promotedSrcOp = this->getTypeConverter()->promoteOperands(
+        loc, op->getOperand(0), operands[0], rewriter);
+
+    // 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.
+    Value srcOffsetIVal = subgroupMmaLoadMatrixOp->getOpOperand(beginInx).get();
+    Value srcOffsetJVal =
+        subgroupMmaLoadMatrixOp->getOpOperand(beginInx + 1).get();
+    Value leadingDim32 = rewriter.create<LLVM::ConstantOp>(
+        loc, llvmTypes.int32Type, leadDimension);
+    Value numElemsLeadDim = rewriter.create<LLVM::MulOp>(
+        loc, llvmTypes.int32Type, leadingDim32, srcOffsetIVal);
+    Value loadOffset = rewriter.create<LLVM::AddOp>(
+        loc, llvmTypes.int32Type, numElemsLeadDim, srcOffsetJVal);
+
+    Value promotedSrcOpToUse;
+    promotedSrcOpToUse = promotedSrcOp[2];
+    Value actualOffset = rewriter.create<LLVM::AddOp>(
+        loc, llvmTypes.int32Type, loadOffset, promotedSrcOpToUse);
+    Value loadAddress = rewriter.create<LLVM::GEPOp>(
+        loc,
+        LLVM::LLVMPointerType::get(llvmTypes.f16Ty,
+                                   srcMemrefType.getMemorySpaceAsInt()),
+        promotedSrcOp[1], 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 mathc with the
+    // intrinsic exposed by NVPTX backend.
+    Value loadAddressCasted = rewriter.create<LLVM::BitcastOp>(
+        loc,
+        LLVM::LLVMPointerType::get(llvmTypes.int32Type,
+                                   srcMemrefType.getMemorySpaceAsInt()),
+        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;
+    // StringRef operandStr = operand.cast<mlir::StringAttr>().getValue();
+    StringRef operandStr = retType.getOperand();
+    if (operandStr.equals("AOp") || operandStr.equals("BOp")) {
+      resType = llvmTypes.fragArrayABTy;
+    } else {
+      if (srcMemrefType.getElementType().isF16())
+        resType = llvmTypes.fragArrayCDTy;
+      else if (srcMemrefType.getElementType().isF32())
+        resType = llvmTypes.fragArrayCDF32Ty;
+      else
+        return failure();
+    }
+
+    // Create nvvm.mma_load op according to the operand types.
+    SmallVector<Value, 2> loadOpOperands({loadAddressCasted, leadingDim32});
+    if (operandStr.equals("AOp")) {
+      if (retTypeShape[0] == 16 && retTypeShape[1] == 16) {
+        NVVM::WMMALoadAM16N16K16Op wmmaLoadAOp =
+            rewriter.create<NVVM::WMMALoadAM16N16K16Op>(loc, resType,
+                                                        loadOpOperands);
+        rewriter.replaceOp(op, wmmaLoadAOp.getResult());
+      } else {
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+      }
+    } else if (operandStr.equals("BOp")) {
+      if (retTypeShape[0] == 16 && retTypeShape[1] == 16) {
+        NVVM::WMMALoadBM16N16K16Op wmmaLoadBOp =
+            rewriter.create<NVVM::WMMALoadBM16N16K16Op>(loc, resType,
+                                                        loadOpOperands);
+        rewriter.replaceOp(op, wmmaLoadBOp.getResult());
+      } else {
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+      }
+    } else {
+      if (retTypeShape[0] == 16 && retTypeShape[1] == 16) {
+        if (srcMemrefType.getElementType().isF16()) {
+          NVVM::WMMALoadCF16M16N16K16Op wmmaLoadCOp =
+              rewriter.create<NVVM::WMMALoadCF16M16N16K16Op>(loc, resType,
+                                                             loadOpOperands);
+          rewriter.replaceOp(op, wmmaLoadCOp.getResult());
+        } else if (srcMemrefType.getElementType().isF32()) {
+          NVVM::WMMALoadCF32M16N16K16Op wmmaLoadCOp =
+              rewriter.create<NVVM::WMMALoadCF32M16N16K16Op>(loc, resType,
+                                                             loadOpOperands);
+          rewriter.replaceOp(op, wmmaLoadCOp.getResult());
+        }
+      } else {
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+      }
+    }
+    return success();
+  }
+
+private:
+  /// Contains definitions of all the LLVM types which are used for lowering
+  /// this GPU subgroupMmaLoadMatrixOp.
+  CommonLLVMTypes llvmTypes;
+};
+
+/// This class implements the conversion of GPU MMA storeOp to wmma.store op
+/// in the NVVM dialect. The conversion not only emits the NVVM op but also
+/// emits code that is necessary to unpack the data in the source and convert
+/// the data in the format that is needed by the NVVM op.
+struct WmmaStoreOpToNVVMLowering
+    : public ConvertOpToLLVMPattern<gpu::SubgroupMmaStoreMatrixOp> {
+public:
+  MLIRContext *context = &this->getTypeConverter()->getContext();
+
+  explicit WmmaStoreOpToNVVMLowering(LLVMTypeConverter &typeConverter)
+      : ConvertOpToLLVMPattern<gpu::SubgroupMmaStoreMatrixOp>(typeConverter),
+        llvmTypes(context) {}
+
+  LogicalResult
+  matchAndRewrite(gpu::SubgroupMmaStoreMatrixOp subgroupMmaStoreMatrixOp,
+                  ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    Operation *op = subgroupMmaStoreMatrixOp.getOperation();
+    if (failed(areAllLLVMTypes(op, operands, rewriter)))
+      return failure();
+
+    int8_t 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 meref to be 32-bit wide.");
+
+    Location loc = op->getLoc();
+
+    // Destination memref of the original op.
+    MemRefType dstMemrefType =
+        subgroupMmaStoreMatrixOp.dstMemref().getType().cast<MemRefType>();
+
+    auto promotedDstOp = this->getTypeConverter()->promoteOperands(
+        loc, op->getOperand(1), operands[1], rewriter);
+
+    auto leadDimension = subgroupMmaStoreMatrixOp.leadDimensionAttr();
+    unsigned beginInx =
+        subgroupMmaStoreMatrixOp.indices().getBeginOperandIndex();
+
+    // Emit ops which compute the store offset using `dstOffsetI`,
+    // `dstOffsetJ`. The actualOffset is (memrefOffset + (alignedPtr +
+    // ((leadDimension * dstOffsetI) + dstOffsetJ)).
+    Value dstOffsetIVal = subgroupMmaStoreMatrixOp.getOperand(beginInx);
+    Value dstOffsetJVal = subgroupMmaStoreMatrixOp.getOperand(beginInx + 1);
+    Value leadingDim32 = rewriter.create<LLVM::ConstantOp>(
+        loc, llvmTypes.int32Type, leadDimension);
+    Value numElemsLeadDim = rewriter.create<LLVM::MulOp>(
+        loc, llvmTypes.int32Type, leadingDim32, dstOffsetIVal);
+    Value loadOffset = rewriter.create<LLVM::AddOp>(
+        loc, llvmTypes.int32Type, numElemsLeadDim, dstOffsetJVal);
+
+    Value promotedDstOpToUse;
+    promotedDstOpToUse = promotedDstOp[2];
+    Value actualOffset = rewriter.create<LLVM::AddOp>(
+        loc, llvmTypes.int32Type, loadOffset, promotedDstOpToUse);
+    Value storeAddress = rewriter.create<LLVM::GEPOp>(
+        loc,
+        LLVM::LLVMPointerType::get(llvmTypes.f16Ty,
+                                   dstMemrefType.getMemorySpaceAsInt()),
+        promotedDstOp[1], 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 mathc with the
+    // intrinsic exposed by NVPTX backend.
+    Value storeAddressCasted = rewriter.create<LLVM::BitcastOp>(
+        loc,
+        LLVM::LLVMPointerType::get(llvmTypes.int32Type,
+                                   dstMemrefType.getMemorySpaceAsInt()),
+        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();
+
+    // Unpack the results from the source.
+    if (subgroupMmaStoreMatrixOp.src()
+            .getType()
+            .cast<gpu::MMAMatrixType>()
+            .getElementType() == llvmTypes.f16Ty) {
+      for (unsigned i = 0, e = llvmTypes.numHalfsInOpFrags[llvmTypes.D]; i < e;
+           ++i) {
+        Value toUse = rewriter.create<LLVM::ExtractValueOp>(
+            loc, llvmTypes.f16x2Ty, operands[0], rewriter.getI32ArrayAttr(i));
+        storeOpOperands.push_back(toUse);
+      }
+      storeOpOperands.push_back(leadingDim32);
+
+      // Create nvvm.mma_store op.
+      if (srcTypeShape[0] == 16 && srcTypeShape[1] == 16)
+        rewriter.create<NVVM::WMMAStoreF16M16N16K16Op>(loc, storeOpOperands);
+      else
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+
+      rewriter.eraseOp(op);
+      return success();
+    }
+    if (subgroupMmaStoreMatrixOp.src()
+            .getType()
+            .cast<gpu::MMAMatrixType>()
+            .getElementType() == llvmTypes.f32Ty) {
+      for (unsigned i = 0, e = 8; i < e; ++i) {
+        Value toUse = rewriter.create<LLVM::ExtractValueOp>(
+            loc, llvmTypes.f32Ty, operands[0], rewriter.getI32ArrayAttr(i));
+        storeOpOperands.push_back(toUse);
+      }
+      storeOpOperands.push_back(leadingDim32);
+
+      // Create nvvm.mma_store op.
+      if (srcTypeShape[0] == 16 && srcTypeShape[1] == 16)
+        rewriter.create<NVVM::WMMAStoreF32M16N16K16Op>(loc, storeOpOperands);
+      else
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+
+      rewriter.eraseOp(op);
+      return success();
+    }
+
+    return failure();
+  }
+
+private:
+  /// Definitions of all the LLVM types which are used for lowering this GPU
+  /// SubgroupMmaStoreMatrixOp.
+  CommonLLVMTypes llvmTypes;
+};
+} // namespace mlir
+
+#endif

mlir/lib/Conversion/GPUToNVVM/WmmaMmaOptoNvvmLowering.h

+//===--- WmmaMmaOpToNVVMLowering.h - MmaOp to NVVM Op lowering -*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains patterns to lower the GPU subgroup MMA_computeOp to the
+// NVVM Dialect.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_LIB_CONVERSION_GPUTONVVM_WMMASTOREOPTONVVMLOWERING_H
+#define MLIR_LIB_CONVERSION_GPUTONVVM_WMMASTOREOPTONVVMLOWERING_H
+
+#include "CommonTypes.h"
+#include "mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h"
+#include "mlir/Dialect/GPU/GPUDialect.h"
+#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
+#include "mlir/Dialect/LLVMIR/NVVMDialect.h"
+
+namespace mlir {
+/// This class implemtents the conversion of GPU MMA computeOp to wmma.mma op
+/// in the NVVM dialect.
+struct WmmaMmaOpToNVVMLowering
+    : public ConvertOpToLLVMPattern<gpu::SubgroupMmaComputeOp> {
+public:
+  MLIRContext *context = &this->getTypeConverter()->getContext();
+
+  explicit WmmaMmaOpToNVVMLowering(LLVMTypeConverter &typeConverter)
+      : ConvertOpToLLVMPattern<gpu::SubgroupMmaComputeOp>(typeConverter),
+        llvmTypes(context) {}
+
+  static LogicalResult areAllLLVMTypes(Operation *op, ValueRange operands,
+                                       ConversionPatternRewriter &rewriter) {
+    if (!llvm::all_of(operands, [](Value value) {
+          return LLVM::isCompatibleType(value.getType());
+        }))
+      return rewriter.notifyMatchFailure(
+          op, "Cannot convert if operands aren't of LLVM type.");
+
+    return success();
+  }
+
+  LogicalResult
+  matchAndRewrite(gpu::SubgroupMmaComputeOp subgroupMmaComputeOp,
+                  ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    Operation *op = subgroupMmaComputeOp.getOperation();
+    if (failed(areAllLLVMTypes(op, operands, rewriter)))
+      return failure();
+
+    Location loc = op->getLoc();
+
+    // The wmma.mma intrinsic in llvm requires the operands as individual
+    // values. So individual elements from the memrefs need to be extracted and
+    // then passed on to the intrinsic call. Emit llvm ops to extract individual
+    // values form lowered memrefs.
+    SmallVector<Value, 24> unpackedOps;
+
+    auto unpackOp = [&](CommonLLVMTypes::OperandMap op, Value operand,
+                        unsigned numElems, Type elemType) {
+      for (unsigned i = 0; i < numElems; ++i) {
+        Value toUse = rewriter.create<LLVM::ExtractValueOp>(
+            loc, elemType, operand, rewriter.getI32ArrayAttr(i));
+        unpackedOps.push_back(toUse);
+      }
+    };
+
+    // Get the shapes of the MMAMatrix type being used. The shapes will
+    // choose which intrinsic this op will be lowered to.
+    gpu::MMAMatrixType aType =
+        subgroupMmaComputeOp.opA().getType().cast<gpu::MMAMatrixType>();
+    ArrayRef<int64_t> aTypeShape = aType.getShape();
+    gpu::MMAMatrixType bType =
+        subgroupMmaComputeOp.opA().getType().cast<gpu::MMAMatrixType>();
+    ArrayRef<int64_t> bTypeShape = bType.getShape();
+    gpu::MMAMatrixType cType =
+        subgroupMmaComputeOp.opA().getType().cast<gpu::MMAMatrixType>();
+    ArrayRef<int64_t> cTypeShape = cType.getShape();
+
+    if (subgroupMmaComputeOp.opC()
+            .getType()
+            .cast<gpu::MMAMatrixType>()
+            .getElementType() == llvmTypes.f16Ty) {
+      unpackOp(llvmTypes.A, operands[0],
+               llvmTypes.numHalfsInOpFrags[llvmTypes.A], llvmTypes.f16x2Ty);
+      unpackOp(llvmTypes.B, operands[1],
+               llvmTypes.numHalfsInOpFrags[llvmTypes.B], llvmTypes.f16x2Ty);
+      unpackOp(llvmTypes.C, operands[2],
+               llvmTypes.numHalfsInOpFrags[llvmTypes.C], llvmTypes.f16x2Ty);
+
+      if (aTypeShape[0] == 16 && aTypeShape[1] == 16 && bTypeShape[0] == 16 &&
+          bTypeShape[1] == 16 && cTypeShape[0] == 16 && cTypeShape[1] == 16) {
+        // Create nvvm.wmma.mma op.
+        NVVM::WMMAMmaF16F16M16N16K16Op wmmaMmaOp =
+            rewriter.create<NVVM::WMMAMmaF16F16M16N16K16Op>(
+                loc, llvmTypes.fragArrayCDTy, unpackedOps);
+
+        rewriter.replaceOp(op, wmmaMmaOp.getResult());
+        return success();
+      } else {
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+      }
+    }
+
+    if (subgroupMmaComputeOp.opC()
+            .getType()
+            .cast<gpu::MMAMatrixType>()
+            .getElementType() == llvmTypes.f32Ty) {
+      unpackOp(llvmTypes.A, operands[0],
+               llvmTypes.numHalfsInOpFrags[llvmTypes.A], llvmTypes.f16x2Ty);
+      unpackOp(llvmTypes.B, operands[1],
+               llvmTypes.numHalfsInOpFrags[llvmTypes.B], llvmTypes.f16x2Ty);
+      unpackOp(llvmTypes.C, operands[2], 8, llvmTypes.f32Ty);
+
+      if (aTypeShape[0] == 16 && aTypeShape[1] == 16 && bTypeShape[0] == 16 &&
+          bTypeShape[1] == 16 && cTypeShape[0] == 16 && cTypeShape[1] == 16) {
+        // Create nvvm.wmma.mma op.
+        NVVM::WMMAMmaF32F32M16N16K16Op wmmaMmaOp =
+            rewriter.create<NVVM::WMMAMmaF32F32M16N16K16Op>(
+                loc, llvmTypes.fragArrayCDF32Ty, unpackedOps);
+
+        rewriter.replaceOp(op, wmmaMmaOp.getResult());
+        return success();
+      } else {
+        // Only M16N16K16 version implemented. Add cases as more versions are
+        // implemented.
+        return failure();
+      }
+    }
+
+    return failure();
+  }
+
+private:
+  /// Definitions of all the LLVM types which are used for lowering
+  /// this GPU subgroupMmaComputeOp.
+  CommonLLVMTypes llvmTypes;
+};
+} // namespace mlir
+
+#endif

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
+
+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>)> {
+  //func @gpu_wmma_load_op() -> (!gpu.mmafragment<8, vector<2xf16>>) {
+  func @gpu_wmma_load_op() -> (!gpu.mma_matrix<16x16xf16, "AOp">) {
+    %wg = memref.alloca() {alignment = 32} : memref<32x32xf16, 3>
+    %i = constant 16 : index
+    %j = constant 16 : index
+    //%0 = gpu.subgroup_mma_load_matrix %wg[%i, %j] {operand = "AOp", leadDimension = 32 : index} : memref<32x32xf16, 3> -> !gpu.mmafragment<8, vector<2xf16>>
+    %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:  %[[BASE:.*]] = llvm.extractvalue %{{.*}}[1] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    //CHECK-NEXT:  %[[OFFSET:.*]] = llvm.extractvalue %{{.*}}[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    //CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
+    //CHECK-NEXT:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
+    //CHECK-NEXT:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
+    //CHECK-NEXT:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
+    //CHECK-NEXT:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    //CHECK-NEXT:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
+    //CHECK-NEXT:  %[[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-NEXT:  llvm.return %[[FRAG]] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //return %0 : !gpu.mmafragment<8, 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>)>) {
+  func @gpu_wmma_store_op(%arg0 : !gpu.mma_matrix<16x16xf16, "DOp">) -> () {
+    %sg = memref.alloca(){alignment = 32} : memref<32x32xf16, 3>
+    %i = constant 16 : index
+    %j = constant 16 : index
+    gpu.subgroup_mma_store_matrix %arg0, %sg[%i,%j] {leadDimension= 32 : index} : !gpu.mma_matrix<16x16xf16, "DOp">, memref<32x32xf16, 3>
+    //CHECK:  %[[INX:.*]] = llvm.mlir.constant(16 : index) : 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-NEXT:  %[[OFFSET:.*]] = llvm.extractvalue %17[2] : !llvm.struct<(ptr<f16, 3>, ptr<f16, 3>, i32, array<2 x i32>, array<2 x i32>)>
+    //CHECK:  %[[LDM:.*]] = llvm.mlir.constant(32 : index) : i32
+    //CHECK-NEXT:  %[[LI:.*]] = llvm.mul %[[LDM]], %[[INX]]  : i32
+    //CHECK-NEXT:  %[[LIJ:.*]] = llvm.add %[[LI]], %[[INX]]  : i32
+    //CHECK-NEXT:  %[[LIJO:.*]] = llvm.add %[[LIJ]], %[[OFFSET]]  : i32
+    //CHECK-NEXT:  %[[ADDRESS:.*]] = llvm.getelementptr %[[BASE]][%[[LIJO]]] : (!llvm.ptr<f16, 3>, i32) -> !llvm.ptr<f16, 3>
+    //CHECK-NEXT:  %[[CADDRESS:.*]] = llvm.bitcast %[[ADDRESS]] : !llvm.ptr<f16, 3> to !llvm.ptr<i32, 3>
+    //CHECK-NEXT:  %[[EL1:.*]] = llvm.extractvalue %[[D]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[EL2:.*]] = llvm.extractvalue %[[D]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[EL3:.*]] = llvm.extractvalue %[[D]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[EL4:.*]] = llvm.extractvalue %[[D]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  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-NEXT:  llvm.return
+    return
+  }
+}
+
+// -----
+
+gpu.module @test_module {
+
+  // CHECK-LABEL: func @gpu_wmma_mma_op
+  // CHECK-SAME: (%[[A:.*]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>, %[[B:.*]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>, %[[C:.*]]: !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>) {
+  func @gpu_wmma_mma_op(%A : !gpu.mma_matrix<16x16xf16, "AOp">, %B : !gpu.mma_matrix<16x16xf16, "BOp">, %C : !gpu.mma_matrix<16x16xf16, "COp">) -> () {
+    %D = gpu.subgroup_mma_compute %A, %B, %C : !gpu.mma_matrix<16x16xf16, "AOp">, !gpu.mma_matrix<16x16xf16, "BOp">, !gpu.mma_matrix<16x16xf16, "COp"> -> !gpu.mma_matrix<16x16xf16, "DOp">
+    //CHECK:  %[[A1:.*]] = llvm.extractvalue %[[A]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A2:.*]] = llvm.extractvalue %[[A]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A3:.*]] = llvm.extractvalue %[[A]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A4:.*]] = llvm.extractvalue %[[A]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A5:.*]] = llvm.extractvalue %[[A]][4 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A6:.*]] = llvm.extractvalue %[[A]][5 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A7:.*]] = llvm.extractvalue %[[A]][6 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[A8:.*]] = llvm.extractvalue %[[A]][7 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B1:.*]] = llvm.extractvalue %[[B]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B2:.*]] = llvm.extractvalue %[[B]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B3:.*]] = llvm.extractvalue %[[B]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B4:.*]] = llvm.extractvalue %[[B]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B5:.*]] = llvm.extractvalue %[[B]][4 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B6:.*]] = llvm.extractvalue %[[B]][5 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B7:.*]] = llvm.extractvalue %[[B]][6 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[B8:.*]] = llvm.extractvalue %[[B]][7 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[C1:.*]] = llvm.extractvalue %[[C]][0 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[C2:.*]] = llvm.extractvalue %[[C]][1 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[C3:.*]] = llvm.extractvalue %[[C]][2 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %[[C4:.*]] = llvm.extractvalue %[[C]][3 : i32] : !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  %{{.*}} = nvvm.wmma.m16n16k16.mma.row.row.f16.f16 %[[A1]], %[[A2]], %[[A3]], %[[A4]], %[[A5]], %[[A6]], %[[A7]], %[[A8]], %[[B1]], %[[B2]], %[[B3]], %[[B4]], %[[B5]], %[[B6]], %[[B7]], %[[B8]], %[[C1]], %[[C2]], %[[C3]], %[[C4]] : vector<2xf16> -> !llvm.struct<(vector<2xf16>, vector<2xf16>, vector<2xf16>, vector<2xf16>)>
+    //CHECK-NEXT:  llvm.return
+    return
+  }
+}