[NVIDIA][Backend] Add CoalesceAsyncCopy Pass for in-DotOpEnc Upcasting

include/triton/Dialect/TritonGPU/Transforms/Passes.td

@@ -192,4 +192,16 @@ def TritonGPULoopScheduling: Pass<"tritongpu-loop-scheduling", "mlir::ModuleOp">
            "number of pipeline stages">
   ];
 }
+
+def TritonGPUCoalesceAsyncCopy: Pass<"tritongpu-coalesce-async-copy", "mlir::ModuleOp"> {
+  let summary = "Improve coalescing for async global to local copies";
+
+  let description = "For AsyncCopyGlobalToLocal ops where the shared encoding's vec is less than "
+                    "the blocked encoding's sizePerThread, this pass improves coalescing by clipping the "
+                    "sizePerThread value";
+
+  let dependentDialects = ["mlir::triton::gpu::TritonGPUDialect",
+                           "mlir::triton::TritonDialect"];
+}
+
 #endif

include/triton/Dialect/TritonGPU/Transforms/Utility.h

@@ -202,6 +202,11 @@ enum class MMALoadType {
                  // pipelining
 };
 MMALoadType getMMALoadType(Operation *loadOp);
+
+// Returns composed LinearLayout for register to shared copy
+std::optional<triton::LinearLayout>
+getRegToSharedLayout(MLIRContext *ctx, ArrayRef<int64_t> shape,
+                     Attribute srcEnc, Attribute dstEnc, int elemBitWidth);
 } // namespace mlir
 
 #endif // TRITON_DIALECT_TRITONGPU_TRANSFORMS_UTILITY_H_

lib/Conversion/TritonGPUToLLVM/Utility.cpp

@@ -4,6 +4,7 @@
 #include "triton/Conversion/TritonGPUToLLVM/TargetInfoBase.h"
 #include "triton/Dialect/TritonGPU/IR/Attributes.h"
 #include "triton/Dialect/TritonGPU/IR/LinearLayoutConversions.h"
+#include "triton/Dialect/TritonGPU/Transforms/Utility.h"
 #include "llvm/ADT/STLExtras.h"
 
 namespace mlir {
@@ -174,41 +175,17 @@ bool emitTransferBetweenRegistersAndShared(
   StringAttr kLane = str_attr("lane");
   StringAttr kWarp = str_attr("warp");
 
-  std::optional<LinearLayout> regLayout =
-      triton::gpu::toLinearLayout(shape, registerTy.getEncoding());
-  std::optional<LinearLayout> sharedLayout = triton::gpu::toLinearLayout(
-      shape, sharedTy.getEncoding(), elemLlvmTy.getIntOrFloatBitWidth());
-  if (!regLayout.has_value() || !sharedLayout.has_value()) {
+  auto regToSharedLayout = getRegToSharedLayout(
+      ctx, shape, registerTy.getEncoding(), sharedTy.getEncoding(),
+      elemLlvmTy.getIntOrFloatBitWidth());
+  if (!regToSharedLayout.has_value())
     return false;
-  }
-  auto sharedOrder = triton::gpu::getOrder(sharedTy.getEncoding());
-
-  // sharedLayout's in-dims are currently (offset, block).  Reshape to
-  // (offsetX1, offsetX2, ..., block) so that we can apply the N-dimensional
-  // shmem strides.  (The offsetX's appear in minor-to-major order.)
-  auto sharedLegacy =
-      cast<triton::gpu::SharedEncodingAttr>(sharedTy.getEncoding());
-  SmallVector<std::pair<StringAttr, int32_t>> multiDimSharedSize;
-  for (int i = 0; i < rank; i++) {
-    int dim = sharedOrder[i];
-    int64_t size = std::max(
-        int64_t{1},
-        shape[dim] / sharedLegacy.getCTALayout().getCTASplitNum()[dim]);
-    multiDimSharedSize.push_back(
-        {str_attr("offset" + std::to_string(dim)), size});
-  }
-  multiDimSharedSize.push_back({kBlock, sharedLayout->getInDimSize(kBlock)});
-  sharedLayout = sharedLayout->reshapeIns(multiDimSharedSize);
-
-  // regToSharedLayout maps from (register, lane, warp, block) to (offsetX1,
-  // ..., offsetXN, block), where the offsetX's are in minor-to-major order.
-  LinearLayout regToSharedLayout = regLayout->invertAndCompose(*sharedLayout);
 
   // TODO(jlebar): We don't currently support loading from shared memory in a
   // different CTA.  We'd need to emit `mapa.shared::cluster` instructions.
-  for (int inBlock = 1; inBlock < regToSharedLayout.getInDimSize(kBlock);
+  for (int inBlock = 1; inBlock < regToSharedLayout->getInDimSize(kBlock);
        inBlock *= 2) {
-    auto idx = llvm::to_vector(llvm::make_second_range(regToSharedLayout.apply(
+    auto idx = llvm::to_vector(llvm::make_second_range(regToSharedLayout->apply(
         {{kRegister, 0}, {kLane, 0}, {kWarp, 0}, {kBlock, inBlock}})));
     // offsetX1, ..., offsetXN must all be 0.
     if (!llvm::all_of(ArrayRef(idx).drop_back(1),
@@ -234,15 +211,15 @@ bool emitTransferBetweenRegistersAndShared(
   // which have known strides.  This would allow us to vectorize across multiple
   // shmem out dimensions where possible.
   const int vecElems =
-      std::min(regToSharedLayout.getNumConsecutiveInOut(),
+      std::min(regToSharedLayout->getNumConsecutiveInOut(),
                maxVecElems.value_or(std::numeric_limits<int>::max()));
 
   Value threadId = getThreadId(rewriter, loc);
-  Value threadsPerWarp = i32_val(regToSharedLayout.getInDimSize(kLane));
+  Value threadsPerWarp = i32_val(regToSharedLayout->getInDimSize(kLane));
   Value laneId = urem(threadId, threadsPerWarp);
   Value warpId = udiv(threadId, threadsPerWarp);
 
-  int numElems = regToSharedLayout.getInDimSize(kRegister);
+  int numElems = regToSharedLayout->getInDimSize(kRegister);
   auto vecTy = vec_ty(elemLlvmTy, vecElems);
   auto ptrTy = shmemBase.getType();
   Value zero = i32_val(0);
@@ -253,14 +230,15 @@ bool emitTransferBetweenRegistersAndShared(
     // we drop_end to drop block, which we know from above will be 0.
     auto multiDimShmemOffset =
         llvm::to_vector(llvm::drop_end(llvm::make_second_range(
-            applyLinearLayout(loc, rewriter, regToSharedLayout,
+            applyLinearLayout(loc, rewriter, *regToSharedLayout,
                               {{kRegister, i32_val(i * vecElems)},
                                {kLane, laneId},
                                {kWarp, warpId},
                                {kBlock, zero}}))));
 
     // Reorder strides according to `order`.  This way they match the
     // multi-dimensional offsets in regToSharedLayout.
+    auto sharedOrder = triton::gpu::getOrder(sharedTy.getEncoding());
     Value shmemOffset = dot(rewriter, loc, multiDimShmemOffset,
                             applyPermutation(shmemStrides, sharedOrder));
     auto vecAddr = gep(ptrTy, elemLlvmTy, shmemBase, shmemOffset);

lib/Dialect/TritonGPU/Transforms/CMakeLists.txt

@@ -18,6 +18,7 @@ add_triton_library(TritonGPUTransforms
   Prefetch.cpp
   RemoveLayoutConversions.cpp
   ReorderInstructions.cpp
+  CoalesceAsyncCopy.cpp
   Utility.cpp
 
   DEPENDS

lib/Dialect/TritonGPU/Transforms/CoalesceAsyncCopy.cpp

@@ -0,0 +1,124 @@
+#include "mlir/Support/LLVM.h"
+#include "mlir/Transforms/Passes.h"
+#include "triton/Analysis/Utility.h"
+#include "triton/Dialect/TritonGPU/IR/Dialect.h"
+#include "triton/Dialect/TritonGPU/Transforms/Utility.h"
+
+namespace mlir {
+namespace triton {
+namespace gpu {
+
+#define GEN_PASS_DEF_TRITONGPUCOALESCEASYNCCOPY
+#include "triton/Dialect/TritonGPU/Transforms/Passes.h.inc"
+
+// This pass currently only applies if the following are all true...
+//   1) Operand A for WGMMA is to be loaded in registers
+//   2) We upcast operand A in registers before the WGMMA
+//      (downcasting is not yet supported)
+//   3) Pipelining is enabled for loading A
+//
+// ...then for the AsyncCopyGlobalToLocal op, the SharedEncoding
+// vec will be less than BlockedEncoding's sizePerThread for k-dim. E.g. if
+// we're upcasting from int8 to bf16, then shared vec is 8 and sizePerThread
+// for k is 16. In this case, AsyncCopyGlobalToLocal will generate two
+// 8-byte-cp.async's for each contiguous 16B global data owned by each
+// thread. This breaks coalescing (i.e. results 2x the minimum required
+// transactions).
+//
+// This issue occurs for cp.async because it combines load and store into one
+// instruction. The fix is to clip each dim of sizePerThread by shared vec, so
+// that the vectorization of load and store are equal along the contiguous
+// dimension. In the above example, each thread will then only own 8B contiguous
+// global data.
+struct ClipAsyncCopySizePerThread
+    : public OpRewritePattern<AsyncCopyGlobalToLocalOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(AsyncCopyGlobalToLocalOp copyOp,
+                                PatternRewriter &rewriter) const override {
+    Value src = copyOp.getSrc();
+    Value mask = copyOp.getMask();
+    Value other = copyOp.getOther();
+    auto srcTy = cast<RankedTensorType>(src.getType());
+    auto dstTy = cast<MemDescType>(copyOp.getResult().getType());
+    auto blockEnc = dyn_cast<BlockedEncodingAttr>(srcTy.getEncoding());
+    if (!blockEnc)
+      return rewriter.notifyMatchFailure(copyOp,
+                                         "src must be of blocked encoding");
+    auto sharedEnc = cast<SharedEncodingAttr>(dstTy.getEncoding());
+    auto sharedVec = sharedEnc.getVec();
+
+    // obtain max contiguous copy size
+    // Note this can be further optimized, as copyContigSize can be even
+    // smaller when lowering, depending on contiguity and mask alignment
+    // (see AsyncCopyGlobalToLocalOpConversion)
+    auto elemBitWidth = dstTy.getElementTypeBitWidth();
+    auto regToSharedLayout =
+        getRegToSharedLayout(rewriter.getContext(), srcTy.getShape(), blockEnc,
+                             sharedEnc, elemBitWidth);
+    auto copyContigSize = regToSharedLayout->getNumConsecutiveInOut();
+
+    // obtain block sizePerThread along contig dim
+    auto sizePerThread = blockEnc.getSizePerThread();
+    auto blockContigSize = sizePerThread[blockEnc.getOrder()[0]];
+
+    if (blockContigSize <= copyContigSize)
+      return rewriter.notifyMatchFailure(
+          copyOp,
+          "blocked sizePerThread along contiguous dim must be greater than the "
+          "max contiguous copy size ");
+
+    sizePerThread[blockEnc.getOrder()[0]] = copyContigSize;
+
+    // obtain new blockedEnc based on clipped sizePerThread
+    auto mod = copyOp->getParentOfType<ModuleOp>();
+    int numWarps = TritonGPUDialect::getNumWarps(mod);
+    int threadsPerWarp = TritonGPUDialect::getThreadsPerWarp(mod);
+    auto newBlockEnc = BlockedEncodingAttr::get(
+        copyOp.getContext(), srcTy.getShape(), sizePerThread,
+        blockEnc.getOrder(), numWarps, threadsPerWarp, blockEnc.getCTALayout());
+
+    // insert cvt's after src, mask, and other
+    auto convertBlockLayout = [&](Value src, BlockedEncodingAttr enc) {
+      auto ty = cast<TensorType>(src.getType());
+      auto newTy =
+          RankedTensorType::get(ty.getShape(), ty.getElementType(), enc);
+      auto cvt = rewriter.create<ConvertLayoutOp>(copyOp->getLoc(), newTy, src);
+      return cvt.getResult();
+    };
+    src = convertBlockLayout(src, newBlockEnc);
+    if (mask)
+      mask = convertBlockLayout(mask, newBlockEnc);
+    if (other)
+      other = convertBlockLayout(other, newBlockEnc);
+
+    rewriter.modifyOpInPlace(copyOp, [&]() {
+      copyOp.getSrcMutable().assign(src);
+      if (mask)
+        copyOp.getMaskMutable().assign(mask);
+      if (other)
+        copyOp.getOtherMutable().assign(other);
+    });
+
+    return success();
+  }
+};
+
+class CoalesceAsyncCopyPass
+    : public impl::TritonGPUCoalesceAsyncCopyBase<CoalesceAsyncCopyPass> {
+public:
+  void runOnOperation() override {
+    ModuleOp m = getOperation();
+    MLIRContext *context = &getContext();
+
+    mlir::RewritePatternSet patterns(context);
+    patterns.add<ClipAsyncCopySizePerThread>(context);
+
+    if (failed(applyPatternsAndFoldGreedily(m, std::move(patterns))))
+      signalPassFailure();
+  }
+};
+
+} // namespace gpu
+} // namespace triton
+} // namespace mlir

lib/Dialect/TritonGPU/Transforms/Utility.cpp

@@ -1153,4 +1153,40 @@ void populateForOpDeadArgumentElimination(RewritePatternSet &patterns) {
   patterns.add<ForOpDeadArgElimination>(patterns.getContext());
 }
 
+std::optional<LinearLayout>
+getRegToSharedLayout(MLIRContext *ctx, ArrayRef<int64_t> shape,
+                     Attribute srcEnc, Attribute dstEnc, int elemBitWidth) {
+  StringAttr kBlock = StringAttr::get(ctx, ("block"));
+  int rank = shape.size();
+
+  std::optional<LinearLayout> regLayout =
+      triton::gpu::toLinearLayout(shape, srcEnc);
+  std::optional<LinearLayout> sharedLayout =
+      triton::gpu::toLinearLayout(shape, dstEnc, elemBitWidth);
+  if (!regLayout.has_value() || !sharedLayout.has_value()) {
+    return std::nullopt;
+  }
+  auto sharedOrder = triton::gpu::getOrder(dstEnc);
+
+  // sharedLayout's in-dims are currently (offset, block).  Reshape to
+  // (offsetX1, offsetX2, ..., block) so that we can apply the N-dimensional
+  // shmem strides.  (The offsetX's appear in minor-to-major order.)
+  auto sharedLegacy = cast<triton::gpu::SharedEncodingAttr>(dstEnc);
+  SmallVector<std::pair<StringAttr, int32_t>> multiDimSharedSize;
+  for (int i = 0; i < rank; i++) {
+    int dim = sharedOrder[i];
+    int64_t size = std::max(
+        int64_t{1},
+        shape[dim] / sharedLegacy.getCTALayout().getCTASplitNum()[dim]);
+    multiDimSharedSize.push_back(
+        {StringAttr::get(ctx, ("offset" + std::to_string(dim))), size});
+  }
+  multiDimSharedSize.push_back({kBlock, sharedLayout->getInDimSize(kBlock)});
+  sharedLayout = sharedLayout->reshapeIns(multiDimSharedSize);
+
+  // regToSharedLayout maps from (register, lane, warp, block) to (offsetX1,
+  // ..., offsetXN, block), where the offsetX's are in minor-to-major order.
+  return regLayout->invertAndCompose(*sharedLayout);
+}
+
 } // namespace mlir

python/src/passes.cc

@@ -72,6 +72,8 @@ void init_triton_passes_ttgpuir(py::module &&m) {
                      createTritonGPUOptimizeAccumulatorInit);
   ADD_PASS_OPTION_WRAPPER_1("add_loop_scheduling",
                             createTritonGPULoopScheduling, int);
+  ADD_PASS_WRAPPER_0("add_coalesce_async_copy",
+                     createTritonGPUCoalesceAsyncCopy);
 }
 
 void init_triton_passes_convert(py::module &&m) {

test/TritonGPU/coalesce-async-copy.mlir

@@ -0,0 +1,35 @@
+// RUN: triton-opt %s -split-input-file -tritongpu-coalesce-async-copy | FileCheck %s
+
+// CHECK: #[[NEW_BLOCKED:.*]] = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [16, 2], warpsPerCTA = [4, 1], order = [1, 0]}>
+// CHECK: %{{.*}} = triton_gpu.convert_layout %{{.*}} : {{.*}} -> tensor<64x16x!tt.ptr<i8>, #[[NEW_BLOCKED]]>
+// CHECK: %{{.*}} = triton_gpu.convert_layout %{{.*}} : {{.*}} -> tensor<64x16xi1, #[[NEW_BLOCKED]]>
+// CHECK: %{{.*}} = triton_gpu.convert_layout %{{.*}} : {{.*}} -> tensor<64x16xi8, #[[NEW_BLOCKED]]>
+// CHECK: %{{.*}} = triton_gpu.async_copy_global_to_local %{{.*}}: tensor<64x16x!tt.ptr<i8>, #[[NEW_BLOCKED]]>
+#blocked = #triton_gpu.blocked<{sizePerThread = [1, 16], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
+#shared = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, order = [1, 0], hasLeadingOffset = false}>
+
+module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32} {
+tt.func @async_copy_i8(%input: tensor<64x16x!tt.ptr<i8>, #blocked>,
+    %view: !triton_gpu.memdesc<64x16xi8, #shared, #triton_gpu.shared_memory, mutable>,
+    %mask: tensor<64x16xi1, #blocked>,
+    %other: tensor<64x16xi8, #blocked>) {
+  %token = triton_gpu.async_copy_global_to_local %input, %view mask %mask other %other: tensor<64x16x!tt.ptr<i8>, #blocked> -> <64x16xi8, #shared, #triton_gpu.shared_memory, mutable>
+  tt.return
+}
+}
+
+// -----
+
+// CHECK: #[[NEW_BLOCKED:.*]] = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [16, 2], warpsPerCTA = [4, 1], order = [1, 0]}>
+// CHECK: %{{.*}} = triton_gpu.convert_layout %{{.*}} : {{.*}} -> tensor<64x16x!tt.ptr<i8>, #[[NEW_BLOCKED]]>
+// CHECK: %{{.*}} = triton_gpu.async_copy_global_to_local %{{.*}}: tensor<64x16x!tt.ptr<i8>, #[[NEW_BLOCKED]]>
+#blocked = #triton_gpu.blocked<{sizePerThread = [1, 16], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
+#shared = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, order = [1, 0], hasLeadingOffset = false}>
+
+module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32} {
+tt.func @async_copy_i8_no_mask_or_other(%input: tensor<64x16x!tt.ptr<i8>, #blocked>,
+    %view: !triton_gpu.memdesc<64x16xi8, #shared, #triton_gpu.shared_memory, mutable>) {
+  %token = triton_gpu.async_copy_global_to_local %input, %view : tensor<64x16x!tt.ptr<i8>, #blocked> -> <64x16xi8, #shared, #triton_gpu.shared_memory, mutable>
+  tt.return
+}
+}

third_party/nvidia/backend/compiler.py

@@ -234,6 +234,7 @@ def make_ttgir(mod, metadata, opt, capability):
             passes.ttgpuir.add_pipeline(pm, opt.num_stages)
         passes.ttgpuir.add_prefetch(pm)
         passes.ttgpuir.add_optimize_dot_operands(pm, capability >= 80)
+        passes.ttgpuir.add_coalesce_async_copy(pm)
         passes.ttgpuir.add_remove_layout_conversions(pm)
         passes.ttgpuir.add_reduce_data_duplication(pm)
         passes.ttgpuir.add_reorder_instructions(pm)