[Mlir-commits] [mlir] [mlir][vector][xegpu] Vector to XeGPU conversion pass (PR #107419)

[Adam Siemieniuk]

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@@ -0,0 +1,257 @@
+//===- VectorToXeGPU.cpp - Convert vector to XeGPU dialect ------*- 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 implements lowering of vector operations to XeGPU dialect ops.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Conversion/VectorToXeGPU/VectorToXeGPU.h"
+
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/Vector/IR/VectorOps.h"
+#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "mlir/Transforms/Passes.h"
+#include "llvm/ADT/TypeSwitch.h"
+
+#include <algorithm>
+#include <optional>
+
+namespace mlir {
+#define GEN_PASS_DEF_CONVERTVECTORTOXEGPU
+#include "mlir/Conversion/Passes.h.inc"
+} // namespace mlir
+
+using namespace mlir;
+
+namespace {
+
+static bool isZeroConstant(Value val) {
+  auto constant = val.getDefiningOp<arith::ConstantOp>();
+  if (!constant)
+    return false;
+
+  return TypeSwitch<Attribute, bool>(constant.getValue())
+      .Case<FloatAttr>(
+          [](auto floatAttr) { return floatAttr.getValue().isZero(); })
+      .Case<IntegerAttr>(
+          [](auto intAttr) { return intAttr.getValue().isZero(); })
+      .Default([](auto) { return false; });
+}
+
+static LogicalResult transferPreconditions(PatternRewriter &rewriter,
+                                           VectorTransferOpInterface xferOp) {
+  if (xferOp.getMask())
+    return rewriter.notifyMatchFailure(xferOp,
+                                       "Masked transfer is not supported");
+
+  auto srcTy = dyn_cast<MemRefType>(xferOp.getShapedType());
+  if (!srcTy)
+    return rewriter.notifyMatchFailure(xferOp, "Expects memref source");
+  VectorType vecTy = xferOp.getVectorType();
+  unsigned vecRank = vecTy.getRank();
+  if (!(vecRank == 1 || vecRank == 2))
+    return rewriter.notifyMatchFailure(xferOp, "Expects 1D or 2D vector");
+
+  SmallVector<int64_t> strides;
+  int64_t offset;
+  if (failed(getStridesAndOffset(srcTy, strides, offset)) ||
+      strides.back() != 1)
+    return rewriter.notifyMatchFailure(
+        xferOp, "Buffer must be contiguous in the innermost dimension");
+
+  AffineMap map = xferOp.getPermutationMap();
+  if (!map.isProjectedPermutation(/*allowZeroInResults=*/false))
+    return rewriter.notifyMatchFailure(xferOp, "Unsupported permutation map");
+  unsigned numInputDims = map.getNumInputs();
+  for (AffineExpr expr : map.getResults().take_back(vecRank)) {
+    auto dim = dyn_cast<AffineDimExpr>(expr);
+    if (dim.getPosition() < (numInputDims - vecRank))
+      return rewriter.notifyMatchFailure(
+          xferOp, "Only the innermost dimensions can be accessed");
+  }
+
+  return success();
+}
+
+static xegpu::CreateNdDescOp
+createNdDescriptor(PatternRewriter &rewriter, Location loc,
+                   xegpu::TensorDescType descType, TypedValue<MemRefType> src,
+                   Operation::operand_range offsets) {
+  MemRefType srcTy = src.getType();
+  auto [strides, offset] = getStridesAndOffset(srcTy);
+
+  xegpu::CreateNdDescOp ndDesc;
+  if (srcTy.hasStaticShape()) {
+    ndDesc = rewriter.create<xegpu::CreateNdDescOp>(loc, descType, src,
+                                                    getAsOpFoldResult(offsets));
+  } else {
+    // In case of any dynamic shapes, source's shape and strides have to be
+    // explicitly provided.
+    SmallVector<Value> sourceDims;
+    unsigned srcRank = srcTy.getRank();
+    for (unsigned i = 0; i < srcRank; ++i)
+      sourceDims.push_back(rewriter.create<memref::DimOp>(loc, src, i));
+
+    SmallVector<int64_t> constOffsets;
+    SmallVector<Value> dynOffsets;
+    for (Value offset : offsets) {
+      std::optional<int64_t> staticVal = getConstantIntValue(offset);
+      if (!staticVal)
+        dynOffsets.push_back(offset);
+      constOffsets.push_back(staticVal ? *staticVal : ShapedType::kDynamic);
+    }
+
+    SmallVector<Value> dynShapes;
+    for (auto [idx, shape] : llvm::enumerate(srcTy.getShape())) {
+      if (shape == ShapedType::kDynamic)
+        dynShapes.push_back(sourceDims[idx]);
+    }
+
+    // Compute strides in reverse order.
+    SmallVector<Value> dynStrides;
+    Value accStride = rewriter.create<arith::ConstantIndexOp>(loc, 1);
+    // Last stride is guaranteed to be static and unit.
+    for (int i = static_cast<int>(strides.size()) - 2; i >= 0; --i) {
+      accStride =
+          rewriter.create<arith::MulIOp>(loc, accStride, sourceDims[i + 1]);
+      if (strides[i] == ShapedType::kDynamic)
+        dynStrides.push_back(accStride);
+    }
+    std::reverse(dynStrides.begin(), dynStrides.end());
+
+    ndDesc = rewriter.create<xegpu::CreateNdDescOp>(
+        loc, descType, src, dynOffsets, dynShapes, dynStrides,
+        DenseI64ArrayAttr::get(rewriter.getContext(), constOffsets),
+        DenseI64ArrayAttr::get(rewriter.getContext(), srcTy.getShape()),
+        DenseI64ArrayAttr::get(rewriter.getContext(), strides));
+  }
+
+  return ndDesc;
+}
+
+struct TransferReadLowering : public OpRewritePattern<vector::TransferReadOp> {
+  using OpRewritePattern<vector::TransferReadOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(vector::TransferReadOp readOp,
+                                PatternRewriter &rewriter) const override {
+    Location loc = readOp.getLoc();
+
+    if (failed(transferPreconditions(rewriter, readOp)))
+      return failure();
+
+    bool isOutOfBounds = readOp.hasOutOfBoundsDim();
+    if (isOutOfBounds && !isZeroConstant(readOp.getPadding()))
+      return rewriter.notifyMatchFailure(
+          readOp, "Unsupported non-zero padded out-of-bounds read");
+
+    AffineMap readMap = readOp.getPermutationMap();
+    bool isTransposeLoad = !readMap.isMinorIdentity();
+
+    VectorType vecTy = readOp.getVectorType();
+    Type elementType = vecTy.getElementType();
+    unsigned minTransposeBitWidth = 32;
+    if (isTransposeLoad &&
+        elementType.getIntOrFloatBitWidth() < minTransposeBitWidth)
+      return rewriter.notifyMatchFailure(
+          readOp, "Unsupported data type for tranposition");
+
+    // If load is transposed, get the base shape for the tensor descriptor.
+    SmallVector<int64_t> descShape{vecTy.getShape()};
+    if (isTransposeLoad)
+      std::reverse(descShape.begin(), descShape.end());
+    auto descType = xegpu::TensorDescType::get(
+        descShape, elementType, /*scattered=*/false, /*array_length=*/1,
+        xegpu::MemoryScope::Global,
+        /*boundary_check=*/isOutOfBounds);
+
+    xegpu::CreateNdDescOp ndDesc =
+        createNdDescriptor(rewriter, loc, descType,
+                           dyn_cast<TypedValue<MemRefType>>(readOp.getSource()),
+                           readOp.getIndices());
+
+    DenseI64ArrayAttr transposeAttr =
+        !isTransposeLoad ? nullptr
+                         : DenseI64ArrayAttr::get(rewriter.getContext(),
+                                                  ArrayRef<int64_t>{1, 0});
+    // By default, no specific caching policy is assigned.
+    xegpu::CachePolicyAttr hint = nullptr;
+    auto loadOp = rewriter.create<xegpu::LoadNdOp>(
+        loc, vecTy, ndDesc, /*packed=*/nullptr, transposeAttr,
+        /*l1_hint=*/hint,
+        /*l2_hint=*/hint, /*l3_hint=*/hint);
+    rewriter.replaceOp(readOp, loadOp);
+
+    return success();
+  }
+};
+
+struct TransferWriteLowering
+    : public OpRewritePattern<vector::TransferWriteOp> {
+  using OpRewritePattern<vector::TransferWriteOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(vector::TransferWriteOp writeOp,
+                                PatternRewriter &rewriter) const override {
+    Location loc = writeOp.getLoc();
+
+    if (failed(transferPreconditions(rewriter, writeOp)))
+      return failure();
+
+    if (writeOp.hasOutOfBoundsDim())
----------------
adam-smnk wrote:

@chencha3 Actually I'm not sure if out of bound `store_nd` is supported 

https://github.com/llvm/llvm-project/pull/107419