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8c074cb0b7
Fix call sites. The method will be removed 2 weeks later. Reviewed By: rriddle Differential Revision: https://reviews.llvm.org/D97464
4248 lines
166 KiB
C++
4248 lines
166 KiB
C++
//===- Ops.cpp - Standard MLIR Operations ---------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/CommonFolders.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/AffineExpr.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Matchers.h"
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#include "mlir/IR/OpImplementation.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/IR/Value.h"
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#include "mlir/Support/MathExtras.h"
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#include "mlir/Transforms/InliningUtils.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/StringSwitch.h"
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#include "llvm/Support/FormatVariadic.h"
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#include "llvm/Support/raw_ostream.h"
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// Pull in all enum type definitions and utility function declarations.
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#include "mlir/Dialect/StandardOps/IR/OpsEnums.cpp.inc"
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using namespace mlir;
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/// Helper function to dispatch an OpFoldResult into either the `dynamicVec` if
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/// it is a Value or into `staticVec` if it is an IntegerAttr.
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/// In the case of a Value, a copy of the `sentinel` value is also pushed to
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/// `staticVec`. This is useful to extract mixed static and dynamic entries that
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/// come from an AttrSizedOperandSegments trait.
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static void dispatchIndexOpFoldResult(OpFoldResult ofr,
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SmallVectorImpl<Value> &dynamicVec,
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SmallVectorImpl<int64_t> &staticVec,
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int64_t sentinel) {
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if (auto v = ofr.dyn_cast<Value>()) {
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dynamicVec.push_back(v);
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staticVec.push_back(sentinel);
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return;
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}
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APInt apInt = ofr.dyn_cast<Attribute>().cast<IntegerAttr>().getValue();
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staticVec.push_back(apInt.getSExtValue());
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}
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static void dispatchIndexOpFoldResults(ArrayRef<OpFoldResult> ofrs,
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SmallVectorImpl<Value> &dynamicVec,
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SmallVectorImpl<int64_t> &staticVec,
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int64_t sentinel) {
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for (auto ofr : ofrs)
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dispatchIndexOpFoldResult(ofr, dynamicVec, staticVec, sentinel);
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}
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/// Return true if ofr1 and ofr2 are the same integer constant attribute values
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/// or the same SSA value.
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/// Ignore integer bitwitdh and type mismatch that come from the fact there is
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/// no IndexAttr and that IndexType have no bitwidth.
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bool mlir::isEqualConstantIntOrValue(OpFoldResult op1, OpFoldResult op2) {
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auto getConstantIntValue = [](OpFoldResult ofr) -> llvm::Optional<int64_t> {
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Attribute attr = ofr.dyn_cast<Attribute>();
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// Note: isa+cast-like pattern allows writing the condition below as 1 line.
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if (!attr && ofr.get<Value>().getDefiningOp<ConstantOp>())
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attr = ofr.get<Value>().getDefiningOp<ConstantOp>().getValue();
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if (auto intAttr = attr.dyn_cast_or_null<IntegerAttr>())
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return intAttr.getValue().getSExtValue();
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return llvm::None;
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};
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auto cst1 = getConstantIntValue(op1), cst2 = getConstantIntValue(op2);
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if (cst1 && cst2 && *cst1 == *cst2)
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return true;
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auto v1 = op1.dyn_cast<Value>(), v2 = op2.dyn_cast<Value>();
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return v1 && v2 && v1 == v2;
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}
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//===----------------------------------------------------------------------===//
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// StandardOpsDialect Interfaces
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//===----------------------------------------------------------------------===//
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namespace {
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/// This class defines the interface for handling inlining with standard
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/// operations.
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struct StdInlinerInterface : public DialectInlinerInterface {
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using DialectInlinerInterface::DialectInlinerInterface;
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//===--------------------------------------------------------------------===//
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// Analysis Hooks
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//===--------------------------------------------------------------------===//
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/// All call operations within standard ops can be inlined.
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bool isLegalToInline(Operation *call, Operation *callable,
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bool wouldBeCloned) const final {
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return true;
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}
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/// All operations within standard ops can be inlined.
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bool isLegalToInline(Operation *, Region *, bool,
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BlockAndValueMapping &) const final {
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return true;
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}
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//===--------------------------------------------------------------------===//
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// Transformation Hooks
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//===--------------------------------------------------------------------===//
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/// Handle the given inlined terminator by replacing it with a new operation
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/// as necessary.
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void handleTerminator(Operation *op, Block *newDest) const final {
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// Only "std.return" needs to be handled here.
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auto returnOp = dyn_cast<ReturnOp>(op);
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if (!returnOp)
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return;
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// Replace the return with a branch to the dest.
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OpBuilder builder(op);
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builder.create<BranchOp>(op->getLoc(), newDest, returnOp.getOperands());
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op->erase();
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}
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/// Handle the given inlined terminator by replacing it with a new operation
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/// as necessary.
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void handleTerminator(Operation *op,
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ArrayRef<Value> valuesToRepl) const final {
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// Only "std.return" needs to be handled here.
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auto returnOp = cast<ReturnOp>(op);
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// Replace the values directly with the return operands.
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assert(returnOp.getNumOperands() == valuesToRepl.size());
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for (const auto &it : llvm::enumerate(returnOp.getOperands()))
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valuesToRepl[it.index()].replaceAllUsesWith(it.value());
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}
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};
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} // end anonymous namespace
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//===----------------------------------------------------------------------===//
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// StandardOpsDialect
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//===----------------------------------------------------------------------===//
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/// A custom unary operation printer that omits the "std." prefix from the
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/// operation names.
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static void printStandardUnaryOp(Operation *op, OpAsmPrinter &p) {
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assert(op->getNumOperands() == 1 && "unary op should have one operand");
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assert(op->getNumResults() == 1 && "unary op should have one result");
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int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
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p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
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<< op->getOperand(0);
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p.printOptionalAttrDict(op->getAttrs());
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p << " : " << op->getOperand(0).getType();
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}
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/// A custom binary operation printer that omits the "std." prefix from the
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/// operation names.
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static void printStandardBinaryOp(Operation *op, OpAsmPrinter &p) {
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assert(op->getNumOperands() == 2 && "binary op should have two operands");
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assert(op->getNumResults() == 1 && "binary op should have one result");
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// If not all the operand and result types are the same, just use the
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// generic assembly form to avoid omitting information in printing.
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auto resultType = op->getResult(0).getType();
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if (op->getOperand(0).getType() != resultType ||
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op->getOperand(1).getType() != resultType) {
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p.printGenericOp(op);
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return;
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}
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int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
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p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
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<< op->getOperand(0) << ", " << op->getOperand(1);
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p.printOptionalAttrDict(op->getAttrs());
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// Now we can output only one type for all operands and the result.
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p << " : " << op->getResult(0).getType();
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}
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/// A custom ternary operation printer that omits the "std." prefix from the
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/// operation names.
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static void printStandardTernaryOp(Operation *op, OpAsmPrinter &p) {
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assert(op->getNumOperands() == 3 && "ternary op should have three operands");
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assert(op->getNumResults() == 1 && "ternary op should have one result");
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// If not all the operand and result types are the same, just use the
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// generic assembly form to avoid omitting information in printing.
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auto resultType = op->getResult(0).getType();
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if (op->getOperand(0).getType() != resultType ||
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op->getOperand(1).getType() != resultType ||
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op->getOperand(2).getType() != resultType) {
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p.printGenericOp(op);
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return;
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}
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int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
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p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
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<< op->getOperand(0) << ", " << op->getOperand(1) << ", "
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<< op->getOperand(2);
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p.printOptionalAttrDict(op->getAttrs());
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// Now we can output only one type for all operands and the result.
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p << " : " << op->getResult(0).getType();
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}
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/// A custom cast operation printer that omits the "std." prefix from the
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/// operation names.
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static void printStandardCastOp(Operation *op, OpAsmPrinter &p) {
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int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
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p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
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<< op->getOperand(0) << " : " << op->getOperand(0).getType() << " to "
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<< op->getResult(0).getType();
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}
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void StandardOpsDialect::initialize() {
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getContext()->loadDialect<tensor::TensorDialect>();
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addOperations<DmaStartOp, DmaWaitOp,
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#define GET_OP_LIST
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#include "mlir/Dialect/StandardOps/IR/Ops.cpp.inc"
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>();
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addInterfaces<StdInlinerInterface>();
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}
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/// Materialize a single constant operation from a given attribute value with
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/// the desired resultant type.
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Operation *StandardOpsDialect::materializeConstant(OpBuilder &builder,
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Attribute value, Type type,
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Location loc) {
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return builder.create<ConstantOp>(loc, type, value);
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}
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/// Matches a ConstantIndexOp.
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/// TODO: This should probably just be a general matcher that uses m_Constant
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/// and checks the operation for an index type.
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static detail::op_matcher<ConstantIndexOp> m_ConstantIndex() {
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return detail::op_matcher<ConstantIndexOp>();
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}
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//===----------------------------------------------------------------------===//
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// Common canonicalization pattern support logic
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//===----------------------------------------------------------------------===//
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/// This is a common class used for patterns of the form
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/// "someop(memrefcast) -> someop". It folds the source of any memref_cast
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/// into the root operation directly.
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static LogicalResult foldMemRefCast(Operation *op) {
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bool folded = false;
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for (OpOperand &operand : op->getOpOperands()) {
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auto cast = operand.get().getDefiningOp<MemRefCastOp>();
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if (cast && !cast.getOperand().getType().isa<UnrankedMemRefType>()) {
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operand.set(cast.getOperand());
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folded = true;
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}
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}
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return success(folded);
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}
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//===----------------------------------------------------------------------===//
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// Common cast compatibility check for vector types.
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//===----------------------------------------------------------------------===//
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/// This method checks for cast compatibility of vector types.
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/// If 'a' and 'b' are vector types, and they are cast compatible,
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/// it calls the 'areElementsCastCompatible' function to check for
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/// element cast compatibility.
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/// Returns 'true' if the vector types are cast compatible, and 'false'
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/// otherwise.
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static bool areVectorCastSimpleCompatible(
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Type a, Type b,
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function_ref<bool(TypeRange, TypeRange)> areElementsCastCompatible) {
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if (auto va = a.dyn_cast<VectorType>())
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if (auto vb = b.dyn_cast<VectorType>())
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return va.getShape().equals(vb.getShape()) &&
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areElementsCastCompatible(va.getElementType(),
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vb.getElementType());
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return false;
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}
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//===----------------------------------------------------------------------===//
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// Helpers for Tensor[Load|Store]Op, TensorToMemrefOp, and GlobalMemrefOp
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//===----------------------------------------------------------------------===//
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static Type getTensorTypeFromMemRefType(Type type) {
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if (auto memref = type.dyn_cast<MemRefType>())
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return RankedTensorType::get(memref.getShape(), memref.getElementType());
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if (auto memref = type.dyn_cast<UnrankedMemRefType>())
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return UnrankedTensorType::get(memref.getElementType());
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return NoneType::get(type.getContext());
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}
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//===----------------------------------------------------------------------===//
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// AddFOp
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//===----------------------------------------------------------------------===//
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OpFoldResult AddFOp::fold(ArrayRef<Attribute> operands) {
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return constFoldBinaryOp<FloatAttr>(
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operands, [](APFloat a, APFloat b) { return a + b; });
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}
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//===----------------------------------------------------------------------===//
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// AddIOp
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//===----------------------------------------------------------------------===//
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OpFoldResult AddIOp::fold(ArrayRef<Attribute> operands) {
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/// addi(x, 0) -> x
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if (matchPattern(rhs(), m_Zero()))
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return lhs();
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return constFoldBinaryOp<IntegerAttr>(operands,
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[](APInt a, APInt b) { return a + b; });
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}
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/// Extract int64_t values from the assumed ArrayAttr of IntegerAttr.
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static SmallVector<int64_t, 4> extractFromI64ArrayAttr(Attribute attr) {
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return llvm::to_vector<4>(
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llvm::map_range(attr.cast<ArrayAttr>(), [](Attribute a) -> int64_t {
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return a.cast<IntegerAttr>().getInt();
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}));
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}
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//===----------------------------------------------------------------------===//
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// AllocOp / AllocaOp
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//===----------------------------------------------------------------------===//
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template <typename AllocLikeOp>
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static LogicalResult verifyAllocLikeOp(AllocLikeOp op) {
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static_assert(llvm::is_one_of<AllocLikeOp, AllocOp, AllocaOp>::value,
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"applies to only alloc or alloca");
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auto memRefType = op.getResult().getType().template dyn_cast<MemRefType>();
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if (!memRefType)
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return op.emitOpError("result must be a memref");
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if (static_cast<int64_t>(op.dynamicSizes().size()) !=
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memRefType.getNumDynamicDims())
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return op.emitOpError("dimension operand count does not equal memref "
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"dynamic dimension count");
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unsigned numSymbols = 0;
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if (!memRefType.getAffineMaps().empty())
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numSymbols = memRefType.getAffineMaps().front().getNumSymbols();
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if (op.symbolOperands().size() != numSymbols)
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return op.emitOpError(
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"symbol operand count does not equal memref symbol count");
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return success();
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}
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static LogicalResult verify(AllocOp op) { return verifyAllocLikeOp(op); }
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static LogicalResult verify(AllocaOp op) {
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// An alloca op needs to have an ancestor with an allocation scope trait.
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if (!op->getParentWithTrait<OpTrait::AutomaticAllocationScope>())
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return op.emitOpError(
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"requires an ancestor op with AutomaticAllocationScope trait");
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return verifyAllocLikeOp(op);
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}
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namespace {
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/// Fold constant dimensions into an alloc like operation.
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template <typename AllocLikeOp>
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struct SimplifyAllocConst : public OpRewritePattern<AllocLikeOp> {
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using OpRewritePattern<AllocLikeOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AllocLikeOp alloc,
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PatternRewriter &rewriter) const override {
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// Check to see if any dimensions operands are constants. If so, we can
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// substitute and drop them.
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if (llvm::none_of(alloc.getOperands(), [](Value operand) {
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return matchPattern(operand, m_ConstantIndex());
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}))
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return failure();
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auto memrefType = alloc.getType();
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// Ok, we have one or more constant operands. Collect the non-constant ones
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// and keep track of the resultant memref type to build.
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SmallVector<int64_t, 4> newShapeConstants;
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newShapeConstants.reserve(memrefType.getRank());
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SmallVector<Value, 4> newOperands;
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unsigned dynamicDimPos = 0;
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for (unsigned dim = 0, e = memrefType.getRank(); dim < e; ++dim) {
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int64_t dimSize = memrefType.getDimSize(dim);
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// If this is already static dimension, keep it.
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if (dimSize != -1) {
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newShapeConstants.push_back(dimSize);
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continue;
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}
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auto *defOp = alloc.getOperand(dynamicDimPos).getDefiningOp();
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if (auto constantIndexOp = dyn_cast_or_null<ConstantIndexOp>(defOp)) {
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// Dynamic shape dimension will be folded.
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newShapeConstants.push_back(constantIndexOp.getValue());
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} else {
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// Dynamic shape dimension not folded; copy operand from old memref.
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newShapeConstants.push_back(-1);
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newOperands.push_back(alloc.getOperand(dynamicDimPos));
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}
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dynamicDimPos++;
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}
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// Create new memref type (which will have fewer dynamic dimensions).
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MemRefType newMemRefType =
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MemRefType::Builder(memrefType).setShape(newShapeConstants);
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assert(static_cast<int64_t>(newOperands.size()) ==
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newMemRefType.getNumDynamicDims());
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// Create and insert the alloc op for the new memref.
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auto newAlloc = rewriter.create<AllocLikeOp>(alloc.getLoc(), newMemRefType,
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newOperands, IntegerAttr());
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// Insert a cast so we have the same type as the old alloc.
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auto resultCast = rewriter.create<MemRefCastOp>(alloc.getLoc(), newAlloc,
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alloc.getType());
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rewriter.replaceOp(alloc, {resultCast});
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return success();
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}
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};
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/// Fold alloc operations with no uses. Alloc has side effects on the heap,
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/// but can still be deleted if it has zero uses.
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struct SimplifyDeadAlloc : public OpRewritePattern<AllocOp> {
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using OpRewritePattern<AllocOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AllocOp alloc,
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PatternRewriter &rewriter) const override {
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if (alloc.use_empty()) {
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rewriter.eraseOp(alloc);
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return success();
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}
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return failure();
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}
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};
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} // end anonymous namespace.
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void AllocOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
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MLIRContext *context) {
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results.insert<SimplifyAllocConst<AllocOp>, SimplifyDeadAlloc>(context);
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}
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void AllocaOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
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MLIRContext *context) {
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results.insert<SimplifyAllocConst<AllocaOp>>(context);
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}
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//===----------------------------------------------------------------------===//
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// AndOp
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//===----------------------------------------------------------------------===//
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OpFoldResult AndOp::fold(ArrayRef<Attribute> operands) {
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/// and(x, 0) -> 0
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if (matchPattern(rhs(), m_Zero()))
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return rhs();
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/// and(x, allOnes) -> x
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APInt intValue;
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if (matchPattern(rhs(), m_ConstantInt(&intValue)) &&
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intValue.isAllOnesValue())
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return lhs();
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/// and(x,x) -> x
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if (lhs() == rhs())
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|
return rhs();
|
|
|
|
return constFoldBinaryOp<IntegerAttr>(operands,
|
|
[](APInt a, APInt b) { return a & b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// AssertOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace {
|
|
struct EraseRedundantAssertions : public OpRewritePattern<AssertOp> {
|
|
using OpRewritePattern<AssertOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(AssertOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
// Erase assertion if argument is constant true.
|
|
if (matchPattern(op.arg(), m_One())) {
|
|
rewriter.eraseOp(op);
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void AssertOp::getCanonicalizationPatterns(OwningRewritePatternList &patterns,
|
|
MLIRContext *context) {
|
|
patterns.insert<EraseRedundantAssertions>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// AssumeAlignmentOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(AssumeAlignmentOp op) {
|
|
unsigned alignment = op.alignment();
|
|
if (!llvm::isPowerOf2_32(alignment))
|
|
return op.emitOpError("alignment must be power of 2");
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// AtomicRMWOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(AtomicRMWOp op) {
|
|
if (op.getMemRefType().getRank() != op.getNumOperands() - 2)
|
|
return op.emitOpError(
|
|
"expects the number of subscripts to be equal to memref rank");
|
|
switch (op.kind()) {
|
|
case AtomicRMWKind::addf:
|
|
case AtomicRMWKind::maxf:
|
|
case AtomicRMWKind::minf:
|
|
case AtomicRMWKind::mulf:
|
|
if (!op.value().getType().isa<FloatType>())
|
|
return op.emitOpError()
|
|
<< "with kind '" << stringifyAtomicRMWKind(op.kind())
|
|
<< "' expects a floating-point type";
|
|
break;
|
|
case AtomicRMWKind::addi:
|
|
case AtomicRMWKind::maxs:
|
|
case AtomicRMWKind::maxu:
|
|
case AtomicRMWKind::mins:
|
|
case AtomicRMWKind::minu:
|
|
case AtomicRMWKind::muli:
|
|
if (!op.value().getType().isa<IntegerType>())
|
|
return op.emitOpError()
|
|
<< "with kind '" << stringifyAtomicRMWKind(op.kind())
|
|
<< "' expects an integer type";
|
|
break;
|
|
default:
|
|
break;
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// GenericAtomicRMWOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void GenericAtomicRMWOp::build(OpBuilder &builder, OperationState &result,
|
|
Value memref, ValueRange ivs) {
|
|
result.addOperands(memref);
|
|
result.addOperands(ivs);
|
|
|
|
if (auto memrefType = memref.getType().dyn_cast<MemRefType>()) {
|
|
Type elementType = memrefType.getElementType();
|
|
result.addTypes(elementType);
|
|
|
|
Region *bodyRegion = result.addRegion();
|
|
bodyRegion->push_back(new Block());
|
|
bodyRegion->addArgument(elementType);
|
|
}
|
|
}
|
|
|
|
static LogicalResult verify(GenericAtomicRMWOp op) {
|
|
auto &body = op.body();
|
|
if (body.getNumArguments() != 1)
|
|
return op.emitOpError("expected single number of entry block arguments");
|
|
|
|
if (op.getResult().getType() != body.getArgument(0).getType())
|
|
return op.emitOpError(
|
|
"expected block argument of the same type result type");
|
|
|
|
bool hasSideEffects =
|
|
body.walk([&](Operation *nestedOp) {
|
|
if (MemoryEffectOpInterface::hasNoEffect(nestedOp))
|
|
return WalkResult::advance();
|
|
nestedOp->emitError("body of 'generic_atomic_rmw' should contain "
|
|
"only operations with no side effects");
|
|
return WalkResult::interrupt();
|
|
})
|
|
.wasInterrupted();
|
|
return hasSideEffects ? failure() : success();
|
|
}
|
|
|
|
static ParseResult parseGenericAtomicRMWOp(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
OpAsmParser::OperandType memref;
|
|
Type memrefType;
|
|
SmallVector<OpAsmParser::OperandType, 4> ivs;
|
|
|
|
Type indexType = parser.getBuilder().getIndexType();
|
|
if (parser.parseOperand(memref) ||
|
|
parser.parseOperandList(ivs, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseColonType(memrefType) ||
|
|
parser.resolveOperand(memref, memrefType, result.operands) ||
|
|
parser.resolveOperands(ivs, indexType, result.operands))
|
|
return failure();
|
|
|
|
Region *body = result.addRegion();
|
|
if (parser.parseRegion(*body, llvm::None, llvm::None) ||
|
|
parser.parseOptionalAttrDict(result.attributes))
|
|
return failure();
|
|
result.types.push_back(memrefType.cast<MemRefType>().getElementType());
|
|
return success();
|
|
}
|
|
|
|
static void print(OpAsmPrinter &p, GenericAtomicRMWOp op) {
|
|
p << op.getOperationName() << ' ' << op.memref() << "[" << op.indices()
|
|
<< "] : " << op.memref().getType();
|
|
p.printRegion(op.body());
|
|
p.printOptionalAttrDict(op->getAttrs());
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// AtomicYieldOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(AtomicYieldOp op) {
|
|
Type parentType = op->getParentOp()->getResultTypes().front();
|
|
Type resultType = op.result().getType();
|
|
if (parentType != resultType)
|
|
return op.emitOpError() << "types mismatch between yield op: " << resultType
|
|
<< " and its parent: " << parentType;
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// BranchOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Given a successor, try to collapse it to a new destination if it only
|
|
/// contains a passthrough unconditional branch. If the successor is
|
|
/// collapsable, `successor` and `successorOperands` are updated to reference
|
|
/// the new destination and values. `argStorage` is an optional storage to use
|
|
/// if operands to the collapsed successor need to be remapped.
|
|
static LogicalResult collapseBranch(Block *&successor,
|
|
ValueRange &successorOperands,
|
|
SmallVectorImpl<Value> &argStorage) {
|
|
// Check that the successor only contains a unconditional branch.
|
|
if (std::next(successor->begin()) != successor->end())
|
|
return failure();
|
|
// Check that the terminator is an unconditional branch.
|
|
BranchOp successorBranch = dyn_cast<BranchOp>(successor->getTerminator());
|
|
if (!successorBranch)
|
|
return failure();
|
|
// Check that the arguments are only used within the terminator.
|
|
for (BlockArgument arg : successor->getArguments()) {
|
|
for (Operation *user : arg.getUsers())
|
|
if (user != successorBranch)
|
|
return failure();
|
|
}
|
|
// Don't try to collapse branches to infinite loops.
|
|
Block *successorDest = successorBranch.getDest();
|
|
if (successorDest == successor)
|
|
return failure();
|
|
|
|
// Update the operands to the successor. If the branch parent has no
|
|
// arguments, we can use the branch operands directly.
|
|
OperandRange operands = successorBranch.getOperands();
|
|
if (successor->args_empty()) {
|
|
successor = successorDest;
|
|
successorOperands = operands;
|
|
return success();
|
|
}
|
|
|
|
// Otherwise, we need to remap any argument operands.
|
|
for (Value operand : operands) {
|
|
BlockArgument argOperand = operand.dyn_cast<BlockArgument>();
|
|
if (argOperand && argOperand.getOwner() == successor)
|
|
argStorage.push_back(successorOperands[argOperand.getArgNumber()]);
|
|
else
|
|
argStorage.push_back(operand);
|
|
}
|
|
successor = successorDest;
|
|
successorOperands = argStorage;
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
/// Simplify a branch to a block that has a single predecessor. This effectively
|
|
/// merges the two blocks.
|
|
struct SimplifyBrToBlockWithSinglePred : public OpRewritePattern<BranchOp> {
|
|
using OpRewritePattern<BranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(BranchOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
// Check that the successor block has a single predecessor.
|
|
Block *succ = op.getDest();
|
|
Block *opParent = op->getBlock();
|
|
if (succ == opParent || !llvm::hasSingleElement(succ->getPredecessors()))
|
|
return failure();
|
|
|
|
// Merge the successor into the current block and erase the branch.
|
|
rewriter.mergeBlocks(succ, opParent, op.getOperands());
|
|
rewriter.eraseOp(op);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// br ^bb1
|
|
/// ^bb1
|
|
/// br ^bbN(...)
|
|
///
|
|
/// -> br ^bbN(...)
|
|
///
|
|
struct SimplifyPassThroughBr : public OpRewritePattern<BranchOp> {
|
|
using OpRewritePattern<BranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(BranchOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
Block *dest = op.getDest();
|
|
ValueRange destOperands = op.getOperands();
|
|
SmallVector<Value, 4> destOperandStorage;
|
|
|
|
// Try to collapse the successor if it points somewhere other than this
|
|
// block.
|
|
if (dest == op->getBlock() ||
|
|
failed(collapseBranch(dest, destOperands, destOperandStorage)))
|
|
return failure();
|
|
|
|
// Create a new branch with the collapsed successor.
|
|
rewriter.replaceOpWithNewOp<BranchOp>(op, dest, destOperands);
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace.
|
|
|
|
Block *BranchOp::getDest() { return getSuccessor(); }
|
|
|
|
void BranchOp::setDest(Block *block) { return setSuccessor(block); }
|
|
|
|
void BranchOp::eraseOperand(unsigned index) { (*this)->eraseOperand(index); }
|
|
|
|
void BranchOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<SimplifyBrToBlockWithSinglePred, SimplifyPassThroughBr>(
|
|
context);
|
|
}
|
|
|
|
Optional<MutableOperandRange>
|
|
BranchOp::getMutableSuccessorOperands(unsigned index) {
|
|
assert(index == 0 && "invalid successor index");
|
|
return destOperandsMutable();
|
|
}
|
|
|
|
Block *BranchOp::getSuccessorForOperands(ArrayRef<Attribute>) { return dest(); }
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CallOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult CallOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
|
|
// Check that the callee attribute was specified.
|
|
auto fnAttr = (*this)->getAttrOfType<FlatSymbolRefAttr>("callee");
|
|
if (!fnAttr)
|
|
return emitOpError("requires a 'callee' symbol reference attribute");
|
|
FuncOp fn = symbolTable.lookupNearestSymbolFrom<FuncOp>(*this, fnAttr);
|
|
if (!fn)
|
|
return emitOpError() << "'" << fnAttr.getValue()
|
|
<< "' does not reference a valid function";
|
|
|
|
// Verify that the operand and result types match the callee.
|
|
auto fnType = fn.getType();
|
|
if (fnType.getNumInputs() != getNumOperands())
|
|
return emitOpError("incorrect number of operands for callee");
|
|
|
|
for (unsigned i = 0, e = fnType.getNumInputs(); i != e; ++i)
|
|
if (getOperand(i).getType() != fnType.getInput(i))
|
|
return emitOpError("operand type mismatch: expected operand type ")
|
|
<< fnType.getInput(i) << ", but provided "
|
|
<< getOperand(i).getType() << " for operand number " << i;
|
|
|
|
if (fnType.getNumResults() != getNumResults())
|
|
return emitOpError("incorrect number of results for callee");
|
|
|
|
for (unsigned i = 0, e = fnType.getNumResults(); i != e; ++i)
|
|
if (getResult(i).getType() != fnType.getResult(i))
|
|
return emitOpError("result type mismatch");
|
|
|
|
return success();
|
|
}
|
|
|
|
FunctionType CallOp::getCalleeType() {
|
|
return FunctionType::get(getContext(), getOperandTypes(), getResultTypes());
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CallIndirectOp
|
|
//===----------------------------------------------------------------------===//
|
|
namespace {
|
|
/// Fold indirect calls that have a constant function as the callee operand.
|
|
struct SimplifyIndirectCallWithKnownCallee
|
|
: public OpRewritePattern<CallIndirectOp> {
|
|
using OpRewritePattern<CallIndirectOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CallIndirectOp indirectCall,
|
|
PatternRewriter &rewriter) const override {
|
|
// Check that the callee is a constant callee.
|
|
SymbolRefAttr calledFn;
|
|
if (!matchPattern(indirectCall.getCallee(), m_Constant(&calledFn)))
|
|
return failure();
|
|
|
|
// Replace with a direct call.
|
|
rewriter.replaceOpWithNewOp<CallOp>(indirectCall, calledFn,
|
|
indirectCall.getResultTypes(),
|
|
indirectCall.getArgOperands());
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace.
|
|
|
|
void CallIndirectOp::getCanonicalizationPatterns(
|
|
OwningRewritePatternList &results, MLIRContext *context) {
|
|
results.insert<SimplifyIndirectCallWithKnownCallee>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// General helpers for comparison ops
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Return the type of the same shape (scalar, vector or tensor) containing i1.
|
|
static Type getI1SameShape(Type type) {
|
|
auto i1Type = IntegerType::get(type.getContext(), 1);
|
|
if (auto tensorType = type.dyn_cast<RankedTensorType>())
|
|
return RankedTensorType::get(tensorType.getShape(), i1Type);
|
|
if (type.isa<UnrankedTensorType>())
|
|
return UnrankedTensorType::get(i1Type);
|
|
if (auto vectorType = type.dyn_cast<VectorType>())
|
|
return VectorType::get(vectorType.getShape(), i1Type);
|
|
return i1Type;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CmpIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static void buildCmpIOp(OpBuilder &build, OperationState &result,
|
|
CmpIPredicate predicate, Value lhs, Value rhs) {
|
|
result.addOperands({lhs, rhs});
|
|
result.types.push_back(getI1SameShape(lhs.getType()));
|
|
result.addAttribute(CmpIOp::getPredicateAttrName(),
|
|
build.getI64IntegerAttr(static_cast<int64_t>(predicate)));
|
|
}
|
|
|
|
// Compute `lhs` `pred` `rhs`, where `pred` is one of the known integer
|
|
// comparison predicates.
|
|
bool mlir::applyCmpPredicate(CmpIPredicate predicate, const APInt &lhs,
|
|
const APInt &rhs) {
|
|
switch (predicate) {
|
|
case CmpIPredicate::eq:
|
|
return lhs.eq(rhs);
|
|
case CmpIPredicate::ne:
|
|
return lhs.ne(rhs);
|
|
case CmpIPredicate::slt:
|
|
return lhs.slt(rhs);
|
|
case CmpIPredicate::sle:
|
|
return lhs.sle(rhs);
|
|
case CmpIPredicate::sgt:
|
|
return lhs.sgt(rhs);
|
|
case CmpIPredicate::sge:
|
|
return lhs.sge(rhs);
|
|
case CmpIPredicate::ult:
|
|
return lhs.ult(rhs);
|
|
case CmpIPredicate::ule:
|
|
return lhs.ule(rhs);
|
|
case CmpIPredicate::ugt:
|
|
return lhs.ugt(rhs);
|
|
case CmpIPredicate::uge:
|
|
return lhs.uge(rhs);
|
|
}
|
|
llvm_unreachable("unknown comparison predicate");
|
|
}
|
|
|
|
// Returns true if the predicate is true for two equal operands.
|
|
static bool applyCmpPredicateToEqualOperands(CmpIPredicate predicate) {
|
|
switch (predicate) {
|
|
case CmpIPredicate::eq:
|
|
case CmpIPredicate::sle:
|
|
case CmpIPredicate::sge:
|
|
case CmpIPredicate::ule:
|
|
case CmpIPredicate::uge:
|
|
return true;
|
|
case CmpIPredicate::ne:
|
|
case CmpIPredicate::slt:
|
|
case CmpIPredicate::sgt:
|
|
case CmpIPredicate::ult:
|
|
case CmpIPredicate::ugt:
|
|
return false;
|
|
}
|
|
llvm_unreachable("unknown comparison predicate");
|
|
}
|
|
|
|
// Constant folding hook for comparisons.
|
|
OpFoldResult CmpIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "cmpi takes two arguments");
|
|
|
|
if (lhs() == rhs()) {
|
|
auto val = applyCmpPredicateToEqualOperands(getPredicate());
|
|
return BoolAttr::get(getContext(), val);
|
|
}
|
|
|
|
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
|
|
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
|
|
if (!lhs || !rhs)
|
|
return {};
|
|
|
|
auto val = applyCmpPredicate(getPredicate(), lhs.getValue(), rhs.getValue());
|
|
return BoolAttr::get(getContext(), val);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CmpFOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static void buildCmpFOp(OpBuilder &build, OperationState &result,
|
|
CmpFPredicate predicate, Value lhs, Value rhs) {
|
|
result.addOperands({lhs, rhs});
|
|
result.types.push_back(getI1SameShape(lhs.getType()));
|
|
result.addAttribute(CmpFOp::getPredicateAttrName(),
|
|
build.getI64IntegerAttr(static_cast<int64_t>(predicate)));
|
|
}
|
|
|
|
/// Compute `lhs` `pred` `rhs`, where `pred` is one of the known floating point
|
|
/// comparison predicates.
|
|
bool mlir::applyCmpPredicate(CmpFPredicate predicate, const APFloat &lhs,
|
|
const APFloat &rhs) {
|
|
auto cmpResult = lhs.compare(rhs);
|
|
switch (predicate) {
|
|
case CmpFPredicate::AlwaysFalse:
|
|
return false;
|
|
case CmpFPredicate::OEQ:
|
|
return cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::OGT:
|
|
return cmpResult == APFloat::cmpGreaterThan;
|
|
case CmpFPredicate::OGE:
|
|
return cmpResult == APFloat::cmpGreaterThan ||
|
|
cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::OLT:
|
|
return cmpResult == APFloat::cmpLessThan;
|
|
case CmpFPredicate::OLE:
|
|
return cmpResult == APFloat::cmpLessThan || cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::ONE:
|
|
return cmpResult != APFloat::cmpUnordered && cmpResult != APFloat::cmpEqual;
|
|
case CmpFPredicate::ORD:
|
|
return cmpResult != APFloat::cmpUnordered;
|
|
case CmpFPredicate::UEQ:
|
|
return cmpResult == APFloat::cmpUnordered || cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::UGT:
|
|
return cmpResult == APFloat::cmpUnordered ||
|
|
cmpResult == APFloat::cmpGreaterThan;
|
|
case CmpFPredicate::UGE:
|
|
return cmpResult == APFloat::cmpUnordered ||
|
|
cmpResult == APFloat::cmpGreaterThan ||
|
|
cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::ULT:
|
|
return cmpResult == APFloat::cmpUnordered ||
|
|
cmpResult == APFloat::cmpLessThan;
|
|
case CmpFPredicate::ULE:
|
|
return cmpResult == APFloat::cmpUnordered ||
|
|
cmpResult == APFloat::cmpLessThan || cmpResult == APFloat::cmpEqual;
|
|
case CmpFPredicate::UNE:
|
|
return cmpResult != APFloat::cmpEqual;
|
|
case CmpFPredicate::UNO:
|
|
return cmpResult == APFloat::cmpUnordered;
|
|
case CmpFPredicate::AlwaysTrue:
|
|
return true;
|
|
}
|
|
llvm_unreachable("unknown comparison predicate");
|
|
}
|
|
|
|
// Constant folding hook for comparisons.
|
|
OpFoldResult CmpFOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "cmpf takes two arguments");
|
|
|
|
auto lhs = operands.front().dyn_cast_or_null<FloatAttr>();
|
|
auto rhs = operands.back().dyn_cast_or_null<FloatAttr>();
|
|
|
|
// TODO: We could actually do some intelligent things if we know only one
|
|
// of the operands, but it's inf or nan.
|
|
if (!lhs || !rhs)
|
|
return {};
|
|
|
|
auto val = applyCmpPredicate(getPredicate(), lhs.getValue(), rhs.getValue());
|
|
return IntegerAttr::get(IntegerType::get(getContext(), 1), APInt(1, val));
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CondBranchOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace {
|
|
/// cond_br true, ^bb1, ^bb2
|
|
/// -> br ^bb1
|
|
/// cond_br false, ^bb1, ^bb2
|
|
/// -> br ^bb2
|
|
///
|
|
struct SimplifyConstCondBranchPred : public OpRewritePattern<CondBranchOp> {
|
|
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CondBranchOp condbr,
|
|
PatternRewriter &rewriter) const override {
|
|
if (matchPattern(condbr.getCondition(), m_NonZero())) {
|
|
// True branch taken.
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getTrueDest(),
|
|
condbr.getTrueOperands());
|
|
return success();
|
|
} else if (matchPattern(condbr.getCondition(), m_Zero())) {
|
|
// False branch taken.
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getFalseDest(),
|
|
condbr.getFalseOperands());
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
/// cond_br %cond, ^bb1, ^bb2
|
|
/// ^bb1
|
|
/// br ^bbN(...)
|
|
/// ^bb2
|
|
/// br ^bbK(...)
|
|
///
|
|
/// -> cond_br %cond, ^bbN(...), ^bbK(...)
|
|
///
|
|
struct SimplifyPassThroughCondBranch : public OpRewritePattern<CondBranchOp> {
|
|
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CondBranchOp condbr,
|
|
PatternRewriter &rewriter) const override {
|
|
Block *trueDest = condbr.trueDest(), *falseDest = condbr.falseDest();
|
|
ValueRange trueDestOperands = condbr.getTrueOperands();
|
|
ValueRange falseDestOperands = condbr.getFalseOperands();
|
|
SmallVector<Value, 4> trueDestOperandStorage, falseDestOperandStorage;
|
|
|
|
// Try to collapse one of the current successors.
|
|
LogicalResult collapsedTrue =
|
|
collapseBranch(trueDest, trueDestOperands, trueDestOperandStorage);
|
|
LogicalResult collapsedFalse =
|
|
collapseBranch(falseDest, falseDestOperands, falseDestOperandStorage);
|
|
if (failed(collapsedTrue) && failed(collapsedFalse))
|
|
return failure();
|
|
|
|
// Create a new branch with the collapsed successors.
|
|
rewriter.replaceOpWithNewOp<CondBranchOp>(condbr, condbr.getCondition(),
|
|
trueDest, trueDestOperands,
|
|
falseDest, falseDestOperands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// cond_br %cond, ^bb1(A, ..., N), ^bb1(A, ..., N)
|
|
/// -> br ^bb1(A, ..., N)
|
|
///
|
|
/// cond_br %cond, ^bb1(A), ^bb1(B)
|
|
/// -> %select = select %cond, A, B
|
|
/// br ^bb1(%select)
|
|
///
|
|
struct SimplifyCondBranchIdenticalSuccessors
|
|
: public OpRewritePattern<CondBranchOp> {
|
|
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CondBranchOp condbr,
|
|
PatternRewriter &rewriter) const override {
|
|
// Check that the true and false destinations are the same and have the same
|
|
// operands.
|
|
Block *trueDest = condbr.trueDest();
|
|
if (trueDest != condbr.falseDest())
|
|
return failure();
|
|
|
|
// If all of the operands match, no selects need to be generated.
|
|
OperandRange trueOperands = condbr.getTrueOperands();
|
|
OperandRange falseOperands = condbr.getFalseOperands();
|
|
if (trueOperands == falseOperands) {
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, trueDest, trueOperands);
|
|
return success();
|
|
}
|
|
|
|
// Otherwise, if the current block is the only predecessor insert selects
|
|
// for any mismatched branch operands.
|
|
if (trueDest->getUniquePredecessor() != condbr->getBlock())
|
|
return failure();
|
|
|
|
// Generate a select for any operands that differ between the two.
|
|
SmallVector<Value, 8> mergedOperands;
|
|
mergedOperands.reserve(trueOperands.size());
|
|
Value condition = condbr.getCondition();
|
|
for (auto it : llvm::zip(trueOperands, falseOperands)) {
|
|
if (std::get<0>(it) == std::get<1>(it))
|
|
mergedOperands.push_back(std::get<0>(it));
|
|
else
|
|
mergedOperands.push_back(rewriter.create<SelectOp>(
|
|
condbr.getLoc(), condition, std::get<0>(it), std::get<1>(it)));
|
|
}
|
|
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, trueDest, mergedOperands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// ...
|
|
/// cond_br %cond, ^bb1(...), ^bb2(...)
|
|
/// ...
|
|
/// ^bb1: // has single predecessor
|
|
/// ...
|
|
/// cond_br %cond, ^bb3(...), ^bb4(...)
|
|
///
|
|
/// ->
|
|
///
|
|
/// ...
|
|
/// cond_br %cond, ^bb1(...), ^bb2(...)
|
|
/// ...
|
|
/// ^bb1: // has single predecessor
|
|
/// ...
|
|
/// br ^bb3(...)
|
|
///
|
|
struct SimplifyCondBranchFromCondBranchOnSameCondition
|
|
: public OpRewritePattern<CondBranchOp> {
|
|
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CondBranchOp condbr,
|
|
PatternRewriter &rewriter) const override {
|
|
// Check that we have a single distinct predecessor.
|
|
Block *currentBlock = condbr->getBlock();
|
|
Block *predecessor = currentBlock->getSinglePredecessor();
|
|
if (!predecessor)
|
|
return failure();
|
|
|
|
// Check that the predecessor terminates with a conditional branch to this
|
|
// block and that it branches on the same condition.
|
|
auto predBranch = dyn_cast<CondBranchOp>(predecessor->getTerminator());
|
|
if (!predBranch || condbr.getCondition() != predBranch.getCondition())
|
|
return failure();
|
|
|
|
// Fold this branch to an unconditional branch.
|
|
if (currentBlock == predBranch.trueDest())
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.trueDest(),
|
|
condbr.trueDestOperands());
|
|
else
|
|
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.falseDest(),
|
|
condbr.falseDestOperands());
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace
|
|
|
|
void CondBranchOp::getCanonicalizationPatterns(
|
|
OwningRewritePatternList &results, MLIRContext *context) {
|
|
results.insert<SimplifyConstCondBranchPred, SimplifyPassThroughCondBranch,
|
|
SimplifyCondBranchIdenticalSuccessors,
|
|
SimplifyCondBranchFromCondBranchOnSameCondition>(context);
|
|
}
|
|
|
|
Optional<MutableOperandRange>
|
|
CondBranchOp::getMutableSuccessorOperands(unsigned index) {
|
|
assert(index < getNumSuccessors() && "invalid successor index");
|
|
return index == trueIndex ? trueDestOperandsMutable()
|
|
: falseDestOperandsMutable();
|
|
}
|
|
|
|
Block *CondBranchOp::getSuccessorForOperands(ArrayRef<Attribute> operands) {
|
|
if (IntegerAttr condAttr = operands.front().dyn_cast_or_null<IntegerAttr>())
|
|
return condAttr.getValue().isOneValue() ? trueDest() : falseDest();
|
|
return nullptr;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Constant*Op
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static void print(OpAsmPrinter &p, ConstantOp &op) {
|
|
p << "constant ";
|
|
p.printOptionalAttrDict(op->getAttrs(), /*elidedAttrs=*/{"value"});
|
|
|
|
if (op->getAttrs().size() > 1)
|
|
p << ' ';
|
|
p << op.getValue();
|
|
|
|
// If the value is a symbol reference, print a trailing type.
|
|
if (op.getValue().isa<SymbolRefAttr>())
|
|
p << " : " << op.getType();
|
|
}
|
|
|
|
static ParseResult parseConstantOp(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
Attribute valueAttr;
|
|
if (parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseAttribute(valueAttr, "value", result.attributes))
|
|
return failure();
|
|
|
|
// If the attribute is a symbol reference, then we expect a trailing type.
|
|
Type type;
|
|
if (!valueAttr.isa<SymbolRefAttr>())
|
|
type = valueAttr.getType();
|
|
else if (parser.parseColonType(type))
|
|
return failure();
|
|
|
|
// Add the attribute type to the list.
|
|
return parser.addTypeToList(type, result.types);
|
|
}
|
|
|
|
/// The constant op requires an attribute, and furthermore requires that it
|
|
/// matches the return type.
|
|
static LogicalResult verify(ConstantOp &op) {
|
|
auto value = op.getValue();
|
|
if (!value)
|
|
return op.emitOpError("requires a 'value' attribute");
|
|
|
|
Type type = op.getType();
|
|
if (!value.getType().isa<NoneType>() && type != value.getType())
|
|
return op.emitOpError() << "requires attribute's type (" << value.getType()
|
|
<< ") to match op's return type (" << type << ")";
|
|
|
|
if (type.isa<IndexType>() || value.isa<BoolAttr>())
|
|
return success();
|
|
|
|
if (auto intAttr = value.dyn_cast<IntegerAttr>()) {
|
|
IntegerType intType = type.cast<IntegerType>();
|
|
if (!intType.isSignless())
|
|
return op.emitOpError("requires integer result types to be signless");
|
|
|
|
// If the type has a known bitwidth we verify that the value can be
|
|
// represented with the given bitwidth.
|
|
unsigned bitwidth = intType.getWidth();
|
|
APInt intVal = intAttr.getValue();
|
|
if (!intVal.isSignedIntN(bitwidth) && !intVal.isIntN(bitwidth))
|
|
return op.emitOpError("requires 'value' to be an integer within the "
|
|
"range of the integer result type");
|
|
return success();
|
|
}
|
|
|
|
if (type.isa<FloatType>()) {
|
|
if (!value.isa<FloatAttr>())
|
|
return op.emitOpError("requires 'value' to be a floating point constant");
|
|
return success();
|
|
}
|
|
|
|
if (type.isa<ShapedType>()) {
|
|
if (!value.isa<ElementsAttr>())
|
|
return op.emitOpError("requires 'value' to be a shaped constant");
|
|
return success();
|
|
}
|
|
|
|
if (type.isa<FunctionType>()) {
|
|
auto fnAttr = value.dyn_cast<FlatSymbolRefAttr>();
|
|
if (!fnAttr)
|
|
return op.emitOpError("requires 'value' to be a function reference");
|
|
|
|
// Try to find the referenced function.
|
|
auto fn =
|
|
op->getParentOfType<ModuleOp>().lookupSymbol<FuncOp>(fnAttr.getValue());
|
|
if (!fn)
|
|
return op.emitOpError()
|
|
<< "reference to undefined function '" << fnAttr.getValue() << "'";
|
|
|
|
// Check that the referenced function has the correct type.
|
|
if (fn.getType() != type)
|
|
return op.emitOpError("reference to function with mismatched type");
|
|
|
|
return success();
|
|
}
|
|
|
|
if (type.isa<NoneType>() && value.isa<UnitAttr>())
|
|
return success();
|
|
|
|
return op.emitOpError("unsupported 'value' attribute: ") << value;
|
|
}
|
|
|
|
OpFoldResult ConstantOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.empty() && "constant has no operands");
|
|
return getValue();
|
|
}
|
|
|
|
void ConstantOp::getAsmResultNames(
|
|
function_ref<void(Value, StringRef)> setNameFn) {
|
|
Type type = getType();
|
|
if (auto intCst = getValue().dyn_cast<IntegerAttr>()) {
|
|
IntegerType intTy = type.dyn_cast<IntegerType>();
|
|
|
|
// Sugar i1 constants with 'true' and 'false'.
|
|
if (intTy && intTy.getWidth() == 1)
|
|
return setNameFn(getResult(), (intCst.getInt() ? "true" : "false"));
|
|
|
|
// Otherwise, build a complex name with the value and type.
|
|
SmallString<32> specialNameBuffer;
|
|
llvm::raw_svector_ostream specialName(specialNameBuffer);
|
|
specialName << 'c' << intCst.getInt();
|
|
if (intTy)
|
|
specialName << '_' << type;
|
|
setNameFn(getResult(), specialName.str());
|
|
|
|
} else if (type.isa<FunctionType>()) {
|
|
setNameFn(getResult(), "f");
|
|
} else {
|
|
setNameFn(getResult(), "cst");
|
|
}
|
|
}
|
|
|
|
/// Returns true if a constant operation can be built with the given value and
|
|
/// result type.
|
|
bool ConstantOp::isBuildableWith(Attribute value, Type type) {
|
|
// SymbolRefAttr can only be used with a function type.
|
|
if (value.isa<SymbolRefAttr>())
|
|
return type.isa<FunctionType>();
|
|
// The attribute must have the same type as 'type'.
|
|
if (value.getType() != type)
|
|
return false;
|
|
// If the type is an integer type, it must be signless.
|
|
if (IntegerType integerTy = type.dyn_cast<IntegerType>())
|
|
if (!integerTy.isSignless())
|
|
return false;
|
|
// Finally, check that the attribute kind is handled.
|
|
return value.isa<IntegerAttr, FloatAttr, ElementsAttr, UnitAttr>();
|
|
}
|
|
|
|
void ConstantFloatOp::build(OpBuilder &builder, OperationState &result,
|
|
const APFloat &value, FloatType type) {
|
|
ConstantOp::build(builder, result, type, builder.getFloatAttr(type, value));
|
|
}
|
|
|
|
bool ConstantFloatOp::classof(Operation *op) {
|
|
return ConstantOp::classof(op) && op->getResult(0).getType().isa<FloatType>();
|
|
}
|
|
|
|
/// ConstantIntOp only matches values whose result type is an IntegerType.
|
|
bool ConstantIntOp::classof(Operation *op) {
|
|
return ConstantOp::classof(op) &&
|
|
op->getResult(0).getType().isSignlessInteger();
|
|
}
|
|
|
|
void ConstantIntOp::build(OpBuilder &builder, OperationState &result,
|
|
int64_t value, unsigned width) {
|
|
Type type = builder.getIntegerType(width);
|
|
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
|
|
}
|
|
|
|
/// Build a constant int op producing an integer with the specified type,
|
|
/// which must be an integer type.
|
|
void ConstantIntOp::build(OpBuilder &builder, OperationState &result,
|
|
int64_t value, Type type) {
|
|
assert(type.isSignlessInteger() &&
|
|
"ConstantIntOp can only have signless integer type");
|
|
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
|
|
}
|
|
|
|
/// ConstantIndexOp only matches values whose result type is Index.
|
|
bool ConstantIndexOp::classof(Operation *op) {
|
|
return ConstantOp::classof(op) && op->getResult(0).getType().isIndex();
|
|
}
|
|
|
|
void ConstantIndexOp::build(OpBuilder &builder, OperationState &result,
|
|
int64_t value) {
|
|
Type type = builder.getIndexType();
|
|
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// DeallocOp
|
|
//===----------------------------------------------------------------------===//
|
|
namespace {
|
|
/// Fold Dealloc operations that are deallocating an AllocOp that is only used
|
|
/// by other Dealloc operations.
|
|
struct SimplifyDeadDealloc : public OpRewritePattern<DeallocOp> {
|
|
using OpRewritePattern<DeallocOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(DeallocOp dealloc,
|
|
PatternRewriter &rewriter) const override {
|
|
// Check that the memref operand's defining operation is an AllocOp.
|
|
Value memref = dealloc.memref();
|
|
if (!isa_and_nonnull<AllocOp>(memref.getDefiningOp()))
|
|
return failure();
|
|
|
|
// Check that all of the uses of the AllocOp are other DeallocOps.
|
|
for (auto *user : memref.getUsers())
|
|
if (!isa<DeallocOp>(user))
|
|
return failure();
|
|
|
|
// Erase the dealloc operation.
|
|
rewriter.eraseOp(dealloc);
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace.
|
|
|
|
static LogicalResult verify(DeallocOp op) {
|
|
if (!op.memref().getType().isa<MemRefType>())
|
|
return op.emitOpError("operand must be a memref");
|
|
return success();
|
|
}
|
|
|
|
void DeallocOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<SimplifyDeadDealloc>(context);
|
|
}
|
|
|
|
LogicalResult DeallocOp::fold(ArrayRef<Attribute> cstOperands,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
/// dealloc(memrefcast) -> dealloc
|
|
return foldMemRefCast(*this);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// DimOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void DimOp::build(OpBuilder &builder, OperationState &result,
|
|
Value memrefOrTensor, int64_t index) {
|
|
auto loc = result.location;
|
|
Value indexValue = builder.create<ConstantIndexOp>(loc, index);
|
|
build(builder, result, memrefOrTensor, indexValue);
|
|
}
|
|
|
|
void DimOp::build(OpBuilder &builder, OperationState &result,
|
|
Value memrefOrTensor, Value index) {
|
|
auto indexTy = builder.getIndexType();
|
|
build(builder, result, indexTy, memrefOrTensor, index);
|
|
}
|
|
|
|
Optional<int64_t> DimOp::getConstantIndex() {
|
|
if (auto constantOp = index().getDefiningOp<ConstantOp>())
|
|
return constantOp.getValue().cast<IntegerAttr>().getInt();
|
|
return {};
|
|
}
|
|
|
|
static LogicalResult verify(DimOp op) {
|
|
// Assume unknown index to be in range.
|
|
Optional<int64_t> index = op.getConstantIndex();
|
|
if (!index.hasValue())
|
|
return success();
|
|
|
|
// Check that constant index is not knowingly out of range.
|
|
auto type = op.memrefOrTensor().getType();
|
|
if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
|
|
if (index.getValue() >= tensorType.getRank())
|
|
return op.emitOpError("index is out of range");
|
|
} else if (auto memrefType = type.dyn_cast<MemRefType>()) {
|
|
if (index.getValue() >= memrefType.getRank())
|
|
return op.emitOpError("index is out of range");
|
|
} else if (type.isa<UnrankedTensorType>() || type.isa<UnrankedMemRefType>()) {
|
|
// Assume index to be in range.
|
|
} else {
|
|
llvm_unreachable("expected operand with tensor or memref type");
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
|
|
auto index = operands[1].dyn_cast_or_null<IntegerAttr>();
|
|
|
|
// All forms of folding require a known index.
|
|
if (!index)
|
|
return {};
|
|
|
|
auto argTy = memrefOrTensor().getType();
|
|
// Fold if the shape extent along the given index is known.
|
|
if (auto shapedTy = argTy.dyn_cast<ShapedType>()) {
|
|
// Folding for unranked types (UnrankedMemRefType, UnrankedTensorType) is
|
|
// not supported.
|
|
if (!shapedTy.hasRank())
|
|
return {};
|
|
if (!shapedTy.isDynamicDim(index.getInt())) {
|
|
Builder builder(getContext());
|
|
return builder.getIndexAttr(shapedTy.getShape()[index.getInt()]);
|
|
}
|
|
}
|
|
|
|
Operation *definingOp = memrefOrTensor().getDefiningOp();
|
|
// dim(tensor_load(memref)) -> dim(memref)
|
|
if (auto tensorLoadOp = dyn_cast_or_null<TensorLoadOp>(definingOp)) {
|
|
setOperand(0, tensorLoadOp.memref());
|
|
return getResult();
|
|
}
|
|
|
|
// Fold dim to the operand of tensor.generate.
|
|
if (auto fromElements = dyn_cast_or_null<tensor::GenerateOp>(definingOp)) {
|
|
auto resultType =
|
|
fromElements.getResult().getType().cast<RankedTensorType>();
|
|
// The case where the type encodes the size of the dimension is handled
|
|
// above.
|
|
assert(resultType.getShape()[index.getInt()] ==
|
|
RankedTensorType::kDynamicSize);
|
|
|
|
// Find the operand of the fromElements that corresponds to this index.
|
|
auto dynExtents = fromElements.dynamicExtents().begin();
|
|
for (auto dim : resultType.getShape().take_front(index.getInt()))
|
|
if (dim == RankedTensorType::kDynamicSize)
|
|
dynExtents++;
|
|
|
|
return Value{*dynExtents};
|
|
}
|
|
|
|
// The size at the given index is now known to be a dynamic size.
|
|
unsigned unsignedIndex = index.getValue().getZExtValue();
|
|
|
|
if (auto subtensor = dyn_cast_or_null<SubTensorOp>(definingOp)) {
|
|
assert(subtensor.isDynamicSize(unsignedIndex) &&
|
|
"Expected dynamic subtensor size");
|
|
return subtensor.getDynamicSize(unsignedIndex);
|
|
}
|
|
|
|
// Fold dim to the size argument for an `AllocOp`, `ViewOp`, or `SubViewOp`.
|
|
auto memrefType = argTy.dyn_cast<MemRefType>();
|
|
if (!memrefType)
|
|
return {};
|
|
|
|
if (auto alloc = dyn_cast_or_null<AllocOp>(definingOp))
|
|
return *(alloc.getDynamicSizes().begin() +
|
|
memrefType.getDynamicDimIndex(unsignedIndex));
|
|
|
|
if (auto view = dyn_cast_or_null<ViewOp>(definingOp))
|
|
return *(view.getDynamicSizes().begin() +
|
|
memrefType.getDynamicDimIndex(unsignedIndex));
|
|
|
|
if (auto subview = dyn_cast_or_null<SubViewOp>(definingOp)) {
|
|
assert(subview.isDynamicSize(unsignedIndex) &&
|
|
"Expected dynamic subview size");
|
|
return subview.getDynamicSize(unsignedIndex);
|
|
}
|
|
|
|
// dim(memrefcast) -> dim
|
|
if (succeeded(foldMemRefCast(*this)))
|
|
return getResult();
|
|
|
|
return {};
|
|
}
|
|
|
|
namespace {
|
|
/// Fold dim of a memref reshape operation to a load into the reshape's shape
|
|
/// operand.
|
|
struct DimOfMemRefReshape : public OpRewritePattern<DimOp> {
|
|
using OpRewritePattern<DimOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(DimOp dim,
|
|
PatternRewriter &rewriter) const override {
|
|
auto reshape = dim.memrefOrTensor().getDefiningOp<MemRefReshapeOp>();
|
|
|
|
if (!reshape)
|
|
return failure();
|
|
|
|
// Place the load directly after the reshape to ensure that the shape memref
|
|
// was not mutated.
|
|
rewriter.setInsertionPointAfter(reshape);
|
|
rewriter.replaceOpWithNewOp<LoadOp>(dim, reshape.shape(),
|
|
llvm::makeArrayRef({dim.index()}));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Fold dim of a dim of a cast into the dim of the source of the tensor cast.
|
|
template <typename CastOpTy>
|
|
struct DimOfCastOp : public OpRewritePattern<DimOp> {
|
|
using OpRewritePattern<DimOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(DimOp dimOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto castOp = dimOp.memrefOrTensor().getDefiningOp<CastOpTy>();
|
|
if (!castOp)
|
|
return failure();
|
|
Value newSource = castOp.getOperand();
|
|
rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.index());
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace.
|
|
|
|
void DimOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<DimOfMemRefReshape, DimOfCastOp<TensorToMemrefOp>,
|
|
DimOfCastOp<tensor::CastOp>>(context);
|
|
}
|
|
|
|
// ---------------------------------------------------------------------------
|
|
// DivFOp
|
|
// ---------------------------------------------------------------------------
|
|
|
|
OpFoldResult DivFOp::fold(ArrayRef<Attribute> operands) {
|
|
return constFoldBinaryOp<FloatAttr>(
|
|
operands, [](APFloat a, APFloat b) { return a / b; });
|
|
}
|
|
|
|
// ---------------------------------------------------------------------------
|
|
// DmaStartOp
|
|
// ---------------------------------------------------------------------------
|
|
|
|
void DmaStartOp::build(OpBuilder &builder, OperationState &result,
|
|
Value srcMemRef, ValueRange srcIndices, Value destMemRef,
|
|
ValueRange destIndices, Value numElements,
|
|
Value tagMemRef, ValueRange tagIndices, Value stride,
|
|
Value elementsPerStride) {
|
|
result.addOperands(srcMemRef);
|
|
result.addOperands(srcIndices);
|
|
result.addOperands(destMemRef);
|
|
result.addOperands(destIndices);
|
|
result.addOperands({numElements, tagMemRef});
|
|
result.addOperands(tagIndices);
|
|
if (stride)
|
|
result.addOperands({stride, elementsPerStride});
|
|
}
|
|
|
|
void DmaStartOp::print(OpAsmPrinter &p) {
|
|
p << "dma_start " << getSrcMemRef() << '[' << getSrcIndices() << "], "
|
|
<< getDstMemRef() << '[' << getDstIndices() << "], " << getNumElements()
|
|
<< ", " << getTagMemRef() << '[' << getTagIndices() << ']';
|
|
if (isStrided())
|
|
p << ", " << getStride() << ", " << getNumElementsPerStride();
|
|
|
|
p.printOptionalAttrDict((*this)->getAttrs());
|
|
p << " : " << getSrcMemRef().getType() << ", " << getDstMemRef().getType()
|
|
<< ", " << getTagMemRef().getType();
|
|
}
|
|
|
|
// Parse DmaStartOp.
|
|
// Ex:
|
|
// %dma_id = dma_start %src[%i, %j], %dst[%k, %l], %size,
|
|
// %tag[%index], %stride, %num_elt_per_stride :
|
|
// : memref<3076 x f32, 0>,
|
|
// memref<1024 x f32, 2>,
|
|
// memref<1 x i32>
|
|
//
|
|
ParseResult DmaStartOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
OpAsmParser::OperandType srcMemRefInfo;
|
|
SmallVector<OpAsmParser::OperandType, 4> srcIndexInfos;
|
|
OpAsmParser::OperandType dstMemRefInfo;
|
|
SmallVector<OpAsmParser::OperandType, 4> dstIndexInfos;
|
|
OpAsmParser::OperandType numElementsInfo;
|
|
OpAsmParser::OperandType tagMemrefInfo;
|
|
SmallVector<OpAsmParser::OperandType, 4> tagIndexInfos;
|
|
SmallVector<OpAsmParser::OperandType, 2> strideInfo;
|
|
|
|
SmallVector<Type, 3> types;
|
|
auto indexType = parser.getBuilder().getIndexType();
|
|
|
|
// Parse and resolve the following list of operands:
|
|
// *) source memref followed by its indices (in square brackets).
|
|
// *) destination memref followed by its indices (in square brackets).
|
|
// *) dma size in KiB.
|
|
if (parser.parseOperand(srcMemRefInfo) ||
|
|
parser.parseOperandList(srcIndexInfos, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseComma() || parser.parseOperand(dstMemRefInfo) ||
|
|
parser.parseOperandList(dstIndexInfos, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseComma() || parser.parseOperand(numElementsInfo) ||
|
|
parser.parseComma() || parser.parseOperand(tagMemrefInfo) ||
|
|
parser.parseOperandList(tagIndexInfos, OpAsmParser::Delimiter::Square))
|
|
return failure();
|
|
|
|
// Parse optional stride and elements per stride.
|
|
if (parser.parseTrailingOperandList(strideInfo))
|
|
return failure();
|
|
|
|
bool isStrided = strideInfo.size() == 2;
|
|
if (!strideInfo.empty() && !isStrided) {
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"expected two stride related operands");
|
|
}
|
|
|
|
if (parser.parseColonTypeList(types))
|
|
return failure();
|
|
if (types.size() != 3)
|
|
return parser.emitError(parser.getNameLoc(), "fewer/more types expected");
|
|
|
|
if (parser.resolveOperand(srcMemRefInfo, types[0], result.operands) ||
|
|
parser.resolveOperands(srcIndexInfos, indexType, result.operands) ||
|
|
parser.resolveOperand(dstMemRefInfo, types[1], result.operands) ||
|
|
parser.resolveOperands(dstIndexInfos, indexType, result.operands) ||
|
|
// size should be an index.
|
|
parser.resolveOperand(numElementsInfo, indexType, result.operands) ||
|
|
parser.resolveOperand(tagMemrefInfo, types[2], result.operands) ||
|
|
// tag indices should be index.
|
|
parser.resolveOperands(tagIndexInfos, indexType, result.operands))
|
|
return failure();
|
|
|
|
if (isStrided) {
|
|
if (parser.resolveOperands(strideInfo, indexType, result.operands))
|
|
return failure();
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult DmaStartOp::verify() {
|
|
unsigned numOperands = getNumOperands();
|
|
|
|
// Mandatory non-variadic operands are: src memref, dst memref, tag memref and
|
|
// the number of elements.
|
|
if (numOperands < 4)
|
|
return emitOpError("expected at least 4 operands");
|
|
|
|
// Check types of operands. The order of these calls is important: the later
|
|
// calls rely on some type properties to compute the operand position.
|
|
// 1. Source memref.
|
|
if (!getSrcMemRef().getType().isa<MemRefType>())
|
|
return emitOpError("expected source to be of memref type");
|
|
if (numOperands < getSrcMemRefRank() + 4)
|
|
return emitOpError() << "expected at least " << getSrcMemRefRank() + 4
|
|
<< " operands";
|
|
if (!getSrcIndices().empty() &&
|
|
!llvm::all_of(getSrcIndices().getTypes(),
|
|
[](Type t) { return t.isIndex(); }))
|
|
return emitOpError("expected source indices to be of index type");
|
|
|
|
// 2. Destination memref.
|
|
if (!getDstMemRef().getType().isa<MemRefType>())
|
|
return emitOpError("expected destination to be of memref type");
|
|
unsigned numExpectedOperands = getSrcMemRefRank() + getDstMemRefRank() + 4;
|
|
if (numOperands < numExpectedOperands)
|
|
return emitOpError() << "expected at least " << numExpectedOperands
|
|
<< " operands";
|
|
if (!getDstIndices().empty() &&
|
|
!llvm::all_of(getDstIndices().getTypes(),
|
|
[](Type t) { return t.isIndex(); }))
|
|
return emitOpError("expected destination indices to be of index type");
|
|
|
|
// 3. Number of elements.
|
|
if (!getNumElements().getType().isIndex())
|
|
return emitOpError("expected num elements to be of index type");
|
|
|
|
// 4. Tag memref.
|
|
if (!getTagMemRef().getType().isa<MemRefType>())
|
|
return emitOpError("expected tag to be of memref type");
|
|
numExpectedOperands += getTagMemRefRank();
|
|
if (numOperands < numExpectedOperands)
|
|
return emitOpError() << "expected at least " << numExpectedOperands
|
|
<< " operands";
|
|
if (!getTagIndices().empty() &&
|
|
!llvm::all_of(getTagIndices().getTypes(),
|
|
[](Type t) { return t.isIndex(); }))
|
|
return emitOpError("expected tag indices to be of index type");
|
|
|
|
// DMAs from different memory spaces supported.
|
|
if (getSrcMemorySpace() == getDstMemorySpace())
|
|
return emitOpError("DMA should be between different memory spaces");
|
|
|
|
// Optional stride-related operands must be either both present or both
|
|
// absent.
|
|
if (numOperands != numExpectedOperands &&
|
|
numOperands != numExpectedOperands + 2)
|
|
return emitOpError("incorrect number of operands");
|
|
|
|
// 5. Strides.
|
|
if (isStrided()) {
|
|
if (!getStride().getType().isIndex() ||
|
|
!getNumElementsPerStride().getType().isIndex())
|
|
return emitOpError(
|
|
"expected stride and num elements per stride to be of type index");
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult DmaStartOp::fold(ArrayRef<Attribute> cstOperands,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
/// dma_start(memrefcast) -> dma_start
|
|
return foldMemRefCast(*this);
|
|
}
|
|
|
|
// ---------------------------------------------------------------------------
|
|
// DmaWaitOp
|
|
// ---------------------------------------------------------------------------
|
|
|
|
void DmaWaitOp::build(OpBuilder &builder, OperationState &result,
|
|
Value tagMemRef, ValueRange tagIndices,
|
|
Value numElements) {
|
|
result.addOperands(tagMemRef);
|
|
result.addOperands(tagIndices);
|
|
result.addOperands(numElements);
|
|
}
|
|
|
|
void DmaWaitOp::print(OpAsmPrinter &p) {
|
|
p << "dma_wait " << getTagMemRef() << '[' << getTagIndices() << "], "
|
|
<< getNumElements();
|
|
p.printOptionalAttrDict((*this)->getAttrs());
|
|
p << " : " << getTagMemRef().getType();
|
|
}
|
|
|
|
// Parse DmaWaitOp.
|
|
// Eg:
|
|
// dma_wait %tag[%index], %num_elements : memref<1 x i32, (d0) -> (d0), 4>
|
|
//
|
|
ParseResult DmaWaitOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
OpAsmParser::OperandType tagMemrefInfo;
|
|
SmallVector<OpAsmParser::OperandType, 2> tagIndexInfos;
|
|
Type type;
|
|
auto indexType = parser.getBuilder().getIndexType();
|
|
OpAsmParser::OperandType numElementsInfo;
|
|
|
|
// Parse tag memref, its indices, and dma size.
|
|
if (parser.parseOperand(tagMemrefInfo) ||
|
|
parser.parseOperandList(tagIndexInfos, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseComma() || parser.parseOperand(numElementsInfo) ||
|
|
parser.parseColonType(type) ||
|
|
parser.resolveOperand(tagMemrefInfo, type, result.operands) ||
|
|
parser.resolveOperands(tagIndexInfos, indexType, result.operands) ||
|
|
parser.resolveOperand(numElementsInfo, indexType, result.operands))
|
|
return failure();
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult DmaWaitOp::fold(ArrayRef<Attribute> cstOperands,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
/// dma_wait(memrefcast) -> dma_wait
|
|
return foldMemRefCast(*this);
|
|
}
|
|
|
|
LogicalResult DmaWaitOp::verify() {
|
|
// Mandatory non-variadic operands are tag and the number of elements.
|
|
if (getNumOperands() < 2)
|
|
return emitOpError() << "expected at least 2 operands";
|
|
|
|
// Check types of operands. The order of these calls is important: the later
|
|
// calls rely on some type properties to compute the operand position.
|
|
if (!getTagMemRef().getType().isa<MemRefType>())
|
|
return emitOpError() << "expected tag to be of memref type";
|
|
|
|
if (getNumOperands() != 2 + getTagMemRefRank())
|
|
return emitOpError() << "expected " << 2 + getTagMemRefRank()
|
|
<< " operands";
|
|
|
|
if (!getTagIndices().empty() &&
|
|
!llvm::all_of(getTagIndices().getTypes(),
|
|
[](Type t) { return t.isIndex(); }))
|
|
return emitOpError() << "expected tag indices to be of index type";
|
|
|
|
if (!getNumElements().getType().isIndex())
|
|
return emitOpError()
|
|
<< "expected the number of elements to be of index type";
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// FPExtOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
bool FPExtOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (auto fa = a.dyn_cast<FloatType>())
|
|
if (auto fb = b.dyn_cast<FloatType>())
|
|
return fa.getWidth() < fb.getWidth();
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// FPToSIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
bool FPToSIOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (a.isa<FloatType>() && b.isSignlessInteger())
|
|
return true;
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// FPToUIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
bool FPToUIOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (a.isa<FloatType>() && b.isSignlessInteger())
|
|
return true;
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// FPTruncOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
bool FPTruncOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (auto fa = a.dyn_cast<FloatType>())
|
|
if (auto fb = b.dyn_cast<FloatType>())
|
|
return fa.getWidth() > fb.getWidth();
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// GlobalMemrefOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static void printGlobalMemrefOpTypeAndInitialValue(OpAsmPrinter &p,
|
|
GlobalMemrefOp op,
|
|
TypeAttr type,
|
|
Attribute initialValue) {
|
|
p << type;
|
|
if (!op.isExternal()) {
|
|
p << " = ";
|
|
if (op.isUninitialized())
|
|
p << "uninitialized";
|
|
else
|
|
p.printAttributeWithoutType(initialValue);
|
|
}
|
|
}
|
|
|
|
static ParseResult
|
|
parseGlobalMemrefOpTypeAndInitialValue(OpAsmParser &parser, TypeAttr &typeAttr,
|
|
Attribute &initialValue) {
|
|
Type type;
|
|
if (parser.parseType(type))
|
|
return failure();
|
|
|
|
auto memrefType = type.dyn_cast<MemRefType>();
|
|
if (!memrefType || !memrefType.hasStaticShape())
|
|
return parser.emitError(parser.getNameLoc())
|
|
<< "type should be static shaped memref, but got " << type;
|
|
typeAttr = TypeAttr::get(type);
|
|
|
|
if (parser.parseOptionalEqual())
|
|
return success();
|
|
|
|
if (succeeded(parser.parseOptionalKeyword("uninitialized"))) {
|
|
initialValue = UnitAttr::get(parser.getBuilder().getContext());
|
|
return success();
|
|
}
|
|
|
|
Type tensorType = getTensorTypeFromMemRefType(memrefType);
|
|
if (parser.parseAttribute(initialValue, tensorType))
|
|
return failure();
|
|
if (!initialValue.isa<ElementsAttr>())
|
|
return parser.emitError(parser.getNameLoc())
|
|
<< "initial value should be a unit or elements attribute";
|
|
return success();
|
|
}
|
|
|
|
static LogicalResult verify(GlobalMemrefOp op) {
|
|
auto memrefType = op.type().dyn_cast<MemRefType>();
|
|
if (!memrefType || !memrefType.hasStaticShape())
|
|
return op.emitOpError("type should be static shaped memref, but got ")
|
|
<< op.type();
|
|
|
|
// Verify that the initial value, if present, is either a unit attribute or
|
|
// an elements attribute.
|
|
if (op.initial_value().hasValue()) {
|
|
Attribute initValue = op.initial_value().getValue();
|
|
if (!initValue.isa<UnitAttr>() && !initValue.isa<ElementsAttr>())
|
|
return op.emitOpError("initial value should be a unit or elements "
|
|
"attribute, but got ")
|
|
<< initValue;
|
|
|
|
// Check that the type of the initial value is compatible with the type of
|
|
// the global variable.
|
|
if (initValue.isa<ElementsAttr>()) {
|
|
Type initType = initValue.getType();
|
|
Type tensorType = getTensorTypeFromMemRefType(memrefType);
|
|
if (initType != tensorType)
|
|
return op.emitOpError("initial value expected to be of type ")
|
|
<< tensorType << ", but was of type " << initType;
|
|
}
|
|
}
|
|
|
|
// TODO: verify visibility for declarations.
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// GetGlobalMemrefOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult
|
|
GetGlobalMemrefOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
|
|
// Verify that the result type is same as the type of the referenced
|
|
// global_memref op.
|
|
auto global =
|
|
symbolTable.lookupNearestSymbolFrom<GlobalMemrefOp>(*this, nameAttr());
|
|
if (!global)
|
|
return emitOpError("'")
|
|
<< name() << "' does not reference a valid global memref";
|
|
|
|
Type resultType = result().getType();
|
|
if (global.type() != resultType)
|
|
return emitOpError("result type ")
|
|
<< resultType << " does not match type " << global.type()
|
|
<< " of the global memref @" << name();
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// IndexCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Index cast is applicable from index to integer and backwards.
|
|
bool IndexCastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (a.isa<ShapedType>() && b.isa<ShapedType>()) {
|
|
auto aShaped = a.cast<ShapedType>();
|
|
auto bShaped = b.cast<ShapedType>();
|
|
|
|
return (aShaped.getShape() == bShaped.getShape()) &&
|
|
areCastCompatible(aShaped.getElementType(),
|
|
bShaped.getElementType());
|
|
}
|
|
|
|
return (a.isIndex() && b.isSignlessInteger()) ||
|
|
(a.isSignlessInteger() && b.isIndex());
|
|
}
|
|
|
|
OpFoldResult IndexCastOp::fold(ArrayRef<Attribute> cstOperands) {
|
|
// Fold IndexCast(IndexCast(x)) -> x
|
|
auto cast = getOperand().getDefiningOp<IndexCastOp>();
|
|
if (cast && cast.getOperand().getType() == getType())
|
|
return cast.getOperand();
|
|
|
|
// Fold IndexCast(constant) -> constant
|
|
// A little hack because we go through int. Otherwise, the size
|
|
// of the constant might need to change.
|
|
if (auto value = cstOperands[0].dyn_cast_or_null<IntegerAttr>())
|
|
return IntegerAttr::get(getType(), value.getInt());
|
|
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// LoadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(LoadOp op) {
|
|
if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
|
|
return op.emitOpError("incorrect number of indices for load");
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult LoadOp::fold(ArrayRef<Attribute> cstOperands) {
|
|
/// load(memrefcast) -> load
|
|
if (succeeded(foldMemRefCast(*this)))
|
|
return getResult();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
namespace {
|
|
/// Fold a load on a tensor_to_memref operation into an tensor.extract on the
|
|
/// corresponding tensor.
|
|
struct LoadOfTensorToMemref : public OpRewritePattern<LoadOp> {
|
|
using OpRewritePattern<LoadOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(LoadOp load,
|
|
PatternRewriter &rewriter) const override {
|
|
auto tensorToMemref = load.memref().getDefiningOp<TensorToMemrefOp>();
|
|
if (!tensorToMemref)
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::ExtractOp>(
|
|
load, tensorToMemref.tensor(), load.indices());
|
|
return success();
|
|
}
|
|
};
|
|
} // end anonymous namespace.
|
|
|
|
void LoadOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<LoadOfTensorToMemref>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MemRefCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
Value MemRefCastOp::getViewSource() { return source(); }
|
|
|
|
bool MemRefCastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
auto aT = a.dyn_cast<MemRefType>();
|
|
auto bT = b.dyn_cast<MemRefType>();
|
|
|
|
auto uaT = a.dyn_cast<UnrankedMemRefType>();
|
|
auto ubT = b.dyn_cast<UnrankedMemRefType>();
|
|
|
|
if (aT && bT) {
|
|
if (aT.getElementType() != bT.getElementType())
|
|
return false;
|
|
if (aT.getAffineMaps() != bT.getAffineMaps()) {
|
|
int64_t aOffset, bOffset;
|
|
SmallVector<int64_t, 4> aStrides, bStrides;
|
|
if (failed(getStridesAndOffset(aT, aStrides, aOffset)) ||
|
|
failed(getStridesAndOffset(bT, bStrides, bOffset)) ||
|
|
aStrides.size() != bStrides.size())
|
|
return false;
|
|
|
|
// Strides along a dimension/offset are compatible if the value in the
|
|
// source memref is static and the value in the target memref is the
|
|
// same. They are also compatible if either one is dynamic (see
|
|
// description of MemRefCastOp for details).
|
|
auto checkCompatible = [](int64_t a, int64_t b) {
|
|
return (a == MemRefType::getDynamicStrideOrOffset() ||
|
|
b == MemRefType::getDynamicStrideOrOffset() || a == b);
|
|
};
|
|
if (!checkCompatible(aOffset, bOffset))
|
|
return false;
|
|
for (auto aStride : enumerate(aStrides))
|
|
if (!checkCompatible(aStride.value(), bStrides[aStride.index()]))
|
|
return false;
|
|
}
|
|
if (aT.getMemorySpace() != bT.getMemorySpace())
|
|
return false;
|
|
|
|
// They must have the same rank, and any specified dimensions must match.
|
|
if (aT.getRank() != bT.getRank())
|
|
return false;
|
|
|
|
for (unsigned i = 0, e = aT.getRank(); i != e; ++i) {
|
|
int64_t aDim = aT.getDimSize(i), bDim = bT.getDimSize(i);
|
|
if (aDim != -1 && bDim != -1 && aDim != bDim)
|
|
return false;
|
|
}
|
|
return true;
|
|
} else {
|
|
if (!aT && !uaT)
|
|
return false;
|
|
if (!bT && !ubT)
|
|
return false;
|
|
// Unranked to unranked casting is unsupported
|
|
if (uaT && ubT)
|
|
return false;
|
|
|
|
auto aEltType = (aT) ? aT.getElementType() : uaT.getElementType();
|
|
auto bEltType = (bT) ? bT.getElementType() : ubT.getElementType();
|
|
if (aEltType != bEltType)
|
|
return false;
|
|
|
|
auto aMemSpace = (aT) ? aT.getMemorySpace() : uaT.getMemorySpace();
|
|
auto bMemSpace = (bT) ? bT.getMemorySpace() : ubT.getMemorySpace();
|
|
if (aMemSpace != bMemSpace)
|
|
return false;
|
|
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
OpFoldResult MemRefCastOp::fold(ArrayRef<Attribute> operands) {
|
|
return succeeded(foldMemRefCast(*this)) ? getResult() : Value();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MemRefReinterpretCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Build a MemRefReinterpretCastOp with all dynamic entries: `staticOffsets`,
|
|
/// `staticSizes` and `staticStrides` are automatically filled with
|
|
/// source-memref-rank sentinel values that encode dynamic entries.
|
|
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
OpFoldResult offset,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(offset, dynamicOffsets, staticOffsets,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
|
|
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
|
|
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
|
|
result.addAttributes(attrs);
|
|
}
|
|
|
|
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
int64_t offset,
|
|
ArrayRef<int64_t> sizes,
|
|
ArrayRef<int64_t> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> sizeValues =
|
|
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
build(b, result, resultType, source, b.getI64IntegerAttr(offset), sizeValues,
|
|
strideValues, attrs);
|
|
}
|
|
|
|
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
Value offset, ValueRange sizes,
|
|
ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
|
|
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
|
|
build(b, result, resultType, source, offset, sizeValues, strideValues, attrs);
|
|
}
|
|
|
|
// TODO: ponder whether we want to allow missing trailing sizes/strides that are
|
|
// completed automatically, like we have for subview and subtensor.
|
|
static LogicalResult verify(MemRefReinterpretCastOp op) {
|
|
// The source and result memrefs should be in the same memory space.
|
|
auto srcType = op.source().getType().cast<BaseMemRefType>();
|
|
auto resultType = op.getType().cast<MemRefType>();
|
|
if (srcType.getMemorySpace() != resultType.getMemorySpace())
|
|
return op.emitError("different memory spaces specified for source type ")
|
|
<< srcType << " and result memref type " << resultType;
|
|
if (srcType.getElementType() != resultType.getElementType())
|
|
return op.emitError("different element types specified for source type ")
|
|
<< srcType << " and result memref type " << resultType;
|
|
|
|
// Match sizes in result memref type and in static_sizes attribute.
|
|
for (auto &en :
|
|
llvm::enumerate(llvm::zip(resultType.getShape(),
|
|
extractFromI64ArrayAttr(op.static_sizes())))) {
|
|
int64_t resultSize = std::get<0>(en.value());
|
|
int64_t expectedSize = std::get<1>(en.value());
|
|
if (resultSize != expectedSize)
|
|
return op.emitError("expected result type with size = ")
|
|
<< expectedSize << " instead of " << resultSize
|
|
<< " in dim = " << en.index();
|
|
}
|
|
|
|
// Match offset and strides in static_offset and static_strides attributes if
|
|
// result memref type has an affine map specified.
|
|
if (!resultType.getAffineMaps().empty()) {
|
|
int64_t resultOffset;
|
|
SmallVector<int64_t, 4> resultStrides;
|
|
if (failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
|
|
return failure();
|
|
|
|
// Match offset in result memref type and in static_offsets attribute.
|
|
int64_t expectedOffset =
|
|
extractFromI64ArrayAttr(op.static_offsets()).front();
|
|
if (resultOffset != expectedOffset)
|
|
return op.emitError("expected result type with offset = ")
|
|
<< resultOffset << " instead of " << expectedOffset;
|
|
|
|
// Match strides in result memref type and in static_strides attribute.
|
|
for (auto &en : llvm::enumerate(llvm::zip(
|
|
resultStrides, extractFromI64ArrayAttr(op.static_strides())))) {
|
|
int64_t resultStride = std::get<0>(en.value());
|
|
int64_t expectedStride = std::get<1>(en.value());
|
|
if (resultStride != expectedStride)
|
|
return op.emitError("expected result type with stride = ")
|
|
<< expectedStride << " instead of " << resultStride
|
|
<< " in dim = " << en.index();
|
|
}
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MemRefReshapeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(MemRefReshapeOp op) {
|
|
Type operandType = op.source().getType();
|
|
Type resultType = op.result().getType();
|
|
|
|
Type operandElementType = operandType.cast<ShapedType>().getElementType();
|
|
Type resultElementType = resultType.cast<ShapedType>().getElementType();
|
|
if (operandElementType != resultElementType)
|
|
return op.emitOpError("element types of source and destination memref "
|
|
"types should be the same");
|
|
|
|
if (auto operandMemRefType = operandType.dyn_cast<MemRefType>())
|
|
if (!operandMemRefType.getAffineMaps().empty())
|
|
return op.emitOpError(
|
|
"source memref type should have identity affine map");
|
|
|
|
int64_t shapeSize = op.shape().getType().cast<MemRefType>().getDimSize(0);
|
|
auto resultMemRefType = resultType.dyn_cast<MemRefType>();
|
|
if (resultMemRefType) {
|
|
if (!resultMemRefType.getAffineMaps().empty())
|
|
return op.emitOpError(
|
|
"result memref type should have identity affine map");
|
|
if (shapeSize == ShapedType::kDynamicSize)
|
|
return op.emitOpError("cannot use shape operand with dynamic length to "
|
|
"reshape to statically-ranked memref type");
|
|
if (shapeSize != resultMemRefType.getRank())
|
|
return op.emitOpError(
|
|
"length of shape operand differs from the result's memref rank");
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MulFOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult MulFOp::fold(ArrayRef<Attribute> operands) {
|
|
return constFoldBinaryOp<FloatAttr>(
|
|
operands, [](APFloat a, APFloat b) { return a * b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MulIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult MulIOp::fold(ArrayRef<Attribute> operands) {
|
|
/// muli(x, 0) -> 0
|
|
if (matchPattern(rhs(), m_Zero()))
|
|
return rhs();
|
|
/// muli(x, 1) -> x
|
|
if (matchPattern(rhs(), m_One()))
|
|
return getOperand(0);
|
|
|
|
// TODO: Handle the overflow case.
|
|
return constFoldBinaryOp<IntegerAttr>(operands,
|
|
[](APInt a, APInt b) { return a * b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// OrOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult OrOp::fold(ArrayRef<Attribute> operands) {
|
|
/// or(x, 0) -> x
|
|
if (matchPattern(rhs(), m_Zero()))
|
|
return lhs();
|
|
/// or(x,x) -> x
|
|
if (lhs() == rhs())
|
|
return rhs();
|
|
|
|
return constFoldBinaryOp<IntegerAttr>(operands,
|
|
[](APInt a, APInt b) { return a | b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// PrefetchOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static void print(OpAsmPrinter &p, PrefetchOp op) {
|
|
p << PrefetchOp::getOperationName() << " " << op.memref() << '[';
|
|
p.printOperands(op.indices());
|
|
p << ']' << ", " << (op.isWrite() ? "write" : "read");
|
|
p << ", locality<" << op.localityHint();
|
|
p << ">, " << (op.isDataCache() ? "data" : "instr");
|
|
p.printOptionalAttrDict(
|
|
op->getAttrs(),
|
|
/*elidedAttrs=*/{"localityHint", "isWrite", "isDataCache"});
|
|
p << " : " << op.getMemRefType();
|
|
}
|
|
|
|
static ParseResult parsePrefetchOp(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
OpAsmParser::OperandType memrefInfo;
|
|
SmallVector<OpAsmParser::OperandType, 4> indexInfo;
|
|
IntegerAttr localityHint;
|
|
MemRefType type;
|
|
StringRef readOrWrite, cacheType;
|
|
|
|
auto indexTy = parser.getBuilder().getIndexType();
|
|
auto i32Type = parser.getBuilder().getIntegerType(32);
|
|
if (parser.parseOperand(memrefInfo) ||
|
|
parser.parseOperandList(indexInfo, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseComma() || parser.parseKeyword(&readOrWrite) ||
|
|
parser.parseComma() || parser.parseKeyword("locality") ||
|
|
parser.parseLess() ||
|
|
parser.parseAttribute(localityHint, i32Type, "localityHint",
|
|
result.attributes) ||
|
|
parser.parseGreater() || parser.parseComma() ||
|
|
parser.parseKeyword(&cacheType) || parser.parseColonType(type) ||
|
|
parser.resolveOperand(memrefInfo, type, result.operands) ||
|
|
parser.resolveOperands(indexInfo, indexTy, result.operands))
|
|
return failure();
|
|
|
|
if (!readOrWrite.equals("read") && !readOrWrite.equals("write"))
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"rw specifier has to be 'read' or 'write'");
|
|
result.addAttribute(
|
|
PrefetchOp::getIsWriteAttrName(),
|
|
parser.getBuilder().getBoolAttr(readOrWrite.equals("write")));
|
|
|
|
if (!cacheType.equals("data") && !cacheType.equals("instr"))
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"cache type has to be 'data' or 'instr'");
|
|
|
|
result.addAttribute(
|
|
PrefetchOp::getIsDataCacheAttrName(),
|
|
parser.getBuilder().getBoolAttr(cacheType.equals("data")));
|
|
|
|
return success();
|
|
}
|
|
|
|
static LogicalResult verify(PrefetchOp op) {
|
|
if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
|
|
return op.emitOpError("too few indices");
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult PrefetchOp::fold(ArrayRef<Attribute> cstOperands,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
// prefetch(memrefcast) -> prefetch
|
|
return foldMemRefCast(*this);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// RankOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult RankOp::fold(ArrayRef<Attribute> operands) {
|
|
// Constant fold rank when the rank of the operand is known.
|
|
auto type = getOperand().getType();
|
|
if (auto shapedType = type.dyn_cast<ShapedType>())
|
|
if (shapedType.hasRank())
|
|
return IntegerAttr::get(IndexType::get(getContext()),
|
|
shapedType.getRank());
|
|
return IntegerAttr();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ReturnOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(ReturnOp op) {
|
|
auto function = cast<FuncOp>(op->getParentOp());
|
|
|
|
// The operand number and types must match the function signature.
|
|
const auto &results = function.getType().getResults();
|
|
if (op.getNumOperands() != results.size())
|
|
return op.emitOpError("has ")
|
|
<< op.getNumOperands() << " operands, but enclosing function (@"
|
|
<< function.getName() << ") returns " << results.size();
|
|
|
|
for (unsigned i = 0, e = results.size(); i != e; ++i)
|
|
if (op.getOperand(i).getType() != results[i])
|
|
return op.emitError()
|
|
<< "type of return operand " << i << " ("
|
|
<< op.getOperand(i).getType()
|
|
<< ") doesn't match function result type (" << results[i] << ")"
|
|
<< " in function @" << function.getName();
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SelectOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SelectOp::fold(ArrayRef<Attribute> operands) {
|
|
auto condition = getCondition();
|
|
|
|
// select true, %0, %1 => %0
|
|
if (matchPattern(condition, m_One()))
|
|
return getTrueValue();
|
|
|
|
// select false, %0, %1 => %1
|
|
if (matchPattern(condition, m_Zero()))
|
|
return getFalseValue();
|
|
return nullptr;
|
|
}
|
|
|
|
static void print(OpAsmPrinter &p, SelectOp op) {
|
|
p << "select " << op.getOperands();
|
|
p.printOptionalAttrDict(op->getAttrs());
|
|
p << " : ";
|
|
if (ShapedType condType = op.getCondition().getType().dyn_cast<ShapedType>())
|
|
p << condType << ", ";
|
|
p << op.getType();
|
|
}
|
|
|
|
static ParseResult parseSelectOp(OpAsmParser &parser, OperationState &result) {
|
|
Type conditionType, resultType;
|
|
SmallVector<OpAsmParser::OperandType, 3> operands;
|
|
if (parser.parseOperandList(operands, /*requiredOperandCount=*/3) ||
|
|
parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonType(resultType))
|
|
return failure();
|
|
|
|
// Check for the explicit condition type if this is a masked tensor or vector.
|
|
if (succeeded(parser.parseOptionalComma())) {
|
|
conditionType = resultType;
|
|
if (parser.parseType(resultType))
|
|
return failure();
|
|
} else {
|
|
conditionType = parser.getBuilder().getI1Type();
|
|
}
|
|
|
|
result.addTypes(resultType);
|
|
return parser.resolveOperands(operands,
|
|
{conditionType, resultType, resultType},
|
|
parser.getNameLoc(), result.operands);
|
|
}
|
|
|
|
static LogicalResult verify(SelectOp op) {
|
|
Type conditionType = op.getCondition().getType();
|
|
if (conditionType.isSignlessInteger(1))
|
|
return success();
|
|
|
|
// If the result type is a vector or tensor, the type can be a mask with the
|
|
// same elements.
|
|
Type resultType = op.getType();
|
|
if (!resultType.isa<TensorType, VectorType>())
|
|
return op.emitOpError()
|
|
<< "expected condition to be a signless i1, but got "
|
|
<< conditionType;
|
|
Type shapedConditionType = getI1SameShape(resultType);
|
|
if (conditionType != shapedConditionType)
|
|
return op.emitOpError()
|
|
<< "expected condition type to have the same shape "
|
|
"as the result type, expected "
|
|
<< shapedConditionType << ", but got " << conditionType;
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SignExtendIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(SignExtendIOp op) {
|
|
// Get the scalar type (which is either directly the type of the operand
|
|
// or the vector's/tensor's element type.
|
|
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
|
|
auto dstType = getElementTypeOrSelf(op.getType());
|
|
|
|
// For now, index is forbidden for the source and the destination type.
|
|
if (srcType.isa<IndexType>())
|
|
return op.emitError() << srcType << " is not a valid operand type";
|
|
if (dstType.isa<IndexType>())
|
|
return op.emitError() << dstType << " is not a valid result type";
|
|
|
|
if (srcType.cast<IntegerType>().getWidth() >=
|
|
dstType.cast<IntegerType>().getWidth())
|
|
return op.emitError("result type ")
|
|
<< dstType << " must be wider than operand type " << srcType;
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SignedDivIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SignedDivIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "binary operation takes two operands");
|
|
|
|
// Don't fold if it would overflow or if it requires a division by zero.
|
|
bool overflowOrDiv0 = false;
|
|
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
|
|
if (overflowOrDiv0 || !b) {
|
|
overflowOrDiv0 = true;
|
|
return a;
|
|
}
|
|
return a.sdiv_ov(b, overflowOrDiv0);
|
|
});
|
|
|
|
// Fold out division by one. Assumes all tensors of all ones are splats.
|
|
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
|
|
if (rhs.getValue() == 1)
|
|
return lhs();
|
|
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
|
|
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
|
|
return lhs();
|
|
}
|
|
|
|
return overflowOrDiv0 ? Attribute() : result;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SignedFloorDivIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static APInt signedCeilNonnegInputs(APInt a, APInt b, bool &overflow) {
|
|
// Returns (a-1)/b + 1
|
|
APInt one(a.getBitWidth(), 1, true); // Signed value 1.
|
|
APInt val = a.ssub_ov(one, overflow).sdiv_ov(b, overflow);
|
|
return val.sadd_ov(one, overflow);
|
|
}
|
|
|
|
OpFoldResult SignedFloorDivIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "binary operation takes two operands");
|
|
|
|
// Don't fold if it would overflow or if it requires a division by zero.
|
|
bool overflowOrDiv0 = false;
|
|
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
|
|
if (overflowOrDiv0 || !b) {
|
|
overflowOrDiv0 = true;
|
|
return a;
|
|
}
|
|
unsigned bits = a.getBitWidth();
|
|
APInt zero = APInt::getNullValue(bits);
|
|
if (a.sge(zero) && b.sgt(zero)) {
|
|
// Both positive (or a is zero), return a / b.
|
|
return a.sdiv_ov(b, overflowOrDiv0);
|
|
} else if (a.sle(zero) && b.slt(zero)) {
|
|
// Both negative (or a is zero), return -a / -b.
|
|
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
|
|
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
|
|
return posA.sdiv_ov(posB, overflowOrDiv0);
|
|
} else if (a.slt(zero) && b.sgt(zero)) {
|
|
// A is negative, b is positive, return - ceil(-a, b).
|
|
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
|
|
APInt ceil = signedCeilNonnegInputs(posA, b, overflowOrDiv0);
|
|
return zero.ssub_ov(ceil, overflowOrDiv0);
|
|
} else {
|
|
// A is positive, b is negative, return - ceil(a, -b).
|
|
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
|
|
APInt ceil = signedCeilNonnegInputs(a, posB, overflowOrDiv0);
|
|
return zero.ssub_ov(ceil, overflowOrDiv0);
|
|
}
|
|
});
|
|
|
|
// Fold out floor division by one. Assumes all tensors of all ones are
|
|
// splats.
|
|
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
|
|
if (rhs.getValue() == 1)
|
|
return lhs();
|
|
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
|
|
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
|
|
return lhs();
|
|
}
|
|
|
|
return overflowOrDiv0 ? Attribute() : result;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SignedCeilDivIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SignedCeilDivIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "binary operation takes two operands");
|
|
|
|
// Don't fold if it would overflow or if it requires a division by zero.
|
|
bool overflowOrDiv0 = false;
|
|
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
|
|
if (overflowOrDiv0 || !b) {
|
|
overflowOrDiv0 = true;
|
|
return a;
|
|
}
|
|
unsigned bits = a.getBitWidth();
|
|
APInt zero = APInt::getNullValue(bits);
|
|
if (a.sgt(zero) && b.sgt(zero)) {
|
|
// Both positive, return ceil(a, b).
|
|
return signedCeilNonnegInputs(a, b, overflowOrDiv0);
|
|
} else if (a.slt(zero) && b.slt(zero)) {
|
|
// Both negative, return ceil(-a, -b).
|
|
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
|
|
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
|
|
return signedCeilNonnegInputs(posA, posB, overflowOrDiv0);
|
|
} else if (a.slt(zero) && b.sgt(zero)) {
|
|
// A is negative, b is positive, return - ( -a / b).
|
|
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
|
|
APInt div = posA.sdiv_ov(b, overflowOrDiv0);
|
|
return zero.ssub_ov(div, overflowOrDiv0);
|
|
} else {
|
|
// A is positive (or zero), b is negative, return - (a / -b).
|
|
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
|
|
APInt div = a.sdiv_ov(posB, overflowOrDiv0);
|
|
return zero.ssub_ov(div, overflowOrDiv0);
|
|
}
|
|
});
|
|
|
|
// Fold out floor division by one. Assumes all tensors of all ones are
|
|
// splats.
|
|
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
|
|
if (rhs.getValue() == 1)
|
|
return lhs();
|
|
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
|
|
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
|
|
return lhs();
|
|
}
|
|
|
|
return overflowOrDiv0 ? Attribute() : result;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SignedRemIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SignedRemIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "remi_signed takes two operands");
|
|
|
|
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
|
|
if (!rhs)
|
|
return {};
|
|
auto rhsValue = rhs.getValue();
|
|
|
|
// x % 1 = 0
|
|
if (rhsValue.isOneValue())
|
|
return IntegerAttr::get(rhs.getType(), APInt(rhsValue.getBitWidth(), 0));
|
|
|
|
// Don't fold if it requires division by zero.
|
|
if (rhsValue.isNullValue())
|
|
return {};
|
|
|
|
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
|
|
if (!lhs)
|
|
return {};
|
|
return IntegerAttr::get(lhs.getType(), lhs.getValue().srem(rhsValue));
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SIToFPOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// sitofp is applicable from integer types to float types.
|
|
bool SIToFPOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (a.isSignlessInteger() && b.isa<FloatType>())
|
|
return true;
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SplatOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(SplatOp op) {
|
|
// TODO: we could replace this by a trait.
|
|
if (op.getOperand().getType() !=
|
|
op.getType().cast<ShapedType>().getElementType())
|
|
return op.emitError("operand should be of elemental type of result type");
|
|
|
|
return success();
|
|
}
|
|
|
|
// Constant folding hook for SplatOp.
|
|
OpFoldResult SplatOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 1 && "splat takes one operand");
|
|
|
|
auto constOperand = operands.front();
|
|
if (!constOperand || !constOperand.isa<IntegerAttr, FloatAttr>())
|
|
return {};
|
|
|
|
auto shapedType = getType().cast<ShapedType>();
|
|
assert(shapedType.getElementType() == constOperand.getType() &&
|
|
"incorrect input attribute type for folding");
|
|
|
|
// SplatElementsAttr::get treats single value for second arg as being a splat.
|
|
return SplatElementsAttr::get(shapedType, {constOperand});
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// StoreOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(StoreOp op) {
|
|
if (op.getNumOperands() != 2 + op.getMemRefType().getRank())
|
|
return op.emitOpError("store index operand count not equal to memref rank");
|
|
|
|
return success();
|
|
}
|
|
|
|
LogicalResult StoreOp::fold(ArrayRef<Attribute> cstOperands,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
/// store(memrefcast) -> store
|
|
return foldMemRefCast(*this);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SubFOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SubFOp::fold(ArrayRef<Attribute> operands) {
|
|
return constFoldBinaryOp<FloatAttr>(
|
|
operands, [](APFloat a, APFloat b) { return a - b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SubIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SubIOp::fold(ArrayRef<Attribute> operands) {
|
|
// subi(x,x) -> 0
|
|
if (getOperand(0) == getOperand(1))
|
|
return Builder(getContext()).getZeroAttr(getType());
|
|
// subi(x,0) -> x
|
|
if (matchPattern(rhs(), m_Zero()))
|
|
return lhs();
|
|
|
|
return constFoldBinaryOp<IntegerAttr>(operands,
|
|
[](APInt a, APInt b) { return a - b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// UIToFPOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// uitofp is applicable from integer types to float types.
|
|
bool UIToFPOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
|
|
if (inputs.size() != 1 || outputs.size() != 1)
|
|
return false;
|
|
Type a = inputs.front(), b = outputs.front();
|
|
if (a.isSignlessInteger() && b.isa<FloatType>())
|
|
return true;
|
|
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SubViewOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace {
|
|
/// Helpers to write more idiomatic operations.
|
|
namespace saturated_arith {
|
|
struct Wrapper {
|
|
explicit Wrapper(int64_t v) : v(v) {}
|
|
operator int64_t() { return v; }
|
|
int64_t v;
|
|
};
|
|
Wrapper operator+(Wrapper a, int64_t b) {
|
|
if (ShapedType::isDynamicStrideOrOffset(a) ||
|
|
ShapedType::isDynamicStrideOrOffset(b))
|
|
return Wrapper(ShapedType::kDynamicStrideOrOffset);
|
|
return Wrapper(a.v + b);
|
|
}
|
|
Wrapper operator*(Wrapper a, int64_t b) {
|
|
if (ShapedType::isDynamicStrideOrOffset(a) ||
|
|
ShapedType::isDynamicStrideOrOffset(b))
|
|
return Wrapper(ShapedType::kDynamicStrideOrOffset);
|
|
return Wrapper(a.v * b);
|
|
}
|
|
} // end namespace saturated_arith
|
|
} // end namespace
|
|
|
|
/// A subview result type can be fully inferred from the source type and the
|
|
/// static representation of offsets, sizes and strides. Special sentinels
|
|
/// encode the dynamic case.
|
|
Type SubViewOp::inferResultType(MemRefType sourceMemRefType,
|
|
ArrayRef<int64_t> leadingStaticOffsets,
|
|
ArrayRef<int64_t> leadingStaticSizes,
|
|
ArrayRef<int64_t> leadingStaticStrides) {
|
|
// A subview may specify only a leading subset of offset/sizes/strides in
|
|
// which case we complete with offset=0, sizes from memref type and strides=1.
|
|
unsigned rank = sourceMemRefType.getRank();
|
|
assert(leadingStaticOffsets.size() <= rank &&
|
|
"unexpected leadingStaticOffsets overflow");
|
|
assert(leadingStaticSizes.size() <= rank &&
|
|
"unexpected leadingStaticSizes overflow");
|
|
assert(leadingStaticStrides.size() <= rank &&
|
|
"unexpected leadingStaticStrides overflow");
|
|
auto staticOffsets = llvm::to_vector<4>(leadingStaticOffsets);
|
|
auto staticSizes = llvm::to_vector<4>(leadingStaticSizes);
|
|
auto staticStrides = llvm::to_vector<4>(leadingStaticStrides);
|
|
unsigned numTrailingOffsets = rank - staticOffsets.size();
|
|
unsigned numTrailingSizes = rank - staticSizes.size();
|
|
unsigned numTrailingStrides = rank - staticStrides.size();
|
|
staticOffsets.append(numTrailingOffsets, 0);
|
|
llvm::append_range(staticSizes,
|
|
sourceMemRefType.getShape().take_back(numTrailingSizes));
|
|
staticStrides.append(numTrailingStrides, 1);
|
|
|
|
// Extract source offset and strides.
|
|
int64_t sourceOffset;
|
|
SmallVector<int64_t, 4> sourceStrides;
|
|
auto res = getStridesAndOffset(sourceMemRefType, sourceStrides, sourceOffset);
|
|
assert(succeeded(res) && "SubViewOp expected strided memref type");
|
|
(void)res;
|
|
|
|
// Compute target offset whose value is:
|
|
// `sourceOffset + sum_i(staticOffset_i * sourceStrides_i)`.
|
|
int64_t targetOffset = sourceOffset;
|
|
for (auto it : llvm::zip(staticOffsets, sourceStrides)) {
|
|
auto staticOffset = std::get<0>(it), targetStride = std::get<1>(it);
|
|
using namespace saturated_arith;
|
|
targetOffset = Wrapper(targetOffset) + Wrapper(staticOffset) * targetStride;
|
|
}
|
|
|
|
// Compute target stride whose value is:
|
|
// `sourceStrides_i * staticStrides_i`.
|
|
SmallVector<int64_t, 4> targetStrides;
|
|
targetStrides.reserve(staticOffsets.size());
|
|
for (auto it : llvm::zip(sourceStrides, staticStrides)) {
|
|
auto sourceStride = std::get<0>(it), staticStride = std::get<1>(it);
|
|
using namespace saturated_arith;
|
|
targetStrides.push_back(Wrapper(sourceStride) * staticStride);
|
|
}
|
|
|
|
// The type is now known.
|
|
return MemRefType::get(
|
|
staticSizes, sourceMemRefType.getElementType(),
|
|
makeStridedLinearLayoutMap(targetStrides, targetOffset,
|
|
sourceMemRefType.getContext()),
|
|
sourceMemRefType.getMemorySpace());
|
|
}
|
|
|
|
Type SubViewOp::inferResultType(MemRefType sourceMemRefType,
|
|
ArrayRef<OpFoldResult> leadingStaticOffsets,
|
|
ArrayRef<OpFoldResult> leadingStaticSizes,
|
|
ArrayRef<OpFoldResult> leadingStaticStrides) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
|
|
staticOffsets, ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
|
|
staticStrides, ShapedType::kDynamicStrideOrOffset);
|
|
return SubViewOp::inferResultType(sourceMemRefType, staticOffsets,
|
|
staticSizes, staticStrides);
|
|
}
|
|
|
|
static void
|
|
getPositionsOfShapeOne(unsigned rank, ArrayRef<int64_t> shape,
|
|
llvm::SmallDenseSet<unsigned> &dimsToProject) {
|
|
dimsToProject.reserve(rank);
|
|
for (unsigned pos = 0, e = shape.size(); pos < e && rank > 0; ++pos) {
|
|
if (shape[pos] == 1) {
|
|
dimsToProject.insert(pos);
|
|
--rank;
|
|
}
|
|
}
|
|
}
|
|
|
|
Type SubViewOp::inferRankReducedResultType(
|
|
unsigned resultRank, MemRefType sourceRankedTensorType,
|
|
ArrayRef<int64_t> leadingStaticOffsets,
|
|
ArrayRef<int64_t> leadingStaticSizes,
|
|
ArrayRef<int64_t> leadingStaticStrides) {
|
|
auto inferredType =
|
|
inferResultType(sourceRankedTensorType, leadingStaticOffsets,
|
|
leadingStaticSizes, leadingStaticStrides)
|
|
.cast<MemRefType>();
|
|
assert(inferredType.getRank() >= resultRank && "expected ");
|
|
int rankDiff = inferredType.getRank() - resultRank;
|
|
if (rankDiff > 0) {
|
|
auto shape = inferredType.getShape();
|
|
llvm::SmallDenseSet<unsigned> dimsToProject;
|
|
getPositionsOfShapeOne(rankDiff, shape, dimsToProject);
|
|
SmallVector<int64_t> projectedShape;
|
|
for (unsigned pos = 0, e = shape.size(); pos < e; ++pos)
|
|
if (!dimsToProject.contains(pos))
|
|
projectedShape.push_back(shape[pos]);
|
|
|
|
AffineMap map;
|
|
auto maps = inferredType.getAffineMaps();
|
|
if (!maps.empty() && maps.front())
|
|
map = getProjectedMap(maps.front(), dimsToProject);
|
|
inferredType =
|
|
MemRefType::get(projectedShape, inferredType.getElementType(), map,
|
|
inferredType.getMemorySpace());
|
|
}
|
|
return inferredType;
|
|
}
|
|
|
|
Type SubViewOp::inferRankReducedResultType(
|
|
unsigned resultRank, MemRefType sourceRankedTensorType,
|
|
ArrayRef<OpFoldResult> leadingStaticOffsets,
|
|
ArrayRef<OpFoldResult> leadingStaticSizes,
|
|
ArrayRef<OpFoldResult> leadingStaticStrides) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
|
|
staticOffsets, ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
|
|
staticStrides, ShapedType::kDynamicStrideOrOffset);
|
|
return SubViewOp::inferRankReducedResultType(
|
|
resultRank, sourceRankedTensorType, staticOffsets, staticSizes,
|
|
staticStrides);
|
|
}
|
|
|
|
// Build a SubViewOp with mixed static and dynamic entries and custom result
|
|
// type. If the type passed is nullptr, it is inferred.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
ArrayRef<OpFoldResult> offsets,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
auto sourceMemRefType = source.getType().cast<MemRefType>();
|
|
// Structuring implementation this way avoids duplication between builders.
|
|
if (!resultType) {
|
|
resultType = SubViewOp::inferResultType(sourceMemRefType, staticOffsets,
|
|
staticSizes, staticStrides)
|
|
.cast<MemRefType>();
|
|
}
|
|
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
|
|
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
|
|
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
|
|
result.addAttributes(attrs);
|
|
}
|
|
|
|
// Build a SubViewOp with mixed static and dynamic entries and inferred result
|
|
// type.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
|
|
ArrayRef<OpFoldResult> offsets,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
build(b, result, MemRefType(), source, offsets, sizes, strides, attrs);
|
|
}
|
|
|
|
// Build a SubViewOp with static entries and inferred result type.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
|
|
ArrayRef<int64_t> offsets, ArrayRef<int64_t> sizes,
|
|
ArrayRef<int64_t> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
|
|
llvm::map_range(offsets, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
SmallVector<OpFoldResult> sizeValues =
|
|
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
build(b, result, source, offsetValues, sizeValues, strideValues, attrs);
|
|
}
|
|
|
|
// Build a SubViewOp with dynamic entries and custom result type. If the
|
|
// type passed is nullptr, it is inferred.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
ArrayRef<int64_t> offsets, ArrayRef<int64_t> sizes,
|
|
ArrayRef<int64_t> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
|
|
llvm::map_range(offsets, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
SmallVector<OpFoldResult> sizeValues =
|
|
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
|
|
return b.getI64IntegerAttr(v);
|
|
}));
|
|
build(b, result, resultType, source, offsetValues, sizeValues, strideValues,
|
|
attrs);
|
|
}
|
|
|
|
// Build a SubViewOp with dynamic entries and custom result type. If the type
|
|
// passed is nullptr, it is inferred.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
|
|
MemRefType resultType, Value source,
|
|
ValueRange offsets, ValueRange sizes,
|
|
ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
|
|
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
|
|
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
|
|
build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
|
|
}
|
|
|
|
// Build a SubViewOp with dynamic entries and inferred result type.
|
|
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
|
|
ValueRange offsets, ValueRange sizes,
|
|
ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
build(b, result, MemRefType(), source, offsets, sizes, strides, attrs);
|
|
}
|
|
|
|
/// For ViewLikeOpInterface.
|
|
Value SubViewOp::getViewSource() { return source(); }
|
|
|
|
/// Given an `originalShape` and a `reducedShape` assumed to be a subset of
|
|
/// `originalShape` with some `1` entries erased, return the set of indices
|
|
/// that specifies which of the entries of `originalShape` are dropped to obtain
|
|
/// `reducedShape`. The returned mask can be applied as a projection to
|
|
/// `originalShape` to obtain the `reducedShape`. This mask is useful to track
|
|
/// which dimensions must be kept when e.g. compute MemRef strides under
|
|
/// rank-reducing operations. Return None if reducedShape cannot be obtained
|
|
/// by dropping only `1` entries in `originalShape`.
|
|
llvm::Optional<llvm::SmallDenseSet<unsigned>>
|
|
mlir::computeRankReductionMask(ArrayRef<int64_t> originalShape,
|
|
ArrayRef<int64_t> reducedShape) {
|
|
size_t originalRank = originalShape.size(), reducedRank = reducedShape.size();
|
|
llvm::SmallDenseSet<unsigned> unusedDims;
|
|
unsigned reducedIdx = 0;
|
|
for (unsigned originalIdx = 0; originalIdx < originalRank; ++originalIdx) {
|
|
// Greedily insert `originalIdx` if no match.
|
|
if (reducedIdx < reducedRank &&
|
|
originalShape[originalIdx] == reducedShape[reducedIdx]) {
|
|
reducedIdx++;
|
|
continue;
|
|
}
|
|
|
|
unusedDims.insert(originalIdx);
|
|
// If no match on `originalIdx`, the `originalShape` at this dimension
|
|
// must be 1, otherwise we bail.
|
|
if (originalShape[originalIdx] != 1)
|
|
return llvm::None;
|
|
}
|
|
// The whole reducedShape must be scanned, otherwise we bail.
|
|
if (reducedIdx != reducedRank)
|
|
return llvm::None;
|
|
return unusedDims;
|
|
}
|
|
|
|
enum SubViewVerificationResult {
|
|
Success,
|
|
RankTooLarge,
|
|
SizeMismatch,
|
|
ElemTypeMismatch,
|
|
MemSpaceMismatch,
|
|
AffineMapMismatch
|
|
};
|
|
|
|
/// Checks if `original` Type type can be rank reduced to `reduced` type.
|
|
/// This function is slight variant of `is subsequence` algorithm where
|
|
/// not matching dimension must be 1.
|
|
static SubViewVerificationResult
|
|
isRankReducedType(Type originalType, Type candidateReducedType,
|
|
std::string *errMsg = nullptr) {
|
|
if (originalType == candidateReducedType)
|
|
return SubViewVerificationResult::Success;
|
|
if (!originalType.isa<RankedTensorType>() && !originalType.isa<MemRefType>())
|
|
return SubViewVerificationResult::Success;
|
|
if (originalType.isa<RankedTensorType>() &&
|
|
!candidateReducedType.isa<RankedTensorType>())
|
|
return SubViewVerificationResult::Success;
|
|
if (originalType.isa<MemRefType>() && !candidateReducedType.isa<MemRefType>())
|
|
return SubViewVerificationResult::Success;
|
|
|
|
ShapedType originalShapedType = originalType.cast<ShapedType>();
|
|
ShapedType candidateReducedShapedType =
|
|
candidateReducedType.cast<ShapedType>();
|
|
|
|
// Rank and size logic is valid for all ShapedTypes.
|
|
ArrayRef<int64_t> originalShape = originalShapedType.getShape();
|
|
ArrayRef<int64_t> candidateReducedShape =
|
|
candidateReducedShapedType.getShape();
|
|
unsigned originalRank = originalShape.size(),
|
|
candidateReducedRank = candidateReducedShape.size();
|
|
if (candidateReducedRank > originalRank)
|
|
return SubViewVerificationResult::RankTooLarge;
|
|
|
|
auto optionalUnusedDimsMask =
|
|
computeRankReductionMask(originalShape, candidateReducedShape);
|
|
|
|
// Sizes cannot be matched in case empty vector is returned.
|
|
if (!optionalUnusedDimsMask.hasValue())
|
|
return SubViewVerificationResult::SizeMismatch;
|
|
|
|
if (originalShapedType.getElementType() !=
|
|
candidateReducedShapedType.getElementType())
|
|
return SubViewVerificationResult::ElemTypeMismatch;
|
|
|
|
// We are done for the tensor case.
|
|
if (originalType.isa<RankedTensorType>())
|
|
return SubViewVerificationResult::Success;
|
|
|
|
// Strided layout logic is relevant for MemRefType only.
|
|
MemRefType original = originalType.cast<MemRefType>();
|
|
MemRefType candidateReduced = candidateReducedType.cast<MemRefType>();
|
|
if (original.getMemorySpace() != candidateReduced.getMemorySpace())
|
|
return SubViewVerificationResult::MemSpaceMismatch;
|
|
|
|
llvm::SmallDenseSet<unsigned> unusedDims = optionalUnusedDimsMask.getValue();
|
|
auto inferredType =
|
|
getProjectedMap(getStridedLinearLayoutMap(original), unusedDims);
|
|
AffineMap candidateLayout;
|
|
if (candidateReduced.getAffineMaps().empty())
|
|
candidateLayout = getStridedLinearLayoutMap(candidateReduced);
|
|
else
|
|
candidateLayout = candidateReduced.getAffineMaps().front();
|
|
assert(inferredType.getNumResults() == 1 &&
|
|
candidateLayout.getNumResults() == 1);
|
|
if (inferredType.getNumSymbols() != candidateLayout.getNumSymbols() ||
|
|
inferredType.getNumDims() != candidateLayout.getNumDims()) {
|
|
if (errMsg) {
|
|
llvm::raw_string_ostream os(*errMsg);
|
|
os << "inferred type: " << inferredType;
|
|
}
|
|
return SubViewVerificationResult::AffineMapMismatch;
|
|
}
|
|
// Check that the difference of the affine maps simplifies to 0.
|
|
AffineExpr diffExpr =
|
|
inferredType.getResult(0) - candidateLayout.getResult(0);
|
|
diffExpr = simplifyAffineExpr(diffExpr, inferredType.getNumDims(),
|
|
inferredType.getNumSymbols());
|
|
auto cst = diffExpr.dyn_cast<AffineConstantExpr>();
|
|
if (!(cst && cst.getValue() == 0)) {
|
|
if (errMsg) {
|
|
llvm::raw_string_ostream os(*errMsg);
|
|
os << "inferred type: " << inferredType;
|
|
}
|
|
return SubViewVerificationResult::AffineMapMismatch;
|
|
}
|
|
return SubViewVerificationResult::Success;
|
|
}
|
|
|
|
template <typename OpTy>
|
|
static LogicalResult produceSubViewErrorMsg(SubViewVerificationResult result,
|
|
OpTy op, Type expectedType,
|
|
StringRef errMsg = "") {
|
|
auto memrefType = expectedType.cast<ShapedType>();
|
|
switch (result) {
|
|
case SubViewVerificationResult::Success:
|
|
return success();
|
|
case SubViewVerificationResult::RankTooLarge:
|
|
return op.emitError("expected result rank to be smaller or equal to ")
|
|
<< "the source rank. " << errMsg;
|
|
case SubViewVerificationResult::SizeMismatch:
|
|
return op.emitError("expected result type to be ")
|
|
<< expectedType
|
|
<< " or a rank-reduced version. (mismatch of result sizes) "
|
|
<< errMsg;
|
|
case SubViewVerificationResult::ElemTypeMismatch:
|
|
return op.emitError("expected result element type to be ")
|
|
<< memrefType.getElementType() << errMsg;
|
|
case SubViewVerificationResult::MemSpaceMismatch:
|
|
return op.emitError("expected result and source memory spaces to match.")
|
|
<< errMsg;
|
|
case SubViewVerificationResult::AffineMapMismatch:
|
|
return op.emitError("expected result type to be ")
|
|
<< expectedType
|
|
<< " or a rank-reduced version. (mismatch of result affine map) "
|
|
<< errMsg;
|
|
}
|
|
llvm_unreachable("unexpected subview verification result");
|
|
}
|
|
|
|
/// Verifier for SubViewOp.
|
|
static LogicalResult verify(SubViewOp op) {
|
|
MemRefType baseType = op.getSourceType();
|
|
MemRefType subViewType = op.getType();
|
|
|
|
// The base memref and the view memref should be in the same memory space.
|
|
if (baseType.getMemorySpace() != subViewType.getMemorySpace())
|
|
return op.emitError("different memory spaces specified for base memref "
|
|
"type ")
|
|
<< baseType << " and subview memref type " << subViewType;
|
|
|
|
// Verify that the base memref type has a strided layout map.
|
|
if (!isStrided(baseType))
|
|
return op.emitError("base type ") << baseType << " is not strided";
|
|
|
|
// Verify result type against inferred type.
|
|
auto expectedType = SubViewOp::inferResultType(
|
|
baseType, extractFromI64ArrayAttr(op.static_offsets()),
|
|
extractFromI64ArrayAttr(op.static_sizes()),
|
|
extractFromI64ArrayAttr(op.static_strides()));
|
|
|
|
std::string errMsg;
|
|
auto result = isRankReducedType(expectedType, subViewType, &errMsg);
|
|
return produceSubViewErrorMsg(result, op, expectedType, errMsg);
|
|
}
|
|
|
|
raw_ostream &mlir::operator<<(raw_ostream &os, Range &range) {
|
|
return os << "range " << range.offset << ":" << range.size << ":"
|
|
<< range.stride;
|
|
}
|
|
|
|
/// Return the list of Range (i.e. offset, size, stride). Each Range
|
|
/// entry contains either the dynamic value or a ConstantIndexOp constructed
|
|
/// with `b` at location `loc`.
|
|
SmallVector<Range, 8> mlir::getOrCreateRanges(OffsetSizeAndStrideOpInterface op,
|
|
OpBuilder &b, Location loc) {
|
|
std::array<unsigned, 3> ranks = op.getArrayAttrMaxRanks();
|
|
assert(ranks[0] == ranks[1] && "expected offset and sizes of equal ranks");
|
|
assert(ranks[1] == ranks[2] && "expected sizes and strides of equal ranks");
|
|
SmallVector<Range, 8> res;
|
|
unsigned rank = ranks[0];
|
|
res.reserve(rank);
|
|
for (unsigned idx = 0; idx < rank; ++idx) {
|
|
Value offset =
|
|
op.isDynamicOffset(idx)
|
|
? op.getDynamicOffset(idx)
|
|
: b.create<ConstantIndexOp>(loc, op.getStaticOffset(idx));
|
|
Value size = op.isDynamicSize(idx)
|
|
? op.getDynamicSize(idx)
|
|
: b.create<ConstantIndexOp>(loc, op.getStaticSize(idx));
|
|
Value stride =
|
|
op.isDynamicStride(idx)
|
|
? op.getDynamicStride(idx)
|
|
: b.create<ConstantIndexOp>(loc, op.getStaticStride(idx));
|
|
res.emplace_back(Range{offset, size, stride});
|
|
}
|
|
return res;
|
|
}
|
|
|
|
namespace {
|
|
|
|
/// Detects the `values` produced by a ConstantIndexOp and places the new
|
|
/// constant in place of the corresponding sentinel value.
|
|
void canonicalizeSubViewPart(SmallVectorImpl<OpFoldResult> &values,
|
|
llvm::function_ref<bool(int64_t)> isDynamic) {
|
|
for (OpFoldResult &ofr : values) {
|
|
if (ofr.is<Attribute>())
|
|
continue;
|
|
// Newly static, move from Value to constant.
|
|
if (auto cstOp = ofr.dyn_cast<Value>().getDefiningOp<ConstantIndexOp>())
|
|
ofr = OpBuilder(cstOp).getIndexAttr(cstOp.getValue());
|
|
}
|
|
}
|
|
|
|
static void replaceWithNewOp(PatternRewriter &rewriter, SubViewOp op,
|
|
SubViewOp newOp) {
|
|
rewriter.replaceOpWithNewOp<MemRefCastOp>(op, newOp, op.getType());
|
|
}
|
|
|
|
static void replaceWithNewOp(PatternRewriter &rewriter, SubTensorOp op,
|
|
SubTensorOp newOp) {
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
|
|
}
|
|
|
|
/// Pattern to rewrite a subview op with constant arguments.
|
|
template <typename OpType>
|
|
class OpWithOffsetSizesAndStridesConstantArgumentFolder final
|
|
: public OpRewritePattern<OpType> {
|
|
public:
|
|
using OpRewritePattern<OpType>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(OpType op,
|
|
PatternRewriter &rewriter) const override {
|
|
// No constant operand, just return;
|
|
if (llvm::none_of(op.getOperands(), [](Value operand) {
|
|
return matchPattern(operand, m_ConstantIndex());
|
|
}))
|
|
return failure();
|
|
|
|
// At least one of offsets/sizes/strides is a new constant.
|
|
// Form the new list of operands and constant attributes from the existing.
|
|
SmallVector<OpFoldResult> mixedOffsets(op.getMixedOffsets());
|
|
SmallVector<OpFoldResult> mixedSizes(op.getMixedSizes());
|
|
SmallVector<OpFoldResult> mixedStrides(op.getMixedStrides());
|
|
canonicalizeSubViewPart(mixedOffsets, ShapedType::isDynamicStrideOrOffset);
|
|
canonicalizeSubViewPart(mixedSizes, ShapedType::isDynamic);
|
|
canonicalizeSubViewPart(mixedStrides, ShapedType::isDynamicStrideOrOffset);
|
|
|
|
// Create the new op in canonical form.
|
|
auto newOp = rewriter.create<OpType>(op.getLoc(), op.source(), mixedOffsets,
|
|
mixedSizes, mixedStrides);
|
|
|
|
replaceWithNewOp(rewriter, op, newOp);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // end anonymous namespace
|
|
|
|
/// Determines whether MemRefCastOp casts to a more dynamic version of the
|
|
/// source memref. This is useful to to fold a memref_cast into a consuming op
|
|
/// and implement canonicalization patterns for ops in different dialects that
|
|
/// may consume the results of memref_cast operations. Such foldable memref_cast
|
|
/// operations are typically inserted as `view` and `subview` ops are
|
|
/// canonicalized, to preserve the type compatibility of their uses.
|
|
///
|
|
/// Returns true when all conditions are met:
|
|
/// 1. source and result are ranked memrefs with strided semantics and same
|
|
/// element type and rank.
|
|
/// 2. each of the source's size, offset or stride has more static information
|
|
/// than the corresponding result's size, offset or stride.
|
|
///
|
|
/// Example 1:
|
|
/// ```mlir
|
|
/// %1 = memref_cast %0 : memref<8x16xf32> to memref<?x?xf32>
|
|
/// %2 = consumer %1 ... : memref<?x?xf32> ...
|
|
/// ```
|
|
///
|
|
/// may fold into:
|
|
///
|
|
/// ```mlir
|
|
/// %2 = consumer %0 ... : memref<8x16xf32> ...
|
|
/// ```
|
|
///
|
|
/// Example 2:
|
|
/// ```
|
|
/// %1 = memref_cast %0 : memref<?x16xf32, affine_map<(i, j)->(16 * i + j)>>
|
|
/// to memref<?x?xf32>
|
|
/// consumer %1 : memref<?x?xf32> ...
|
|
/// ```
|
|
///
|
|
/// may fold into:
|
|
///
|
|
/// ```
|
|
/// consumer %0 ... : memref<?x16xf32, affine_map<(i, j)->(16 * i + j)>>
|
|
/// ```
|
|
bool mlir::canFoldIntoConsumerOp(MemRefCastOp castOp) {
|
|
MemRefType sourceType = castOp.source().getType().dyn_cast<MemRefType>();
|
|
MemRefType resultType = castOp.getType().dyn_cast<MemRefType>();
|
|
|
|
// Requires ranked MemRefType.
|
|
if (!sourceType || !resultType)
|
|
return false;
|
|
|
|
// Requires same elemental type.
|
|
if (sourceType.getElementType() != resultType.getElementType())
|
|
return false;
|
|
|
|
// Requires same rank.
|
|
if (sourceType.getRank() != resultType.getRank())
|
|
return false;
|
|
|
|
// Only fold casts between strided memref forms.
|
|
int64_t sourceOffset, resultOffset;
|
|
SmallVector<int64_t, 4> sourceStrides, resultStrides;
|
|
if (failed(getStridesAndOffset(sourceType, sourceStrides, sourceOffset)) ||
|
|
failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
|
|
return false;
|
|
|
|
// If cast is towards more static sizes along any dimension, don't fold.
|
|
for (auto it : llvm::zip(sourceType.getShape(), resultType.getShape())) {
|
|
auto ss = std::get<0>(it), st = std::get<1>(it);
|
|
if (ss != st)
|
|
if (MemRefType::isDynamic(ss) && !MemRefType::isDynamic(st))
|
|
return false;
|
|
}
|
|
|
|
// If cast is towards more static offset along any dimension, don't fold.
|
|
if (sourceOffset != resultOffset)
|
|
if (MemRefType::isDynamicStrideOrOffset(sourceOffset) &&
|
|
!MemRefType::isDynamicStrideOrOffset(resultOffset))
|
|
return false;
|
|
|
|
// If cast is towards more static strides along any dimension, don't fold.
|
|
for (auto it : llvm::zip(sourceStrides, resultStrides)) {
|
|
auto ss = std::get<0>(it), st = std::get<1>(it);
|
|
if (ss != st)
|
|
if (MemRefType::isDynamicStrideOrOffset(ss) &&
|
|
!MemRefType::isDynamicStrideOrOffset(st))
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
namespace {
|
|
/// Pattern to rewrite a subview op with MemRefCast arguments.
|
|
/// This essentially pushes memref_cast past its consuming subview when
|
|
/// `canFoldIntoConsumerOp` is true.
|
|
///
|
|
/// Example:
|
|
/// ```
|
|
/// %0 = memref_cast %V : memref<16x16xf32> to memref<?x?xf32>
|
|
/// %1 = subview %0[0, 0][3, 4][1, 1] :
|
|
/// memref<?x?xf32> to memref<3x4xf32, offset:?, strides:[?, 1]>
|
|
/// ```
|
|
/// is rewritten into:
|
|
/// ```
|
|
/// %0 = subview %V: memref<16x16xf32> to memref<3x4xf32, #[[map0]]>
|
|
/// %1 = memref_cast %0: memref<3x4xf32, offset:0, strides:[16, 1]> to
|
|
/// memref<3x4xf32, offset:?, strides:[?, 1]>
|
|
/// ```
|
|
class SubViewOpMemRefCastFolder final : public OpRewritePattern<SubViewOp> {
|
|
public:
|
|
using OpRewritePattern<SubViewOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(SubViewOp subViewOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Any constant operand, just return to let SubViewOpConstantFolder kick in.
|
|
if (llvm::any_of(subViewOp.getOperands(), [](Value operand) {
|
|
return matchPattern(operand, m_ConstantIndex());
|
|
}))
|
|
return failure();
|
|
|
|
auto castOp = subViewOp.source().getDefiningOp<MemRefCastOp>();
|
|
if (!castOp)
|
|
return failure();
|
|
|
|
if (!canFoldIntoConsumerOp(castOp))
|
|
return failure();
|
|
|
|
/// Deduce the resultType of the SubViewOp using `inferSubViewResultType` on
|
|
/// the cast source operand type and the SubViewOp static information. This
|
|
/// is the resulting type if the MemRefCastOp were folded.
|
|
auto resultType = SubViewOp::inferRankReducedResultType(
|
|
subViewOp.getType().getRank(),
|
|
castOp.source().getType().cast<MemRefType>(),
|
|
subViewOp.getMixedOffsets(), subViewOp.getMixedSizes(),
|
|
subViewOp.getMixedStrides());
|
|
Value newSubView = rewriter.create<SubViewOp>(
|
|
subViewOp.getLoc(), resultType, castOp.source(), subViewOp.offsets(),
|
|
subViewOp.sizes(), subViewOp.strides(), subViewOp.static_offsets(),
|
|
subViewOp.static_sizes(), subViewOp.static_strides());
|
|
rewriter.replaceOpWithNewOp<MemRefCastOp>(subViewOp, subViewOp.getType(),
|
|
newSubView);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void SubViewOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<OpWithOffsetSizesAndStridesConstantArgumentFolder<SubViewOp>,
|
|
SubViewOpMemRefCastFolder>(context);
|
|
}
|
|
|
|
OpFoldResult SubViewOp::fold(ArrayRef<Attribute> operands) {
|
|
if (getResult().getType().cast<ShapedType>().getRank() == 0 &&
|
|
source().getType().cast<ShapedType>().getRank() == 0)
|
|
return getViewSource();
|
|
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SubTensorOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// A subtensor result type can be fully inferred from the source type and the
|
|
/// static representation of offsets, sizes and strides. Special sentinels
|
|
/// encode the dynamic case.
|
|
Type SubTensorOp::inferResultType(RankedTensorType sourceRankedTensorType,
|
|
ArrayRef<int64_t> leadingStaticOffsets,
|
|
ArrayRef<int64_t> leadingStaticSizes,
|
|
ArrayRef<int64_t> leadingStaticStrides) {
|
|
// A subtensor may specify only a leading subset of offset/sizes/strides in
|
|
// which case we complete with offset=0, sizes from memref type and strides=1.
|
|
unsigned rank = sourceRankedTensorType.getRank();
|
|
assert(leadingStaticSizes.size() <= rank &&
|
|
"unexpected leadingStaticSizes overflow");
|
|
auto staticSizes = llvm::to_vector<4>(leadingStaticSizes);
|
|
unsigned numTrailingSizes = rank - staticSizes.size();
|
|
llvm::append_range(staticSizes, sourceRankedTensorType.getShape().take_back(
|
|
numTrailingSizes));
|
|
return RankedTensorType::get(staticSizes,
|
|
sourceRankedTensorType.getElementType());
|
|
}
|
|
|
|
Type SubTensorOp::inferResultType(RankedTensorType sourceRankedTensorType,
|
|
ArrayRef<OpFoldResult> leadingStaticOffsets,
|
|
ArrayRef<OpFoldResult> leadingStaticSizes,
|
|
ArrayRef<OpFoldResult> leadingStaticStrides) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
|
|
staticOffsets, ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
|
|
staticStrides, ShapedType::kDynamicStrideOrOffset);
|
|
return SubTensorOp::inferResultType(sourceRankedTensorType, staticOffsets,
|
|
staticSizes, staticStrides);
|
|
}
|
|
|
|
/// A subtensor result type can be fully inferred from the source type and the
|
|
/// static representation of offsets, sizes and strides. Special sentinels
|
|
/// encode the dynamic case.
|
|
Type SubTensorOp::inferRankReducedResultType(
|
|
unsigned resultRank, RankedTensorType sourceRankedTensorType,
|
|
ArrayRef<int64_t> leadingStaticOffsets,
|
|
ArrayRef<int64_t> leadingStaticSizes,
|
|
ArrayRef<int64_t> leadingStaticStrides) {
|
|
auto inferredType =
|
|
inferResultType(sourceRankedTensorType, leadingStaticOffsets,
|
|
leadingStaticSizes, leadingStaticStrides)
|
|
.cast<RankedTensorType>();
|
|
int rankDiff = inferredType.getRank() - resultRank;
|
|
if (rankDiff > 0) {
|
|
auto shape = inferredType.getShape();
|
|
llvm::SmallDenseSet<unsigned> dimsToProject;
|
|
getPositionsOfShapeOne(rankDiff, shape, dimsToProject);
|
|
SmallVector<int64_t> projectedShape;
|
|
for (unsigned pos = 0, e = shape.size(); pos < e; ++pos)
|
|
if (!dimsToProject.contains(pos))
|
|
projectedShape.push_back(shape[pos]);
|
|
inferredType =
|
|
RankedTensorType::get(projectedShape, inferredType.getElementType());
|
|
}
|
|
return inferredType;
|
|
}
|
|
|
|
Type SubTensorOp::inferRankReducedResultType(
|
|
unsigned resultRank, RankedTensorType sourceRankedTensorType,
|
|
ArrayRef<OpFoldResult> leadingStaticOffsets,
|
|
ArrayRef<OpFoldResult> leadingStaticSizes,
|
|
ArrayRef<OpFoldResult> leadingStaticStrides) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
|
|
staticOffsets, ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
|
|
staticStrides, ShapedType::kDynamicStrideOrOffset);
|
|
return SubTensorOp::inferRankReducedResultType(
|
|
resultRank, sourceRankedTensorType, staticOffsets, staticSizes,
|
|
staticStrides);
|
|
}
|
|
|
|
// Build a SubTensorOp with mixed static and dynamic entries and custom result
|
|
// type. If the type passed is nullptr, it is inferred.
|
|
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
|
|
RankedTensorType resultType, Value source,
|
|
ArrayRef<OpFoldResult> offsets,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
auto sourceRankedTensorType = source.getType().cast<RankedTensorType>();
|
|
// Structuring implementation this way avoids duplication between builders.
|
|
if (!resultType) {
|
|
resultType =
|
|
SubTensorOp::inferResultType(sourceRankedTensorType, staticOffsets,
|
|
staticSizes, staticStrides)
|
|
.cast<RankedTensorType>();
|
|
}
|
|
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
|
|
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
|
|
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
|
|
result.addAttributes(attrs);
|
|
}
|
|
|
|
// Build a SubTensorOp with mixed static and dynamic entries and inferred result
|
|
// type.
|
|
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
|
|
Value source, ArrayRef<OpFoldResult> offsets,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
|
|
}
|
|
|
|
// Build a SubTensorOp with dynamic entries and custom result type. If the type
|
|
// passed is nullptr, it is inferred.
|
|
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
|
|
RankedTensorType resultType, Value source,
|
|
ValueRange offsets, ValueRange sizes,
|
|
ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
|
|
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
|
|
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
|
|
build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
|
|
}
|
|
|
|
// Build a SubTensorOp with dynamic entries and inferred result type.
|
|
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
|
|
Value source, ValueRange offsets,
|
|
ValueRange sizes, ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
|
|
}
|
|
|
|
/// Verifier for SubTensorOp.
|
|
static LogicalResult verify(SubTensorOp op) {
|
|
// Verify result type against inferred type.
|
|
auto expectedType = SubTensorOp::inferResultType(
|
|
op.getSourceType(), extractFromI64ArrayAttr(op.static_offsets()),
|
|
extractFromI64ArrayAttr(op.static_sizes()),
|
|
extractFromI64ArrayAttr(op.static_strides()));
|
|
auto result = isRankReducedType(expectedType, op.getType());
|
|
return produceSubViewErrorMsg(result, op, expectedType);
|
|
}
|
|
|
|
namespace {
|
|
/// Pattern to rewrite a subtensor op with tensor::Cast arguments.
|
|
/// This essentially pushes memref_cast past its consuming subtensor when
|
|
/// `canFoldIntoConsumerOp` is true.
|
|
///
|
|
/// Example:
|
|
/// ```
|
|
/// %0 = tensorcast %V : tensor<16x16xf32> to tensor<?x?xf32>
|
|
/// %1 = subtensor %0[0, 0][3, 4][1, 1] : tensor<?x?xf32> to tensor<3x4xf32>
|
|
/// ```
|
|
/// is rewritten into:
|
|
/// ```
|
|
/// %0 = subtensor %V[0, 0][3, 4][1, 1] : tensor<16x16xf32> to tensor<3x4xf32>
|
|
/// %1 = tensor.cast %0: tensor<3x4xf32> to tensor<3x4xf32>
|
|
/// ```
|
|
class SubTensorOpCastFolder final : public OpRewritePattern<SubTensorOp> {
|
|
public:
|
|
using OpRewritePattern<SubTensorOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(SubTensorOp subTensorOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Any constant operand, just return to let SubViewOpConstantFolder kick in.
|
|
if (llvm::any_of(subTensorOp.getOperands(), [](Value operand) {
|
|
return matchPattern(operand, m_ConstantIndex());
|
|
}))
|
|
return failure();
|
|
|
|
auto castOp = subTensorOp.source().getDefiningOp<tensor::CastOp>();
|
|
if (!castOp)
|
|
return failure();
|
|
|
|
if (!canFoldIntoConsumerOp(castOp))
|
|
return failure();
|
|
|
|
/// Deduce the resultType of SubTensorOp with `inferRankReducedResultType`
|
|
/// on the cast source operand type and the SubTensorOp static information.
|
|
/// This is the resulting type if the tensor::CastOp were folded and
|
|
/// rank-reduced to the desired result rank.
|
|
auto resultType = SubTensorOp::inferRankReducedResultType(
|
|
subTensorOp.getType().getRank(),
|
|
castOp.source().getType().cast<RankedTensorType>(),
|
|
subTensorOp.getMixedOffsets(), subTensorOp.getMixedSizes(),
|
|
subTensorOp.getMixedStrides());
|
|
Value newSubTensor = rewriter.create<SubTensorOp>(
|
|
subTensorOp.getLoc(), resultType, castOp.source(),
|
|
subTensorOp.offsets(), subTensorOp.sizes(), subTensorOp.strides(),
|
|
subTensorOp.static_offsets(), subTensorOp.static_sizes(),
|
|
subTensorOp.static_strides());
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(
|
|
subTensorOp, subTensorOp.getType(), newSubTensor);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void SubTensorOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<OpWithOffsetSizesAndStridesConstantArgumentFolder<SubTensorOp>,
|
|
SubTensorOpCastFolder>(context);
|
|
}
|
|
|
|
//
|
|
static LogicalResult
|
|
foldIdentityOffsetSizeAndStrideOpInterface(OffsetSizeAndStrideOpInterface op,
|
|
ShapedType shapedType) {
|
|
OpBuilder b(op.getContext());
|
|
for (OpFoldResult ofr : op.getMixedOffsets())
|
|
if (!isEqualConstantIntOrValue(ofr, b.getIndexAttr(0)))
|
|
return failure();
|
|
// Rank-reducing noops only need to inspect the leading dimensions: llvm::zip
|
|
// is appropriate.
|
|
auto shape = shapedType.getShape();
|
|
for (auto it : llvm::zip(op.getMixedSizes(), shape))
|
|
if (!isEqualConstantIntOrValue(std::get<0>(it),
|
|
b.getIndexAttr(std::get<1>(it))))
|
|
return failure();
|
|
for (OpFoldResult ofr : op.getMixedStrides())
|
|
if (!isEqualConstantIntOrValue(ofr, b.getIndexAttr(1)))
|
|
return failure();
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult SubTensorOp::fold(ArrayRef<Attribute>) {
|
|
if (getSourceType() == getType() &&
|
|
succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
|
|
return this->source();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SubTensorInsertOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Build a SubTensorInsertOp with mixed static and dynamic entries.
|
|
void mlir::SubTensorInsertOp::build(OpBuilder &b, OperationState &result,
|
|
Value source, Value dest,
|
|
ArrayRef<OpFoldResult> offsets,
|
|
ArrayRef<OpFoldResult> sizes,
|
|
ArrayRef<OpFoldResult> strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
|
|
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
|
|
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
|
|
ShapedType::kDynamicSize);
|
|
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
|
|
ShapedType::kDynamicStrideOrOffset);
|
|
build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
|
|
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
|
|
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
|
|
result.addAttributes(attrs);
|
|
}
|
|
|
|
// Build a SubTensorInsertOp with dynamic entries.
|
|
void mlir::SubTensorInsertOp::build(OpBuilder &b, OperationState &result,
|
|
Value source, Value dest,
|
|
ValueRange offsets, ValueRange sizes,
|
|
ValueRange strides,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
|
|
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
|
|
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
|
|
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
|
|
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
|
|
build(b, result, source, dest, offsetValues, sizeValues, strideValues);
|
|
}
|
|
|
|
OpFoldResult SubTensorInsertOp::fold(ArrayRef<Attribute>) {
|
|
if (getSourceType() == getType() &&
|
|
succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
|
|
return this->source();
|
|
if (succeeded(tensor::foldTensorCast(*this)))
|
|
return this->source();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TensorLoadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult TensorLoadOp::fold(ArrayRef<Attribute>) {
|
|
if (auto tensorToMemref = memref().getDefiningOp<TensorToMemrefOp>())
|
|
return tensorToMemref.tensor();
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TensorToMemrefOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult TensorToMemrefOp::fold(ArrayRef<Attribute>) {
|
|
if (auto tensorLoad = tensor().getDefiningOp<TensorLoadOp>())
|
|
if (tensorLoad.memref().getType() == getType())
|
|
return tensorLoad.memref();
|
|
return {};
|
|
}
|
|
|
|
namespace {
|
|
/// Replace tensor_cast + tensor_to_memref by tensor_to_memref + memref_cast.
|
|
struct TensorCastToMemref : public OpRewritePattern<TensorToMemrefOp> {
|
|
using OpRewritePattern<TensorToMemrefOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TensorToMemrefOp tensorToMemRef,
|
|
PatternRewriter &rewriter) const final {
|
|
auto tensorCastOperand =
|
|
tensorToMemRef.getOperand().getDefiningOp<tensor::CastOp>();
|
|
if (!tensorCastOperand)
|
|
return failure();
|
|
auto srcTensorType =
|
|
tensorCastOperand.getOperand().getType().dyn_cast<RankedTensorType>();
|
|
if (!srcTensorType)
|
|
return failure();
|
|
auto memrefType = MemRefType::get(srcTensorType.getShape(),
|
|
srcTensorType.getElementType());
|
|
Value memref = rewriter.create<TensorToMemrefOp>(
|
|
tensorToMemRef.getLoc(), memrefType, tensorCastOperand.getOperand());
|
|
rewriter.replaceOpWithNewOp<MemRefCastOp>(tensorToMemRef,
|
|
tensorToMemRef.getType(), memref);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Canonicalize tensor_load + tensor_to_memref to memref_cast when type
|
|
/// mismatches prevent `TensorToMemrefOp::fold` to kick in.
|
|
struct TensorLoadToMemref : public OpRewritePattern<TensorToMemrefOp> {
|
|
using OpRewritePattern<TensorToMemrefOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TensorToMemrefOp tensorToMemRef,
|
|
PatternRewriter &rewriter) const final {
|
|
auto tensorLoad = tensorToMemRef.tensor().getDefiningOp<TensorLoadOp>();
|
|
// Bail unless we have a tensor_load + tensor_to_memref with different
|
|
// types. `TensorToMemrefOp::fold` handles the same type case.
|
|
if (!tensorLoad ||
|
|
tensorLoad.memref().getType() == tensorToMemRef.getType())
|
|
return failure();
|
|
// If types are not cast-compatible, bail.
|
|
if (!MemRefCastOp::areCastCompatible(tensorLoad.memref().getType(),
|
|
tensorToMemRef.getType()))
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<MemRefCastOp>(
|
|
tensorToMemRef, tensorToMemRef.getType(), tensorLoad.memref());
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void TensorToMemrefOp::getCanonicalizationPatterns(
|
|
OwningRewritePatternList &results, MLIRContext *context) {
|
|
results.insert<TensorCastToMemref, TensorLoadToMemref>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TransposeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Build a strided memref type by applying `permutationMap` tp `memRefType`.
|
|
static MemRefType inferTransposeResultType(MemRefType memRefType,
|
|
AffineMap permutationMap) {
|
|
auto rank = memRefType.getRank();
|
|
auto originalSizes = memRefType.getShape();
|
|
// Compute permuted sizes.
|
|
SmallVector<int64_t, 4> sizes(rank, 0);
|
|
for (auto en : llvm::enumerate(permutationMap.getResults()))
|
|
sizes[en.index()] =
|
|
originalSizes[en.value().cast<AffineDimExpr>().getPosition()];
|
|
|
|
// Compute permuted strides.
|
|
int64_t offset;
|
|
SmallVector<int64_t, 4> strides;
|
|
auto res = getStridesAndOffset(memRefType, strides, offset);
|
|
assert(succeeded(res) && strides.size() == static_cast<unsigned>(rank));
|
|
(void)res;
|
|
auto map =
|
|
makeStridedLinearLayoutMap(strides, offset, memRefType.getContext());
|
|
map = permutationMap ? map.compose(permutationMap) : map;
|
|
return MemRefType::Builder(memRefType).setShape(sizes).setAffineMaps(map);
|
|
}
|
|
|
|
void TransposeOp::build(OpBuilder &b, OperationState &result, Value in,
|
|
AffineMapAttr permutation,
|
|
ArrayRef<NamedAttribute> attrs) {
|
|
auto permutationMap = permutation.getValue();
|
|
assert(permutationMap);
|
|
|
|
auto memRefType = in.getType().cast<MemRefType>();
|
|
// Compute result type.
|
|
MemRefType resultType = inferTransposeResultType(memRefType, permutationMap);
|
|
|
|
build(b, result, resultType, in, attrs);
|
|
result.addAttribute(TransposeOp::getPermutationAttrName(), permutation);
|
|
}
|
|
|
|
// transpose $in $permutation attr-dict : type($in) `to` type(results)
|
|
static void print(OpAsmPrinter &p, TransposeOp op) {
|
|
p << "transpose " << op.in() << " " << op.permutation();
|
|
p.printOptionalAttrDict(op->getAttrs(),
|
|
{TransposeOp::getPermutationAttrName()});
|
|
p << " : " << op.in().getType() << " to " << op.getType();
|
|
}
|
|
|
|
static ParseResult parseTransposeOp(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
OpAsmParser::OperandType in;
|
|
AffineMap permutation;
|
|
MemRefType srcType, dstType;
|
|
if (parser.parseOperand(in) || parser.parseAffineMap(permutation) ||
|
|
parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonType(srcType) ||
|
|
parser.resolveOperand(in, srcType, result.operands) ||
|
|
parser.parseKeywordType("to", dstType) ||
|
|
parser.addTypeToList(dstType, result.types))
|
|
return failure();
|
|
|
|
result.addAttribute(TransposeOp::getPermutationAttrName(),
|
|
AffineMapAttr::get(permutation));
|
|
return success();
|
|
}
|
|
|
|
static LogicalResult verify(TransposeOp op) {
|
|
if (!op.permutation().isPermutation())
|
|
return op.emitOpError("expected a permutation map");
|
|
if (op.permutation().getNumDims() != op.getShapedType().getRank())
|
|
return op.emitOpError(
|
|
"expected a permutation map of same rank as the input");
|
|
|
|
auto srcType = op.in().getType().cast<MemRefType>();
|
|
auto dstType = op.getType().cast<MemRefType>();
|
|
auto transposedType = inferTransposeResultType(srcType, op.permutation());
|
|
if (dstType != transposedType)
|
|
return op.emitOpError("output type ")
|
|
<< dstType << " does not match transposed input type " << srcType
|
|
<< ", " << transposedType;
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult TransposeOp::fold(ArrayRef<Attribute>) {
|
|
if (succeeded(foldMemRefCast(*this)))
|
|
return getResult();
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TruncateIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(TruncateIOp op) {
|
|
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
|
|
auto dstType = getElementTypeOrSelf(op.getType());
|
|
|
|
if (srcType.isa<IndexType>())
|
|
return op.emitError() << srcType << " is not a valid operand type";
|
|
if (dstType.isa<IndexType>())
|
|
return op.emitError() << dstType << " is not a valid result type";
|
|
|
|
if (srcType.cast<IntegerType>().getWidth() <=
|
|
dstType.cast<IntegerType>().getWidth())
|
|
return op.emitError("operand type ")
|
|
<< srcType << " must be wider than result type " << dstType;
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// UnsignedDivIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult UnsignedDivIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "binary operation takes two operands");
|
|
|
|
// Don't fold if it would require a division by zero.
|
|
bool div0 = false;
|
|
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
|
|
if (div0 || !b) {
|
|
div0 = true;
|
|
return a;
|
|
}
|
|
return a.udiv(b);
|
|
});
|
|
|
|
// Fold out division by one. Assumes all tensors of all ones are splats.
|
|
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
|
|
if (rhs.getValue() == 1)
|
|
return lhs();
|
|
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
|
|
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
|
|
return lhs();
|
|
}
|
|
|
|
return div0 ? Attribute() : result;
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// UnsignedRemIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult UnsignedRemIOp::fold(ArrayRef<Attribute> operands) {
|
|
assert(operands.size() == 2 && "remi_unsigned takes two operands");
|
|
|
|
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
|
|
if (!rhs)
|
|
return {};
|
|
auto rhsValue = rhs.getValue();
|
|
|
|
// x % 1 = 0
|
|
if (rhsValue.isOneValue())
|
|
return IntegerAttr::get(rhs.getType(), APInt(rhsValue.getBitWidth(), 0));
|
|
|
|
// Don't fold if it requires division by zero.
|
|
if (rhsValue.isNullValue())
|
|
return {};
|
|
|
|
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
|
|
if (!lhs)
|
|
return {};
|
|
return IntegerAttr::get(lhs.getType(), lhs.getValue().urem(rhsValue));
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ViewOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static ParseResult parseViewOp(OpAsmParser &parser, OperationState &result) {
|
|
OpAsmParser::OperandType srcInfo;
|
|
SmallVector<OpAsmParser::OperandType, 1> offsetInfo;
|
|
SmallVector<OpAsmParser::OperandType, 4> sizesInfo;
|
|
auto indexType = parser.getBuilder().getIndexType();
|
|
Type srcType, dstType;
|
|
llvm::SMLoc offsetLoc;
|
|
if (parser.parseOperand(srcInfo) || parser.getCurrentLocation(&offsetLoc) ||
|
|
parser.parseOperandList(offsetInfo, OpAsmParser::Delimiter::Square))
|
|
return failure();
|
|
|
|
if (offsetInfo.size() != 1)
|
|
return parser.emitError(offsetLoc) << "expects 1 offset operand";
|
|
|
|
return failure(
|
|
parser.parseOperandList(sizesInfo, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonType(srcType) ||
|
|
parser.resolveOperand(srcInfo, srcType, result.operands) ||
|
|
parser.resolveOperands(offsetInfo, indexType, result.operands) ||
|
|
parser.resolveOperands(sizesInfo, indexType, result.operands) ||
|
|
parser.parseKeywordType("to", dstType) ||
|
|
parser.addTypeToList(dstType, result.types));
|
|
}
|
|
|
|
static void print(OpAsmPrinter &p, ViewOp op) {
|
|
p << op.getOperationName() << ' ' << op.getOperand(0) << '[';
|
|
p.printOperand(op.byte_shift());
|
|
p << "][" << op.sizes() << ']';
|
|
p.printOptionalAttrDict(op->getAttrs());
|
|
p << " : " << op.getOperand(0).getType() << " to " << op.getType();
|
|
}
|
|
|
|
static LogicalResult verify(ViewOp op) {
|
|
auto baseType = op.getOperand(0).getType().cast<MemRefType>();
|
|
auto viewType = op.getType();
|
|
|
|
// The base memref should have identity layout map (or none).
|
|
if (baseType.getAffineMaps().size() > 1 ||
|
|
(baseType.getAffineMaps().size() == 1 &&
|
|
!baseType.getAffineMaps()[0].isIdentity()))
|
|
return op.emitError("unsupported map for base memref type ") << baseType;
|
|
|
|
// The result memref should have identity layout map (or none).
|
|
if (viewType.getAffineMaps().size() > 1 ||
|
|
(viewType.getAffineMaps().size() == 1 &&
|
|
!viewType.getAffineMaps()[0].isIdentity()))
|
|
return op.emitError("unsupported map for result memref type ") << viewType;
|
|
|
|
// The base memref and the view memref should be in the same memory space.
|
|
if (baseType.getMemorySpace() != viewType.getMemorySpace())
|
|
return op.emitError("different memory spaces specified for base memref "
|
|
"type ")
|
|
<< baseType << " and view memref type " << viewType;
|
|
|
|
// Verify that we have the correct number of sizes for the result type.
|
|
unsigned numDynamicDims = viewType.getNumDynamicDims();
|
|
if (op.sizes().size() != numDynamicDims)
|
|
return op.emitError("incorrect number of size operands for type ")
|
|
<< viewType;
|
|
|
|
return success();
|
|
}
|
|
|
|
Value ViewOp::getViewSource() { return source(); }
|
|
|
|
namespace {
|
|
|
|
struct ViewOpShapeFolder : public OpRewritePattern<ViewOp> {
|
|
using OpRewritePattern<ViewOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ViewOp viewOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if none of the operands are constants.
|
|
if (llvm::none_of(viewOp.getOperands(), [](Value operand) {
|
|
return matchPattern(operand, m_ConstantIndex());
|
|
}))
|
|
return failure();
|
|
|
|
// Get result memref type.
|
|
auto memrefType = viewOp.getType();
|
|
|
|
// Get offset from old memref view type 'memRefType'.
|
|
int64_t oldOffset;
|
|
SmallVector<int64_t, 4> oldStrides;
|
|
if (failed(getStridesAndOffset(memrefType, oldStrides, oldOffset)))
|
|
return failure();
|
|
assert(oldOffset == 0 && "Expected 0 offset");
|
|
|
|
SmallVector<Value, 4> newOperands;
|
|
|
|
// Offset cannot be folded into result type.
|
|
|
|
// Fold any dynamic dim operands which are produced by a constant.
|
|
SmallVector<int64_t, 4> newShapeConstants;
|
|
newShapeConstants.reserve(memrefType.getRank());
|
|
|
|
unsigned dynamicDimPos = 0;
|
|
unsigned rank = memrefType.getRank();
|
|
for (unsigned dim = 0, e = rank; dim < e; ++dim) {
|
|
int64_t dimSize = memrefType.getDimSize(dim);
|
|
// If this is already static dimension, keep it.
|
|
if (!ShapedType::isDynamic(dimSize)) {
|
|
newShapeConstants.push_back(dimSize);
|
|
continue;
|
|
}
|
|
auto *defOp = viewOp.sizes()[dynamicDimPos].getDefiningOp();
|
|
if (auto constantIndexOp = dyn_cast_or_null<ConstantIndexOp>(defOp)) {
|
|
// Dynamic shape dimension will be folded.
|
|
newShapeConstants.push_back(constantIndexOp.getValue());
|
|
} else {
|
|
// Dynamic shape dimension not folded; copy operand from old memref.
|
|
newShapeConstants.push_back(dimSize);
|
|
newOperands.push_back(viewOp.sizes()[dynamicDimPos]);
|
|
}
|
|
dynamicDimPos++;
|
|
}
|
|
|
|
// Create new memref type with constant folded dims.
|
|
MemRefType newMemRefType =
|
|
MemRefType::Builder(memrefType).setShape(newShapeConstants);
|
|
// Nothing new, don't fold.
|
|
if (newMemRefType == memrefType)
|
|
return failure();
|
|
|
|
// Create new ViewOp.
|
|
auto newViewOp = rewriter.create<ViewOp>(viewOp.getLoc(), newMemRefType,
|
|
viewOp.getOperand(0),
|
|
viewOp.byte_shift(), newOperands);
|
|
// Insert a cast so we have the same type as the old memref type.
|
|
rewriter.replaceOpWithNewOp<MemRefCastOp>(viewOp, newViewOp,
|
|
viewOp.getType());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
struct ViewOpMemrefCastFolder : public OpRewritePattern<ViewOp> {
|
|
using OpRewritePattern<ViewOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ViewOp viewOp,
|
|
PatternRewriter &rewriter) const override {
|
|
Value memrefOperand = viewOp.getOperand(0);
|
|
MemRefCastOp memrefCastOp = memrefOperand.getDefiningOp<MemRefCastOp>();
|
|
if (!memrefCastOp)
|
|
return failure();
|
|
Value allocOperand = memrefCastOp.getOperand();
|
|
AllocOp allocOp = allocOperand.getDefiningOp<AllocOp>();
|
|
if (!allocOp)
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<ViewOp>(viewOp, viewOp.getType(), allocOperand,
|
|
viewOp.byte_shift(), viewOp.sizes());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // end anonymous namespace
|
|
|
|
void ViewOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
|
|
MLIRContext *context) {
|
|
results.insert<ViewOpShapeFolder, ViewOpMemrefCastFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// XOrOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult XOrOp::fold(ArrayRef<Attribute> operands) {
|
|
/// xor(x, 0) -> x
|
|
if (matchPattern(rhs(), m_Zero()))
|
|
return lhs();
|
|
/// xor(x,x) -> 0
|
|
if (lhs() == rhs())
|
|
return Builder(getContext()).getZeroAttr(getType());
|
|
|
|
return constFoldBinaryOp<IntegerAttr>(operands,
|
|
[](APInt a, APInt b) { return a ^ b; });
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ZeroExtendIOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verify(ZeroExtendIOp op) {
|
|
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
|
|
auto dstType = getElementTypeOrSelf(op.getType());
|
|
|
|
if (srcType.isa<IndexType>())
|
|
return op.emitError() << srcType << " is not a valid operand type";
|
|
if (dstType.isa<IndexType>())
|
|
return op.emitError() << dstType << " is not a valid result type";
|
|
|
|
if (srcType.cast<IntegerType>().getWidth() >=
|
|
dstType.cast<IntegerType>().getWidth())
|
|
return op.emitError("result type ")
|
|
<< dstType << " must be wider than operand type " << srcType;
|
|
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TableGen'd op method definitions
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#define GET_OP_CLASSES
|
|
#include "mlir/Dialect/StandardOps/IR/Ops.cpp.inc"
|