b2b793dfb5
Most notably, this PR: - changes the traits (and assembly format) of addptr so it can handle offsets that have arbitrary integer width. - adds support for `cat`
72 lines
2.7 KiB
C++
72 lines
2.7 KiB
C++
#include "triton/Dialect/Triton/IR/Traits.h"
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static mlir::LogicalResult verifySameEncoding(mlir::Type tyA, mlir::Type tyB) {
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using namespace mlir;
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auto encA = tyA.dyn_cast<RankedTensorType>();
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auto encB = tyA.dyn_cast<RankedTensorType>();
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if (!encA || !encB)
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return success();
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return encA.getEncoding() == encB.getEncoding() ? success() : failure();
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}
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mlir::LogicalResult
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mlir::OpTrait::impl::verifySameOperandsAndResultEncoding(Operation *op) {
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if (failed(verifyAtLeastNOperands(op, 1)) ||
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failed(verifyAtLeastNResults(op, 1)))
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return failure();
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auto type = op->getOperand(0).getType();
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for (auto resultType : op->getResultTypes())
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if (failed(verifySameEncoding(resultType, type)))
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return op->emitOpError()
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<< "requires the same encoding for all operands and results";
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return verifySameOperandsEncoding(op);
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}
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mlir::LogicalResult
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mlir::OpTrait::impl::verifySameOperandsEncoding(Operation *op) {
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if (failed(verifyAtLeastNOperands(op, 1)))
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return failure();
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auto type = op->getOperand(0).getType();
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for (auto opType : llvm::drop_begin(op->getOperandTypes(), 1))
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if (failed(verifySameEncoding(opType, type)))
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return op->emitOpError() << "requires the same encoding for all operands";
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return success();
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}
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mlir::LogicalResult mlir::OpTrait::impl::verifyTensorSize(Operation *op) {
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for (auto opType : op->getOperandTypes()) {
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if (auto tensorType = opType.dyn_cast<RankedTensorType>()) {
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int64_t numElements = 1;
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for (int64_t s : tensorType.getShape())
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numElements *= s;
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if (numElements > maxTensorNumElements)
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return op->emitError("Maximum allowed number of elements is ")
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<< maxTensorNumElements << ", but " << *op
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<< " has more than that";
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if ((numElements & (numElements - 1)) != 0)
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return op->emitError("Number of elements must be power-of-two, but ")
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<< *op << " doesn't follow the rule (" << numElements << ")"
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<< " elements";
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}
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}
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for (auto opType : op->getResultTypes()) {
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if (auto tensorType = opType.dyn_cast<RankedTensorType>()) {
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int64_t numElements = 1;
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for (int64_t s : tensorType.getShape())
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numElements *= s;
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if (numElements > maxTensorNumElements)
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return op->emitError("Maximum allowed number of elements is ")
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<< maxTensorNumElements << ", but " << *op
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<< " has more than that";
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if ((numElements & (numElements - 1)) != 0)
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return op->emitError("Number of elements must be power-of-two, but ")
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<< *op << " doesn't follow the rule (" << numElements << ")"
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<< " elements";
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}
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}
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return success();
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}
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