55 lines
1.8 KiB
C++
55 lines
1.8 KiB
C++
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//===- onnx_ops.cpp - MLIR ONNX Operations --------------------------------===//
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//
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// Copyright 2019 The IBM Research Authors.
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//
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// =============================================================================
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//
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// This file defines ONNX operations in the MLIR operation set.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/ADT/SetVector.h"
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#include "llvm/ADT/SmallBitVector.h"
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#include "mlir/IR/Block.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/Function.h"
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#include "mlir/IR/IntegerSet.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 "onnx_ops.hpp"
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using namespace mlir;
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//===----------------------------------------------------------------------===//
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// ONNXOpsDialect
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//===----------------------------------------------------------------------===//
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/// Dialect creation, the instance will be owned by the context. This is the
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/// point of registration of custom types and operations for the dialect.
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ONNXOpsDialect::ONNXOpsDialect(mlir::MLIRContext* ctx)
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: mlir::Dialect(getDialectNamespace(), ctx) {
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addOperations<
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#define GET_OP_LIST
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#include "src/compiler/onnx.cpp.inc"
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>();
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}
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//===----------------------------------------------------------------------===//
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// ONNX Operations
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//===----------------------------------------------------------------------===//
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static void buildONNXAddOp(mlir::Builder* builder, mlir::OperationState& state,
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mlir::Value* lhs, mlir::Value* rhs) {
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state.addTypes(UnrankedTensorType::get(builder->getF32Type()));
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state.addOperands({lhs, rhs});
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}
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//===----------------------------------------------------------------------===//
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// TableGen'd op method definitions
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//===----------------------------------------------------------------------===//
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#define GET_OP_CLASSES
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#include "src/compiler/onnx.cpp.inc"
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