mirror of https://github.com/llvm/torch-mlir
1911 lines
85 KiB
C++
1911 lines
85 KiB
C++
//===------------------------------------------------------------*- C++ -*-===//
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//
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// This file is licensed 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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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h"
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#include "torch-mlir/Conversion/TorchOnnxToTorch/Utils.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "llvm/ADT/ArrayRef.h"
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#include "llvm/ADT/SmallVector.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::onnx_c;
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// Simple rewrites for the default domain.
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// See: https://onnx.ai/onnx/operators/
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// For operators that are effectively version invariant, we register with
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// sinceVersion==1. We interpret this to include the following spec
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// diffs that are irrelevant to this level of lowering:
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// * Supported element types.
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// * Limited broadcasting to full broadcasting support.
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//
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// There are a lot of spec revisions that basically generalized elementwise
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// to be more normal and a direct translation vs a special case. This
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// results in a lot of ONNX test cases that all reduce to the exact same
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// thing here, so we simplify.
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// utilities
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// Templatized function to get an item op of a type
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namespace {
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template <typename T>
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Value getItemOp(OpBinder binder, ConversionPatternRewriter &rewriter,
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Value &ofItem) {
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return rewriter.create<Torch::AtenItemOp>(binder.getLoc(),
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rewriter.getType<T>(), ofItem);
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}
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} // namespace
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void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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OnnxCustomOpConversionPattern &patterns) {
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patterns.onOp(
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"QuantizeLinear", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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if (binder.tensorOperands(operands, 3) ||
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binder.tensorResultType(resultType))
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return failure();
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Value operand = operands[0];
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Value scale = operands[1];
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Value zeropoint = operands[2];
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auto scaleTy = scale.getType().dyn_cast<Torch::ValueTensorType>();
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if (!scaleTy || !scaleTy.hasSizes())
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return rewriter.notifyMatchFailure(binder.op, "requires known rank");
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if (!resultType.hasDtype())
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return rewriter.notifyMatchFailure(binder.op,
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"requires known result dtype");
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if (scaleTy.getSizes().size() == 0) {
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Type qTy = resultType.getDtype();
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if (qTy.isUnsignedInteger(8)) {
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qTy = rewriter.getType<Torch::QUInt8Type>();
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} else if (qTy.isSignedInteger(8)) {
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qTy = rewriter.getType<Torch::QInt8Type>();
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} else if (qTy.isSignedInteger(32)) {
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qTy = rewriter.getType<Torch::QInt32Type>();
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} else {
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return rewriter.notifyMatchFailure(binder.op,
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"unsupported result dtype");
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}
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auto qTensorTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(), qTy);
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auto torchqTy = Torch::getScalarTypeForType(qTy);
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Value tyConst = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64),
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static_cast<int64_t>(torchqTy)));
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scale = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(), scale);
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zeropoint = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(), zeropoint);
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auto quantize = rewriter.create<Torch::AtenQuantizePerTensorOp>(
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binder.getLoc(), qTensorTy, operand, scale, zeropoint, tyConst);
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rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(
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binder.op, resultType, quantize);
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return success();
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}
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return failure();
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});
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patterns.onOp(
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"QLinearConv", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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if ((binder.tensorOperands(operands, 8) &&
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binder.tensorOperands(operands, 9)) ||
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binder.tensorResultType(resultType))
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return failure();
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Value a = operands[0];
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Value aScale = operands[1];
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Value aZp = operands[2];
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Value b = operands[3];
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Value bScale = operands[4];
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Value bZp = operands[5];
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Value cScale = operands[6];
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Value cZp = operands[7];
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Value c = operands.size() == 9 ? operands[8] : nullptr;
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auto check = [](Value v) {
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auto vTy = v.getType().cast<Torch::ValueTensorType>();
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return llvm::all_of(vTy.getSizes(), [](int64_t d) { return d == 1; });
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};
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if (!check(aScale) || !check(aZp) || !check(bScale) || !check(bZp) ||
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!check(cScale) || !check(cScale))
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return rewriter.notifyMatchFailure(
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binder.op, "not supported for non per-tensor quantization");
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auto extract = [&rewriter, &binder](Value v) {
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auto vTy = v.getType().cast<Torch::ValueTensorType>();
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Type extractTy = rewriter.getType<Torch::FloatType>();
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if (isa<IntegerType>(vTy.getDtype()))
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extractTy = rewriter.getType<Torch::IntType>();
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return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
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v);
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};
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aZp = extract(aZp);
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bZp = extract(bZp);
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cZp = extract(cZp);
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aScale = extract(aScale);
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bScale = extract(bScale);
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cScale = extract(cScale);
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auto make = [&rewriter, &binder](Value v, Value scale,
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Value zp) -> Value {
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auto ty = v.getType().cast<Torch::ValueTensorType>();
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auto newTy = getQTorchTypeFromTorchIntType(ty);
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return rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
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binder.getLoc(), newTy, v, scale, zp);
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};
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a = make(a, aScale, aZp);
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b = make(b, bScale, bZp);
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auto cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(),
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rewriter.getIntegerType(32, /*issigned=*/true));
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// TODO(suderman): insert convolution operator.
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llvm::SmallVector<Value> newOperands = {a, b};
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if (c)
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newOperands.push_back(c);
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cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(),
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rewriter.getType<Torch::QInt32Type>());
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llvm::SmallVector<NamedAttribute> newAttributes;
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newAttributes.push_back(
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rewriter.getNamedAttr("name", rewriter.getStringAttr("onnx.Conv")));
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for (auto namedAttr : binder.op->getAttrDictionary()) {
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if (namedAttr.getName().getValue().compare("name") == 0)
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continue;
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llvm::errs() << namedAttr.getName() << "\n";
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newAttributes.push_back(namedAttr);
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}
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c = rewriter
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.create<Torch::OperatorOp>(binder.getLoc(), cTy, newOperands,
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newAttributes,
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binder.op->getRegions().size())
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.getResult(0);
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Value outScale = rewriter.create<Torch::AtenMulFloatOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(), aScale,
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bScale);
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Value outZp = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
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c = rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
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binder.getLoc(), cTy, c, outScale, outZp);
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cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(), rewriter.getF32Type());
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c = rewriter.create<Torch::AtenDequantizeSelfOp>(binder.getLoc(), cTy,
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c);
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cTy = dyn_cast<Torch::ValueTensorType>(
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getQTorchTypeFromTorchIntType(resultType));
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Value dtyVal = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(
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rewriter.getIntegerType(64),
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static_cast<int64_t>(
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Torch::getScalarTypeForType(cTy.getDtype()))));
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c = rewriter.create<Torch::AtenQuantizePerTensorOp>(
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binder.getLoc(), cTy, c, cScale, cZp, dtyVal);
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rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(binder.op, resultType,
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c);
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return success();
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});
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patterns.onOp(
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"QLinearMatMul", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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if (binder.tensorOperands(operands, 8) ||
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binder.tensorResultType(resultType))
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return failure();
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Value a = operands[0];
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Value aScale = operands[1];
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Value aZp = operands[2];
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Value b = operands[3];
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Value bScale = operands[4];
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Value bZp = operands[5];
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Value cScale = operands[6];
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Value cZp = operands[7];
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auto check = [](Value v) {
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auto vTy = v.getType().cast<Torch::ValueTensorType>();
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for (auto dim : vTy.getSizes())
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if (dim != 1)
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return false;
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return true;
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};
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if (!check(aScale) || !check(aZp) || !check(bScale) || !check(bZp) ||
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!check(cScale) || !check(cScale))
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return rewriter.notifyMatchFailure(
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binder.op, "not supported for non per-tensor quantization");
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Value emptyList = rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(),
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rewriter.getType<Torch::ListType>(
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rewriter.getType<Torch::IntType>()),
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ValueRange{});
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auto extract = [&rewriter, &binder, &emptyList](Value v) {
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auto vTy = v.getType().cast<Torch::ValueTensorType>();
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if (!vTy.getSizes().empty()) {
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vTy = rewriter.getType<Torch::ValueTensorType>(
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ArrayRef<int64_t>({}), vTy.getOptionalDtype());
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v = rewriter.create<Torch::AtenReshapeOp>(binder.getLoc(), vTy, v,
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emptyList);
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}
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Type extractTy = rewriter.getType<Torch::FloatType>();
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if (isa<IntegerType>(vTy.getDtype()))
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extractTy = rewriter.getType<Torch::IntType>();
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return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
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v);
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};
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aZp = extract(aZp);
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bZp = extract(bZp);
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cZp = extract(cZp);
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aScale = extract(aScale);
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bScale = extract(bScale);
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cScale = extract(cScale);
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auto make = [&rewriter, &binder](Value v, Value scale,
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Value zp) -> Value {
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auto ty = v.getType().cast<Torch::ValueTensorType>();
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auto newTy = getQTorchTypeFromTorchIntType(ty);
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return rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
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binder.getLoc(), newTy, v, scale, zp);
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};
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a = make(a, aScale, aZp);
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b = make(b, bScale, bZp);
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auto cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(),
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rewriter.getIntegerType(32, /*issigned=*/true));
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Value c;
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if (cTy.getSizes().size() == 2) {
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c = rewriter.create<Torch::AtenMmOp>(binder.getLoc(), cTy, a, b);
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} else {
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c = rewriter.create<Torch::AtenBmmOp>(binder.getLoc(), cTy, a, b);
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}
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cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(),
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rewriter.getType<Torch::QInt32Type>());
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Value mmScale = rewriter.create<Torch::AtenMulFloatOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(), aScale,
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bScale);
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Value mmZp = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
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c = rewriter.create<Torch::Aten_MakePerTensorQuantizedTensorOp>(
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binder.getLoc(), cTy, c, mmScale, mmZp);
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cTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(), rewriter.getF32Type());
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c = rewriter.create<Torch::AtenDequantizeSelfOp>(binder.getLoc(), cTy,
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c);
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cTy = dyn_cast<Torch::ValueTensorType>(
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getQTorchTypeFromTorchIntType(resultType));
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Value dtyVal = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(
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rewriter.getIntegerType(64),
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static_cast<int64_t>(
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Torch::getScalarTypeForType(cTy.getDtype()))));
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c = rewriter.create<Torch::AtenQuantizePerTensorOp>(
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binder.getLoc(), cTy, c, cScale, cZp, dtyVal);
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rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(binder.op, resultType,
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c);
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return success();
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});
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patterns.onOp("Reciprocal", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand;
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenReciprocalOp>(
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binder.op, resultType, operand);
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return success();
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});
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patterns.onOp(
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"Relu", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value x;
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if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenReluOp>(binder.op, resultType,
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x);
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return success();
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});
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patterns.onOp("Round", 11,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand;
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenRoundOp>(
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binder.op, resultType, operand);
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return success();
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});
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patterns.onOp(
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"ScatterElements", 18,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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SmallVector<Value> valList;
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int64_t axis;
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std::string reduction;
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int64_t numOperands = binder.op->getNumOperands();
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if (binder.tensorOperands(valList, numOperands) ||
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binder.s64IntegerAttr(axis, "axis", 0) ||
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binder.customOpNameStringAttr(reduction, "reduction", "none") ||
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binder.tensorResultType(resultType))
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return failure();
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Value data = valList[0];
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Value indices = valList[1];
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Value updates = valList[2];
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// ONNX allows negative axis.
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if (axis < 0)
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axis +=
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cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
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Value constAxis = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), axis));
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if (reduction == "none") {
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rewriter.replaceOpWithNewOp<Torch::AtenScatterSrcOp>(
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binder.op, resultType, data, constAxis, indices, updates);
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return success();
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}
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// TODO: Implement max and min cases
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if (reduction == "mul") {
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reduction = "multiply";
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} else if (reduction == "max" || reduction == "min") {
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return rewriter.notifyMatchFailure(
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binder.op, "max/min reduction unsupported for scatter elements");
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}
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Value cstStrReduction =
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rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), reduction);
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rewriter.replaceOpWithNewOp<Torch::AtenScatterReduceOp>(
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binder.op, resultType, data, constAxis, indices, updates,
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cstStrReduction);
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return success();
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});
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patterns.onOp(
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"Sigmoid", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value x;
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if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenSigmoidOp>(binder.op, resultType,
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x);
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return success();
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});
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patterns.onOp("Sin", 7,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand;
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenSinOp>(
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binder.op, resultType, operand);
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return success();
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});
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patterns.onOp("Tanh", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand;
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenTanhOp>(
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binder.op, resultType, operand);
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return success();
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});
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patterns.onOp("Sqrt", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand;
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if (binder.tensorOperand(operand) ||
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binder.tensorResultType(resultType))
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return failure();
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rewriter.replaceOpWithNewOp<Torch::AtenSqrtOp>(
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binder.op, resultType, operand);
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return success();
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});
|
|
patterns.onOp(
|
|
"Sub", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value x;
|
|
Value y;
|
|
if (binder.tensorOperands(x, y) || binder.tensorResultType(resultType))
|
|
return failure();
|
|
Value const1 = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 1));
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSubTensorOp>(
|
|
binder.op, resultType, x, y, const1);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Sum", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
if (binder.op->getNumOperands() == 1) {
|
|
Torch::ValueTensorType resultType;
|
|
Value x;
|
|
if (binder.tensorOperand(x) || binder.tensorResultType(resultType))
|
|
return failure();
|
|
rewriter.replaceOp(binder.op, x);
|
|
return success();
|
|
}
|
|
Torch::ValueTensorType resultType;
|
|
SmallVector<Value> valList;
|
|
int64_t numOperands = binder.op->getNumOperands();
|
|
if (binder.tensorOperands(valList, numOperands) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
Value const1 = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 1));
|
|
// Short circuit to binary add
|
|
if (numOperands == 2) {
|
|
rewriter.replaceOpWithNewOp<Torch::AtenAddTensorOp>(
|
|
binder.op, resultType, valList[0], valList[1], const1);
|
|
return success();
|
|
}
|
|
// When binder.op->getNumOperands() > 2
|
|
Value curr = rewriter.create<Torch::AtenAddTensorOp>(
|
|
binder.getLoc(), resultType, valList[0], valList[1], const1);
|
|
for (int i = 2; i < numOperands; i++) {
|
|
if (i == numOperands - 1) {
|
|
curr = rewriter.create<Torch::AtenAddTensorOp>(
|
|
binder.getLoc(), resultType, curr, valList[i], const1);
|
|
} else {
|
|
SmallVector<int64_t> resultBroadcastShapeInt;
|
|
SmallVector<Value> resultBroadcastShapeValue;
|
|
Torch::computeBroadcastShape(rewriter, binder.getLoc(), curr,
|
|
valList[i], resultBroadcastShapeInt,
|
|
resultBroadcastShapeValue);
|
|
auto baseType = Torch::ValueTensorType::get(
|
|
binder.op->getContext(), resultBroadcastShapeInt,
|
|
resultType.getOptionalDtype());
|
|
curr = rewriter.create<Torch::AtenAddTensorOp>(
|
|
binder.getLoc(), baseType, curr, valList[i], const1);
|
|
}
|
|
}
|
|
rewriter.replaceOp(binder.op, curr);
|
|
return success();
|
|
});
|
|
patterns.onOp("Where", 1,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
SmallVector<Value> valList;
|
|
int64_t numOperands = binder.op->getNumOperands();
|
|
if (binder.tensorOperands(valList, numOperands) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
Value condition = valList[0];
|
|
Value x = valList[1];
|
|
Value y = valList[2];
|
|
rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
|
|
binder.op, resultType, condition, x, y);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Xor", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value x;
|
|
Value y;
|
|
if (binder.tensorOperands(x, y) || binder.tensorResultType(resultType))
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<Torch::AtenLogicalXorOp>(binder.op,
|
|
resultType, x, y);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Squeeze", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value axes;
|
|
if (binder.tensorOperands(data, axes) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
|
|
auto sizes =
|
|
dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
|
|
if (sizes.size() == 0) {
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSqueezeOp>(binder.op,
|
|
resultType, data);
|
|
return success();
|
|
}
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
Value adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
adjustmentInt));
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
rewriter.replaceOpWithNewOp<Torch::PrimsSqueezeOp>(
|
|
binder.op, resultType, data, dimValueList);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Unsqueeze", 13,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
// Unlike squeeze where we are able to lower to Torch::PrimsSqueezeOp,
|
|
// pytorch does not support torch.unsqueeze to insert multiple new dims.
|
|
// discussion can be found here:
|
|
// https://github.com/pytorch/pytorch/issues/9410
|
|
// So, for now, we unroll into multiple unsqueezes.
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value axes;
|
|
if (binder.tensorOperands(data, axes) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
|
|
auto sizes =
|
|
dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
|
|
if (sizes.size() == 0) {
|
|
rewriter.replaceOp(binder.op, data);
|
|
return success();
|
|
}
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
Value adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
adjustmentInt));
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
Value cstFalse =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
|
|
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
Value updatedAxes = rewriter.create<Torch::AtenTensorOp>(
|
|
binder.getLoc(),
|
|
axesType.getWithSizesAndDtype(sizes, axesType.getOptionalDtype()),
|
|
dimValueList, /*dtype=*/noneVal, /*device=*/noneVal, cstFalse);
|
|
// Sort the list of dims, so we don't run into this situation:
|
|
// data.sizes = [2, 3, 4]
|
|
// dims = [4, 0]
|
|
// index 4 will be invalid to add a singleton dimension because
|
|
// data.sizes.size == 3 We have to work with sorted dims to avoid this
|
|
// situation.
|
|
auto sortIndicesType = axesType.getWithSizesAndDtype(
|
|
axesType.getOptionalSizes(),
|
|
IntegerType::get(binder.op->getContext(), 64, IntegerType::Signed));
|
|
auto sortOpResult = rewriter.create<Torch::AtenSortOp>(
|
|
binder.getLoc(), axes.getType(), sortIndicesType, updatedAxes, zero,
|
|
cstFalse);
|
|
Value result;
|
|
auto baseType = Torch::ValueTensorType::getWithLeastStaticInformation(
|
|
binder.op->getContext());
|
|
// Go through the updated, sorted axes. Do unsqueeze for each dim.
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, sortOpResult->getResult(0),
|
|
zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
if (sizes[0] == 1) {
|
|
result = rewriter.create<Torch::AtenUnsqueezeOp>(
|
|
binder.getLoc(), resultType, data, dim);
|
|
} else if (i == 0) {
|
|
result = rewriter.create<Torch::AtenUnsqueezeOp>(
|
|
binder.getLoc(), baseType, data, dim);
|
|
} else if (i == sizes[0] - 1) {
|
|
result = rewriter.create<Torch::AtenUnsqueezeOp>(
|
|
binder.getLoc(), resultType, result, dim);
|
|
} else {
|
|
result = rewriter.create<Torch::AtenUnsqueezeOp>(
|
|
binder.getLoc(), baseType, result, dim);
|
|
}
|
|
}
|
|
rewriter.replaceOp(binder.op, result);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Softmax", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value input;
|
|
int64_t axis;
|
|
if (binder.tensorOperand(input) ||
|
|
binder.s64IntegerAttr(axis, "axis", -1) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
// ONNX allows negative axis.
|
|
if (axis < 0)
|
|
axis +=
|
|
cast<Torch::ValueTensorType>(input.getType()).getSizes().size();
|
|
|
|
Value constAxis = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), axis));
|
|
|
|
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSoftmaxIntOp>(
|
|
binder.op, resultType, input, constAxis, /*dtype=*/noneVal);
|
|
return success();
|
|
});
|
|
|
|
patterns.onOp(
|
|
"Selu", 6, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
float alpha, gamma;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.f32FloatAttr(alpha, "alpha") ||
|
|
binder.f32FloatAttr(gamma, "gamma") ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
Value vAlpha = rewriter.create<Torch::ConstantFloatOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
|
|
rewriter.getFloatAttr(rewriter.getF64Type(), alpha));
|
|
|
|
Value vScale = rewriter.create<Torch::ConstantFloatOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
|
|
rewriter.getFloatAttr(rewriter.getF64Type(), gamma));
|
|
|
|
Value vInputScale = rewriter.create<Torch::ConstantFloatOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
|
|
rewriter.getFloatAttr(rewriter.getF64Type(), 1.0));
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenEluOp>(
|
|
binder.op, resultType, operand, vAlpha, vScale, vInputScale);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"ReduceMax", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
SmallVector<Value, 1> operands;
|
|
int64_t keepDims, noop_with_empty_axes;
|
|
|
|
if (binder.tensorOperandsList(operands) ||
|
|
binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
|
|
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
|
|
0))
|
|
return failure();
|
|
Value data = operands[0];
|
|
|
|
if (operands.size() == 1) {
|
|
if (noop_with_empty_axes == 0) {
|
|
MLIRContext *context = binder.op->getContext();
|
|
int rank =
|
|
data.getType().cast<Torch::ValueTensorType>().getSizes().size();
|
|
SmallVector<Value, 1> dims;
|
|
for (int i = 0; i < rank; i++) {
|
|
dims.push_back(rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getI64IntegerAttr(i)));
|
|
}
|
|
Value dimsList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(context)), dims);
|
|
Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
|
|
Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
|
|
binder.getLoc(), keepDimsConstInt);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenAmaxOp>(
|
|
binder.op, resultType, data, /*dim=*/dimsList,
|
|
/*keepdim=*/keepDimsBool);
|
|
} else {
|
|
rewriter.replaceOp(binder.op, data);
|
|
}
|
|
return success();
|
|
}
|
|
|
|
Value axes = operands[1];
|
|
|
|
SmallVector<Value> dimList;
|
|
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
|
|
auto sizes =
|
|
dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
|
|
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
Value adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
adjustmentInt));
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
|
|
Value keepDimBool;
|
|
if (keepDims == 1) {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
|
|
} else {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
|
|
}
|
|
rewriter.replaceOpWithNewOp<Torch::AtenAmaxOp>(
|
|
binder.op, resultType, data, dimValueList, keepDimBool);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"ReduceSum", 13,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value axes;
|
|
int64_t keepDims;
|
|
int64_t noop_with_empty_axes;
|
|
if (binder.tensorOperands(data, axes) ||
|
|
binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
|
|
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
|
|
0))
|
|
return failure();
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
|
|
auto sizes =
|
|
dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
|
|
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
// Deal with case when axes is empty
|
|
if (sizes.size() == 1 && sizes[0] == 0) {
|
|
if (noop_with_empty_axes == 0) {
|
|
Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
|
|
Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
|
|
binder.getLoc(), keepDimsConstInt);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
|
|
binder.op, resultType, data, /*dim=*/noneVal,
|
|
/*keepdim=*/keepDimsBool, /*dtype=*/noneVal);
|
|
} else {
|
|
rewriter.replaceOp(binder.op, data);
|
|
}
|
|
return success();
|
|
}
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
Value adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
adjustmentInt));
|
|
// convert axes (tensor) into torch int list while dealing with neg axis
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
Value keepDimBool;
|
|
if (keepDims == 1) {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
|
|
} else {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
|
|
}
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
|
|
binder.op, resultType, data, dimValueList, keepDimBool,
|
|
/*dtype=*/noneVal);
|
|
return success();
|
|
});
|
|
// onnx.ReduceMean with axes provided as argument introduced in opset 18
|
|
patterns.onOp(
|
|
"ReduceMean", 18,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value axes;
|
|
int64_t keepDims;
|
|
int64_t noop_with_empty_axes;
|
|
if (binder.tensorOperands(data, axes) ||
|
|
binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
|
|
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
|
|
0))
|
|
return failure();
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
|
|
auto sizes =
|
|
dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
|
|
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
// deal with case when axes is empty
|
|
if (sizes.size() == 1 && sizes[0] == 0) {
|
|
if (noop_with_empty_axes == 0) {
|
|
Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
|
|
Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
|
|
binder.getLoc(), keepDimsConstInt);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
|
|
binder.op, resultType, data, /*dim=*/noneVal, keepDimsBool,
|
|
/*dtype=*/noneVal);
|
|
} else {
|
|
rewriter.replaceOp(binder.op, data);
|
|
}
|
|
return success();
|
|
}
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
Value adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
adjustmentInt));
|
|
// convert axes (tensor) into torch int list while dealing with neg axis
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
Value keepDimBool;
|
|
if (keepDims == 1) {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
|
|
} else {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
|
|
}
|
|
rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
|
|
binder.op, resultType, data, dimValueList, keepDimBool,
|
|
/*dtype=*/noneVal);
|
|
return success();
|
|
});
|
|
|
|
// onnx.ReduceMean with axes provided as attribute
|
|
patterns.onOp(
|
|
"ReduceMean", 1,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
llvm::SmallVector<int64_t> axes;
|
|
int64_t keepDims;
|
|
int64_t noop_with_empty_axes;
|
|
if (binder.tensorOperand(data) || binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerArrayAttr(axes, "axes", 0) ||
|
|
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
|
|
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
|
|
0))
|
|
return failure();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
// deal with case when axes is empty
|
|
if (axes.size() == 0) {
|
|
if (noop_with_empty_axes == 0) {
|
|
Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
|
|
Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
|
|
binder.getLoc(), keepDimsConstInt);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
|
|
binder.op, resultType, data, /*dim=*/noneVal, keepDimsBool,
|
|
/*dtype=*/noneVal);
|
|
} else {
|
|
rewriter.replaceOp(binder.op, data);
|
|
}
|
|
return success();
|
|
}
|
|
int64_t adjustmentInt =
|
|
cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
|
|
// convert axes (tensor) into torch int list while dealing with neg axis
|
|
for (uint64_t i = 0; i < axes.size(); i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
int64_t dim = axes[i];
|
|
if (dim < 0) {
|
|
dim += adjustmentInt;
|
|
}
|
|
// deal with neg axis: if (axis < 0) axis += rank
|
|
Value finalDim = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
Value keepDimBool;
|
|
if (keepDims == 1) {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
|
|
} else {
|
|
keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
|
|
}
|
|
rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
|
|
binder.op, resultType, data, dimValueList, keepDimBool,
|
|
/*dtype=*/noneVal);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"ReduceMin", 13,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
// AtenAminOp allows us to pass a list of dims
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value axes;
|
|
int64_t keepDims;
|
|
int64_t noop_with_empty_axes;
|
|
if (binder.tensorOperandAtIndex(data, 0) ||
|
|
binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
|
|
binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
|
|
0))
|
|
return failure();
|
|
|
|
auto dataTy = cast<Torch::BaseTensorType>(data.getType());
|
|
Torch::IntType torchIntTy = rewriter.getType<Torch::IntType>();
|
|
|
|
// If any of the input dims are 0 we set to the upper limit:
|
|
if (llvm::any_of(dataTy.getSizes(), [](int64_t d) { return d == 0; }) &&
|
|
(llvm::any_of(dataTy.getSizes(),
|
|
[](int64_t d) { return d == Torch::kUnknownSize; }) ||
|
|
keepDims)) {
|
|
auto dty = dataTy.getDtype();
|
|
Value scalar;
|
|
if (FloatType fpTy = dyn_cast<FloatType>(dty)) {
|
|
auto inf = APFloat::getInf(fpTy.getFloatSemantics());
|
|
scalar = rewriter.create<Torch::ConstantFloatOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
|
|
rewriter.getFloatAttr(rewriter.getF64Type(),
|
|
inf.convertToDouble()));
|
|
}
|
|
|
|
if (IntegerType intTy = dyn_cast<IntegerType>(dty)) {
|
|
auto mx =
|
|
intTy.isSigned()
|
|
? APInt::getSignedMaxValue(intTy.getIntOrFloatBitWidth())
|
|
: APInt::getMaxValue(intTy.getIntOrFloatBitWidth());
|
|
scalar = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), torchIntTy,
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
mx.getSExtValue()));
|
|
}
|
|
|
|
llvm::SmallVector<Value> fillDims;
|
|
for (int i = 0, s = resultType.getSizes().size(); i < s; ++i) {
|
|
auto staticDim = resultType.getSizes()[i];
|
|
if (staticDim != Torch::kUnknownSize) {
|
|
fillDims.push_back(rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), torchIntTy,
|
|
rewriter.getI64IntegerAttr(staticDim)));
|
|
continue;
|
|
}
|
|
|
|
Value iv = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), torchIntTy, rewriter.getI64IntegerAttr(i));
|
|
fillDims.push_back(rewriter.create<Torch::AtenSizeIntOp>(
|
|
binder.getLoc(), torchIntTy, data, iv));
|
|
}
|
|
|
|
Value none = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
|
|
Value fillDimsList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(), Torch::ListType::get(torchIntTy), fillDims);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
|
|
binder.op, resultType, fillDimsList, scalar, none, none, none,
|
|
none);
|
|
return success();
|
|
}
|
|
|
|
// Previous version of the operation had the axes as an attribute:
|
|
SmallVector<Value> axesList;
|
|
llvm::SmallVector<int64_t> axesAttr;
|
|
if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
|
|
for (int i = 0, s = axesAttr.size(); i < s; ++i) {
|
|
axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), torchIntTy,
|
|
rewriter.getI64IntegerAttr(axesAttr[i])));
|
|
}
|
|
}
|
|
|
|
// Extract the axes values from the axes operand:
|
|
if (!binder.tensorOperandAtIndex(axes, 1)) {
|
|
Torch::BaseTensorType axesType =
|
|
axes.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<int64_t> selectSizes{1};
|
|
Type selectResultType = axesType.getWithSizesAndDtype(
|
|
selectSizes, axesType.getOptionalDtype());
|
|
auto sizes = axesType.getSizes();
|
|
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
|
|
// Extract the value of each axes:
|
|
for (int i = 0; i < sizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, axes, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
axesList.push_back(dim);
|
|
}
|
|
}
|
|
|
|
// Handle the noop case:
|
|
if (axesList.empty() && noop_with_empty_axes) {
|
|
rewriter.replaceOp(binder.op, data);
|
|
return success();
|
|
}
|
|
|
|
// Deal with case when no axes arg is passed but not a noop:
|
|
if (axesList.empty()) {
|
|
int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
|
|
.getSizes()
|
|
.size();
|
|
for (int i = 0; i < numDims; i++) {
|
|
Value curr = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
axesList.push_back(curr);
|
|
}
|
|
}
|
|
|
|
// Handle negative axis:
|
|
Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
|
|
torchIntTy, data);
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getI64IntegerAttr(0));
|
|
for (Value &axes : axesList) {
|
|
Value isNegative =
|
|
rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
|
|
isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
|
|
isNegative);
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isNegative, rankVal);
|
|
axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
|
|
finalOffset);
|
|
}
|
|
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
|
|
Value keepDimBool =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
|
|
binder.op, resultType, data, dimValueList, keepDimBool);
|
|
return success();
|
|
});
|
|
|
|
patterns.onOp("Shape", 9,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<Torch::Aten_ShapeAsTensorOp>(
|
|
binder.op, resultType, operand);
|
|
return success();
|
|
});
|
|
|
|
patterns.onOp("Sinh", 9,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSinhOp>(
|
|
binder.op, resultType, operand);
|
|
return success();
|
|
});
|
|
|
|
// split with fixed-size parts
|
|
// Arguments:
|
|
// - input: the tensor to split
|
|
// Attributes:
|
|
// - axis: the axis along which to split the input
|
|
// - num_outputs: the number of outputs to produce
|
|
// Outputs:
|
|
// - outputs: the produced outputs. Variadic with num_outputs elements.
|
|
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
|
|
// tensors
|
|
// so we need to unpack the list
|
|
patterns.onOp(
|
|
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Value self;
|
|
int64_t axis;
|
|
int64_t num_outputs;
|
|
if (binder.tensorOperand(self))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Not converting to AtenSplitTensorOp due to input "
|
|
"tensor mismatch");
|
|
if (binder.s64IntegerAttr(axis, "axis", 0))
|
|
return rewriter.notifyMatchFailure(binder.op,
|
|
"Failed to get axis attribute");
|
|
if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Failed to get num_outputs attribute");
|
|
|
|
auto result0Ty =
|
|
binder.op->getResult(0).getType().cast<Torch::ValueTensorType>();
|
|
auto selfTy = self.getType().cast<Torch::ValueTensorType>();
|
|
|
|
int64_t dim = axis;
|
|
if (dim < 0)
|
|
dim += selfTy.getSizes().size();
|
|
|
|
// set intermediate shape to the shape of the first result
|
|
// if the results are of different shapes
|
|
// set the splitted axis to variable shape
|
|
llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
|
|
for (auto result : binder.op->getResultTypes()) {
|
|
int64_t d = result.cast<Torch::ValueTensorType>().getSizes()[dim];
|
|
intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
|
|
}
|
|
|
|
Value dimValue = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));
|
|
|
|
Value splitSize = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), num_outputs));
|
|
|
|
// TODO: Attempting to use the shape expected by the ONNX mlir as ground
|
|
// truth. For now just use dynamic shapes.
|
|
auto resultOuterType =
|
|
Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
|
|
/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
|
|
result0Ty.getOptionalDtype()));
|
|
Torch::AtenSplitTensorOp new_op =
|
|
rewriter.create<Torch::AtenSplitTensorOp>(
|
|
binder.getLoc(), resultOuterType, self, splitSize, dimValue);
|
|
|
|
// the onnx op is variadic with multiple results, but AtenSplitWithSizes
|
|
// outputs a list so we need to unpack the list
|
|
rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
|
|
binder.op, binder.op->getResults().getType(), new_op.getResult());
|
|
|
|
return success();
|
|
});
|
|
|
|
// split with variable parts
|
|
// Arguments:
|
|
// - input: the tensor to split
|
|
// - split: the sizes of the splits to be produced
|
|
// Attributes:
|
|
// - axis: the axis along which to split the input
|
|
// - num_outputs: the number of outputs to produce
|
|
// Outputs:
|
|
// - outputs: the produced outputs. Variadic with num_outputs elements.
|
|
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
|
|
// tensors
|
|
// so we need to unpack the list
|
|
patterns.onOp(
|
|
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Value self;
|
|
Value split;
|
|
int64_t axis;
|
|
int64_t num_outputs;
|
|
if (binder.tensorOperandAtIndex(self, 0) ||
|
|
binder.tensorOperandAtIndex(split, 1))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Not converting to AtenSplitWithSizesOp due to input "
|
|
"tensor mismatch");
|
|
if (binder.s64IntegerAttr(axis, "axis", 0))
|
|
return rewriter.notifyMatchFailure(binder.op,
|
|
"Failed to get axis attribute");
|
|
if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Failed to get num_outputs attribute");
|
|
|
|
auto result0Ty =
|
|
binder.op->getResult(0).getType().cast<Torch::ValueTensorType>();
|
|
auto selfTy =
|
|
cast<Torch::ValueTensorType>(binder.op->getOperand(0).getType());
|
|
|
|
int64_t dim = axis;
|
|
if (dim < 0)
|
|
dim += selfTy.getSizes().size();
|
|
|
|
llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
|
|
for (auto result : binder.op->getResultTypes()) {
|
|
int64_t d = result.cast<Torch::ValueTensorType>().getSizes()[dim];
|
|
intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
|
|
}
|
|
|
|
Torch::PrimTolistOp splitToList = rewriter.create<Torch::PrimTolistOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(rewriter.getType<Torch::IntType>()), split);
|
|
|
|
Value dimValue = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));
|
|
|
|
// TODO: Attempting to use the shape expected by the ONNX mlir as ground
|
|
// truth. For now just use dynamic shapes.
|
|
auto resultOuterType =
|
|
Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
|
|
/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
|
|
result0Ty.getOptionalDtype()));
|
|
Torch::AtenSplitWithSizesOp new_op =
|
|
rewriter.create<Torch::AtenSplitWithSizesOp>(
|
|
binder.getLoc(), resultOuterType, self,
|
|
splitToList.getResult(0), dimValue);
|
|
|
|
// the onnx op is variadic with multiple results, but AtenSplitWithSizes
|
|
// outputs a list so we need to unpack the list
|
|
rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
|
|
binder.op, binder.op->getResults().getType(), new_op.getResult());
|
|
|
|
return success();
|
|
});
|
|
|
|
patterns.onOp("Tan", 7,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<Torch::AtenTanOp>(
|
|
binder.op, resultType, operand);
|
|
return success();
|
|
});
|
|
|
|
patterns.onOp(
|
|
"Transpose", 13,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
auto loc = binder.getLoc();
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
auto operandType = operand.getType().cast<Torch::ValueTensorType>();
|
|
TensorType tensorType = operandType.toBuiltinTensor();
|
|
if (!tensorType || !tensorType.hasRank())
|
|
return failure();
|
|
|
|
// Default permutation is to reverse orders:
|
|
int64_t rank = tensorType.getRank();
|
|
llvm::SmallVector<int64_t> reverse(rank);
|
|
for (int64_t i = 0; i < rank; ++i) {
|
|
reverse[i] = rank - i - 1;
|
|
}
|
|
|
|
llvm::SmallVector<int64_t> permutations;
|
|
if (failed(binder.s64IntegerArrayAttr(permutations, "perm", reverse)))
|
|
return rewriter.notifyMatchFailure(binder.op,
|
|
"Failed to obtain permutations");
|
|
|
|
if (static_cast<int64_t>(permutations.size()) != rank)
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Permutation length does not match operand rank");
|
|
|
|
llvm::SmallVector<int64_t> shape(tensorType.getShape());
|
|
llvm::SmallVector<int64_t> current(rank);
|
|
for (int64_t i = 0; i < rank; ++i) {
|
|
current[i] = i;
|
|
}
|
|
|
|
for (auto &dim : permutations)
|
|
dim = dim < 0 ? dim + rank : dim;
|
|
|
|
// We need to override to the destination if known:
|
|
if (resultType.hasSizes()) {
|
|
for (int i = 0; i < rank; ++i) {
|
|
shape[permutations[i]] = resultType.getSizes()[i];
|
|
}
|
|
}
|
|
|
|
// Convert dynamic shape dimension:
|
|
for (unsigned i = 0; i < shape.size(); i++) {
|
|
if (shape[i] == ShapedType::kDynamic)
|
|
shape[i] = Torch::kUnknownSize;
|
|
}
|
|
|
|
for (int64_t i = 0; i < rank; ++i) {
|
|
if (current[i] == permutations[i])
|
|
continue;
|
|
|
|
int64_t target = i + 1;
|
|
for (; target < rank; ++target) {
|
|
if (current[target] == permutations[i])
|
|
break;
|
|
}
|
|
|
|
std::swap(shape[i], shape[target]);
|
|
std::swap(current[i], current[target]);
|
|
|
|
Value dim0 = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
|
|
Value dim1 = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), target));
|
|
|
|
operand = rewriter.create<Torch::AtenTransposeIntOp>(
|
|
loc,
|
|
Torch::ValueTensorType::get(tensorType.getContext(), shape,
|
|
operandType.getOptionalDtype()),
|
|
operand, dim0, dim1);
|
|
}
|
|
|
|
rewriter.replaceOp(binder.op, operand);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Slice", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultTorchType;
|
|
Value operand, starts, ends;
|
|
// Handle if axes are not provided
|
|
|
|
if (binder.tensorOperandAtIndex(operand, 0) ||
|
|
binder.tensorOperandAtIndex(starts, 1) ||
|
|
binder.tensorOperandAtIndex(ends, 2) ||
|
|
binder.tensorResultType(resultTorchType)) {
|
|
return failure();
|
|
}
|
|
|
|
auto context = rewriter.getContext();
|
|
auto operandTorchTy = operand.getType().cast<Torch::ValueTensorType>();
|
|
auto operandTy =
|
|
operandTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
|
|
|
|
if (!operandTy)
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op,
|
|
"Expected tensor operator argument to be a ranked tensor type");
|
|
|
|
auto startsTorchTy = starts.getType().cast<Torch::ValueTensorType>();
|
|
auto startsTy =
|
|
startsTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
|
|
int startSize = startsTy.getDimSize(0);
|
|
|
|
auto endsTorchTy = ends.getType().cast<Torch::ValueTensorType>();
|
|
auto endsTy =
|
|
endsTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
|
|
int endSize = endsTy.getDimSize(0);
|
|
auto resultTy =
|
|
resultTorchType.toBuiltinTensor().dyn_cast<RankedTensorType>();
|
|
if (!resultTy)
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Expected result type to be a ranked tensor type");
|
|
|
|
Location loc = binder.getLoc();
|
|
|
|
// Binding `axes` from its arguments or through a default value
|
|
Value axes;
|
|
if (binder.getNumOperands() >= 4) {
|
|
if (binder.tensorOperandAtIndex(axes, 3)) {
|
|
return failure();
|
|
}
|
|
}
|
|
|
|
// Binding `steps` from its arguments or through a default value
|
|
Value steps;
|
|
if (binder.getNumOperands() >= 5) {
|
|
if (binder.tensorOperandAtIndex(steps, 4)) {
|
|
return failure();
|
|
}
|
|
} else {
|
|
// The default `steps` value is a 1d tensor filled with ones with a
|
|
// size equal to the size of `starts` and `ends`.
|
|
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
|
|
Value sizeStepInput = rewriter.create<Torch::ConstantIntOp>(
|
|
loc, rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), startSize));
|
|
Value sizeStepsInput = rewriter.create<Torch::PrimListConstructOp>(
|
|
loc,
|
|
Torch::ListType::get(
|
|
Torch::IntType::get(binder.op->getContext())),
|
|
sizeStepInput);
|
|
steps = rewriter.create<Torch::AtenOnesOp>(
|
|
loc, startsTorchTy, sizeStepsInput, none, none, none, none);
|
|
}
|
|
|
|
if (!(endsTy.getRank() == 1 && startsTy.getRank() == 1 &&
|
|
startSize == endSize))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Expected the rank of starts and ends tensors to be 1 "
|
|
"and their dimensions to match");
|
|
|
|
if (axes) {
|
|
auto axesTorchTy = axes.getType().cast<Torch::ValueTensorType>();
|
|
auto axesTy =
|
|
axesTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
|
|
int64_t numAxes = axesTy.getDimSize(0);
|
|
|
|
if (!(axesTy && numAxes == endSize))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Axes should be the same size of starts and ends");
|
|
}
|
|
|
|
auto stepsTy = steps.getType()
|
|
.cast<Torch::ValueTensorType>()
|
|
.toBuiltinTensor()
|
|
.dyn_cast<RankedTensorType>();
|
|
|
|
if (!(stepsTy && stepsTy.getDimSize(0) == endsTy.getDimSize(0)))
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Steps should be the same size of starts and ends");
|
|
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
loc, rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
|
|
auto select = [&](Value v, Value k) -> Value {
|
|
auto ty = v.getType().cast<Torch::ValueTensorType>();
|
|
auto sel = rewriter.create<Torch::AtenIndexSelectOp>(
|
|
loc,
|
|
Torch::ValueTensorType::get(ty.getContext(), ArrayRef<int64_t>{1},
|
|
ty.getOptionalDtype()),
|
|
v, zero, k);
|
|
Value item = rewriter.create<Torch::AtenItemOp>(
|
|
loc, rewriter.getType<Torch::IntType>(), sel);
|
|
return item;
|
|
};
|
|
|
|
llvm::SmallVector<int64_t> intermediateShape(operandTy.getShape());
|
|
for (int i = 0, s = operandTy.getRank(); i < s; ++i) {
|
|
if (operandTy.getDimSize(i) != resultTy.getDimSize(i))
|
|
intermediateShape[i] = -1;
|
|
if (intermediateShape[i] == ShapedType::kDynamic)
|
|
intermediateShape[i] = Torch::kUnknownSize;
|
|
}
|
|
auto intermediateType = Torch::ValueTensorType::get(
|
|
context, intermediateShape, resultTorchType.getOptionalDtype());
|
|
for (int i = 0; i < endSize; ++i) {
|
|
|
|
Value k = rewriter.create<Torch::ConstantIntOp>(
|
|
loc, rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value kTensor = rewriter.create<Torch::PrimNumToTensorScalarOp>(
|
|
loc,
|
|
Torch::ValueTensorType::get(
|
|
context, ArrayRef<int64_t>{1},
|
|
rewriter.getIntegerType(64, /*signed*/ 1)),
|
|
k);
|
|
|
|
Value start = select(starts, kTensor);
|
|
Value end = select(ends, kTensor);
|
|
Value axis = axes ? select(axes, kTensor) : k;
|
|
Value step = select(steps, kTensor);
|
|
|
|
auto sliceType = intermediateType;
|
|
sliceType = i == (endSize - 1) ? resultTorchType : sliceType;
|
|
operand = rewriter.create<Torch::AtenSliceTensorOp>(
|
|
loc, sliceType, operand, axis, start, end, step);
|
|
}
|
|
|
|
rewriter.replaceOp(binder.op, operand);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Reshape", 5, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value data;
|
|
Value shape;
|
|
int64_t allowzero;
|
|
if (binder.tensorOperands(data, shape) ||
|
|
binder.tensorResultType(resultType) ||
|
|
binder.s64IntegerAttr(allowzero, "allowzero", 0))
|
|
return failure();
|
|
|
|
// If the result shape is static then we can create a result shape list
|
|
// directly using the result shape values (integers).
|
|
if (resultType.hasSizes()) {
|
|
bool hasStaticShape = resultType.areAllSizesKnown();
|
|
ArrayRef<int64_t> resultShapeInt = resultType.getSizes();
|
|
if (hasStaticShape) {
|
|
SmallVector<Value> resultShape;
|
|
for (int64_t dim : resultShapeInt) {
|
|
resultShape.push_back(rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getI64IntegerAttr(dim)));
|
|
}
|
|
Value resultShapeList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(
|
|
Torch::IntType::get(binder.op->getContext())),
|
|
resultShape);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
|
|
binder.op, resultType, data, resultShapeList);
|
|
return success();
|
|
}
|
|
}
|
|
|
|
Torch::BaseTensorType shapeType =
|
|
shape.getType().cast<Torch::BaseTensorType>();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Type selectResultType = shapeType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
|
|
auto shapeSizes =
|
|
dyn_cast<Torch::ValueTensorType>(shape.getType()).getSizes();
|
|
auto dataSizes =
|
|
dyn_cast<Torch::ValueTensorType>(data.getType()).getSizes();
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
if (allowzero == 0) {
|
|
// convert shape (tensor) into torch int list while dealing with zero
|
|
// vals
|
|
for (int i = 0; i < shapeSizes[0]; i++) {
|
|
// Go through the shape list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, shape, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
// deal with zero axis values: replace with original dim value in
|
|
// input
|
|
Value isZero =
|
|
rewriter.create<Torch::AtenEqIntOp>(binder.getLoc(), dim, zero);
|
|
isZero =
|
|
rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(), isZero);
|
|
Value adjustment;
|
|
int64_t inputDimsSize = dataSizes.size();
|
|
if (i < inputDimsSize) {
|
|
adjustment = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64),
|
|
dataSizes[i]));
|
|
}
|
|
// Will never have a 0 in the shape tensor input at an index out of
|
|
// bounds of original input dims Therefore, no need to adjust
|
|
else {
|
|
adjustment = zero;
|
|
}
|
|
Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
|
|
binder.getLoc(), isZero, adjustment);
|
|
Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
|
|
binder.getLoc(), dim, finalOffset);
|
|
dimList.push_back(finalDim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(
|
|
Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(
|
|
binder.op, resultType, data, dimValueList);
|
|
return success();
|
|
}
|
|
// convert axes (tensor) into torch int list
|
|
for (int i = 0; i < shapeSizes[0]; i++) {
|
|
// Go through the axes list and get each dim in the list
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, shape, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
dimList.push_back(dim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenReshapeOp>(binder.op, resultType,
|
|
data, dimValueList);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Range", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
// ONNX.Range(start, limit, delta) -- limit is exclusive
|
|
|
|
Torch::ValueTensorType resultType;
|
|
Value start, limit, delta;
|
|
auto loc = binder.getLoc();
|
|
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
|
|
if (binder.tensorOperandAtIndex(start, 0) ||
|
|
binder.tensorOperandAtIndex(limit, 1) ||
|
|
binder.tensorOperandAtIndex(delta, 2) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
// Convert a 0-dimensional/Scalar Tensor ([]) to Scalar Torch Numeric
|
|
// Value torch.tensor(1.1) equivalent in ONNX to 1.1 as an example
|
|
// type of start, limit, delta can be one of: double, float, int16,
|
|
// int32, int64 Assuming start, limit and delta to be same type (could
|
|
// they be different?)
|
|
Torch::BaseTensorType startTensorType =
|
|
start.getType().cast<Torch::BaseTensorType>();
|
|
bool isFloatDType = startTensorType.getDtype().isF64() ||
|
|
startTensorType.getDtype().isF32();
|
|
bool isIntDType = startTensorType.getDtype().isInteger(16) ||
|
|
startTensorType.getDtype().isInteger(32) ||
|
|
startTensorType.getDtype().isInteger(64);
|
|
if (!isFloatDType && !isIntDType) {
|
|
return rewriter.notifyMatchFailure(
|
|
binder.op, "Expected the start, limit, delta to be one of "
|
|
"double, float, int16, int32, int64");
|
|
}
|
|
Value scalarStart, scalarLimit, scalarDelta;
|
|
if (isFloatDType) {
|
|
scalarStart = getItemOp<Torch::FloatType>(binder, rewriter, start);
|
|
scalarLimit = getItemOp<Torch::FloatType>(binder, rewriter, limit);
|
|
scalarDelta = getItemOp<Torch::FloatType>(binder, rewriter, delta);
|
|
} else {
|
|
scalarStart = getItemOp<Torch::IntType>(binder, rewriter, start);
|
|
scalarLimit = getItemOp<Torch::IntType>(binder, rewriter, limit);
|
|
scalarDelta = getItemOp<Torch::IntType>(binder, rewriter, delta);
|
|
}
|
|
rewriter.replaceOpWithNewOp<Torch::AtenArangeStartStepOp>(
|
|
binder.op, resultType, scalarStart, scalarLimit, scalarDelta, none,
|
|
none, none, none);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Tile", 6, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
Value repeatDims;
|
|
if (binder.tensorOperands(operand, repeatDims) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
// convert repeatDims tensor to list of ints
|
|
auto repeatDimsSizes =
|
|
dyn_cast<Torch::ValueTensorType>(repeatDims.getType()).getSizes();
|
|
SmallVector<Value> dimList;
|
|
SmallVector<int64_t> selectSizes;
|
|
selectSizes.push_back(1);
|
|
Torch::BaseTensorType shapeType =
|
|
repeatDims.getType().cast<Torch::BaseTensorType>();
|
|
Type selectResultType = shapeType.getWithSizesAndDtype(
|
|
llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
|
|
Value zero = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
|
|
for (int i = 0; i < repeatDimsSizes[0]; i++) {
|
|
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(),
|
|
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
|
|
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
|
|
binder.getLoc(), selectResultType, repeatDims, zero, selectIndex);
|
|
Value dim = rewriter.create<Torch::AtenItemOp>(
|
|
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
|
|
dimList.push_back(dim);
|
|
}
|
|
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
|
|
binder.getLoc(),
|
|
Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
|
|
dimList);
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenTileOp>(binder.op, resultType,
|
|
operand, dimValueList);
|
|
return success();
|
|
});
|
|
patterns.onOp(
|
|
"Topk", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType Values_type, Indices_type;
|
|
Value X, K;
|
|
int64_t axis;
|
|
bool largest, sorted;
|
|
if (binder.tensorOperandAtIndex(X, 0) ||
|
|
binder.tensorOperandAtIndex(K, 1) ||
|
|
binder.s64IntegerAttr(axis, "axis", -1) ||
|
|
binder.s64BoolAttr(largest, "largest", true) ||
|
|
binder.s64BoolAttr(sorted, "sorted", true) ||
|
|
binder.tensorResultTypeAtIndex(Values_type, 0) ||
|
|
binder.tensorResultTypeAtIndex(Indices_type, 1))
|
|
return failure();
|
|
std::optional<unsigned> maybeRank = Torch::getTensorRank(X);
|
|
if (!maybeRank)
|
|
return rewriter.notifyMatchFailure(binder.op,
|
|
"Unimplemented: unranked tensor");
|
|
unsigned rank = *maybeRank;
|
|
axis = Torch::toPositiveDim(axis, rank);
|
|
Value cstAxis = rewriter.create<Torch::ConstantIntOp>(
|
|
binder.getLoc(), rewriter.getI64IntegerAttr(axis));
|
|
Value cstLargest =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), largest);
|
|
Value cstSorted =
|
|
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), sorted);
|
|
rewriter.replaceOpWithNewOp<Torch::AtenTopkOp>(
|
|
binder.op, Values_type, Indices_type, X, K, cstAxis, cstLargest,
|
|
cstSorted);
|
|
return success();
|
|
});
|
|
patterns.onOp("Sign", 9,
|
|
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
|
|
Torch::ValueTensorType resultType;
|
|
Value operand;
|
|
if (binder.tensorOperand(operand) ||
|
|
binder.tensorResultType(resultType))
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<Torch::AtenSignOp>(
|
|
binder.op, resultType, operand);
|
|
return success();
|
|
});
|
|
}
|