add PIM accelerator
This commit is contained in:
NiccoloN
499
src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialCommon.cpp
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499
src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialCommon.cpp
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@@ -0,0 +1,499 @@
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.h"
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#include "mlir/IR/BuiltinAttributes.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Location.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/Value.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "llvm/ADT/SmallVector.h"
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#include "llvm/ADT/Twine.h"
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#include "llvm/Support/Casting.h"
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#include <cassert>
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#include <optional>
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#include <utility>
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#include "ONNXToSpatialCommon.hpp"
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#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
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#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
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#include "src/Dialect/ONNX/ONNXOps.hpp"
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using namespace mlir;
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namespace onnx_mlir {
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SmallVector<Value> sliceTensor(
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const Value& tensorToSlice, size_t axis, int64_t sliceSize, ConversionPatternRewriter& rewriter, Location loc) {
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ArrayRef<long> shape = getTensorShape(tensorToSlice);
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assert("Invalid axis" && axis < shape.size());
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SmallVector<OpFoldResult> strides(shape.size(), rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets(shape.size(), rewriter.getIndexAttr(0));
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SmallVector<OpFoldResult> sizes;
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sizes.reserve(shape.size());
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for (const auto size : shape)
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sizes.push_back(rewriter.getIndexAttr(size));
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sizes[axis] = rewriter.getIndexAttr(sliceSize);
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long length = shape[axis];
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auto [numSlices, lastSliceSize] = ceilIntegerDivideWithRemainder(length, sliceSize);
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SmallVector<Value> slices;
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slices.reserve(numSlices);
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for (int64_t i = 0; i < numSlices; i++) {
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offsets[axis] = rewriter.getIndexAttr(i * sliceSize);
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if (i == numSlices - 1 && lastSliceSize != 0)
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sizes[axis] = rewriter.getIndexAttr(lastSliceSize);
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Value slice = rewriter.create<tensor::ExtractSliceOp>(loc, tensorToSlice, offsets, sizes, strides);
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slices.push_back(slice);
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}
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return slices;
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}
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SmallVector<Value>
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sliceVector(const Value& vectorToSlice, int64_t sliceSize, ConversionPatternRewriter& rewriter, Location loc) {
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ArrayRef<long> shape = getTensorShape(vectorToSlice);
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assert("Not a vector" && isVectorShape(shape));
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size_t axis = shape[0] != 1 ? 0 : 1;
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return sliceTensor(vectorToSlice, axis, sliceSize, rewriter, loc);
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}
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DenseMap<CoreId, SmallVector<Value>>
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sliceVectorPerCrossbarPerCore(const Value& vectorToSlice, ConversionPatternRewriter& rewriter, Location loc) {
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SmallVector<Value> slices = sliceVector(vectorToSlice, crossbarSize, rewriter, loc);
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DenseMap<CoreId, SmallVector<Value>> slicesPerCore;
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for (size_t sliceId = 0; sliceId < slices.size(); sliceId++) {
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size_t coreId = sliceId / crossbarCountInCore;
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slicesPerCore[coreId].push_back(slices[sliceId]);
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}
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return slicesPerCore;
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}
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DenseMap<HSliceId, DenseMap<CoreId, SmallVector<Value>>> tileMatrix(
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Value& matrixToTile, int64_t hSliceSize, int64_t vSliceSize, ConversionPatternRewriter& rewriter, Location& loc) {
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assert("Not a matrix" && isMatrixShape(getTensorShape(matrixToTile)));
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DenseMap<HSliceId, DenseMap<CoreId, SmallVector<Value>>> tiles;
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SmallVector<Value> hSlices = sliceTensor(matrixToTile, 1, hSliceSize, rewriter, loc);
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size_t numHSlices = hSlices.size();
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for (size_t hSliceId = 0; hSliceId < numHSlices; hSliceId++) {
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Value hSlice = hSlices[hSliceId];
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SmallVector<Value> vSlices = sliceTensor(hSlice, 0, vSliceSize, rewriter, loc);
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for (size_t vSliceId = 0; vSliceId < vSlices.size(); vSliceId++) {
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size_t coreId = vSliceId / crossbarCountInCore;
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Value vSlice = vSlices[vSliceId];
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tiles[hSliceId][coreId].push_back(vSlice);
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}
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}
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return tiles;
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}
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tensor::SplatOp
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broadcastToVector(Value scalarToBroadcast, int64_t length, ConversionPatternRewriter& rewriter, Location loc) {
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auto oldType = cast<RankedTensorType>(scalarToBroadcast.getType());
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Type elementType = oldType.getElementType();
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int64_t shape[2] = {1, length};
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Type type = oldType.cloneWith(ArrayRef(shape), elementType);
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auto zero = rewriter.create<arith::ConstantIndexOp>(loc, 0).getResult();
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SmallVector<Value> index(oldType.getRank(), zero);
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auto elementValue = rewriter.create<tensor::ExtractOp>(loc, scalarToBroadcast, index).getResult();
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return rewriter.create<tensor::SplatOp>(loc, type, elementValue);
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}
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Value sumTensors(ArrayRef<Value> tensors, ConversionPatternRewriter& rewriter) {
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if (tensors.size() == 1)
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return tensors[0];
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SmallVector<Value> tensors1 = {tensors.begin(), tensors.end()};
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SmallVector<Value> tensors2;
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tensors2.reserve(tensors.size() / 2);
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auto* currTensors = &tensors1;
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auto* nextTensors = &tensors2;
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while (currTensors->size() > 1) {
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for (size_t i = 0; i < currTensors->size() - 1; i += 2) {
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Value a = (*currTensors)[i];
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Value b = (*currTensors)[i + 1];
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rewriter.setInsertionPointAfterValue(b);
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auto addedValue = rewriter.create<spatial::SpatVAddOp>(a.getLoc(), a.getType(), a, b);
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nextTensors->push_back(addedValue);
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}
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if (currTensors->size() % 2 == 1)
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nextTensors->push_back(currTensors->back());
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std::swap(currTensors, nextTensors);
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nextTensors->clear();
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}
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assert(currTensors->size() == 1 && "Expected a single input at this point.");
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return (*currTensors)[0];
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}
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Value createMapOperation(PatternRewriter& rewriter, MapOperations mapOp, const Value& input) {
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switch (mapOp) {
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case MapOperations::None: assert(false && "Invalid map operation during map operation creation.");
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case MapOperations::ONNXSoftmaxOp: return rewriter.create<ONNXSoftmaxOp>(input.getLoc(), input.getType(), input);
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case MapOperations::ONNXReluOp: return rewriter.create<ONNXReluOp>(input.getLoc(), input.getType(), input);
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case MapOperations::ONNXLeakyReluOp: return rewriter.create<ONNXLeakyReluOp>(input.getLoc(), input.getType(), input);
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case MapOperations::ONNXExpOp: return rewriter.create<ONNXExpOp>(input.getLoc(), input.getType(), input);
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}
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}
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void unpackOptionalPairVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& value1, size_t& value2) {
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if (auto unpackedStrides = valuesArray) {
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value1 = mlir::cast<IntegerAttr>(unpackedStrides->getValue()[0]).getInt();
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value2 = mlir::cast<IntegerAttr>(unpackedStrides->getValue()[1]).getInt();
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}
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else {
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value1 = 1;
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value2 = 1;
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}
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}
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std::optional<llvm::Twine>
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unpackOptionalPadsVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& pad_x, size_t& pad_y) {
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if (valuesArray.has_value()) {
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auto pads = mlir::ArrayAttr(*valuesArray);
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if (pads.size() != 4)
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return "pads must have 4 elements.";
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pad_x = cast<IntegerAttr>(pads[2]).getInt();
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pad_y = cast<IntegerAttr>(pads[3]).getInt();
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}
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else {
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// Default padding is 0 unless specified otherwise.
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// https://onnx.ai/onnx/operators/onnx__Conv.html
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pad_x = pad_y = 0;
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}
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return std::nullopt;
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}
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void tileImageTensorByChannel(Value imageTensor,
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SmallVector<SmallVector<SmallVector<Value>>>& tiles,
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size_t tileSize,
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ConversionPatternRewriter& rewriter) {
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ShapedType imageShape = mlir::cast<ShapedType>(imageTensor.getType());
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size_t input_h = GET_IMAGE_HEIGHT(imageShape);
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size_t input_w = GET_IMAGE_WIDTH(imageShape);
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size_t tileCount = ceilIntegerDivide(GET_IMAGE_CHANNEL(imageShape), tileSize);
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size_t tileRest = GET_IMAGE_CHANNEL(imageShape) % tileSize;
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SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
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SmallVector<OpFoldResult> sizes = {
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rewriter.getIndexAttr(1), rewriter.getIndexAttr(tileSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
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Location loc = imageTensor.getLoc();
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for (size_t i = 0; i < tileCount; i++) {
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if (i == tileCount - 1 && tileRest != 0)
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sizes[1] = rewriter.getIndexAttr(tileRest);
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for (size_t x = 0; x < input_w; x++) {
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for (size_t y = 0; y < input_h; y++) {
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offsets[1] = rewriter.getIndexAttr(i * tileSize);
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offsets[2] = rewriter.getIndexAttr(x);
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offsets[3] = rewriter.getIndexAttr(y);
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tiles[i][x][y] = rewriter.create<tensor::ExtractSliceOp>(loc, imageTensor, offsets, sizes, strides);
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}
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}
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}
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}
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Value createImgConcatOp(SmallVector<SmallVector<SmallVector<Value>>>& outputTiles,
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ConversionPatternRewriter& rewriter,
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Location& loc,
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Type outputType) {
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// Populate the outputTiles for the concat in the given order:
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// 1. Start top left pixel
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// 2. Continue on its right pixel till the end of the row
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// 3. Restart on the next row
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size_t outputTileCount = outputTiles.size();
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size_t output_w = outputTiles[0].size();
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size_t output_h = outputTiles[0][0].size();
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SmallVector<Value> tilesToConcat;
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tilesToConcat.reserve(output_h * output_w * outputTileCount * crossbarSize);
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for (size_t outX = 0; outX < output_h; outX++)
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for (size_t outY = 0; outY < output_w; outY++)
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for (size_t outTile = 0; outTile < outputTileCount; outTile++)
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tilesToConcat.push_back(outputTiles[outTile][outX][outY]);
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return rewriter.create<spatial::SpatImgConcatOp>(loc, outputType, tilesToConcat);
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}
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LogicalResult
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verifyWithinBoundsAndPaddings(size_t input_w, size_t input_h, int inX, int inY, size_t pad_x, size_t pad_y) {
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if (inX < 0) {
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assert((size_t) (-inX) <= pad_x && "verifyWithinBoundsAndPaddings: Negative x value out of padding");
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return failure();
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}
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if (inY < 0) {
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assert((size_t) (-inY) <= pad_y && "verifyWithinBoundsAndPaddings: Negative y value out of padding");
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return failure();
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}
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if ((size_t) inX >= input_w || (size_t) inY >= input_h) {
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assert((size_t) inX < input_w + pad_x && "verifyWithinBoundsAndPaddings: Positive x out of bounds");
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assert((size_t) inY < input_h + pad_y && "verifyWithinBoundsAndPaddings: Positive y out of bounds");
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return failure();
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}
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return success();
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}
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Value createExtractSliceImg(Value valToSlice,
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size_t x,
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size_t y,
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size_t t,
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size_t channelTileCount,
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size_t channelTileRest,
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size_t input_w,
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size_t input_h,
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PatternRewriter& rewriter) {
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SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
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SmallVector<OpFoldResult> sizes = {
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rewriter.getIndexAttr(1), rewriter.getIndexAttr(crossbarSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
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if (t == channelTileCount - 1 && channelTileRest != 0)
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sizes[1] = rewriter.getIndexAttr(channelTileRest);
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offsets[1] = rewriter.getIndexAttr(t * crossbarSize);
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offsets[2] = rewriter.getIndexAttr(x);
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offsets[3] = rewriter.getIndexAttr(y);
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return rewriter.create<tensor::ExtractSliceOp>(valToSlice.getLoc(), valToSlice, offsets, sizes, strides);
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}
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Value indexImgValue(Value v,
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size_t x,
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size_t y,
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size_t t,
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size_t channelTileCount,
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size_t channelTileRest,
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size_t input_w,
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size_t input_h,
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ConversionPatternRewriter& rewriter) {
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auto newV = rewriter.getRemappedValue(v);
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if (newV)
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v = newV;
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if (!v.getDefiningOp())
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return createExtractSliceImg(v, x, y, t, channelTileCount, channelTileRest, input_w, input_h, rewriter);
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if (auto computeOp = v.getDefiningOp<spatial::SpatWeightedCompute>()) {
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// We found the computeOp that produces the tile we want, just return this
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// value.
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// TODO: Should we assert that x,y,t are zero?
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assert(x == 0 && y == 0 && t == 0 && "indexImgValue: WeightedComputeOp tile indeces should be zero");
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return v;
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}
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if (auto receiveOp = v.getDefiningOp<spatial::SpatChannelReceiveOp>()) {
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// This is a receiveOp, just return its value which will be resolved later
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assert(x == 0 && y == 0 && t == 0 && "indexImgValue: receiveOp tile indeces should be zero");
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return v;
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}
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if (auto imgConcatOp = v.getDefiningOp<spatial::SpatImgConcatOp>()) {
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auto imgConcatInput = imgConcatOp.getInputTile(x, y, t);
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// TODO: Is this correct?
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// Above we already index exactly the tile we want, so `x=y=t=0` in
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// recursive call
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return indexImgValue(imgConcatInput, 0, 0, 0, channelTileCount, channelTileRest, input_w, input_h, rewriter);
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}
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if (auto tensorConcatOp = v.getDefiningOp<tensor::ConcatOp>()) {
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// This can be recursive.
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// First, get the input tensors of the tensor.concatOp
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// Then, find the input tensor that contains the tile we want
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// Finally, recursive call asking for the tile
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auto concatAxis = tensorConcatOp.getDim();
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assert(concatAxis != 0 && "Expecting to concat on channel/x/y axis");
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assert(concatAxis == 1 && "TODO: Make sure this works and makes sense for other axis.");
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SmallVector<size_t, 4> indexDims = {1, t * crossbarSize, x, y};
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// Find the input tensor that contains the tile we want
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size_t currentTile = 0;
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for (auto concatInput : tensorConcatOp.getInputs()) {
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auto concatInputShape = cast<ShapedType>(concatInput.getType());
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assert(concatInputShape.getRank() == 4 && "Expecting an image tensor");
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auto concatInputSizeOnAxis = concatInputShape.getDimSize(concatAxis);
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if (currentTile + concatInputSizeOnAxis > indexDims[concatAxis]) {
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// This input tensor contains the tile we want
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indexDims[concatAxis] -= currentTile;
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if (indexDims[1] % crossbarSize != 0) {
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assert(ignoreConcatError
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&& "TODO: Handle non-tile aligned tensor, or set "
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"--ignore-concat-error=true");
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}
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return indexImgValue(concatInput,
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indexDims[2],
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indexDims[3],
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indexDims[1] / crossbarSize,
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channelTileCount,
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channelTileRest,
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input_w,
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input_h,
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rewriter);
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}
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currentTile += concatInputSizeOnAxis;
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}
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assert(false
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&& "Could not find the input tensor that contains the tile "
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"within tensor.ConcatOp");
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}
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v.dump();
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assert(false && "indexImgValue: unsupported operation");
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}
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void resolveInputTensorTilesBlockArg(Value wholeInputTensor,
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SmallVector<SmallVector<SmallVector<Value>>>& inputTiles,
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size_t channelTileCount,
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size_t channelTileRest,
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size_t input_w,
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size_t input_h,
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PatternRewriter& rewriter) {
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SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
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SmallVector<OpFoldResult> sizes = {
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rewriter.getIndexAttr(1), rewriter.getIndexAttr(crossbarSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
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Location loc = wholeInputTensor.getLoc();
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for (size_t t = 0; t < channelTileCount; t++) {
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if (t == channelTileCount - 1 && channelTileRest != 0)
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sizes[1] = rewriter.getIndexAttr(channelTileRest);
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for (size_t x = 0; x < input_w; x++) {
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for (size_t y = 0; y < input_h; y++) {
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offsets[1] = rewriter.getIndexAttr(t * crossbarSize);
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offsets[2] = rewriter.getIndexAttr(x);
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offsets[3] = rewriter.getIndexAttr(y);
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inputTiles[t][x][y] = rewriter.create<tensor::ExtractSliceOp>(loc, wholeInputTensor, offsets, sizes, strides);
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}
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}
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}
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}
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std::optional<Twine> resolveImgInputTiles(Value wholeInputTensor,
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SmallVector<SmallVector<SmallVector<Value>>>& inputTiles,
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size_t channelTileCount,
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size_t channelTileRest,
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size_t input_w,
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size_t input_h,
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ConversionPatternRewriter& rewriter) {
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for (size_t t = 0; t < channelTileCount; t++) {
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for (size_t x = 0; x < input_w; x++) {
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for (size_t y = 0; y < input_h; y++) {
|
||||
inputTiles[t][x][y] =
|
||||
indexImgValue(wholeInputTensor, x, y, t, channelTileCount, channelTileRest, input_w, input_h, rewriter);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return std::nullopt;
|
||||
}
|
||||
|
||||
LogicalResult handleFlattenLikeOp(SmallVector<SmallVector<Value>>& inputTiles,
|
||||
const size_t inputTilesCount,
|
||||
const size_t lastInputTileDimension,
|
||||
TensorType inputShape,
|
||||
TensorType outputShape,
|
||||
Value reshapeInput,
|
||||
ConversionPatternRewriter& rewriter) {
|
||||
// Only support reshape between an image and a vector (i.e. flatten)
|
||||
if (inputShape.getRank() != 4 || outputShape.getRank() != 2) {
|
||||
return rewriter.notifyMatchFailure(reshapeInput.getDefiningOp(),
|
||||
"resolveVecInputTiles only supports reshapes from 4D to 2D tensors");
|
||||
}
|
||||
|
||||
/*
|
||||
* From a 4D tensor <N, C, W, H> to a 2D tensor <N, C*H*W>
|
||||
*/
|
||||
auto N = inputShape.getDimSize(0);
|
||||
auto C = inputShape.getDimSize(1);
|
||||
auto H = inputShape.getDimSize(2);
|
||||
auto W = inputShape.getDimSize(3);
|
||||
assert(N == 1 && "Only support N = 1 for image tensors");
|
||||
|
||||
for (size_t i = 0; i < inputTilesCount; i++) {
|
||||
auto c = (i / (H * W)) % C;
|
||||
// TODO: Is this correct? Or should I invert h and w?
|
||||
auto w = (i / H) % W;
|
||||
auto h = i % H;
|
||||
|
||||
Value curTile = indexImgValue(reshapeInput, w, h, c, inputTilesCount, lastInputTileDimension, W, H, rewriter);
|
||||
|
||||
// Assert the shape of the tile, and reshape it
|
||||
auto curTileShape = cast<TensorType>(curTile.getType());
|
||||
assert(curTileShape.getRank() == 4 && "We just reshaped an image tensor, why rank != 4?");
|
||||
assert(curTileShape.getDimSize(0) == 1 && "We just reshaped an image tensor with N = 1, why is it now != 1?");
|
||||
assert(curTileShape.getDimSize(2) == 1 && "We should have just looked up a single pixel why W != 1?");
|
||||
assert(curTileShape.getDimSize(3) == 1 && "We should have just looked up a single pixel why H != 1?");
|
||||
|
||||
// Reshape this pixel tensor into a vector, for compatibility with the
|
||||
// rest
|
||||
SmallVector<int64_t> newShapeVals = {curTileShape.getDimSize(0), curTileShape.getDimSize(1)};
|
||||
auto shapeType = RankedTensorType::get({static_cast<int64_t>(newShapeVals.size())}, rewriter.getI64Type());
|
||||
Value shapeTensor =
|
||||
rewriter.create<arith::ConstantOp>(reshapeInput.getLoc(), DenseIntElementsAttr::get(shapeType, newShapeVals));
|
||||
auto reshapedType = RankedTensorType::get(newShapeVals, curTileShape.getElementType());
|
||||
auto reshapedCurTile = tosa::ReshapeOp::create(rewriter, reshapeInput.getLoc(), reshapedType, curTile, shapeTensor);
|
||||
|
||||
size_t coreIndex = i / crossbarCountInCore;
|
||||
inputTiles[coreIndex].push_back(reshapedCurTile);
|
||||
}
|
||||
|
||||
return success();
|
||||
}
|
||||
|
||||
std::pair<size_t, size_t> kernel_get_start_and_end(
|
||||
int64_t out_pos, int64_t input_width, int64_t krn_width, int64_t stride, int64_t dilation, int64_t pad) {
|
||||
int64_t firstValid = std::ceil(static_cast<float>(pad) / dilation) * dilation - pad;
|
||||
int64_t start = std::max(firstValid, out_pos * stride - pad);
|
||||
int64_t end = std::min(input_width, out_pos * stride + (krn_width - 1) * dilation + 1 - pad);
|
||||
|
||||
assert(start >= 0 && "Start position must be non-negative.");
|
||||
assert(end >= 0 && "End position must be non-negative.");
|
||||
return std::make_pair(start, end);
|
||||
}
|
||||
|
||||
void incrementWeightedComputeInputsSegmentSize(spatial::SpatWeightedCompute wcomputeOp, int increment) {
|
||||
auto oldSegmentSizes = wcomputeOp->getAttrOfType<DenseI32ArrayAttr>(wcomputeOp.getOperandSegmentSizesAttrName());
|
||||
|
||||
auto newSegmentSizes =
|
||||
DenseI32ArrayAttr::get(wcomputeOp->getContext(), {oldSegmentSizes[0], oldSegmentSizes[1] + increment});
|
||||
|
||||
wcomputeOp->setAttr(wcomputeOp.getOperandSegmentSizesAttrName(), newSegmentSizes);
|
||||
}
|
||||
|
||||
int getResultIndex(Operation* op, Value v) {
|
||||
int resultNumber = -1;
|
||||
for (auto result : op->getResults()) {
|
||||
if (result == v) {
|
||||
resultNumber = result.getResultNumber();
|
||||
break;
|
||||
}
|
||||
}
|
||||
assert(resultNumber >= 0 && "Value not found in given operation's results.");
|
||||
|
||||
return resultNumber;
|
||||
}
|
||||
|
||||
}; // namespace onnx_mlir
|
||||
Reference in New Issue
Block a user