c8961d408e
A new converter with per axis quantization parameters is added to quantize a dense elements attribute. For each slice along the quantization axis, it creates an uniform quantized value converter, with different scale and zero point, and quantizes the values in the slice. The current implementation doesn't handle sparse elements attributes. PiperOrigin-RevId: 270121986
112 lines
4.0 KiB
C++
112 lines
4.0 KiB
C++
//===- UniformSupport.cpp - Support utilities for uniform quant -----------===//
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//
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// Copyright 2019 The MLIR Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// =============================================================================
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#include "mlir/Dialect/QuantOps/UniformSupport.h"
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#include "mlir/IR/StandardTypes.h"
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#include <numeric>
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using namespace mlir;
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using namespace mlir::quant;
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static bool isQuantizablePrimitiveType(Type inputType) {
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return inputType.isa<FloatType>();
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}
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const ExpressedToQuantizedConverter
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ExpressedToQuantizedConverter::forInputType(Type inputType) {
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switch (inputType.getKind()) {
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default:
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if (isQuantizablePrimitiveType(inputType)) {
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// Supported primitive type (which just is the expressed type).
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return ExpressedToQuantizedConverter{inputType, inputType};
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}
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// Unsupported.
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return ExpressedToQuantizedConverter{inputType, nullptr};
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case StandardTypes::RankedTensor:
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case StandardTypes::UnrankedTensor:
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case StandardTypes::Vector: {
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Type elementType = inputType.cast<ShapedType>().getElementType();
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if (!isQuantizablePrimitiveType(elementType)) {
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// Unsupported.
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return ExpressedToQuantizedConverter{inputType, nullptr};
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}
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return ExpressedToQuantizedConverter{
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inputType, inputType.cast<ShapedType>().getElementType()};
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}
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}
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}
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Type ExpressedToQuantizedConverter::convert(QuantizedType elementalType) const {
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assert(expressedType && "convert() on unsupported conversion");
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switch (inputType.getKind()) {
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default:
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if (isQuantizablePrimitiveType(elementalType)) {
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// For primitives, just use the new elemental type.
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return elementalType;
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}
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// Unsupported.
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return nullptr;
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case StandardTypes::RankedTensor:
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return RankedTensorType::get(inputType.cast<RankedTensorType>().getShape(),
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elementalType);
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case StandardTypes::UnrankedTensor:
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return UnrankedTensorType::get(elementalType);
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case StandardTypes::Vector:
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return VectorType::get(inputType.cast<VectorType>().getShape(),
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elementalType);
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}
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}
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ElementsAttr
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UniformQuantizedPerAxisValueConverter::convert(Attribute realValue) {
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if (auto attr = realValue.dyn_cast<DenseFPElementsAttr>()) {
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return convert(attr);
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}
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// TODO(fengliuai): handles sparse elements attribute
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return nullptr;
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}
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DenseElementsAttr
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UniformQuantizedPerAxisValueConverter::convert(DenseFPElementsAttr attr) {
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// Creates the converter for each chunk. Normally the size of the
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// quantization dim is 3, so we can cache all the converters.
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ShapedType type = attr.getType();
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size_t dimSize = type.getDimSize(quantizationDim);
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if (dimSize != scales.size()) {
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return {};
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}
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SmallVector<UniformQuantizedValueConverter, 4> converters;
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converters.reserve(dimSize);
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for (int i = 0, e = dimSize; i != e; ++i) {
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converters.push_back(getPerChunkConverter(i));
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}
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// Scan the elements of the dense elements attributes and quantize them by
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// using the right quantization parameters.
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int64_t flattenIndex = 0;
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auto shape = type.getShape();
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int64_t chunkSize =
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std::accumulate(std::next(shape.begin(), quantizationDim + 1),
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shape.end(), 1, std::multiplies<int64_t>());
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Type newElementType = IntegerType::get(storageBitWidth, attr.getContext());
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return attr.mapValues(newElementType, [&](const APFloat &old) {
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int chunkIndex = (flattenIndex++) / chunkSize;
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return converters[chunkIndex % dimSize].quantizeFloatToInt(old);
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});
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}
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