1//===- UniformSupport.cpp - Support utilities for uniform quant -----------===//
2//
3// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4// See https://llvm.org/LICENSE.txt for license information.
5// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6//
7//===----------------------------------------------------------------------===//
8
9#include "mlir/Dialect/Quant/UniformSupport.h"
10#include "mlir/IR/BuiltinTypes.h"
11#include <numeric>
12
13using namespace mlir;
14using namespace mlir::quant;
15
16static bool isQuantizablePrimitiveType(Type inputType) {
17 return isa<FloatType>(Val: inputType);
18}
19
20ExpressedToQuantizedConverter
21ExpressedToQuantizedConverter::forInputType(Type inputType) {
22 if (isa<TensorType, VectorType>(Val: inputType)) {
23 Type elementType = cast<ShapedType>(inputType).getElementType();
24 if (!isQuantizablePrimitiveType(inputType: elementType))
25 return ExpressedToQuantizedConverter{.inputType: inputType, .expressedType: nullptr};
26 return ExpressedToQuantizedConverter{.inputType: inputType, .expressedType: elementType};
27 }
28 // Supported primitive type (which just is the expressed type).
29 if (isQuantizablePrimitiveType(inputType))
30 return ExpressedToQuantizedConverter{.inputType: inputType, .expressedType: inputType};
31 // Unsupported.
32 return ExpressedToQuantizedConverter{.inputType: inputType, .expressedType: nullptr};
33}
34
35Type ExpressedToQuantizedConverter::convert(QuantizedType elementalType) const {
36 assert(expressedType && "convert() on unsupported conversion");
37 if (auto tensorType = dyn_cast<RankedTensorType>(inputType))
38 return RankedTensorType::get(tensorType.getShape(), elementalType);
39 if (dyn_cast<UnrankedTensorType>(inputType))
40 return UnrankedTensorType::get(elementalType);
41 if (auto vectorType = dyn_cast<VectorType>(inputType))
42 return VectorType::get(vectorType.getShape(), elementalType);
43
44 // If the expressed types match, just use the new elemental type.
45 if (elementalType.getExpressedType() == expressedType)
46 return elementalType;
47 // Unsupported.
48 return nullptr;
49}
50
51ElementsAttr
52UniformQuantizedPerAxisValueConverter::convert(Attribute realValue) {
53 if (auto attr = dyn_cast<DenseFPElementsAttr>(realValue)) {
54 return convert(attr);
55 }
56 // TODO: handles sparse elements attribute
57 return nullptr;
58}
59
60DenseElementsAttr
61UniformQuantizedPerAxisValueConverter::convert(DenseFPElementsAttr attr) {
62 // Creates the converter for each chunk. Normally the size of the
63 // quantization dim is 3, so we can cache all the converters.
64 ShapedType type = attr.getType();
65 size_t dimSize = type.getDimSize(quantizationDim);
66 if (dimSize != scales.size()) {
67 return {};
68 }
69 SmallVector<UniformQuantizedValueConverter, 4> converters;
70 converters.reserve(N: dimSize);
71 for (int i = 0, e = dimSize; i != e; ++i) {
72 converters.push_back(Elt: getPerChunkConverter(index: i));
73 }
74
75 // Scan the elements of the dense elements attributes and quantize them by
76 // using the right quantization parameters.
77 int64_t flattenIndex = 0;
78 auto shape = type.getShape();
79 int64_t chunkSize =
80 std::accumulate(std::next(shape.begin(), quantizationDim + 1),
81 shape.end(), 1, std::multiplies<int64_t>());
82 Type newElementType = IntegerType::get(attr.getContext(), storageBitWidth);
83 return attr.mapValues(newElementType, [&](const APFloat &old) {
84 int chunkIndex = (flattenIndex++) / chunkSize;
85 return converters[chunkIndex % dimSize].quantizeFloatToInt(old);
86 });
87}
88

source code of mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp