MLIR Quantization

This document outlines the design of the MLIR quantization system. While the term "quantization" is highly overloaded, in this case, it refers to a fairly narrow scope of techniques in use to enable conversion of floating-point computations to corresponding and plausible variants expressed in integer math for inference, as has historically been supported by low-bit depth inference engines such as TFLite, various accelerator hardware, and many DSPs.

Much of this is inspired by the approach taken in this paper with many extensions and adaptations folded in. It specifically documents the positions that MLIR has taken on the topic, and is not a general reference.

[TOC]

Uniform quantization

The primary quantization mechanism supported by MLIR is a scheme which can express fixed point and affine transformations via uniformly spaced point on the Real number line.

Further, the scheme can be applied:

• per-layer : Applying to every value within the target type.
• per-axis (also called per-channel) : Applying individually to each index along a specific axis of a tensor type.

Fixed point values

Fixed point values are a Real number divided by a scale. We will call the result of the divided Real the scaled value.

$$ real_value = scaled_value * scale $$

The scale can be interpreted as the distance, in Real units, between neighboring scaled values. For example, if the scale is $$ \pi $$, then fixed point values with this scale can only represent multiples of $$ \pi $$, and nothing in between. The maximum rounding error to convert an arbitrary Real to a fixed point value with a given $$ scale $$ is $$ \frac{scale}{2} $$. Continuing the previous example, when $$ scale = \pi $$, the maximum rounding error will be $$ \frac{\pi}{2} $$.

Multiplication can be performed on scaled values with different scales, using the same algorithm as multiplication of Real values (note that product scaled value has $$ scale_{product} = scale_{left \mbox{ } operand} * scale_{right \mbox{ } operand} $$). Addition can be performed on scaled values, as long as they have the same scale, using the same algorithm as addition of Real values. This makes it convenient to represent scaled values on a computer as signed integers, and perform arithmetic on those signed integers, because the results will be correct scaled values.

Affine values

Mathematically speaking, affine values are the result of adding a Real-valued zero point, to a scaled value. Or equivalently, subtracting a zero point from an affine value results in a scaled value:

$$ real_value = scaled_value * scale = (affine_value - zero_point) * scale $$

Essentially, affine values are a shifting of the scaled values by some constant amount. Arithmetic (i.e., addition, subtraction, multiplication, division) cannot, in general, be directly performed on affine values; you must first convert them to the equivalent scaled values.

As alluded to above, the motivation for using affine values is to more efficiently represent the Real values that will actually be encountered during computation. Frequently, the Real values that will be encountered are not symmetric around the Real zero. We also make the assumption that the Real zero is encountered during computation, and should thus be represented.

In this case, it's inefficient to store scaled values represented by signed integers, as some of the signed integers will never be used. The bit patterns corresponding to those signed integers are going to waste.

In order to exactly represent the Real zero with an integral-valued affine value, the zero point must be an integer between the minimum and maximum affine value (inclusive). For example, given an affine value represented by an 8 bit unsigned integer, we have: $$ 0 \leq zero_point \leq 255$$. This is important, because in deep neural networks' convolution-like operations, we frequently need to zero-pad inputs and outputs, so zero must be exactly representable, or the result will be biased.

Relation

Real values, fixed point values, and affine values relate through the following equation, which demonstrates how to convert one type of number to another:

$$ real_value = scaled_value * scale = (affine_value - zero_point) * scale $$

Note that computers generally store mathematical values using a finite number of bits. Thus, while the above conversions are exact, to store the result in a finite number of bits, we must, in general, round the result of the conversion (this applies to both cases: storing using floating point and storing using fixed point). Note that a full discussion of rounding behavior is outside the scope of this document, and it is safe to assume unless otherwise stated that rounding should be according to the IEEE754 default of RNE (where hardware permits).

Converting between Real and fixed point or affine

To convert a Real value to a fixed point value, you must know the scale. To convert a Real value to an affine value, you must know the scale and zero point.

Real to affine

To convert an input tensor of Real-valued elements (usually represented by a floating point format, frequently Single precision) to a tensor of affine elements represented by an integral type (e.g. 8-bit unsigned integer), the following conversion can be performed (note that it is not required that all representable values of the integral type are used):

$$ \begin{align*} af&fine_value_{uint8 \, or \, uint16} \ &= clampToTargetSize(roundToNearestInteger( \frac{real_value_{Single}}{scale_{Single}}){sint32} + zero_point{uint8 \, or \, uint16}) \end{align*} $$

In the above, we assume that $$real_value$$ is a Single, $$scale$$ is a Single, $$roundToNearestInteger$$ returns a signed 32 bit integer, and $$zero_point$$ is an unsigned 8 or 16 bit integer. Note that bit depth and number of fixed point values are indicative of common types on typical hardware but is not constrained to particular bit depths or a requirement that the entire range of an N-bit integer is used.

Affine to Real

To convert an output tensor of affine elements represented by uint8 or uint16 to a tensor of Real-valued elements (usually represented with a floating point format, frequently Single precision), the following conversion can be performed:

$$ \begin{align*} re&al_value_{Single} \ &= roundToNearestFloat((affine_value_{uint8 \, or \, uint16} - zero_point_{uint8 \, or \, uint16}){sint32}){Single} * scale_{Single} \end{align*} $$

In the above, we assume that the result of subtraction is in 32-bit signed integer format, and that $$roundToNearestFloat$$ returns a Single.

Affine to fixed point

When the affine and fixed point scales are the same, subtract the zero point from the affine value to get the equivalent fixed point value.

$$ scaled_value = affine_value_{non\mbox}negative} - zero_point_{non\mbox{negative} $$

Fixed point to affine

When the affine and fixed point scales are the same, add the zero point to the fixed point value to get the equivalent affine value.

$$ affine_value_{non\mbox}negative} = scaled_value + zero_point_{non\mbox{negative} $$

Usage within MLIR

There are several components to the quantization system being developed within MLIR:

• Quantization dialect containing:

  • A family of QuantizedTypes which represent the mapping between expressed values (typically of a floating point computer type) and storage values (typically of an integral computer type).
  • Type conversion ops for converting between types based on a QuantizedType and its expressed and storage sub-types.
  • Instrumentation ops for assigning instrumentation points within the computation where runtime statistics may help guide the quantization process.

• Integration with simulated quantization at training time

• TFLite native quantization

  • The TFLite op-set natively supports uniform-quantized variants.
  • Passes and tools exist to convert directly from the TensorFlow dialect to the TFLite quantized op-set.

• FxpMath dialect containing (experimental) generalized representations of fixed-point math ops and conversions:

  • Real math ops representing common combinations of arithmetic operations that closely match corresponding fixed-point math concepts (as opposed to being spread across multiple ops as is typical in source dialects).
  • Fixed-point math ops that for carrying out computations on integers, as are typically needed by uniform quantization schemes.
  • Passes to lower from real math ops to fixed-point math ops.

• Solver tools which can (experimentally and generically operate on computations expressed in the FxpMath dialect in order to convert from floating point types to appropriate QuantizedTypes, allowing the computation to be further lowered to integral math ops.

Not every application of quantization will use all facilities. Specifically, the TensorFlow to TensorFlow Lite conversion uses the QuantizedTypes but has its own ops for type conversion and expression of the backing math.

Quantization Dialect

Quantized type

TODO : Flesh this section out.

• QuantizedType base class
• UniformQuantizedType

Quantized type conversion ops

• qcast : Convert from an expressed type to QuantizedType
• dcast : Convert from a QuantizedType to its expressed type
• scast : Convert between a QuantizedType and its storage type

Instrumentation and constraint ops

• const_fake_quant : Emulates the logic of the historic TensorFlow fake_quant_with_min_max_args op.
• stats_ref : Declares that statistics should be gathered at this point with a unique key and made available to future passes of the solver.
• stats : Declares inline statistics (per layer and per axis) for the point in the computation. stats_ref ops are generally converted to stats ops once trial runs have been performed.
• coupled_ref : Declares points in the computation to be coupled from a type inference perspective based on a unique key.

Integration with simulated quantization at training time

TensorFlow has historically used the tf.quantization.fake_quant_* family of operations to simulate the effect of quantization at training time.

As originally implemented, TensorFlow Lite was the primary user of such operations at inference time. When quantized inference was enabled, if every eligible tensor passed through an appropriate fake_quant node (the rules of which tensors can have fake_quant applied are somewhat involved), then TensorFlow Lite would use the attributes of the fake_quant ops to make a judgment about how to convert to use kernels from its quantized ops subset.

In MLIR-based quantization, fake_quant_* ops are handled by converting them to a sequence of qcast (quantize) followed by dcast (dequantize) with an appropriate UniformQuantizedType as the target of the qcast operation.

This allows subsequent compiler passes to preserve the knowledge that quantization was simulated in a certain way while giving the compiler flexibility to move the casts as it simplifies the computation and converts it to a form based on integral arithmetic.

This scheme also naturally allows computations that are partially quantized where the parts which could not be reduced to integral ops are still carried out in floating point with appropriate conversions at the boundaries.

TFLite Native Quantization

TODO : Flesh this out

General algorithm

1. Take input min/max information and set the ArrayInfo (which really is InputOrOutputArrayInfo.
2. In LegalizeTF, convert ArrayInfo min/max to tf.Quantize and tf.Dequantize nodes. (or tf.FakeQuant) Convert all constant FakeQuants to (tf.FQ -> tfl.Q -> tfl.DQ).
3. Hardcode logic/propagation needs to happen here.
4. Run TF constant folding.
5. In PrepareTFL, convert all tf.FQ to (tfl.Q -> tfl.DQ).
6. Run quantization pass that take (tfl.DQ (for both input and weights) -> op -> tfl.Q) and replaces with (op). Also replace (constant_float -> tfl.Q) with (constant_quant).

FxpMath Dialect

Real math ops

Note that these all support explicit clamps, which allows for simple fusions and representation of some common sequences quantization-compatible math. Of addition, some support explicit biases, which are often represented as separate adds in source dialects.

TODO: This op set is still evolving and needs to be completed.

• RealBinaryOp
  • RealAddEwOp
  • RealSubEwOp
  • RealMulEwOp
  • RealDivEwOp
• RealUnaryOp
  • IDENTITY
  • TANH
  • SIGMOID
  • EXP
  • LOG
  • NEG
  • RSQRT
  • SIN
  • SQUARE
  • SQRT
  • CMPZ
  • CMPNZ
  • CMPLZ
  • CMPGZ

Fixed-point math ops

TODO: This op set only has enough ops to lower a simple power-of-two RealAddEwOp.

• RoundingDivideByPotFxpOp
• SaturatingAddFxpOp

Solver tools

Solver tools exist to analyze an MLIR-computation, expressed in either a supported source dialect or in the real math ops set and solve for appropriate QuantizedTypes that allow the computation to be lowered to integral math.

These tools are an active area of work and may be expanded in the future to adjacent areas such as solving for transformations to other kinds of lower precision types (i.e. bfloat16 or fp16).

Solver tools are expected to operate in several modes, depending on the computation and the manner in which it was trained:

• Transform : With all available information in the MLIR computation, infer boundaries where the computation can be carried out with integral math and change types accordingly to appropriate QuantizedTypes:

  • For passthrough ops which do not perform active math, change them to operate directly on the storage type, converting in and out at the edges via scast ops.
  • For ops that have the Quantizable trait, the type can be set directly. This includes ops from the [real math ops set]{#real-math-ops}.
  • For others, encase them in appropriate dcast/qcast ops, presuming that some follow-on pass will know what to do with them.

• Instrument : Most of the time, there are not sufficient implied constraints within a computation to perform many transformations. For this reason, the solver can insert instrumentation ops at points where additional runtime statistics may yield solutions. It is expected that such computations will be lowered as-is for execution, run over an appropriate eval set, and statistics at each instrumentation point made available for a future invocation of the solver.

• Simplify : A variety of passes and simplifications are applied once QuantizedTypes are added in order to arrive at a computation that is expressed in as much integral math, with the fewest number of casts as possible.