Step 2. Markup Transformations

This step defines the optimal FakeQuantize decomposition precisions for the best inference performance via operations markup with runtime attribute instances. Attributes are created for input and output ports and operations. Transformations do not change the operation output port precisions. A model markup low precision logic is decomposed and implemented into the following common markup transformations. The order of transformations is important:

  1. MarkupBias

  2. MarkupCanBeQuantized

  3. MarkupPrecisions

  4. MarkupPerTensorQuantization

  5. MarkupAvgPoolPrecisionPreserved

  6. PropagatePrecisions

  7. AlignQuantizationIntervals

  8. AlignQuantizationParameters

Transformation name

Create attributes

Use attributes

MarkupBias

Bias

MarkupCanBeQuantized

Precisions

MarkupPrecisions

Precisions,PrecisionPreserved

MarkupPerTensorQuantization

PerTensorQuantization

MarkupAvgPoolPrecisionPreserved

AvgPoolPrecisionPreserved

Precisions, PrecisionPreserved

PropagatePrecisions

Precisions

Precisions, PrecisionPreserved

AlignQuantizationIntervals

IntervalsAlignment

PrecisionPreserved

AlignQuantizationParameters

QuantizationAlignment

PrecisionPreserved, PerTensorQuantization

Note

The same type of attribute instances can be created in different transformations. This approach is the result of the transformation single-responsibility principle. For example, Precision attribute instances are created in MarkupCanBeQuantized and MarkupPrecisions transformations, but the reasons for their creation are different

Common markup transformations can be decomposed into simpler utility markup transformations. The order of Markup utility transformations is not important:

Let’s explore all transformations and their relations in detail, using one and the same model:

_images/step2_markup_original.svg

The original model key features:

  • The first concat1 concatenation operation has not quantized convolution1 consumer.

  • The second concat2 concatenation operation has quantized convolution2 consumer with requirements:

    • support unsigned int8 on activations,

    • per-tensor quantization.

  • Between the concat2 concatenation operation and Convolution there is an AvgPool operation, which mathematically should return an f32 tensor. But the MarkupAvgPoolPrecisionPreserved transformation is active. This allows the low precision transformation, that goes after the AvgPool, to propagate low precision tensor to the next consumer.

Transformations are run with the following parameters:

auto supportedPrecisions = std::vector<PrecisionsRestriction>({
    PrecisionsRestriction::create<ov::opset1::Convolution>({
        {{0}, {ov::element::u8}},
        {{1}, {ov::element::i8}},
    }),
});

auto perTensorQuantization = std::vector<QuantizationGranularityRestriction>({
    QuantizationGranularityRestriction::create<ov::opset1::Convolution>({0})
});

ov::pass::Manager lptManager;
lptManager.register_pass<ov::pass::low_precision::LowPrecision>(supportedPrecisions, perTensorQuantization);
lptManager.run_passes(model);

1. MarkupCanBeQuantized

The transformation marks operations that cannot be quantized. No attributes are required before the transformation.

Changes in the example model after MarkupCanBeQuantized transformation:

  • Not quantized convolution1 operation is marked by the Precisions attribute with empty values. This attribute allows the next transformation to ignore not quantized operation.

Result model:

MarkupCanBeQuantize

Model display features (here and below):

  • The attributes added by the current transformation are marked in bold.

  • If attributes do not fit into one line, then one line consists of only one attribute.

2. MarkupPrecisions

The transformation is required and includes two tasks:

  1. Mark operation input ports (create Precision attribute instance) by provided restrictions: input port index and required precisions. Restrictions are provided as input argument in ov::pass::low_precision::LowPrecision constructor.

  2. Mark precision preserved operations.

No attributes are required before the transformation. Changes in the example model after MarkupPrecisions transformation:

  • Both concatenation operations are marked as precision preserved operations. It allows to propagate precision via these operations.

  • Quantized convolution2 operation is marked by the Precisions attribute with u8 precision on activations and i8 precisions on weights according to the provided restrictions. This attribute instance allows to specify which precisions are required for quantized Convolution operation.

Result model:

MarkupPrecisions result

3. MarkupPerTensorQuantization

The transformation is required and marks operations (create PerTensorQuantization attribute instance) by provided restrictions: an operation that requires per-tensor quantization. No attributes are required before the transformation.

Changes in the example model after MarkupPerTensorQuantization transformation:

  • both Convolution operations are marked by PerTensorQuantization

Result model:

MarkupPerTensorQuantization result

4. MarkupAvgPoolPrecisionPreserved

The transformation is optional. MarkupAvgPoolPrecisionPreserved marks AvgPool operations as precision preserved or not precision preserved. AvgPool operation is precision preserved if next not precision preserved operation can be inferred in low precision. In other words, AvgPool operations become precision preserved operations to speed up model inference. The transformation uses PrecisionPreserved attributes created before. The transformation is combined and uses:

  • CreatePrecisionsDependentAttribute

  • PropagateThroughPrecisionPreserved

  • UpdateSharedPrecisionPreserved

Changes in the example model after MarkupAvgPoolPrecisionPreserved transformation:

  • AvgPool operations are marked by PrecisionPreserved and AvgPoolPrecisionPreserved (not used below).

Result model:

arkupAvgPoolPrecisionPreserved

5. PropagatePrecisions

The transformation is required. PropagatePrecision is a key transformation in the markup pipeline, which marks FakeQuantize output port precisions. The transformation uses PrecisionPreserved attribute instances created before. The transformation is combined and uses:

  • CreateAttribute

  • PropagateThroughPrecisionPreserved

  • PropagateToInput

Changes in the example model after PropagatePrecisions transformation:

  • All precision preserved operations are marked by the Precisions attribute instance, which defines the required precision for the operation.

  • FakeQuantize operation output ports are marked by Precisions attribute instances, which define target precision for decomposition. In the sample model, FakeQuantize operations have signed intervals, but the Precisions attributes are initialized by u8 (unsigned int8) values as the result applied during transformations restrictions for Convolution operations.

Result model:

PropagatePrecisions

Note

AlignQuantizationIntervals and AlignQuantizationParameters transformations are required if the model has quantized concatenation operations.

6. AlignQuantizationIntervals

The transformation is required for models with the quantized operation. The transformation marks FakeQuantize operation and precision preserved consumers to combine quantization information from different FakeQuantize operations for future quantization intervals alignment. The transformation is combined and uses:

  • CreateAttribute

  • PropagateThroughPrecisionPreserved

Changes in the example model after AlignQuantizationIntervals transformation:

  • All FakeQuantize operations and their precision preserved consumers are marked by the IntervalsAlignment attribute instance.

Result model:

AlignQuantizationIntervals

7. AlignQuantizationParameters

The transformation is required for models with quantized concatenation operation. The transformation marks FakeQuantize precision preserved consumers to align quantization intervals. The transformation is combined and uses:

  • CreateAttribute

  • PropagateThroughPrecisionPreserved

  • UpdateSharedPrecisionPreserved

Changes in the example model after AlignQuantizationParameters transformation:

  • All FakeQuantize precision preserved consumers are marked by QuantizationAlignment attribute instance. convolution1 input ports are marked by Precisions attribute instances with empty precisions collection. As a result, the convolution1 operation was detected as not quantized, and the QuantizationAlignment attribute default value false does not change. convolution2 input ports are marked by Precisions attribute instances with not empty precisions collection. convolution2 operation was detected as quantized with the PerTensorQuantization attribute, and the QuantizationAlignment attribute default value changed to true.

Final model:

AlignQuantizationParameters