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CompressQuantizeWeights - handle decompressed inputs to FakeQuantize (#19061)

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Decompression attribute (that is present in models with FP16 precision)
prevents the weights to be constantfolded. Weights constantfolding is
required by CompressQuantizeWeights to compress the weights to low
precision format.

Ticket: CVS-117310
This commit is contained in:
Mateusz Tabaka authored and GitHub committed 2023-08-08 16:52:40 +00:00
1 parent 0b9d0e232f
commit 8ceddf4be7
2 files changed
+51 -4
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src/common/transformations/tests/utils/compress_quantize_weights.cpp
+39 -1
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@@ -10,6 +10,7 @@
#include <ngraph/opsets/opset8.hpp>
#include <ngraph/pass/manager.hpp>
#include <transformations/init_node_info.hpp>
#include <transformations/rt_info/decompression.hpp>
#include <transformations/utils/utils.hpp>
#include "common_test_utils/ngraph_test_utils.hpp"
@@ -41,8 +42,10 @@ class CompressQuantizeWeightsTests
std::tie(param, data_prc) = GetParam();
{
std::shared_ptr<Node> data = opset8::Constant::create(data_prc, param.shape, param.weights);
if (data_prc == element::f16)
if (data_prc == element::f16) {
data = std::make_shared<opset8::Convert>(data, element::f32);
ov::mark_as_decompression(data);
}
auto input_low = opset8::Constant::create(element::f32, Shape{}, {param.in_low});
auto input_high = opset8::Constant::create(element::f32, Shape{}, {param.in_high});
auto output_low = opset8::Constant::create(element::f32, Shape{}, {param.out_low});
@@ -159,6 +162,41 @@ TEST_F(TransformationTestsF, CompressQuantizeWeightsWithDequantizationSubgraph)
comparator.enable(FunctionsComparator::CmpValues::ACCURACY);
}
TEST_F(TransformationTestsF, CompressQuantizeWeightsWithDequantizationSubgraphFP16) {
{
auto data = opset8::Constant::create(element::f16, Shape{2, 4, 1, 1}, {-1, 0, 1, 2, 3, 4, 5, 11});
auto convert_to_f32 = std::make_shared<opset8::Convert>(data, element::f32);
ov::mark_as_decompression(convert_to_f32);
auto input_low = opset8::Constant::create(element::f32, Shape{}, {1});
auto input_high = opset8::Constant::create(element::f32, Shape{}, {9});
auto output_low = opset8::Constant::create(element::f32, Shape{}, {-128});
auto output_high = opset8::Constant::create(element::f32, Shape{}, {127});
auto fq =
std::make_shared<opset8::FakeQuantize>(convert_to_f32, input_low, input_high, output_low, output_high, 256);
auto convert = std::make_shared<opset8::Convert>(fq, element::i8);
auto second_convert = std::make_shared<opset8::Convert>(convert, element::f32);
auto scale = opset8::Constant::create(element::f32, Shape{}, {10.0 / 255});
auto zero_point = opset8::Constant::create(element::f32, Shape{}, {2 - 255.0 / 10});
auto sub = std::make_shared<opset8::Subtract>(second_convert, zero_point);
auto mul = std::make_shared<opset8::Multiply>(sub, scale);
function = std::make_shared<Function>(NodeVector{mul}, ParameterVector{});
manager.register_pass<pass::CompressQuantizeWeights>();
}
{
auto data = opset8::Constant::create(element::i8, Shape{2, 4, 1, 1}, {-128, -128, -128, -96, -64, -32, 0, 127});
auto convert = std::make_shared<opset8::Convert>(data, element::f32);
auto scale = opset8::Constant::create(element::f32, Shape{}, {10.0 / 255});
auto zero_point = opset8::Constant::create(element::f32, Shape{}, {2 - 255.0 / 10});
auto sub = std::make_shared<opset8::Subtract>(convert, zero_point);
auto mul = std::make_shared<opset8::Multiply>(sub, scale);
function_ref = std::make_shared<Function>(NodeVector{mul}, ParameterVector{});
}
comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
comparator.enable(FunctionsComparator::CmpValues::ACCURACY);
}
TEST_F(TransformationTestsF, CompressQuantizeWeightsWithZeroPointOptimizer) {
{
auto data = opset8::Constant::create(element::f32, Shape{3, 1, 1, 1}, {-0.144816, 0.0858578, 0.110928});