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Always represent zero accurately in v1 histogram #3731

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Jan 15, 2025
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Always represent zero accurately in v1 histogram #3731

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234d122
Always represent zero accurately in v1 histogram
quic-kyunggeu Jan 9, 2025
c594ea7
Change test criteria of the failing test cases
quic-kyunggeu Jan 9, 2025
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2 changes: 1 addition & 1 deletion ModelOptimizations/DlQuantization/src/math_functions.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -249,7 +249,7 @@ void InitializePdf(PDF& pdf, DTYPE min_val, DTYPE max_val, bool signed_vals)
for (int i = 0; i < PDF_SIZE; ++i)
{
if (signed_vals)
pdf.xLeft[i] = min_val + i * bucket_size;
pdf.xLeft[i] = floor(min_val / bucket_size + i) * bucket_size;
else
pdf.xLeft[i] = i * bucket_size;
}
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6 changes: 4 additions & 2 deletions ModelOptimizations/DlQuantization/test/TestPercentileEncodingAnalyzer.cpp
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Expand Up @@ -300,7 +300,9 @@ TYPED_TEST(TestPercentileEncodingAnalyzer, Symmetric_Percentile100)
absoluteMax = std::max(std::abs(absoluteMax), std::abs(absoluteMin));
absoluteMin = -absoluteMax;

EXPECT_LT(encoding.max, absoluteMax);
size_t PDF_SIZE = 512;
double HISTOGRAM_BUCKET_SIZE = 3 * (absoluteMax - absoluteMin) / PDF_SIZE;
EXPECT_LT(encoding.max, absoluteMax + PDF_SIZE);

EXPECT_FLOAT_EQ(encoding.delta, (encoding.max - encoding.min) / 255);
EXPECT_FLOAT_EQ(encoding.offset, encoding.min / encoding.delta);
Expand Down Expand Up @@ -367,4 +369,4 @@ TYPED_TEST(TestPercentileEncodingAnalyzer, AllZeroesAsymmetric)
EXPECT_NEAR(encoding1.max, 0.996078, 0.0001);
EXPECT_EQ(encoding1.offset, -128);
EXPECT_EQ(encoding1.bw, 8);
}
}
28 changes: 20 additions & 8 deletions ModelOptimizations/DlQuantization/test/TestTensorQuantizer.cpp
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Expand Up @@ -123,14 +123,18 @@ TEST_F(TestTensorQuantizer, SanityTestCpu)
encoding.min, encoding.max, 8, false);

std::cout << "Encoding min=" << encoding.min << ", max=" << encoding.max << std::endl;
EXPECT_NEAR(encoding.min, -6.52711, 0.001);
EXPECT_NEAR(encoding.max, 8.88412, 0.001);
double MAX = 8.88412;
double MIN = -6.52711;
size_t PDF_SIZE = 512;
double HISTOGRAM_BUCKET_SIZE = 3 * (MAX - MIN) / PDF_SIZE;
EXPECT_NEAR(encoding.min, MIN, HISTOGRAM_BUCKET_SIZE);
EXPECT_NEAR(encoding.max, MAX, HISTOGRAM_BUCKET_SIZE);

std::cout << "input-data=" << inputTensor.data()[0] << ", quantized-data=" << quantizedTensor.data()[0]
<< std::endl;

EXPECT_NE(inputTensor.data()[0], quantizedTensor.data()[0]);
EXPECT_NEAR(quantizedTensor.data()[0], 5.0162, 0.001);
EXPECT_NEAR(quantizedTensor.data()[0], 5.0162, HISTOGRAM_BUCKET_SIZE);
}

TEST_F(TestTensorQuantizer, SanityTestComputeEncodingFromDataAsymmetricTFEnhanced)
Expand All @@ -140,8 +144,12 @@ TEST_F(TestTensorQuantizer, SanityTestComputeEncodingFromDataAsymmetricTFEnhance
TfEncoding encoding {};
enhancedTensorQuant->computeEncodingFromData(8, paramTensor.data(), tensorCount, encoding,
ComputationMode::COMP_MODE_CPU, false, false, false);
EXPECT_NEAR(encoding.min, -6.527, 0.001);
EXPECT_NEAR(encoding.max, 8.884, 0.001);
double MAX = 8.88412;
double MIN = -6.52711;
size_t PDF_SIZE = 512;
double HISTOGRAM_BUCKET_SIZE = 3 * (MAX - MIN) / PDF_SIZE;
EXPECT_NEAR(encoding.min, MIN, HISTOGRAM_BUCKET_SIZE);
EXPECT_NEAR(encoding.max, MAX, HISTOGRAM_BUCKET_SIZE);
}

TEST_F(TestTensorQuantizer, SanityTestComputeEncodingFromDataSymmetricTF)
Expand Down Expand Up @@ -599,13 +607,17 @@ TEST_F(TestTensorQuantizer, SanityTestGpu)
quantTensorBlob.getDataPtrOnDevice(), encoding.min, encoding.max, 8, true);

std::cout << "Encoding min=" << encoding.min << ", max=" << encoding.max << std::endl;
EXPECT_NEAR(encoding.min, -6.52711, 0.001);
EXPECT_NEAR(encoding.max, 8.88412, 0.001);
double MAX = 8.88412;
double MIN = -6.52711;
size_t PDF_SIZE = 512;
double HISTOGRAM_BUCKET_SIZE = 3 * (MAX - MIN) / PDF_SIZE;
EXPECT_NEAR(encoding.min, MIN, HISTOGRAM_BUCKET_SIZE);
EXPECT_NEAR(encoding.max, MAX, HISTOGRAM_BUCKET_SIZE);

std::cout << "input-data=" << inputTensorBlob.getDataPtrOnCpu()[0]
<< ", quantized-data=" << quantTensorBlob.getDataPtrOnCpu()[0] << std::endl;
EXPECT_NE(inputTensorBlob.getDataPtrOnCpu()[0], quantTensorBlob.getDataPtrOnCpu()[0]);
EXPECT_NEAR(quantTensorBlob.getDataPtrOnCpu()[0], 5.0162, 0.001);
EXPECT_NEAR(quantTensorBlob.getDataPtrOnCpu()[0], 5.0162, HISTOGRAM_BUCKET_SIZE);
}

#endif
6 changes: 4 additions & 2 deletions ModelOptimizations/DlQuantization/test/TestTfEnhancedEncodingAnalyzer.cpp
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Expand Up @@ -336,8 +336,10 @@ TYPED_TEST(TestTfEnhancedEncodingAnalyzer, SymmetricUnsigned)
absoluteMax = std::max(std::abs(absoluteMax), std::abs(absoluteMin));
absoluteMin = -absoluteMax;

size_t PDF_SIZE = 512;
double HISTOGRAM_BUCKET_SIZE = 3 * (absoluteMax - absoluteMin) / PDF_SIZE;
EXPECT_EQ(encoding.min, 0);
EXPECT_NEAR(encoding.max, absoluteMax, 0.015);
EXPECT_NEAR(encoding.max, absoluteMax, 0.015 + HISTOGRAM_BUCKET_SIZE);

EXPECT_FLOAT_EQ(encoding.delta, (encoding.max - encoding.min) / 255);
EXPECT_FLOAT_EQ(encoding.offset, encoding.min / encoding.delta);
Expand All @@ -349,4 +351,4 @@ int main(int argc, char** argv)
{
::testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
}
4 changes: 2 additions & 2 deletions TrainingExtensions/tensorflow/test/python/test_adaround_keras.py
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Expand Up @@ -248,8 +248,8 @@ def test_compute_output_with_adarounded_weights():

hard_recons_error, soft_recons_error = AdaroundOptimizer._eval_recons_err_metrics(conv_wrapper, None, inp_tensor,
out_tensor)
assert np.isclose(hard_recons_error, 0.6102066, atol=1e-4)
assert np.isclose(soft_recons_error, 0.6107949, atol=1e-4)
assert np.isclose(hard_recons_error, 0.61097431182861, atol=1e-4)
assert np.isclose(soft_recons_error, 0.61089688539505, atol=1e-4)


# Adaround wrapper tests
Expand Down
4 changes: 2 additions & 2 deletions TrainingExtensions/torch/test/python/v1/test_adaround_optimizer.py
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Expand Up @@ -150,8 +150,8 @@ def test_compute_recons_metrics(self):
out_data)

print(recons_err_hard, recons_err_soft)
self.assertAlmostEqual(recons_err_hard, 0.610206663608551, places=3)
self.assertAlmostEqual(recons_err_soft, 0.6107949018478394, places=3)
self.assertAlmostEqual(recons_err_hard, 0.61097431182861, places=3)
self.assertAlmostEqual(recons_err_soft, 0.61089688539505, places=3)

@pytest.mark.cuda
def test_compute_output_with_adarounded_weights(self):
Expand Down
2 changes: 1 addition & 1 deletion TrainingExtensions/torch/test/python/v1/test_quantizer.py
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Expand Up @@ -2134,7 +2134,7 @@ def forward_pass(model, args):
tensor[0, 0, 0, 0] = 1000
sim.model.conv2.output_quantizers[0].update_encoding_stats(tensor)
sim.model.conv2.output_quantizers[0].compute_encoding()
assert sim.model.conv2.output_quantizers[0].encoding.max < 1.0
assert sim.model.conv2.output_quantizers[0].encoding.max < 1.1

sim.model.conv2.output_quantizers[0].quant_scheme = QuantScheme.post_training_tf
tensor = torch.rand(1, 10, 24, 24)
Expand Down
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