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Move MP traget_kpi from facade to MixedPrecisionQuantizationConfig #990

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merged 8 commits into from
Mar 11, 2024
Merged

Move MP traget_kpi from facade to MixedPrecisionQuantizationConfig #990

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af5970f
move target kpi to mixed precision config instead of API function
Mar 10, 2024
59b23bf
adjust tests with nre API
Mar 11, 2024
de8eed7
Merge main
Mar 11, 2024
5188ecc
fix import
Mar 11, 2024
9e21f7e
fix missing target_kpi moves in tests
Mar 11, 2024
ca10d58
fix missing target_kpi moves in tests
Mar 11, 2024
b4086fc
fix feature runners
Mar 11, 2024
8013914
fix custom layer test
Mar 11, 2024
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adjust tests with nre API
  • Loading branch information
Ofir Gordon authored and Ofir Gordon committed Mar 11, 2024
commit 59b23bf4394717b1fe6e47a30516c7d96d88ecfe
4 changes: 2 additions & 2 deletions tests/common_tests/base_test.py
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Expand Up @@ -35,13 +35,13 @@ def get_input_shapes(self):

def get_core_config(self):
return CoreConfig(quantization_config=self.get_quantization_config(),
mixed_precision_config=self.get_mixed_precision_v2_config(),
mixed_precision_config=self.get_mixed_precision_config(),
debug_config=self.get_debug_config())

def get_quantization_config(self):
return QuantizationConfig()

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return None

def get_debug_config(self):
Expand Down
5 changes: 3 additions & 2 deletions tests/keras_tests/feature_networks_tests/feature_networks/mixed_precision_bops_test.py
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Expand Up @@ -46,8 +46,9 @@ def get_tpc(self):
mp_bitwidth_candidates_list=self.mixed_precision_candidates_list,
name="mp_bopts_test")

def get_mixed_precision_v2_config(self):
return MixedPrecisionQuantizationConfig(num_of_images=1)
def get_mixed_precision_config(self):
return MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi())

def get_input_shapes(self):
return [[self.val_batch_size, 16, 16, 3]]
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6 changes: 4 additions & 2 deletions tests/keras_tests/feature_networks_tests/feature_networks/qat/qat_test.py
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Expand Up @@ -295,11 +295,13 @@ def __init__(self, unit_test, kpi_weights=np.inf, kpi_activation=np.inf, expecte

def run_test(self, **kwargs):
model_float = self.create_networks()
config = mct.core.CoreConfig(mixed_precision_config=MixedPrecisionQuantizationConfig())
config = mct.core.CoreConfig(
mixed_precision_config=MixedPrecisionQuantizationConfig(target_kpi=
mct.core.KPI(weights_memory=self.kpi_weights,
activation_memory=self.kpi_activation)))
qat_ready_model, quantization_info, custom_objects = mct.qat.keras_quantization_aware_training_init_experimental(
model_float,
self.representative_data_gen_experimental,
mct.core.KPI(weights_memory=self.kpi_weights, activation_memory=self.kpi_activation),
core_config=config,
fw_info=self.get_fw_info(),
target_platform_capabilities=self.get_tpc())
Expand Down
4 changes: 2 additions & 2 deletions .../keras_tests/feature_networks_tests/feature_networks/reused_layer_mixed_precision_test.py
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Expand Up @@ -51,8 +51,8 @@ def get_quantization_config(self):
relu_bound_to_power_of_2=True, weights_bias_correction=True,
input_scaling=True, activation_channel_equalization=True)

def get_mixed_precision_v2_config(self):
return MixedPrecisionQuantizationConfig()
def get_mixed_precision_config(self):
return MixedPrecisionQuantizationConfig(target_kpi=self.get_kpi())

def create_networks(self):
layer = layers.Conv2D(3, 4)
Expand Down
12 changes: 6 additions & 6 deletions tests/keras_tests/feature_networks_tests/feature_networks/weights_mixed_precision_tests.py
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Expand Up @@ -44,7 +44,7 @@ def get_quantization_config(self):
relu_bound_to_power_of_2=True, weights_bias_correction=True,
input_scaling=True, activation_channel_equalization=True)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi())

Expand Down Expand Up @@ -81,7 +81,7 @@ def get_quantization_config(self):
relu_bound_to_power_of_2=True, weights_bias_correction=True,
input_scaling=True, activation_channel_equalization=True)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(target_kpi=self.get_kpi())

def get_kpi(self):
Expand Down Expand Up @@ -225,7 +225,7 @@ class MixedPercisionCombinedNMSTest(MixedPercisionBaseTest):
def __init__(self, unit_test):
super().__init__(unit_test)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi(),
use_hessian_based_scores=False)
Expand Down Expand Up @@ -367,7 +367,7 @@ def get_quantization_config(self):
relu_bound_to_power_of_2=False, weights_bias_correction=False,
input_scaling=False, activation_channel_equalization=False)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(target_kpi=self.get_kpi())


Expand All @@ -383,7 +383,7 @@ def get_quantization_config(self):
input_scaling=False,
activation_channel_equalization=False)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi())

Expand Down Expand Up @@ -416,7 +416,7 @@ class MixedPercisionSearchLastLayerDistanceTest(MixedPercisionBaseTest):
def __init__(self, unit_test):
super().__init__(unit_test, val_batch_size=2)

def get_mixed_precision_v2_config(self):
def get_mixed_precision_config(self):
return mct.core.MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi(),
distance_weighting_method=get_last_layer_weights,
Expand Down
6 changes: 3 additions & 3 deletions tests/pytorch_tests/function_tests/test_pytorch_tp_model.py
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Expand Up @@ -29,7 +29,8 @@
from model_compression_toolkit.target_platform_capabilities.target_platform import TargetPlatformCapabilities
from model_compression_toolkit.target_platform_capabilities.target_platform.targetplatform2framework import LayerFilterParams
from model_compression_toolkit.target_platform_capabilities.target_platform.targetplatform2framework.attribute_filter import Greater, Smaller, Eq
from model_compression_toolkit.core.common.mixed_precision.mixed_precision_quantization_config import DEFAULT_MIXEDPRECISION_CONFIG
from model_compression_toolkit.core.common.mixed_precision.mixed_precision_quantization_config import \
DEFAULT_MIXEDPRECISION_CONFIG, MixedPrecisionQuantizationConfig
from model_compression_toolkit.target_platform_capabilities.constants import DEFAULT_TP_MODEL, IMX500_TP_MODEL, \
TFLITE_TP_MODEL, QNNPACK_TP_MODEL, KERNEL_ATTR, WEIGHTS_N_BITS, PYTORCH_KERNEL, BIAS_ATTR, BIAS
from model_compression_toolkit.core.pytorch.pytorch_implementation import PytorchImplementation
Expand Down Expand Up @@ -240,13 +241,12 @@ def rep_data():
rep_data,
target_platform_capabilities=tpc)

mp_qc = copy.deepcopy(DEFAULT_MIXEDPRECISION_CONFIG)
mp_qc = MixedPrecisionQuantizationConfig(target_kpi=mct.core.KPI(np.inf))
mp_qc.num_of_images = 1
core_config = mct.core.CoreConfig(quantization_config=mct.core.QuantizationConfig(),
mixed_precision_config=mp_qc)
quantized_model, _ = mct.ptq.pytorch_post_training_quantization(model,
rep_data,
target_kpi=mct.core.KPI(np.inf),
target_platform_capabilities=tpc,
core_config=core_config)

Expand Down
8 changes: 5 additions & 3 deletions tests/pytorch_tests/model_tests/feature_models/mixed_precision_activation_test.py
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Expand Up @@ -49,7 +49,8 @@ def get_core_configs(self):
qc = mct.core.QuantizationConfig(mct.core.QuantizationErrorMethod.MSE, mct.core.QuantizationErrorMethod.MSE,
relu_bound_to_power_of_2=False, weights_bias_correction=True,
input_scaling=False, activation_channel_equalization=False)
mpc = mct.core.MixedPrecisionQuantizationConfig(num_of_images=1)
mpc = mct.core.MixedPrecisionQuantizationConfig(num_of_images=1,
target_kpi=self.get_kpi())

return {"mixed_precision_activation_model": mct.core.CoreConfig(quantization_config=qc, mixed_precision_config=mpc)}

Expand Down Expand Up @@ -126,8 +127,9 @@ def __init__(self, unit_test):
def get_kpi(self):
return KPI(np.inf, np.inf)

def get_mixed_precision_v2_config(self):
return MixedPrecisionQuantizationConfig(num_of_images=4)
def get_mixed_precision_config(self):
return MixedPrecisionQuantizationConfig(num_of_images=4,
target_kpi=self.get_kpi())

def create_feature_network(self, input_shape):
return MixedPrecisionMultipleInputsNet(input_shape)
Expand Down