def qconv_model():
x = x_in = keras.layers.Input((23, 23, 1), name="input")
x = QActivation("quantized_relu(4)", name="QA_0")(x)
x = QConv2D(16,
2,
2,
kernel_quantizer=quantizers.binary(),
bias_quantizer=quantizers.ternary(),
name="qconv2d_1")(x)
x = QConv2D(8,
2,
2,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qconv2D_2")(x)
x = QConv2D(2,
2,
2,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qconv2d_3")(x)
x = QActivation("quantized_bits(6, 0, 1)", name="QA_4")(x)
model = keras.Model(inputs=[x_in], outputs=[x])
return model
def convert_to_qkeras_quantizer(self,
alpha=None,
use_stochastic_rounding=False):
"""convert qtools quantizer to qkeras quantizer."""
return quantizers.binary(
use_01=self.use_01,
alpha=alpha,
use_stochastic_rounding=use_stochastic_rounding)
def test_Binary():
qkeras_quantizer = quantizers.binary()
qtools_quantizer = quantizer_impl.Binary()
qtools_quantizer.convert_qkeras_quantizer(qkeras_quantizer)
new_quantizer = qtools_quantizer.convert_to_qkeras_quantizer(
alpha=qkeras_quantizer.alpha,
use_stochastic_rounding=qkeras_quantizer.use_stochastic_rounding)
result = new_quantizer.__dict__
for (key, val) in result.items():
assert_equal(val, qkeras_quantizer.__dict__[key])
def test_util_layers():
input_quantizers = None # quantizers.quantized_bits(4, 0, 1)
act = "quantized_bits(6, 0, 1)"
x = x_in = keras.layers.Input((24, 24, 1), name="input")
x = QActivation(act, name="QA_0")(x)
x = keras.layers.Reshape((12 * 12, 4, 1), name="reshape_1")(x)
x = keras.layers.MaxPooling2D(pool_size=(2, 2), name="maxpooling_2")(x)
x = keras.layers.Flatten(name="flatten_3")(x)
x = QDense(30,
kernel_quantizer=quantizers.binary(use_01=1),
bias_quantizer=quantizers.binary(use_01=1),
activation=quantizers.quantized_po2(3, 2),
name="qdense_4")(x)
model = keras.Model(inputs=[x_in], outputs=[x])
dtype_dict = run(model, input_quantizers)
multiplier = dtype_dict["qdense_4"]["multiplier"]
assert multiplier["quantizer_type"] == "quantized_bits"
assert multiplier["bits"] == 6
assert multiplier["int_bits"] == 1
assert multiplier["is_signed"] == 1
assert multiplier["op_type"] == "and"
accumulator = dtype_dict["qdense_4"]["accumulator"]
assert accumulator["quantizer_type"] == "quantized_bits"
assert accumulator["bits"] == 15
assert accumulator["int_bits"] == 10
assert accumulator["is_signed"] == 1
assert accumulator["op_type"] == "add"
output = dtype_dict["qdense_4"]["output_quantizer"]
assert output["quantizer_type"] == "quantized_po2"
assert output["bits"] == 3
assert output["is_signed"] == 1
assert output["max_value"] == 2
def qdense_model_fork():
x = x_in = keras.layers.Input((23, ), name="input")
x = QDense(10,
kernel_quantizer=quantizers.quantized_bits(5, 0, 1),
bias_quantizer=quantizers.quantized_bits(5, 0, 1),
activation=quantizers.quantized_po2(3, 1),
name="qdense_0")(x)
x = QDense(20,
kernel_quantizer=quantizers.quantized_bits(5, 0, 1),
bias_quantizer=quantizers.quantized_bits(5, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qdense_1")(x)
x = QActivation("quantized_relu(4)", name="QA_2")(x)
x_1 = QDense(30,
kernel_quantizer=quantizers.binary(),
bias_quantizer=quantizers.binary(),
name="qdense_3")(x)
x_2 = QActivation("quantized_relu(6,2)", name="QA_3")(x)
model = keras.Model(inputs=[x_in], outputs=[
x_1,
x_2,
])
return model
model.add(BatchNormalization(name='bn'))
model.add(QActivation(activation=activation_quantizer))
model.compile()
return model, is_xnor, test_no
@pytest.fixture(scope='module')
def randX_100_10():
return randX(100, 10)
@pytest.mark.parametrize(
'test_no,N,kernel_quantizer,bias_quantizer,activation_quantizer,use_batchnorm,is_xnor',
[(1, 10, ternary(alpha=1), quantized_bits(5,
2), 'binary_tanh', False, False),
(2, 10, binary(), quantized_bits(5, 2), 'binary_tanh', False, True),
(3, 10, ternary(alpha='auto'), quantized_bits(5,
2), binary(), True, True),
(4, 10, ternary(alpha='auto'), quantized_bits(5,
2), 'ternary', True, False),
(5, 10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.2), True, False),
(6, 10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.8), True, False),
(7, 10, binary(), quantized_bits(5, 2), binary(), False, True)])
def test_btnn(make_btnn, randX_100_10):
model, is_xnor, test_no = make_btnn
X = randX_100_10
cfg = hls4ml.utils.config_from_keras_model(model, granularity='name')
hls_model = hls4ml.converters.convert_from_keras_model(
model,
def test_qenergy():
x = x_in = keras.layers.Input((784, ), name="input")
x = QDense(300,
kernel_quantizer=quantizers.binary(),
bias_quantizer=quantizers.binary(),
name="d0")(x)
x = QActivation("quantized_relu(4,0)", name="d0_qr4")(x)
x = QDense(100,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
name="d1")(x)
x = QAdaptiveActivation("quantized_relu", 4, name="d1_qr4")(x)
x = QDense(10,
kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
name="d2")(x)
x = keras.layers.Activation("softmax", name="softmax")(x)
model = keras.Model(inputs=[x_in], outputs=[x])
# print(model.summary())
reference_internal = "int8"
reference_accumulator = "int32"
# get reference energy cost
q = run_qtools.QTools(model,
process="horowitz",
source_quantizers=reference_internal,
is_inference=False,
weights_path=None,
keras_quantizer=reference_internal,
keras_accumulator=reference_accumulator,
for_reference=True)
ref_energy_dict = q.pe(weights_on_memory="sram",
activations_on_memory="sram",
min_sram_size=8 * 16 * 1024 * 1024,
rd_wr_on_io=False)
reference_size = q.extract_energy_sum(qtools_settings.cfg.include_energy,
ref_energy_dict)
# get trial energy cost
q = run_qtools.QTools(model,
process="horowitz",
source_quantizers=reference_internal,
is_inference=False,
weights_path=None,
keras_quantizer=reference_internal,
keras_accumulator=reference_accumulator,
for_reference=False)
trial_energy_dict = q.pe(weights_on_memory="sram",
activations_on_memory="sram",
min_sram_size=8 * 16 * 1024 * 1024,
rd_wr_on_io=False)
trial_size = q.extract_energy_sum(qtools_settings.cfg.include_energy,
trial_energy_dict)
# Reference energy number is now updated with keras_accumulator as
# output quantizer
tmp = ref_energy_dict["d0"]["energy"]
assert tmp["inputs"] == pytest.approx(372.77, abs=0.1)
assert tmp["outputs"] == pytest.approx(570.57, abs=0.1)
assert tmp["parameters"] == pytest.approx(111975.96, abs=0.1)
assert tmp["op_cost"] == pytest.approx(70560.0, abs=0.1)
tmp = ref_energy_dict["d1"]["energy"]
assert tmp["inputs"] == pytest.approx(570.57, abs=0.1)
assert tmp["outputs"] == pytest.approx(190.19, abs=0.1)
assert tmp["parameters"] == pytest.approx(14313.66, abs=0.1)
assert tmp["op_cost"] == pytest.approx(26500.0, abs=0.1)
tmp = ref_energy_dict["d2"]["energy"]
assert tmp["inputs"] == pytest.approx(190.19, abs=0.1)
assert tmp["outputs"] == pytest.approx(19.02, abs=0.1)
assert tmp["parameters"] == pytest.approx(483.08, abs=0.1)
assert tmp["op_cost"] == pytest.approx(883.33, abs=0.1)
# Trial
tmp = trial_energy_dict["d0"]["energy"]
assert tmp["inputs"] == pytest.approx(372.77, abs=0.1)
assert tmp["outputs"] == pytest.approx(342.34, abs=0.1)
assert tmp["parameters"] == pytest.approx(13997.95, abs=0.1)
assert tmp["op_cost"] == pytest.approx(15729.0, abs=0.1)
tmp = trial_energy_dict["d1"]["energy"]
assert tmp["inputs"] == pytest.approx(72.27, abs=0.1)
assert tmp["outputs"] == pytest.approx(110.31, abs=0.1)
assert tmp["parameters"] == pytest.approx(7158.73, abs=0.1)
assert tmp["op_cost"] == pytest.approx(3250.0, abs=0.1)
tmp = trial_energy_dict["d2"]["energy"]
assert tmp["inputs"] == pytest.approx(26.63, abs=0.1)
assert tmp["outputs"] == pytest.approx(11.41, abs=0.1)
assert tmp["parameters"] == pytest.approx(243.44, abs=0.1)
assert tmp["op_cost"] == pytest.approx(102.08, abs=0.1)
# print(ref_energy_dict)
# print(trial_energy_dict)
assert int(reference_size) == 226629
assert int(trial_size) == 41070
if use_batchnorm:
model.add(BatchNormalization(name='bn'))
model.add(QActivation(activation=activation_quantizer))
model.compile()
return model, is_xnor
@pytest.fixture(scope='module')
def randX_100_10():
return randX(100, 10)
@pytest.mark.parametrize(
'N,kernel_quantizer,bias_quantizer,activation_quantizer,use_batchnorm,is_xnor',
[(10, ternary(alpha=1), quantized_bits(5, 2), 'binary_tanh', False, False),
(10, binary(), quantized_bits(5, 2), 'binary_tanh', False, True),
(10, ternary(alpha='auto'), quantized_bits(5, 2), binary(), True, True),
(10, ternary(alpha='auto'), quantized_bits(5, 2), 'ternary', True, False),
(10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.2), True, False),
(10, ternary(alpha='auto'), quantized_bits(
5, 2), ternary(threshold=0.8), True, False),
(10, binary(), quantized_bits(5, 2), binary(), False, True)])
def test_btnn(make_btnn, randX_100_10):
model, is_xnor = make_btnn
X = randX_100_10
cfg = hls4ml.utils.config_from_keras_model(model, granularity='name')
hls_model = hls4ml.converters.convert_from_keras_model(model,
output_dir='btnn',
hls_config=cfg)
hls_model.compile()
