def test_quantized_tanh_limits(bits, sigmoid_type, use_real_tanh, test_values, expected_values):
"""Test the min and max values of quantized_tanh function with three different sigmoid variants."""
set_internal_sigmoid(sigmoid_type)
x = K.placeholder(ndim=2)
f = K.function([x], [quantized_tanh(bits, symmetric=True, use_real_tanh=use_real_tanh)(x)])
set_internal_sigmoid(_default_sigmoid_type)
result = f([test_values])[0]
min_max = np.array(
[quantized_tanh(bits, symmetric=True, use_real_tanh=use_real_tanh).min(),
quantized_tanh(bits, symmetric=True, use_real_tanh=use_real_tanh).max()])
assert_allclose(result, expected_values, rtol=1e-05)
assert_allclose(result, min_max, rtol=1e-05)
def test_qrnn(rnn, all_weights_signature, expected_output):
K.set_learning_phase(0)
np.random.seed(22)
tf.random.set_seed(22)
x = x_in = Input((2, 4), name='input')
x = rnn(16,
activation=quantized_tanh(bits=8, symmetric=True),
kernel_quantizer=quantized_bits(8, 0, 1, alpha=1.0),
recurrent_quantizer=quantized_bits(8, 0, 1, alpha=1.0),
bias_quantizer=quantized_bits(8, 0, 1, alpha=1.0),
state_quantizer=quantized_bits(4, 0, 1, alpha=1.0),
name='qrnn_0')(x)
x = QDense(4,
kernel_quantizer=quantized_bits(6, 2, 1, alpha=1.0),
bias_quantizer=quantized_bits(4, 0, 1),
name='dense')(x)
x = Activation('softmax', name='softmax')(x)
model = Model(inputs=[x_in], outputs=[x])
# reload the model to ensure saving/loading works
json_string = model.to_json()
clear_session()
model = quantized_model_from_json(json_string)
# Save the model as an h5 file using Keras's model.save()
fd, fname = tempfile.mkstemp('.h5')
model.save(fname)
del model # Delete the existing model
# Return a compiled model identical to the previous one
model = load_qmodel(fname)
# Clean the created h5 file after loading the model
os.close(fd)
os.remove(fname)
# apply quantizer to weights
model_save_quantized_weights(model)
all_weights = []
for layer in model.layers:
for i, weights in enumerate(layer.get_weights()):
w = np.sum(weights)
all_weights.append(w)
all_weights = np.array(all_weights)
assert all_weights.size == all_weights_signature.size
assert np.all(all_weights == all_weights_signature)
# test forward:
inputs = 2 * np.random.rand(10, 2, 4)
actual_output = model.predict(inputs).astype(np.float16)
assert_allclose(actual_output, expected_output, rtol=1e-4)
def test_quantized_tanh(bits, use_real_tanh, test_values, expected_values):
"""Test quantized_tanh function with three different sigmoid variants."""
# store previous sigmoid type
set_internal_sigmoid('hard')
x = K.placeholder(ndim=2)
f = K.function([x], [quantized_tanh(bits, symmetric=True, use_real_tanh=use_real_tanh)(x)])
set_internal_sigmoid(_default_sigmoid_type)
result = f([test_values])[0]
assert_allclose(result, expected_values, rtol=1e-05)
def main():
# check the mean value of samples from stochastic_rounding for po2
np.random.seed(42)
count = 100000
val = 42
a = K.constant([val] * count)
b = quantized_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be close to ", val)
b = quantized_relu_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be close to ", val)
a = K.constant([-1] * count)
b = quantized_relu_po2(use_stochastic_rounding=True)(a)
res = np.sum(K.eval(b)) / count
print(res, "should be all ", 0)
# non-stochastic rounding quantizer.
a = K.constant([-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0])
a = K.constant([0.194336])
print(" a =", K.eval(a).astype(np.float16))
print("qa =", K.eval(quantized_relu(6,2)(a)).astype(np.float16))
print("ss =", K.eval(smooth_sigmoid(a)).astype(np.float16))
print("hs =", K.eval(hard_sigmoid(a)).astype(np.float16))
print("ht =", K.eval(hard_tanh(a)).astype(np.float16))
print("st =", K.eval(smooth_tanh(a)).astype(np.float16))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print("qb_111 =", K.eval(quantized_bits(1,1,1)(c)).astype(np.float16))
print("qb_210 =", K.eval(quantized_bits(2,1,0)(c)).astype(np.float16))
print("qb_211 =", K.eval(quantized_bits(2,1,1)(c)).astype(np.float16))
print("qb_300 =", K.eval(quantized_bits(3,0,0)(c)).astype(np.float16))
print("qb_301 =", K.eval(quantized_bits(3,0,1)(c)).astype(np.float16))
c_1000 = K.constant(np.array([list(K.eval(c))] * 1000))
b = np.sum(K.eval(bernoulli()(c_1000)).astype(np.int32), axis=0) / 1000.0
print(" hs =", K.eval(hard_sigmoid(c)).astype(np.float16))
print(" b_all =", b.astype(np.float16))
T = 0.0
t = K.eval(stochastic_ternary(alpha="auto")(c_1000))
for i in range(10):
print("stochastic_ternary({}) =".format(i), t[i])
print(" st_all =", np.round(
np.sum(t.astype(np.float32), axis=0).astype(np.float16) /
1000.0, 2).astype(np.float16))
print(" ternary =", K.eval(ternary(threshold=0.5)(c)).astype(np.int32))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print(" b_10 =", K.eval(binary(1)(c)).astype(np.float16))
print("qr_10 =", K.eval(quantized_relu(1,0)(c)).astype(np.float16))
print("qr_11 =", K.eval(quantized_relu(1,1)(c)).astype(np.float16))
print("qr_20 =", K.eval(quantized_relu(2,0)(c)).astype(np.float16))
print("qr_21 =", K.eval(quantized_relu(2,1)(c)).astype(np.float16))
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("smooth"); print("with smooth sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("real"); print("with real sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("hard")
print(" c =", K.eval(c).astype(np.float16))
print("q2_31 =", K.eval(quantized_po2(3,1)(c)).astype(np.float16))
print("q2_32 =", K.eval(quantized_po2(3,2)(c)).astype(np.float16))
print("qr2_21 =", K.eval(quantized_relu_po2(2,1)(c)).astype(np.float16))
print("qr2_22 =", K.eval(quantized_relu_po2(2,2)(c)).astype(np.float16))
print("qr2_44 =", K.eval(quantized_relu_po2(4,1)(c)).astype(np.float16))
# stochastic rounding
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print("q2_32_2 =", K.eval(quantized_relu_po2(32,2)(c)).astype(np.float16))
b = K.eval(stochastic_binary()(c_1000)).astype(np.int32)
for i in range(5):
print("sbinary({}) =".format(i), b[i])
print("sbinary =", np.round(np.sum(b, axis=0) / 1000.0, 2).astype(np.float16))
print(" binary =", K.eval(binary()(c)).astype(np.int32))
print(" c =", K.eval(c).astype(np.float16))
for i in range(10):
print(" s_bin({}) =".format(i),
K.eval(binary(use_stochastic_rounding=1)(c)).astype(np.int32))
for i in range(10):
print(" s_po2({}) =".format(i),
K.eval(quantized_po2(use_stochastic_rounding=1)(c)).astype(np.int32))
for i in range(10):
print(
" s_relu_po2({}) =".format(i),
K.eval(quantized_relu_po2(use_stochastic_rounding=1)(c)).astype(
np.int32))
def main():
np.random.seed(42)
a = K.constant([-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0, 3.0])
a = K.constant([0.194336])
print(" a =", K.eval(a).astype(np.float16))
print("qa =", K.eval(quantized_relu(6,2)(a)).astype(np.float16))
print("ss =", K.eval(smooth_sigmoid(a)).astype(np.float16))
print("hs =", K.eval(hard_sigmoid(a)).astype(np.float16))
print("ht =", K.eval(hard_tanh(a)).astype(np.float16))
print("st =", K.eval(smooth_tanh(a)).astype(np.float16))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print("qb_111 =", K.eval(quantized_bits(1,1,1)(c)).astype(np.float16))
print("qb_210 =", K.eval(quantized_bits(2,1,0)(c)).astype(np.float16))
print("qb_211 =", K.eval(quantized_bits(2,1,1)(c)).astype(np.float16))
print("qb_300 =", K.eval(quantized_bits(3,0,0)(c)).astype(np.float16))
print("qb_301 =", K.eval(quantized_bits(3,0,1)(c)).astype(np.float16))
c_1000 = K.constant(np.array([list(K.eval(c))] * 1000))
b = np.sum(K.eval(bernoulli()(c_1000)).astype(np.int32), axis=0) / 1000.0
print(" hs =", K.eval(hard_sigmoid(c)).astype(np.float16))
print(" b_all =", b.astype(np.float16))
T = 0.0
t = K.eval(stochastic_ternary(threshold=T)(c_1000)).astype(np.int32)
for i in range(10):
print("sternary({}) =".format(i), t[i])
print(" st_all =", np.round(
np.sum(t.astype(np.float32), axis=0).astype(np.float16) /
1000.0, 2).astype(np.float16))
print(" ternary =", K.eval(ternary(threshold=0.5)(c)).astype(np.int32))
b = K.eval(stochastic_binary()(c_1000)).astype(np.int32)
for i in range(5):
print("sbinary({}) =".format(i), b[i])
print("sbinary =", np.round(np.sum(b, axis=0) / 1000.0, 2).astype(np.float16))
print(" binary =", K.eval(binary()(c)).astype(np.int32))
c = K.constant(np.arange(-1.5, 1.51, 0.3))
print(" c =", K.eval(c).astype(np.float16))
print(" b_10 =", K.eval(binary(1)(c)).astype(np.float16))
print("qr_10 =", K.eval(quantized_relu(1,0)(c)).astype(np.float16))
print("qr_11 =", K.eval(quantized_relu(1,1)(c)).astype(np.float16))
print("qr_20 =", K.eval(quantized_relu(2,0)(c)).astype(np.float16))
print("qr_21 =", K.eval(quantized_relu(2,1)(c)).astype(np.float16))
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("smooth"); print("with smooth sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("real"); print("with real sigmoid")
print("qr_101 =", K.eval(quantized_relu(1,0,1)(c)).astype(np.float16))
print("qr_111 =", K.eval(quantized_relu(1,1,1)(c)).astype(np.float16))
print("qr_201 =", K.eval(quantized_relu(2,0,1)(c)).astype(np.float16))
print("qr_211 =", K.eval(quantized_relu(2,1,1)(c)).astype(np.float16))
print("qt_200 =", K.eval(quantized_tanh(2,0)(c)).astype(np.float16))
print("qt_210 =", K.eval(quantized_tanh(2,1)(c)).astype(np.float16))
print("qt_201 =", K.eval(quantized_tanh(2,0,1)(c)).astype(np.float16))
print("qt_211 =", K.eval(quantized_tanh(2,1,1)(c)).astype(np.float16))
set_internal_sigmoid("hard")
print(" c =", K.eval(c).astype(np.float16))
print("q2_31 =", K.eval(quantized_po2(3,1)(c)).astype(np.float16))
print("q2_32 =", K.eval(quantized_po2(3,2)(c)).astype(np.float16))
print("qr2_21 =", K.eval(quantized_relu_po2(2,1)(c)).astype(np.float16))
print("qr2_22 =", K.eval(quantized_relu_po2(2,2)(c)).astype(np.float16))
print("qr2_44 =", K.eval(quantized_relu_po2(4,1)(c)).astype(np.float16))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
print("q2_32_2 =", K.eval(quantized_relu_po2(32,2)(c)).astype(np.float16))
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
print(str(w[-1].message))
