def CollectVarHistogram(vs_gs):
"""Adds histogram summaries for variables and gradients."""
for name, (var, grad) in vs_gs.FlattenItems():
with tf.device(var.device), tf.name_scope(name + '/summary'):
if isinstance(grad, tf.IndexedSlices):
var = tf.gather(var, grad.indices)
grad = grad.values
if var.dtype.is_complex:
var = tf.abs(var)
grad = tf.abs(grad)
histogram('var_hist/' + name, var)
histogram('grad_hist/' + name, grad)
def _MelSpectrogram(self, signal):
"""Computes the mel spectrogram from a waveform signal.
Args:
signal: f32 Tensor, shaped [batch_size, num_samples]
Returns:
f32 features Tensor, shaped [batch_size, num_frames, mel_channels]
"""
p = self.params
# FFT.
real_frequency_spectrogram = tf.signal.rfft(signal, [self._fft_size])
magnitude_spectrogram = tf.abs(real_frequency_spectrogram)
# Shape of magnitude_spectrogram is num_frames x (fft_size/2+1)
# Mel_weight is [num_spectrogram_bins, num_mel_bins]
mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
num_mel_bins=p.num_bins,
num_spectrogram_bins=self._fft_size // 2 + 1,
sample_rate=p.sample_rate,
lower_edge_hertz=p.lower_edge_hertz,
upper_edge_hertz=p.upper_edge_hertz,
dtype=tf.float32)
# Weight matrix implemented in the magnitude domain.
batch_size, num_frames, fft_channels = py_utils.GetShape(
magnitude_spectrogram, 3)
mel_spectrogram = tf.matmul(
tf.reshape(magnitude_spectrogram,
[batch_size * num_frames, fft_channels]),
mel_weight_matrix)
mel_spectrogram = tf.reshape(mel_spectrogram,
[batch_size, num_frames, p.num_bins])
return mel_spectrogram
def _iter_body(i, mat_m, mat_h, unused_old_mat_h, error, unused_run_step):
mat_m_i = (1 - alpha) * identity + alpha * mat_m
new_mat_m = tf.matmul(mat_power(mat_m_i, exponent), mat_m)
new_mat_h = tf.matmul(mat_h, mat_m_i)
new_error = tf.reduce_max(tf.abs(new_mat_m - identity))
return (i + 1, new_mat_m, new_mat_h, mat_h, new_error,
new_error < error)
def _SampleGumbelWithMax(phi, target_max, batch_seed, time_step, src_ids,
src_paddings):
"""Samples a set of Gumbel noises with a specified maximum value.
A set of values are sampled from Gumbel distributions with location parameters
`phi` under the condition that their maximum is equal to `target_max`.
The numerical stable implementation from Appendix B.3 of
https://arxiv.org/pdf/1903.06059.pdf is used.
Args:
phi: A float tensor of shape [tgt_batch, k] thtat represents location
parameters of Gumbel distributions.
target_max: A float tensor of shape [tgt_batch, 1] that represents the
target max values.
batch_seed: An int tensor of shape [src_batch] that holds a seed value for
each batch item. src_batch must be equal to tgt_batch / num_hyps_per_beam.
The same seed is used within each consecutive num_hyps_per_beam items
along the tgt_batch axis.
time_step: A float tensor used as a secondary seed.
src_ids: An int tensor of shape [src_batch, src_seq] that represents source
IDs. Used for turning the random seed into a function of source IDs.
src_paddings: A 0/1 float tensor of shape [src_batch, src_seq] where 1 means
that the corresponding element of src_ids is a padding.
Returns:
A float tensor like `phi` where their maximum values along the second axis
is (almost) equal to `target_max`.
"""
dtype = phi.dtype
tgt_batch = tf.shape(phi)[0]
k = tf.shape(phi)[1]
src_batch = tf.shape(batch_seed)[0]
num_hyps_per_beam = tgt_batch // src_batch
# Sample noises from Gumbel distributions with location parameters `phi`.
# shape: [src_batch, num_hyps_per_beam, k]
gumbel_noises = _BatchSampleGumbel(batch_seed, time_step, src_ids,
src_paddings, [num_hyps_per_beam, k],
dtype)
# shape: [num_hyps_per_beam, src_batch, k]
gumbel_noises = tf.transpose(gumbel_noises, perm=[1, 0, 2])
# shape: [tgt_batch, k]
gumbel_noises = tf.reshape(gumbel_noises, tf.shape(phi))
# shape: [tgt_batch, k]
g_phi = phi + gumbel_noises
# shape: [tgt_batch, 1]
z = tf.reduce_max(g_phi, axis=1, keepdims=True)
# Equation (23).
# shape: [tgt_batch, k]
v = target_max - g_phi + tf.math.log1p(
# Without taking max, sometimes the result of log1p would become NaN on
# TPU.
tf.maximum(-tf.exp(g_phi - z), tf.constant(-1., dtype=dtype)))
# Equation (24).
return target_max - tf.nn.relu(v) - tf.math.log1p(tf.exp(-tf.abs(v)))
def CollectVarHistogram(vs_gs):
"""Adds histogram summaries for variables and gradients."""
for name, (var, grad) in vs_gs.FlattenItems():
name = py_utils.SanitizeScopeKey(name)
with tf.device(var.device), tf.name_scope(name + '/summary'):
if isinstance(grad, tf.IndexedSlices):
var = tf.gather(var, grad.indices)
grad = grad.values
if var.dtype.is_complex:
var = tf.abs(var)
grad = tf.abs(grad)
if py_utils.IsEagerMode():
histogram_v2(f'var_hist/{name}', var)
histogram_v2(f'grad_hist/{name}', grad)
else:
histogram(f'var_hist/{name}', var)
histogram(f'grad_hist/{name}', grad)
def _generalized_inverse_pth_root(self, input_t, exponent, epsilon=1e-12):
input_t_f64 = tf.cast(input_t, tf.float64)
s, u, v = tf.linalg.svd(
input_t_f64 +
tf.eye(tf.shape(input_t_f64)[0], dtype=tf.float64) * epsilon,
full_matrices=True)
inv_s = tf.reshape(
tf.pow(tf.maximum(s, epsilon), tf.cast(exponent, tf.float64)),
[1, -1])
val = tf.matmul(u * inv_s, v, adjoint_b=True)
return tf.cast(val, tf.float32), tf.reduce_max(tf.abs(u - v))
def inverse_pth_root(self, input_t, exponent, epsilon=1e-12):
input_t_f64 = tf.cast(input_t, tf.float64)
s, u, v = tf.linalg.svd(
input_t_f64 +
tf.eye(tf.shape(input_t_f64)[0], dtype=tf.float64) * epsilon,
full_matrices=True)
val = tf.matmul(
tf.matmul(
u,
tf.linalg.tensor_diag(
tf.pow(tf.maximum(s, epsilon), tf.cast(exponent, tf.float64)))),
tf.transpose(v))
return tf.cast(val, tf.float32), tf.reduce_max(tf.abs(u - v))
def compute_relative_changes(eps, u, v, w, new_eps, new_u, new_v, new_w):
prev_sum_uvw = tf.stop_gradient((u + v + w) / eps)
sum_uvw = tf.stop_gradient((new_u + new_v + new_w) / new_eps)
# Compute the relative changes on margins of P.
# This will be used for stopping criteria.
# Note the last update on w would guarantee the
# margin constraint c is satisfied, so we don't
# need to check it here.
p = tf.exp(tf.stop_gradient(score_ / new_eps + sum_uvw))
p_a = tf.reduce_sum(p, axis=-1, keepdims=True)
p_b = tf.reduce_sum(p, axis=-2, keepdims=True)
delta_a = tf.abs(a - p_a) / (a + 1e-6)
delta_b = tf.abs(b - p_b) / (b + 1e-6)
new_delta = tf.reduce_max(delta_a)
new_delta = tf.maximum(new_delta, tf.reduce_max(delta_b))
# Compute the relative changes on assignment solution P.
# This will be used for stopping criteria.
delta_p = tf.abs(tf.exp(prev_sum_uvw) -
tf.exp(sum_uvw)) / (tf.exp(sum_uvw) + 1e-6)
new_delta = tf.maximum(new_delta, tf.reduce_max(delta_p))
return new_delta
def _testGradDrop(self, graddrop_params):
batch_size, dims = 4, 5
gd_layer = graddrop_params.Set(name='test_gd_layer').Instantiate()
linear_layer = builder_layers.LinearLayer.Params().Set(
name='test_linear_layer', input_dims=dims,
output_dims=dims).Instantiate()
x = tf.random.uniform((batch_size, dims))
x = linear_layer.FPropDefaultTheta(x)
# Make a copy of x after graddrop.
x_gd = gd_layer.FPropDefaultTheta(x)
# Compute a loss based on graddrop's version of x.
gd_loss_0 = tf.reduce_sum(x_gd**2)
gd_loss_1 = tf.reduce_sum(-tf.abs(x_gd))
gd_layer.SetLosses([
(gd_loss_0, 0.1),
(gd_loss_1, 0.2),
])
gd_total_loss = gd_loss_0 + gd_loss_1
gd_grad = tf.gradients(gd_total_loss, x)
# Compute the same loss based on the regular version of x.
loss_0 = tf.reduce_sum(x**2)
loss_1 = tf.reduce_sum(-tf.abs(x))
total_loss = loss_0 + loss_1
grad = tf.gradients(total_loss, x)
with self.session() as sess:
sess.run(tf.global_variables_initializer())
actual_total_loss, actual_grad, actual_gd_total_loss, actual_gd_grad = (
sess.run([total_loss, grad, gd_total_loss, gd_grad]))
# Verify that losses are similar, but the gradients are different.
self.assertAllClose(actual_total_loss, actual_gd_total_loss)
self.assertNotAllClose(actual_grad, actual_gd_grad)
def _update_mask(self, weights, threshold):
"""Updates the mask for a given weight tensor.
This functions first computes the cdf of the weight tensor, and estimates
the threshold value such that 'desired_sparsity' fraction of weights
have magnitude less than the threshold.
Args:
weights: The weight tensor that needs to be masked.
threshold: The current threshold value. The function will compute a new
threshold and return the exponential moving average using the current
value of threshold
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if sparsity is not defined
"""
if self._sparsity is None:
raise ValueError('Sparsity variable undefined')
sparsity = self._get_sparsity(weights.op.name)
with tf.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = tf.abs(weights)
k = tf.cast(
tf.round(
tf.cast(tf.size(abs_weights), tf.float32) *
(1 - sparsity)), tf.int32)
# Sort the entire array
values, _ = tf.nn.top_k(tf.reshape(abs_weights, [-1]),
k=tf.size(abs_weights))
# Grab the (k-1) th value
current_threshold = tf.gather(values, k - 1)
smoothed_threshold = tf.add_n([
tf.multiply(current_threshold, 1 - self._spec.threshold_decay),
tf.multiply(threshold, self._spec.threshold_decay)
])
new_mask = tf.cast(
tf.greater_equal(abs_weights, smoothed_threshold), tf.float32)
return smoothed_threshold, new_mask
def testGradDropSetLossesTwiceRaisesError(self):
batch_size, dims = 4, 5
gd_layer = graddrop.GradDrop.Params().Set(
name='test_gd_layer').Instantiate()
x = tf.random.uniform((batch_size, dims))
x_gd = gd_layer.FPropDefaultTheta(x)
gd_loss_0 = tf.reduce_sum(x_gd**2)
gd_loss_1 = tf.reduce_sum(-tf.abs(x_gd))
gd_layer.SetLosses([
(gd_loss_0, 0.1),
(gd_loss_1, 0.2),
])
with self.assertRaisesRegex(ValueError, r'.*Losses already set.*'):
gd_layer.SetLosses([
(gd_loss_0, 0.1),
(gd_loss_1, 0.2),
])
def _BBoxArea(bbox):
"""Computes the area of a 2-d bbox.
Vertices must be ordered clockwise or counter-clockwise. This function can
technically handle any kind of convex polygons.
Args:
bbox: a float Tensor of shape [..., 4, 2] of bboxes. The last coordinates
are the four corners of the bbox and (x, y). The corners must be given in
counter-clockwise order.
Returns:
Area of the bbox. Tensor of shape [..., 1].
"""
bbox_roll = tf.roll(bbox, shift=1, axis=-2)
det = tf.reduce_sum(
bbox[..., 0] * bbox_roll[..., 1] - bbox[..., 1] * bbox_roll[..., 0],
axis=-1,
keepdims=True) / 2.0
return tf.abs(det)
def inlined_matrix_inverse_pth_root(mat_g,
mat_g_size,
alpha,
iter_count=100,
error_tolerance=1e-6,
ridge_epsilon=1e-6):
"""Computes mat_g^alpha, where alpha = -1/p, p is one of 2, 4, or 8.
We use an iterative Schur-Newton method from equation 3.2 on page 9 of:
A Schur-Newton Method for the Matrix p-th Root and its Inverse
by Chun-Hua Guo and Nicholas J. Higham
SIAM Journal on Matrix Analysis and Applications,
2006, Vol. 28, No. 3 : pp. 788-804
https://pdfs.semanticscholar.org/0abe/7f77433cf5908bfe2b79aa91af881da83858.pdf
Args:
mat_g: the symmetric PSD matrix whose power it to be computed
mat_g_size: size of mat_g.
alpha: exponent, must be -1/p for p a positive integer.
iter_count: Maximum number of iterations.
error_tolerance: Error indicator, useful for early termination.
ridge_epsilon: Ridge epsilon added to make the matrix positive definite.
Returns:
mat_g^alpha
"""
alpha = tf.cast(alpha, tf.float64)
neg_alpha = -1.0 * alpha
exponent = 1.0 / neg_alpha
identity = tf.eye(tf.cast(mat_g_size, tf.int32), dtype=tf.float64)
def _unrolled_mat_pow_2(mat_m):
"""Computes mat_m^2."""
return tf.matmul(mat_m, mat_m)
def _unrolled_mat_pow_4(mat_m):
"""Computes mat_m^4."""
mat_pow_2 = _unrolled_mat_pow_2(mat_m)
return tf.matmul(mat_pow_2, mat_pow_2)
def _unrolled_mat_pow_8(mat_m):
"""Computes mat_m^4."""
mat_pow_4 = _unrolled_mat_pow_4(mat_m)
return tf.matmul(mat_pow_4, mat_pow_4)
def mat_power(mat_m, p):
"""Computes mat_m^p, for p == 2 or 4 or 8.
Args:
mat_m: a square matrix
p: a positive integer
Returns:
mat_m^p
"""
branch_index = tf.cast(p / 2 - 1, tf.int32)
return tf.switch_case(
branch_index, {
0: functools.partial(_unrolled_mat_pow_2, mat_m),
1: functools.partial(_unrolled_mat_pow_4, mat_m),
2: functools.partial(_unrolled_mat_pow_8, mat_m),
})
def _iter_condition(i, unused_mat_m, unused_mat_h, unused_old_mat_h, error,
run_step):
return tf.math.logical_and(
tf.math.logical_and(i < iter_count, error > error_tolerance),
run_step)
def _iter_body(i, mat_m, mat_h, unused_old_mat_h, error, unused_run_step):
mat_m_i = (1 - alpha) * identity + alpha * mat_m
new_mat_m = tf.matmul(mat_power(mat_m_i, exponent), mat_m)
new_mat_h = tf.matmul(mat_h, mat_m_i)
new_error = tf.reduce_max(tf.abs(new_mat_m - identity))
return (i + 1, new_mat_m, new_mat_h, mat_h, new_error,
new_error < error)
if mat_g_size == 1:
mat_h = tf.pow(mat_g + ridge_epsilon, alpha)
else:
damped_mat_g = mat_g + ridge_epsilon * identity
z = (1 - 1 / alpha) / (2 * tf.norm(damped_mat_g))
# The best value for z is
# (1 - 1/alpha) * (c_max^{-alpha} - c_min^{-alpha}) /
# (c_max^{1-alpha} - c_min^{1-alpha})
# where c_max and c_min are the largest and smallest singular values of
# damped_mat_g.
# The above estimate assumes that c_max > c_min * 2^p. (p = -1/alpha)
# Can replace above line by the one below, but it is less accurate,
# hence needs more iterations to converge.
# z = (1 - 1/alpha) / tf.trace(damped_mat_g)
# If we want the method to always converge, use z = 1 / norm(damped_mat_g)
# or z = 1 / tf.trace(damped_mat_g), but these can result in many
# extra iterations.
new_mat_m_0 = damped_mat_g * z
new_error = tf.reduce_max(tf.abs(new_mat_m_0 - identity))
new_mat_h_0 = identity * tf.pow(z, neg_alpha)
_, mat_m, mat_h, old_mat_h, error, convergence = tf.while_loop(
_iter_condition, _iter_body,
[0, new_mat_m_0, new_mat_h_0, new_mat_h_0, new_error, True])
error = tf.reduce_max(tf.abs(mat_m - identity))
is_converged = tf.cast(convergence, old_mat_h.dtype)
resultant_mat_h = is_converged * mat_h + (1 - is_converged) * old_mat_h
return resultant_mat_h, error
def max_assignment(score: tf.Tensor,
*,
elementwise_upper_bound: tf.Tensor,
row_sums: tf.Tensor,
col_sums: tf.Tensor,
epsilon: float = 0.1,
num_iterations: int = 50,
use_epsilon_scaling: bool = True):
"""Differentiable max assignment with margin and upper bound constraints.
Args:
score: a 3D tensor of size [batch_size, n_rows, n_columns]. score[i, j, k]
denotes the weight if the assignment on this entry is non-zero.
elementwise_upper_bound: a 3D tensor of size [batch_size, n_rows,
n_columns]. Each entry denotes the maximum value assignment[i, j, k] can
take and must be a non-negative value. For example, upper_bound[i, j,
k]=1.0 for binary assignment problem.
row_sums: a 2D tensor of size [batch_size, n_rows]. The row sum constraint.
The output assignment p[i, j, :] must sum to row_sums[i, j].
col_sums: a 2D tensor of size [batch_size, n_columns]. The column sum
constraint. The output assignment p[i, :, k] must sum to col_sums[i, k].
epsilon: the epsilon coefficient of entropy regularization. The value should
be within the range (0, 1]. `0.01` might work better than `0.1`. `0.1` may
not make the assignment close enough to 0 or 1.
num_iterations: the maximum number of iterations to perform.
use_epsilon_scaling: whether to use epsilon scaling. In practice, the
convergence of the iterative algorithm is much better if we start by
solving the optimization with a larger epsilon value and re-use the
solution (i.e. dual variables) for the instance with a smaller epsilon.
This is called the epsilon scaling trick. See [Schmitzer 2019]
(https://arxiv.org/pdf/1610.06519.pdf) as a reference. Here if
use_epsilon_scaling=True, after each iteration we decrease the running
epsilon by a constant factor until it reaches the target epsilon
value. We found this to work well for gradient backward propagation,
while the original scaling trick doesn't.
Returns:
A tuple with the following values.
- assignment: a 3D tensor of size [batch_size, n_rows, n_columns].
The output assignment.
- used_iter: a scalar tensor indicating the number of iterations used.
- eps: a scalar tensor indicating the stopping epsilon value.
- delta: a scalar tensor indicating the stopping delta value (the relative
change on the margins of assignment p in the last iteration).
"""
# Check if all shapes are correct
score_shape = score.shape
bsz = score_shape[0]
n = score_shape[1]
m = score_shape[2]
score = tf.ensure_shape(score, [bsz, n, m])
elementwise_upper_bound = tf.ensure_shape(elementwise_upper_bound,
[bsz, n, m])
row_sums = tf.ensure_shape(tf.expand_dims(row_sums, axis=2), [bsz, n, 1])
col_sums = tf.ensure_shape(tf.expand_dims(col_sums, axis=1), [bsz, 1, m])
# the total sum of row sums must be equal to total sum of column sums
sum_diff = tf.reduce_sum(row_sums, axis=1) - tf.reduce_sum(col_sums,
axis=2)
sum_diff = tf.abs(sum_diff)
tf.Assert(tf.reduce_all(sum_diff < 1e-6), [sum_diff])
# Convert upper_bound constraint into another margin constraint
# by adding auxiliary variables & scores. Tensor `a`, `b` and `c`
# represent the margins (i.e. reduced sum) of 3 axes respectively.
#
max_row_sums = tf.reduce_sum(elementwise_upper_bound,
axis=-1,
keepdims=True)
max_col_sums = tf.reduce_sum(elementwise_upper_bound,
axis=-2,
keepdims=True)
score_ = tf.stack([score, tf.zeros_like(score)], axis=1) # (bsz, 2, n, m)
a = tf.stack([row_sums, max_row_sums - row_sums], axis=1) # (bsz, 2, n, 1)
b = tf.stack([col_sums, max_col_sums - col_sums], axis=1) # (bsz, 2, 1, m)
c = tf.expand_dims(elementwise_upper_bound, axis=1) # (bsz, 1, n, m)
# Clip log(0) to a large negative values -1e+36 to avoid
# getting inf or NaN values in computation. Cannot use larger
# values because float32 would use `-inf` automatically.
#
tf.Assert(tf.reduce_all(a >= 0), [a])
tf.Assert(tf.reduce_all(b >= 0), [b])
tf.Assert(tf.reduce_all(c >= 0), [c])
log_a = tf.maximum(tf.math.log(a), -1e+36)
log_b = tf.maximum(tf.math.log(b), -1e+36)
log_c = tf.maximum(tf.math.log(c), -1e+36)
# Initialize the dual variables of margin constraints
u = tf.zeros_like(a)
v = tf.zeros_like(b)
w = tf.zeros_like(c)
eps = tf.constant(1.0 if use_epsilon_scaling else epsilon,
dtype=score.dtype)
epsilon = tf.constant(epsilon, dtype=score.dtype)
def do_updates(cur_iter, eps, u, v, w): # pylint: disable=unused-argument
# Epsilon scaling, i.e. gradually decreasing `eps` until it
# reaches the target `epsilon` value
cur_iter = tf.cast(cur_iter, u.dtype)
scaling = tf.minimum(0.6 * 1.04**cur_iter, 0.85)
eps = tf.maximum(epsilon, eps * scaling)
score_div_eps = score_ / eps
# Update u
log_q_1 = score_div_eps + (w + v) / eps
log_q_1 = tf.reduce_logsumexp(log_q_1, axis=-1, keepdims=True)
new_u = (log_a - tf.maximum(log_q_1, -1e+30)) * eps
# Update v
log_q_2 = score_div_eps + (w + new_u) / eps
log_q_2 = tf.reduce_logsumexp(log_q_2, axis=-2, keepdims=True)
new_v = (log_b - tf.maximum(log_q_2, -1e+30)) * eps
# Update w
log_q_3 = score_div_eps + (new_u + new_v) / eps
log_q_3 = tf.reduce_logsumexp(log_q_3, axis=-3, keepdims=True)
new_w = (log_c - tf.maximum(log_q_3, -1e+30)) * eps
return eps, new_u, new_v, new_w
def compute_relative_changes(eps, u, v, w, new_eps, new_u, new_v, new_w):
prev_sum_uvw = tf.stop_gradient((u + v + w) / eps)
sum_uvw = tf.stop_gradient((new_u + new_v + new_w) / new_eps)
# Compute the relative changes on margins of P.
# This will be used for stopping criteria.
# Note the last update on w would guarantee the
# margin constraint c is satisfied, so we don't
# need to check it here.
p = tf.exp(tf.stop_gradient(score_ / new_eps + sum_uvw))
p_a = tf.reduce_sum(p, axis=-1, keepdims=True)
p_b = tf.reduce_sum(p, axis=-2, keepdims=True)
delta_a = tf.abs(a - p_a) / (a + 1e-6)
delta_b = tf.abs(b - p_b) / (b + 1e-6)
new_delta = tf.reduce_max(delta_a)
new_delta = tf.maximum(new_delta, tf.reduce_max(delta_b))
# Compute the relative changes on assignment solution P.
# This will be used for stopping criteria.
delta_p = tf.abs(tf.exp(prev_sum_uvw) -
tf.exp(sum_uvw)) / (tf.exp(sum_uvw) + 1e-6)
new_delta = tf.maximum(new_delta, tf.reduce_max(delta_p))
return new_delta
for cur_iter in tf.range(num_iterations):
prev_eps, prev_u, prev_v, prev_w = eps, u, v, w
eps, u, v, w = do_updates(cur_iter, eps, u, v, w)
delta = compute_relative_changes(prev_eps, prev_u, prev_v, prev_w, eps, u,
v, w)
cur_iter = num_iterations
assignment = tf.exp((score_ + u + v + w) / eps)
assignment = assignment[:, 0]
return assignment, cur_iter, eps, delta
def _SmoothL1Norm(a):
"""Smoothed L1 norm."""
# F&F paper formula (3).
# http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf
return tf.where(tf.abs(a) < 1, 0.5 * tf.square(a), tf.abs(a) - 0.5)
def _Gradient(inputs, _, original_grad):
# Compute the gradients for each loss w.r.t. the inputs.
# TODO(jngiam): Look into whether TF dedups this computation.
per_loss_grads = []
for loss, _ in self._losses:
per_loss_grad = tf.gradients(loss, self._output_tensor)[0]
if per_loss_grad is None:
tf.logging.warning(
'Loss %s did not result in a gradient during '
'GradDrop computation.', loss)
else:
per_loss_grads.append(per_loss_grad)
if not per_loss_grads:
raise ValueError('No valid gradients for GradDrop.')
# Multiply the gradients with the inputs.
grads = per_loss_grads
if p.use_input_sign_only:
input_abs = tf.abs(
tf.cast(tf.abs(inputs) <= p.epsilon, tf.float32) + inputs)
grads = [grad * ((inputs) / (input_abs)) for grad in grads]
else:
grads = [grad * inputs for grad in grads]
# Sum gradient over batch, assuming that batch is always on dim 0.
if p.marginalize_batch_dim:
grads = [
tf.reduce_sum(grad, axis=0, keepdims=True)
for grad in grads
]
# First discretize all gradients into their sign values.
grad_sign_positive = [
tf.cast(grad > 0.0, tf.float32) for grad in grads
]
grad_sign_negative = [
tf.cast(grad < 0.0, tf.float32) for grad in grads
]
# Calculate the probability of positive gradients based on equation (1)
# in the GradDrop paper.
grad_abs_sum = tf.add_n([tf.abs(grad) for grad in grads])
prob_pos = (tf.add_n(grads) / (2. * grad_abs_sum + p.epsilon))
# Implementation of different scales for the keep function. Larger
# scales result in steeper keep functions.
prob_pos *= p.keep_prob_function_scale
if p.keep_prob_function == 'sigmoid':
# Standard sigmoid has derivative of 0.25 at 0 so the factor of 4.0
# allows the function scale in sigmoid to be compatible with the
# function scale in the linear case.
prob_pos = tf.sigmoid(4.0 * prob_pos)
elif p.keep_prob_function == 'linear':
prob_pos += 0.5
# The main, default mode of GradDrop. Only gradients of one sign are kept,
# and which sign is calculated via equation (1) of the main paper.
prob_pos = tf.cast(prob_pos >= tf.random.uniform(prob_pos.shape),
tf.float32) - 0.5
grad_masks = [
(gsp - gsn) * prob_pos >= 0
for (gsn, gsp) in zip(grad_sign_negative, grad_sign_positive)
]
# This diag value gives us the percentage of grads which are kept.
gradmask_diag = [tf.cast(gm, tf.float32) for gm in grad_masks]
diag = tf.reduce_mean(tf.add_n(gradmask_diag) / len(grad_masks))
summary_utils.scalar('average_grad_mask', diag)
leak_ratios = [leak_ratio for _, leak_ratio in self._losses]
transformed_per_loss_grads = [
grad * (leak + (1.0 - leak) * tf.cast(grad_mask, tf.float32))
for (leak, grad,
grad_mask) in zip(leak_ratios, per_loss_grads, grad_masks)
]
transformed_grad = tf.cast(tf.add_n(transformed_per_loss_grads),
original_grad.dtype)
if not p.keep_gradnorm_constant:
return transformed_grad
transformed_grad_norm = tf.sqrt(tf.reduce_sum(transformed_grad**2))
original_grad_norm = tf.sqrt(tf.reduce_sum(original_grad**2))
return transformed_grad * original_grad_norm / (
transformed_grad_norm + p.epsilon)
def _maybe_update_block_mask(self, weights, threshold):
"""Performs block-granular masking of the weights.
Block pruning occurs only if the block_height or block_width is > 1 and
if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise
pruning occurs.
Args:
weights: The weight tensor that needs to be masked.
threshold: The current threshold value. The function will compute a new
threshold and return the exponential moving average using the current
value of threshold
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if block pooling function is not AVG or MAX
"""
block_dims = self._get_block_dims(weights.op.name)
squeezed_weights = tf.squeeze(weights)
if squeezed_weights.get_shape().ndims != 2 or block_dims == [1, 1]:
return self._update_mask(weights, threshold)
for i in range(2):
if block_dims[i] == -1:
block_dims[i] = squeezed_weights.get_shape()[i]
if self._block_pooling_function not in ['AVG', 'MAX']:
raise ValueError(
'Unknown pooling function for block sparsity: %s' %
self._block_pooling_function)
with tf.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = tf.abs(squeezed_weights)
pool_window = block_dims
pool_fn = pruning_utils.factorized_pool
squeeze_axis = None
if not self._spec.use_tpu:
pool_fn = tf.nn.pool
abs_weights = tf.reshape(abs_weights, [
1,
abs_weights.get_shape()[0],
abs_weights.get_shape()[1], 1
])
squeeze_axis = [0, 3]
pooled_weights = pool_fn(abs_weights,
window_shape=pool_window,
pooling_type=self._block_pooling_function,
strides=pool_window,
padding='SAME',
name=weights.op.name + '_pooled')
if pooled_weights.get_shape().ndims != 2:
pooled_weights = tf.squeeze(pooled_weights, axis=squeeze_axis)
smoothed_threshold, new_mask = self._update_mask(
pooled_weights, threshold)
updated_mask = pruning_utils.expand_tensor(new_mask, block_dims)
sliced_mask = tf.slice(updated_mask, [0, 0], [
squeezed_weights.get_shape()[0],
squeezed_weights.get_shape()[1]
])
return smoothed_threshold, tf.reshape(sliced_mask, tf.shape(weights))