def _compare_expand_tensor_with_kronecker_product(self, tensor, block_dim):
with self.cached_session() as session:
variables.global_variables_initializer().run()
expanded_tensor = pruning_utils.expand_tensor(tensor, block_dim)
kronecker_product = pruning_utils.kronecker_product(
tensor, array_ops.ones(block_dim))
expanded_tensor_val, kronecker_product_val = session.run(
[expanded_tensor, kronecker_product])
self.assertAllEqual(expanded_tensor_val, kronecker_product_val)
def _compare_expand_tensor_with_kronecker_product(self, tensor, block_dim):
with self.test_session() as session:
variables.global_variables_initializer().run()
expanded_tensor = pruning_utils.expand_tensor(tensor, block_dim)
kronecker_product = pruning_utils.kronecker_product(
tensor, array_ops.ones(block_dim))
expanded_tensor_val, kronecker_product_val = session.run(
[expanded_tensor, kronecker_product])
self.assertAllEqual(expanded_tensor_val, kronecker_product_val)
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
"""
squeezed_weights = array_ops.squeeze(weights)
if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [
1, 1
]:
return self._update_mask(weights, threshold)
if self._block_pooling_function not in ['AVG', 'MAX']:
raise ValueError(
'Unknown pooling function for block sparsity: %s' %
self._block_pooling_function)
with ops.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = math_ops.abs(squeezed_weights)
pool_window = [self._block_dim[0], self._block_dim[1]]
pool_fn = pruning_utils.factorized_pool
squeeze_axis = None
if not self._spec.use_tpu:
pool_fn = nn_ops.pool
abs_weights = array_ops.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 = array_ops.squeeze(pooled_weights,
axis=squeeze_axis)
smoothed_threshold, new_mask = self._update_mask(
pooled_weights, threshold)
updated_mask = pruning_utils.expand_tensor(new_mask,
self._block_dim)
sliced_mask = array_ops.slice(updated_mask, [0, 0], [
squeezed_weights.get_shape()[0],
squeezed_weights.get_shape()[1]
])
return smoothed_threshold, array_ops.reshape(sliced_mask,
array_ops.shape(weights))
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
"""
squeezed_weights = array_ops.squeeze(weights)
if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [1, 1]:
return self._update_mask(weights, threshold)
if self._block_pooling_function not in ['AVG', 'MAX']:
raise ValueError('Unknown pooling function for block sparsity: %s' %
self._block_pooling_function)
with ops.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = math_ops.abs(squeezed_weights)
pool_window = [self._block_dim[0], self._block_dim[1]]
pool_fn = pruning_utils.factorized_pool
squeeze_axis = None
if not self._spec.use_tpu:
pool_fn = nn_ops.pool
abs_weights = array_ops.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 = array_ops.squeeze(pooled_weights, axis=squeeze_axis)
smoothed_threshold, new_mask = self._update_mask(pooled_weights,
threshold)
updated_mask = pruning_utils.expand_tensor(new_mask, self._block_dim)
sliced_mask = array_ops.slice(
updated_mask, [0, 0],
[squeezed_weights.get_shape()[0],
squeezed_weights.get_shape()[1]])
return smoothed_threshold, array_ops.reshape(sliced_mask,
array_ops.shape(weights))