def test_generate_remapping_with_shifted_vocab(self):
"""Tests where vocab is the same, but shifted / ordered differently."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0)
expected_remapping = [2, 0, 1]
expected_num_present = 3
with self.cached_session():
self.assertAllEqual(expected_remapping, self.evaluate(remapping))
self.assertAllEqual(expected_num_present, self.evaluate(num_present))
def test_generate_remapping_with_offset(self):
"""Tests offset and num_new_vocab logic."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=1,
new_vocab_offset=1)
expected_remapping = [0]
expected_num_present = 1
with self.cached_session():
self.assertAllEqual(expected_remapping, self.evaluate(remapping))
self.assertAllEqual(expected_num_present, self.evaluate(num_present))
def test_generate_remapping_with_offset(self):
"""Tests offset and num_new_vocab logic."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=1,
new_vocab_offset=1)
expected_remapping = [0]
expected_num_present = 1
with self.cached_session():
self.assertAllEqual(expected_remapping, remapping.eval())
self.assertAllEqual(expected_num_present, num_present.eval())
def test_generate_remapping_with_no_vocab_changes(self):
"""Tests where vocab does not change at all."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.old_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0)
expected_remapping = range(0, 3)
expected_num_present = 3
with self.cached_session():
self.assertAllEqual(expected_remapping, self.evaluate(remapping))
self.assertAllEqual(expected_num_present, self.evaluate(num_present))
def test_generate_remapping_with_shifted_vocab(self):
"""Tests where vocab is the same, but shifted / ordered differently."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0)
expected_remapping = [2, 0, 1]
expected_num_present = 3
with self.cached_session():
self.assertAllEqual(expected_remapping, remapping.eval())
self.assertAllEqual(expected_num_present, num_present.eval())
def test_generate_remapping_with_no_vocab_changes(self):
"""Tests where vocab does not change at all."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.old_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0)
expected_remapping = range(0, 3)
expected_num_present = 3
with self.cached_session():
self.assertAllEqual(expected_remapping, remapping.eval())
self.assertAllEqual(expected_num_present, num_present.eval())
def test_generate_remapping_with_old_vocab_size(self):
"""Tests where old_vocab_size is specified."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0,
# Old vocabulary becomes ['knitting', 'eminem'].
old_vocab_size=2)
expected_remapping = [-1, 0, 1]
expected_num_present = 2
with self.cached_session():
self.assertAllEqual(expected_remapping, self.evaluate(remapping))
self.assertAllEqual(expected_num_present, self.evaluate(num_present))
def test_generate_remapping_with_old_vocab_size(self):
"""Tests where old_vocab_size is specified."""
remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=self.new_vocab_file,
old_vocab_file=self.old_vocab_file,
num_new_vocab=3,
new_vocab_offset=0,
# Old vocabulary becomes ['knitting', 'eminem'].
old_vocab_size=2)
expected_remapping = [-1, 0, 1]
expected_num_present = 2
with self.cached_session():
self.assertAllEqual(expected_remapping, remapping.eval())
self.assertAllEqual(expected_num_present, num_present.eval())
def _load_and_remap_matrix(ckpt_path,
old_tensor_name,
new_row_vocab_offset,
num_rows_to_load,
new_col_vocab_size,
initializer,
old_row_vocab_size=-1,
old_row_vocab_file=None,
new_row_vocab_file=None,
old_col_vocab_file=None,
new_col_vocab_file=None,
num_row_oov_buckets=0,
num_col_oov_buckets=0,
max_rows_in_memory=-1):
"""Loads a 2-D (matrix) `Tensor` from checkpoint.
Generates 1D-remappings for rows and columns using the
`GenerateVocabRemapping` op, and initializes any anticipated values with the
provided initializer. Then, uses the `LoadAndRemapMatrix` op to create a
matrix that loads existing values from the checkpoint, while filling out
"missing" values with the newly initialized values. See
contrib/framework/ops/checkpoint_ops.cc for more information on the wrapped
functionality (LoadAndRemapMatrix). This wrapper can be used to perform only
row remapping or only col remapping. If only row remapping is desired,
{new,old}_col_vocab_file should be `None`, and vice versa for column
remapping.
NOTE: This only supports div-partitioning the vocabulary on the 1st dimension
(row axis) via `new_row_vocab_offset`.
Args:
ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`)
from which the old matrix `Tensor` will be loaded.
old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
new_row_vocab_offset: A 0-indexed integer representing what line to
start reading at in the new row vocabulary. Used for partitioned
variables.
num_rows_to_load: Number of rows to load for the new vocabulary (note: to
support variable partitioning and partial loading, this does not need to
be the same as the number of entries in `new_row_vocab_file`).
new_col_vocab_size: Number of columns to load - should be the same as the
number of entries in `new_col_vocab_file`, since we don't support
partitioning along the column axis.
initializer: Callable initializer function that accepts a 1-D tensor as the
arg to specify the shape of the returned tensor. Used to initialize
missing values.
old_row_vocab_size: The number of entries to consider in the old vocabulary.
With the default value of -1, the entire old row vocabulary file will be
used. Otherwise, only the first `old_row_vocab_size` entries will be
considered for remapping.Must be smaller than the length of
`old_row_vocab_file`. NOTE: we do not provide an equivalent
`old_col_vocab_size` for classes.
old_row_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old row vocabulary file. Can be None, which represents no
remapping on the row axis.
new_row_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new row vocabulary file. Can be None, which represents no remapping
on the row axis - in which case, `new_row_vocab_offset` and
`num_rows_to_load` work under the assumption that the new row vocab is the
same as the old row vocab.
old_col_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old column vocabulary file. Can be None, which represents no
remapping on the column axis.
new_col_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new column vocabulary file. Can be None, which represents no
remapping on the column axis - in which case, `new_col_vocab_size` works
under the assumption that the new col vocab is the same as the old col
vocab.
num_row_oov_buckets: `int` specifying the number of out-of-vocabulary rows
to append. Must be >= 0.
num_col_oov_buckets: `int` specifying the number of out-of-vocabulary
columns to append. Must be >= 0.
max_rows_in_memory: `int` specifying the maximum number of rows to load from
the checkpoint at once. If less than or equal to 0, the entire matrix will
be loaded into memory. Setting this arg trades increased disk reads for
lower memory usage.
Returns:
A Tensor of shape `[num_rows_to_load + num_row_oov_buckets,
new_col_vocab_size + num_col_oov_buckets]`, with values loaded from the
specified tensor in the checkpoint, and any missing or OOV values
initialized with the given `initializer`.
Raises:
ValueError: If `num_row_oov_buckets` or `num_col_oov_buckets` < 0.
ValueError: If either `old_row_vocab_file` or `new_row_vocab_file` is
provided, while the other is not. Same for `old_col_vocab_file` and
`new_col_vocab_file`.
ValueError: If neither row vocabs or col vocabs are provided.
"""
if num_row_oov_buckets < 0:
raise ValueError("num_row_oov_buckets must be >= 0, but received %d" %
num_row_oov_buckets)
if num_col_oov_buckets < 0:
raise ValueError("num_col_oov_buckets must be >= 0, but received %d" %
num_col_oov_buckets)
if bool(old_row_vocab_file) != bool(new_row_vocab_file):
raise ValueError(
"old_row_vocab_file and new_row_vocab_file must both be specified or "
"left unspecified. old_row_vocab_file='{}', new_row_vocab_file='{}'".
format(old_row_vocab_file, new_row_vocab_file))
if bool(old_col_vocab_file) != bool(new_col_vocab_file):
raise ValueError(
"old_col_vocab_file and new_col_vocab_file must both be specified or "
"left unspecified. old_col_vocab_file='{}', new_col_vocab_file='{}'".
format(old_col_vocab_file, new_col_vocab_file))
remap_rows = new_row_vocab_file and old_row_vocab_file
remap_cols = new_col_vocab_file and old_col_vocab_file
if not (remap_rows or remap_cols):
raise ValueError(
"Must provide either row or column vocab files. If no remapping is "
"necessary, consider using `tf.contrib.framework.init_from_checkpoint` "
"instead.")
num_rows_present = num_rows_to_load
if remap_rows:
row_remapping, num_rows_present = (
gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=new_row_vocab_file,
old_vocab_file=old_row_vocab_file,
new_vocab_offset=new_row_vocab_offset,
num_new_vocab=num_rows_to_load,
old_vocab_size=old_row_vocab_size))
else:
# Even when the rows are not being reordered, we still need to generate a
# remapping to account for initializing partitioned Variables (when
# new_row_vocab_offset is non-zero).
row_remapping = math_ops.range(
new_row_vocab_offset,
new_row_vocab_offset + num_rows_to_load,
dtype=dtypes.int64)
col_remapping = []
num_cols_present = new_col_vocab_size
if remap_cols:
col_remapping, num_cols_present = (
gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=new_col_vocab_file,
old_vocab_file=old_col_vocab_file,
new_vocab_offset=0, # Offset is unused for cols (no partitioning).
num_new_vocab=new_col_vocab_size))
init_vals = initializer([
num_rows_to_load * new_col_vocab_size -
num_rows_present * num_cols_present, 1
])
return_tensor = gen_checkpoint_ops.load_and_remap_matrix(
ckpt_path=ckpt_path,
old_tensor_name=old_tensor_name,
row_remapping=row_remapping,
col_remapping=col_remapping,
initializing_values=init_vals,
num_rows=num_rows_to_load,
num_cols=new_col_vocab_size,
max_rows_in_memory=max_rows_in_memory)
# Add OOV row(s) and column(s).
if num_row_oov_buckets > 0:
init_row_oov_val = initializer([num_row_oov_buckets, new_col_vocab_size])
init_row_oov_val = ops.convert_to_tensor(init_row_oov_val)
return_tensor = array_ops.concat([return_tensor, init_row_oov_val], 0)
if num_col_oov_buckets > 0:
# We need to add any row OOV to the new column shape.
init_col_oov_val = initializer(
[num_rows_to_load + num_row_oov_buckets, num_col_oov_buckets])
init_col_oov_val = ops.convert_to_tensor(init_col_oov_val)
return_tensor = array_ops.concat([return_tensor, init_col_oov_val], 1)
return return_tensor
def _load_and_remap_matrix(ckpt_path,
old_tensor_name,
new_row_vocab_offset,
num_rows_to_load,
new_col_vocab_size,
initializer,
old_row_vocab_size=-1,
old_row_vocab_file=None,
new_row_vocab_file=None,
old_col_vocab_file=None,
new_col_vocab_file=None,
num_row_oov_buckets=0,
num_col_oov_buckets=0,
max_rows_in_memory=-1):
"""Loads a 2-D (matrix) `Tensor` from checkpoint.
Generates 1D-remappings for rows and columns using the
`GenerateVocabRemapping` op, and initializes any anticipated values with the
provided initializer. Then, uses the `LoadAndRemapMatrix` op to create a
matrix that loads existing values from the checkpoint, while filling out
"missing" values with the newly initialized values. See
contrib/framework/ops/checkpoint_ops.cc for more information on the wrapped
functionality (LoadAndRemapMatrix). This wrapper can be used to perform only
row remapping or only col remapping. If only row remapping is desired,
{new,old}_col_vocab_file should be `None`, and vice versa for column
remapping.
NOTE: This only supports div-partitioning the vocabulary on the 1st dimension
(row axis) via `new_row_vocab_offset`.
Args:
ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`)
from which the old matrix `Tensor` will be loaded.
old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
new_row_vocab_offset: A 0-indexed integer representing what line to
start reading at in the new row vocabulary. Used for partitioned
variables.
num_rows_to_load: Number of rows to load for the new vocabulary (note: to
support variable partitioning and partial loading, this does not need to
be the same as the number of entries in `new_row_vocab_file`).
new_col_vocab_size: Number of columns to load - should be the same as the
number of entries in `new_col_vocab_file`, since we don't support
partitioning along the column axis.
initializer: Callable initializer function that accepts a 1-D tensor as the
arg to specify the shape of the returned tensor. Used to initialize
missing values.
old_row_vocab_size: The number of entries to consider in the old vocabulary.
With the default value of -1, the entire old row vocabulary file will be
used. Otherwise, only the first `old_row_vocab_size` entries will be
considered for remapping.Must be smaller than the length of
`old_row_vocab_file`. NOTE: we do not provide an equivalent
`old_col_vocab_size` for classes.
old_row_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old row vocabulary file. Can be None, which represents no
remapping on the row axis.
new_row_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new row vocabulary file. Can be None, which represents no remapping
on the row axis - in which case, `new_row_vocab_offset` and
`num_rows_to_load` work under the assumption that the new row vocab is the
same as the old row vocab.
old_col_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old column vocabulary file. Can be None, which represents no
remapping on the column axis.
new_col_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new column vocabulary file. Can be None, which represents no
remapping on the column axis - in which case, `new_col_vocab_size` works
under the assumption that the new col vocab is the same as the old col
vocab.
num_row_oov_buckets: `int` specifying the number of out-of-vocabulary rows
to append. Must be >= 0.
num_col_oov_buckets: `int` specifying the number of out-of-vocabulary
columns to append. Must be >= 0.
max_rows_in_memory: `int` specifying the maximum number of rows to load from
the checkpoint at once. If less than or equal to 0, the entire matrix will
be loaded into memory. Setting this arg trades increased disk reads for
lower memory usage.
Returns:
A Tensor of shape `[num_rows_to_load + num_row_oov_buckets,
new_col_vocab_size + num_col_oov_buckets]`, with values loaded from the
specified tensor in the checkpoint, and any missing or OOV values
initialized with the given `initializer`.
Raises:
ValueError: If `num_row_oov_buckets` or `num_col_oov_buckets` < 0.
ValueError: If either `old_row_vocab_file` or `new_row_vocab_file` is
provided, while the other is not. Same for `old_col_vocab_file` and
`new_col_vocab_file`.
ValueError: If neither row vocabs or col vocabs are provided.
"""
if num_row_oov_buckets < 0:
raise ValueError("num_row_oov_buckets must be >= 0, but received %d" %
num_row_oov_buckets)
if num_col_oov_buckets < 0:
raise ValueError("num_col_oov_buckets must be >= 0, but received %d" %
num_col_oov_buckets)
if bool(old_row_vocab_file) != bool(new_row_vocab_file):
raise ValueError(
"old_row_vocab_file and new_row_vocab_file must both be specified or "
"left unspecified. old_row_vocab_file='{}', new_row_vocab_file='{}'"
.format(old_row_vocab_file, new_row_vocab_file))
if bool(old_col_vocab_file) != bool(new_col_vocab_file):
raise ValueError(
"old_col_vocab_file and new_col_vocab_file must both be specified or "
"left unspecified. old_col_vocab_file='{}', new_col_vocab_file='{}'"
.format(old_col_vocab_file, new_col_vocab_file))
remap_rows = new_row_vocab_file and old_row_vocab_file
remap_cols = new_col_vocab_file and old_col_vocab_file
if not (remap_rows or remap_cols):
raise ValueError(
"Must provide either row or column vocab files. If no remapping is "
"necessary, consider using `tf.contrib.framework.init_from_checkpoint` "
"instead.")
num_rows_present = num_rows_to_load
if remap_rows:
row_remapping, num_rows_present = (
gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=new_row_vocab_file,
old_vocab_file=old_row_vocab_file,
new_vocab_offset=new_row_vocab_offset,
num_new_vocab=num_rows_to_load,
old_vocab_size=old_row_vocab_size))
else:
# Even when the rows are not being reordered, we still need to generate a
# remapping to account for initializing partitioned Variables (when
# new_row_vocab_offset is non-zero).
row_remapping = math_ops.range(new_row_vocab_offset,
new_row_vocab_offset + num_rows_to_load,
dtype=dtypes.int64)
col_remapping = []
num_cols_present = new_col_vocab_size
if remap_cols:
col_remapping, num_cols_present = (
gen_checkpoint_ops.generate_vocab_remapping(
new_vocab_file=new_col_vocab_file,
old_vocab_file=old_col_vocab_file,
new_vocab_offset=
0, # Offset is unused for cols (no partitioning).
num_new_vocab=new_col_vocab_size))
init_vals = initializer([
num_rows_to_load * new_col_vocab_size -
num_rows_present * num_cols_present, 1
])
return_tensor = gen_checkpoint_ops.load_and_remap_matrix(
ckpt_path=ckpt_path,
old_tensor_name=old_tensor_name,
row_remapping=row_remapping,
col_remapping=col_remapping,
initializing_values=init_vals,
num_rows=num_rows_to_load,
num_cols=new_col_vocab_size,
max_rows_in_memory=max_rows_in_memory)
# Add OOV row(s) and column(s).
if num_row_oov_buckets > 0:
init_row_oov_val = initializer(
[num_row_oov_buckets, new_col_vocab_size])
init_row_oov_val = ops.convert_to_tensor(init_row_oov_val)
return_tensor = array_ops.concat([return_tensor, init_row_oov_val], 0)
if num_col_oov_buckets > 0:
# We need to add any row OOV to the new column shape.
init_col_oov_val = initializer(
[num_rows_to_load + num_row_oov_buckets, num_col_oov_buckets])
init_col_oov_val = ops.convert_to_tensor(init_col_oov_val)
return_tensor = array_ops.concat([return_tensor, init_col_oov_val], 1)
return return_tensor
