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# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Convert checkpoints created by Estimator (tf1) to be Keras compatible."""
import numpy as np
import tensorflow.compat.v1 as tf # TF 1.x
# Mapping between old <=> new names. The source pattern in original variable
# name will be replaced by destination pattern.
BERT_NAME_REPLACEMENTS = (
("bert", "bert_model"),
("embeddings/word_embeddings", "word_embeddings/embeddings"),
("embeddings/token_type_embeddings",
"embedding_postprocessor/type_embeddings"),
("embeddings/position_embeddings",
"embedding_postprocessor/position_embeddings"),
("embeddings/LayerNorm", "embedding_postprocessor/layer_norm"),
("attention/self", "self_attention"),
("attention/output/dense", "self_attention_output"),
("attention/output/LayerNorm", "self_attention_layer_norm"),
("intermediate/dense", "intermediate"),
("output/dense", "output"),
("output/LayerNorm", "output_layer_norm"),
("pooler/dense", "pooler_transform"),
)
BERT_V2_NAME_REPLACEMENTS = (
("bert/", ""),
("encoder", "transformer"),
("embeddings/word_embeddings", "word_embeddings/embeddings"),
("embeddings/token_type_embeddings", "type_embeddings/embeddings"),
("embeddings/position_embeddings", "position_embedding/embeddings"),
("embeddings/LayerNorm", "embeddings/layer_norm"),
("attention/self", "self_attention"),
("attention/output/dense", "self_attention/attention_output"),
("attention/output/LayerNorm", "self_attention_layer_norm"),
("intermediate/dense", "intermediate"),
("output/dense", "output"),
("output/LayerNorm", "output_layer_norm"),
("pooler/dense", "pooler_transform"),
("cls/predictions", "bert/cls/predictions"),
("cls/predictions/output_bias", "cls/predictions/output_bias/bias"),
("cls/seq_relationship/output_bias", "predictions/transform/logits/bias"),
("cls/seq_relationship/output_weights",
"predictions/transform/logits/kernel"),
)
BERT_PERMUTATIONS = ()
BERT_V2_PERMUTATIONS = (("cls/seq_relationship/output_weights", (1, 0)),)
def _bert_name_replacement(var_name, name_replacements):
"""Gets the variable name replacement."""
for src_pattern, tgt_pattern in name_replacements:
if src_pattern in var_name:
old_var_name = var_name
var_name = var_name.replace(src_pattern, tgt_pattern)
tf.logging.info("Converted: %s --> %s", old_var_name, var_name)
return var_name
def _has_exclude_patterns(name, exclude_patterns):
"""Checks if a string contains substrings that match patterns to exclude."""
for p in exclude_patterns:
if p in name:
return True
return False
def _get_permutation(name, permutations):
"""Checks whether a variable requires transposition by pattern matching."""
for src_pattern, permutation in permutations:
if src_pattern in name:
tf.logging.info("Permuted: %s --> %s", name, permutation)
return permutation
return None
def _get_new_shape(name, shape, num_heads):
"""Checks whether a variable requires reshape by pattern matching."""
if "self_attention/attention_output/kernel" in name:
return tuple([num_heads, shape[0] // num_heads, shape[1]])
if "self_attention/attention_output/bias" in name:
return shape
patterns = [
"self_attention/query", "self_attention/value", "self_attention/key"
]
for pattern in patterns:
if pattern in name:
if "kernel" in name:
return tuple([shape[0], num_heads, shape[1] // num_heads])
if "bias" in name:
return tuple([num_heads, shape[0] // num_heads])
return None
def create_v2_checkpoint(model,
src_checkpoint,
output_path,
checkpoint_model_name="model"):
"""Converts a name-based matched TF V1 checkpoint to TF V2 checkpoint."""
# Uses streaming-restore in eager model to read V1 name-based checkpoints.
model.load_weights(src_checkpoint).assert_existing_objects_matched()
if hasattr(model, "checkpoint_items"):
checkpoint_items = model.checkpoint_items
else:
checkpoint_items = {}
checkpoint_items[checkpoint_model_name] = model
checkpoint = tf.train.Checkpoint(**checkpoint_items)
checkpoint.save(output_path)
def convert(checkpoint_from_path,
checkpoint_to_path,
num_heads,
name_replacements,
permutations,
exclude_patterns=None):
"""Migrates the names of variables within a checkpoint.
Args:
checkpoint_from_path: Path to source checkpoint to be read in.
checkpoint_to_path: Path to checkpoint to be written out.
num_heads: The number of heads of the model.
name_replacements: A list of tuples of the form (match_str, replace_str)
describing variable names to adjust.
permutations: A list of tuples of the form (match_str, permutation)
describing permutations to apply to given variables. Note that match_str
should match the original variable name, not the replaced one.
exclude_patterns: A list of string patterns to exclude variables from
checkpoint conversion.
Returns:
A dictionary that maps the new variable names to the Variable objects.
A dictionary that maps the old variable names to the new variable names.
"""
with tf.Graph().as_default():
tf.logging.info("Reading checkpoint_from_path %s", checkpoint_from_path)
reader = tf.train.NewCheckpointReader(checkpoint_from_path)
name_shape_map = reader.get_variable_to_shape_map()
new_variable_map = {}
conversion_map = {}
for var_name in name_shape_map:
if exclude_patterns and _has_exclude_patterns(var_name, exclude_patterns):
continue
# Get the original tensor data.
tensor = reader.get_tensor(var_name)
# Look up the new variable name, if any.
new_var_name = _bert_name_replacement(var_name, name_replacements)
# See if we need to reshape the underlying tensor.
new_shape = None
if num_heads > 0:
new_shape = _get_new_shape(new_var_name, tensor.shape, num_heads)
if new_shape:
tf.logging.info("Veriable %s has a shape change from %s to %s",
var_name, tensor.shape, new_shape)
tensor = np.reshape(tensor, new_shape)
# See if we need to permute the underlying tensor.
permutation = _get_permutation(var_name, permutations)
if permutation:
tensor = np.transpose(tensor, permutation)
# Create a new variable with the possibly-reshaped or transposed tensor.
var = tf.Variable(tensor, name=var_name)
# Save the variable into the new variable map.
new_variable_map[new_var_name] = var
# Keep a list of converter variables for sanity checking.
if new_var_name != var_name:
conversion_map[var_name] = new_var_name
saver = tf.train.Saver(new_variable_map)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tf.logging.info("Writing checkpoint_to_path %s", checkpoint_to_path)
saver.save(sess, checkpoint_to_path, write_meta_graph=False)
tf.logging.info("Summary:")
tf.logging.info(" Converted %d variable name(s).", len(new_variable_map))
tf.logging.info(" Converted: %s", str(conversion_map))
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