Delete tf1_bert_checkpoint_converter_lib.py

tf1_bert_checkpoint_converter_lib.py

@@ -1,201 +0,0 @@
-# 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))