Add ALBERT Conversion Script

tools/checkpoint_conversion/convert_albert_checkpoints.py

@@ -0,0 +1,319 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# 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
+#
+#     https://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.
+import os
+import shutil
+
+import numpy as np
+import tensorflow as tf
+import transformers
+from absl import app
+from absl import flags
+
+import keras_nlp
+from tools.checkpoint_conversion.checkpoint_conversion_utils import (
+    get_md5_checksum,
+)
+
+PRESET_MAP = {
+    "albert_base_en_uncased": "albert-base-v2",
+    "albert_large_en_uncased": "albert-large-v2",
+    "albert_extra_large_en_uncased": "albert-xlarge-v2",
+    "albert_extra_extra_large_en_uncased": "albert-xxlarge-v2",
+}
+
+
+FLAGS = flags.FLAGS
+flags.DEFINE_string(
+    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+)
+
+
+def convert_checkpoints(hf_model):
+    print("\n-> Convert original weights to KerasNLP format.")
+
+    print("\n-> Load KerasNLP model.")
+    keras_nlp_model = keras_nlp.models.AlbertBackbone.from_preset(
+        FLAGS.preset, load_weights=False
+    )
+
+    hf_wts = hf_model.state_dict()
+    print("Original weights:")
+    print(list(hf_wts.keys()))
+
+    num_heads = keras_nlp_model.num_heads
+    hidden_dim = keras_nlp_model.hidden_dim
+
+    keras_nlp_model.get_layer("token_embedding").embeddings.assign(
+        hf_wts["embeddings.word_embeddings.weight"]
+    )
+    keras_nlp_model.get_layer("position_embedding").position_embeddings.assign(
+        hf_wts["embeddings.position_embeddings.weight"]
+    )
+    keras_nlp_model.get_layer("segment_embedding").embeddings.assign(
+        hf_wts["embeddings.token_type_embeddings.weight"]
+    )
+
+    keras_nlp_model.get_layer("embeddings_layer_norm").gamma.assign(
+        hf_wts["embeddings.LayerNorm.weight"]
+    )
+    keras_nlp_model.get_layer("embeddings_layer_norm").beta.assign(
+        hf_wts["embeddings.LayerNorm.bias"]
+    )
+
+    keras_nlp_model.get_layer("embedding_projection").kernel.assign(
+        hf_wts["encoder.embedding_hidden_mapping_in.weight"].T
+    )
+    keras_nlp_model.get_layer("embedding_projection").bias.assign(
+        hf_wts["encoder.embedding_hidden_mapping_in.bias"]
+    )
+
+    for i in range(keras_nlp_model.num_groups):
+        for j in range(keras_nlp_model.num_inner_repetitions):
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._query_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.query.weight"
+                ]
+                .transpose(1, 0)
+                .reshape((hidden_dim, num_heads, -1))
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._query_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.query.bias"
+                ]
+                .reshape((num_heads, -1))
+                .numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._key_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.key.weight"
+                ]
+                .transpose(1, 0)
+                .reshape((hidden_dim, num_heads, -1))
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._key_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.key.bias"
+                ]
+                .reshape((num_heads, -1))
+                .numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._value_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.value.weight"
+                ]
+                .transpose(1, 0)
+                .reshape((hidden_dim, num_heads, -1))
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._value_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.value.bias"
+                ]
+                .reshape((num_heads, -1))
+                .numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._output_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.dense.weight"
+                ]
+                .transpose(1, 0)
+                .reshape((num_heads, -1, hidden_dim))
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layer._output_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.dense.bias"
+                ].numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layernorm.gamma.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.LayerNorm.weight"
+                ].numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._self_attention_layernorm.beta.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.attention.LayerNorm.bias"
+                ].numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_intermediate_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.ffn.weight"
+                ]
+                .transpose(1, 0)
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_intermediate_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.ffn.bias"
+                ].numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_output_dense.kernel.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.ffn_output.weight"
+                ]
+                .transpose(1, 0)
+                .numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_output_dense.bias.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.ffn_output.bias"
+                ].numpy()
+            )
+
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_layernorm.gamma.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.full_layer_layer_norm.weight"
+                ].numpy()
+            )
+            keras_nlp_model.get_layer(
+                f"group_{i}_inner_layer_{j}"
+            )._feedforward_layernorm.beta.assign(
+                hf_wts[
+                    f"encoder.albert_layer_groups.{i}.albert_layers.{j}.full_layer_layer_norm.bias"
+                ].numpy()
+            )
+
+    keras_nlp_model.get_layer("pooled_dense").kernel.assign(
+        hf_wts["pooler.weight"].transpose(1, 0).numpy()
+    )
+    keras_nlp_model.get_layer("pooled_dense").bias.assign(
+        hf_wts["pooler.bias"].numpy()
+    )
+
+    # Save the model.
+    print("\n-> Save KerasNLP model weights.")
+    keras_nlp_model.save_weights(os.path.join(FLAGS.preset, "model.h5"))
+
+    return keras_nlp_model
+
+
+def extract_vocab(hf_tokenizer):
+    spm_path = os.path.join(FLAGS.preset, "spiece.model")
+    print(f"\n-> Save KerasNLP SPM vocabulary file to `{spm_path}`.")
+
+    shutil.copyfile(
+        transformers.utils.hub.get_file_from_repo(
+            hf_tokenizer.name_or_path, "spiece.model"
+        ),
+        spm_path,
+    )
+
+    keras_nlp_tokenizer = keras_nlp.models.AlbertTokenizer(
+        proto=spm_path,
+    )
+    keras_nlp_preprocessor = keras_nlp.models.AlbertPreprocessor(
+        keras_nlp_tokenizer
+    )
+
+    print("-> Print MD5 checksum of the vocab files.")
+    print(f"`{spm_path}` md5sum: ", get_md5_checksum(spm_path))
+
+    return keras_nlp_preprocessor
+
+
+def check_output(
+    keras_nlp_preprocessor,
+    keras_nlp_model,
+    hf_tokenizer,
+    hf_model,
+):
+    print("\n-> Check the outputs.")
+    sample_text = ["cricket is awesome, easily the best sport in the world!"]
+
+    # KerasNLP
+    keras_nlp_inputs = keras_nlp_preprocessor(tf.constant(sample_text))
+    keras_nlp_output = keras_nlp_model.predict(keras_nlp_inputs)[
+        "sequence_output"
+    ]
+
+    # HF
+    hf_inputs = hf_tokenizer(
+        sample_text, padding="max_length", return_tensors="pt"
+    )
+    hf_output = hf_model(**hf_inputs).last_hidden_state
+
+    print("KerasNLP output:", keras_nlp_output[0, 0, :10])
+    print("HF output:", hf_output[0, 0, :10])
+    print("Difference:", np.mean(keras_nlp_output - hf_output.detach().numpy()))
+
+    # Show the MD5 checksum of the model weights.
+    print(
+        "Model md5sum: ",
+        get_md5_checksum(os.path.join(FLAGS.preset, "model.h5")),
+    )
+
+
+def main(_):
+    os.makedirs(FLAGS.preset)
+
+    hf_model_name = PRESET_MAP[FLAGS.preset]
+
+    print("\n-> Load HF model and HF tokenizer.")
+    hf_model = transformers.AutoModel.from_pretrained(hf_model_name)
+    hf_model.eval()
+    hf_tokenizer = transformers.AutoTokenizer.from_pretrained(hf_model_name)
+
+    keras_nlp_model = convert_checkpoints(hf_model)
+    print("\n -> Load KerasNLP preprocessor.")
+    keras_nlp_preprocessor = extract_vocab(hf_tokenizer)
+
+    check_output(
+        keras_nlp_preprocessor,
+        keras_nlp_model,
+        hf_tokenizer,
+        hf_model,
+    )
+
+
+if __name__ == "__main__":
+    flags.mark_flag_as_required("preset")
+    app.run(main)