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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
INIT_COMMON = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
END_COMMON = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
DECODER_PATTERNS = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
REMAINING_PATTERNS = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
KEYS_TO_IGNORE = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def rename_state_dict_key(k, patterns):
for tf_name, hf_name in patterns:
k = k.replace(tf_name, hf_name)
return k
def convert_bigbird_pegasus(tf_weights: dict, config_update: dict) -> BigBirdPegasusForConditionalGeneration:
cfg = BigBirdPegasusConfig(**config_update)
torch_model = BigBirdPegasusForConditionalGeneration(cfg)
state_dict = torch_model.state_dict()
mapping = {}
# separating decoder weights
decoder_weights = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder")}
remaining_weights = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder")}
for k, v in tqdm(decoder_weights.items(), "tf -> hf conversion"):
conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
if any(conditions):
continue
patterns = DECODER_PATTERNS
new_k = rename_state_dict_key(k, patterns)
if new_k not in state_dict:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
v = v.T
mapping[new_k] = torch.from_numpy(v)
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
for k, v in tqdm(remaining_weights.items(), "tf -> hf conversion"):
conditions = [k.endswith(ending) for ending in KEYS_TO_IGNORE]
if any(conditions):
continue
patterns = REMAINING_PATTERNS
new_k = rename_state_dict_key(k, patterns)
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
v = v.T
mapping[new_k] = torch.from_numpy(v)
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
mapping["model.encoder.embed_positions.weight"] = mapping["model.embed_positions.weight"]
mapping["model.decoder.embed_positions.weight"] = mapping.pop("model.embed_positions.weight")
missing, extra = torch_model.load_state_dict(mapping, strict=False)
unexpected_missing = [
k
for k in missing
if k
not in [
"final_logits_bias",
"model.encoder.embed_tokens.weight",
"model.decoder.embed_tokens.weight",
"lm_head.weight",
]
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def get_tf_weights_as_numpy(path) -> Dict:
init_vars = tf.train.list_variables(path)
tf_weights = {}
ignore_name = ["global_step"]
for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
skip_key = any(pat in name for pat in ignore_name)
if skip_key:
continue
array = tf.train.load_variable(path, name)
tf_weights[name] = array
return tf_weights
def convert_bigbird_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str, config_update: dict):
tf_weights = get_tf_weights_as_numpy(ckpt_path)
torch_model = convert_bigbird_pegasus(tf_weights, config_update)
torch_model.save_pretrained(save_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
args = parser.parse_args()
config_update = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
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