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import logging
import torch
from collections import OrderedDict
from transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
BertSelfOutput)
from transformers.modeling_roberta import (RobertaEmbeddings,
RobertaForMaskedLM,
RobertaForSequenceClassification,
RobertaModel)
logger = logging.getLogger(__name__)
# NOTE transformers should be 2.5.1
def convert_cxlm_to_transformers(ckpt_path):
ckpt = torch.load(ckpt_path, map_location="cpu")
args = ckpt["args"]
config = BertConfig(
# vocab_size_or_config_json_file=250002,
vocab_size=250002,
hidden_size=args.encoder_embed_dim,
num_hidden_layers=args.encoder_layers,
num_attention_heads=args.encoder_attention_heads,
intermediate_size=args.encoder_ffn_embed_dim,
max_position_embeddings=args.max_positions + 2,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
print("Our BERT config:", config)
stat_dict = ckpt["model"]
new_stat_dict = {}
model = RobertaForMaskedLM(config)
model.eval()
sent_enc = "decoder.sentence_encoder"
new_stat_dict["roberta.embeddings.word_embeddings.weight"] = stat_dict[sent_enc + ".embed_tokens.weight"]
new_stat_dict["roberta.embeddings.position_embeddings.weight"] = stat_dict[sent_enc + ".embed_positions.weight"]
new_stat_dict["roberta.embeddings.token_type_embeddings.weight"] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight)
new_stat_dict["roberta.embeddings.LayerNorm.weight"] = stat_dict[sent_enc + ".emb_layer_norm.weight"]
new_stat_dict["roberta.embeddings.LayerNorm.bias"] = stat_dict[sent_enc + ".emb_layer_norm.bias"]
for i in range(config.num_hidden_layers):
# Encoder: start of layer
# layer: BertLayer = model.roberta.encoder.layer[i]
layer = "roberta.encoder.layer.%d" % i
roberta_layer = sent_enc + (".layers.%d" % i)
### self attention
# self_attn: BertSelfAttention = layer.attention.self
self_attn = layer + ".attention.self"
assert (
stat_dict[roberta_layer + ".self_attn.k_proj.weight"].data.shape == \
stat_dict[roberta_layer + ".self_attn.q_proj.weight"].data.shape == \
stat_dict[roberta_layer + ".self_attn.v_proj.weight"].data.shape == \
torch.Size((config.hidden_size, config.hidden_size))
)
new_stat_dict[self_attn + ".query.weight"] = stat_dict[roberta_layer + ".self_attn.q_proj.weight"]
new_stat_dict[self_attn + ".query.bias"] = stat_dict[roberta_layer + ".self_attn.q_proj.bias"]
new_stat_dict[self_attn + ".key.weight"] = stat_dict[roberta_layer + ".self_attn.k_proj.weight"]
new_stat_dict[self_attn + ".key.bias"] = stat_dict[roberta_layer + ".self_attn.k_proj.bias"]
new_stat_dict[self_attn + ".value.weight"] = stat_dict[roberta_layer + ".self_attn.v_proj.weight"]
new_stat_dict[self_attn + ".value.bias"] = stat_dict[roberta_layer + ".self_attn.v_proj.bias"]
### self-attention output
# self_output: BertSelfOutput = layer.attention.output
self_output = layer + ".attention.output"
assert (
model.roberta.encoder.layer[i].attention.output.dense.weight.shape == stat_dict[
roberta_layer + ".self_attn.out_proj.weight"].shape
)
new_stat_dict[self_output + ".dense.weight"] = stat_dict[roberta_layer + ".self_attn.out_proj.weight"]
new_stat_dict[self_output + ".dense.bias"] = stat_dict[roberta_layer + ".self_attn.out_proj.bias"]
new_stat_dict[self_output + ".LayerNorm.weight"] = stat_dict[roberta_layer + ".self_attn_layer_norm.weight"]
new_stat_dict[self_output + ".LayerNorm.bias"] = stat_dict[roberta_layer + ".self_attn_layer_norm.bias"]
### intermediate
# intermediate: BertIntermediate = layer.intermediate
intermediate = layer + ".intermediate"
assert (
model.roberta.encoder.layer[i].intermediate.dense.weight.shape == stat_dict[
roberta_layer + ".fc1.weight"].shape
)
# TODO
new_stat_dict[intermediate + ".dense.weight"] = stat_dict[roberta_layer + ".fc1.weight"]
new_stat_dict[intermediate + ".dense.bias"] = stat_dict[roberta_layer + ".fc1.bias"]
### output
# bert_output: BertOutput = layer.output
bert_output = layer + ".output"
assert (
model.roberta.encoder.layer[i].output.dense.weight.shape == stat_dict[
roberta_layer + ".fc2.weight"].shape
)
new_stat_dict[bert_output + ".dense.weight"] = stat_dict[roberta_layer + ".fc2.weight"]
new_stat_dict[bert_output + ".dense.bias"] = stat_dict[roberta_layer + ".fc2.bias"]
new_stat_dict[bert_output + ".LayerNorm.weight"] = stat_dict[roberta_layer + ".final_layer_norm.weight"]
new_stat_dict[bert_output + ".LayerNorm.bias"] = stat_dict[roberta_layer + ".final_layer_norm.bias"]
#### end of layer
new_stat_dict["lm_head.dense.weight"] = stat_dict["decoder.lm_head.dense.weight"]
new_stat_dict["lm_head.dense.bias"] = stat_dict["decoder.lm_head.dense.bias"]
new_stat_dict["lm_head.layer_norm.weight"] = stat_dict["decoder.lm_head.layer_norm.weight"]
new_stat_dict["lm_head.layer_norm.bias"] = stat_dict["decoder.lm_head.layer_norm.bias"]
new_stat_dict["lm_head.decoder.weight"] = stat_dict["decoder.lm_head.weight"]
new_stat_dict["lm_head.bias"] = stat_dict["decoder.lm_head.bias"]
new_stat_dict["lm_head.decoder.bias"] = stat_dict["decoder.lm_head.bias"]
new_stat_dict["roberta.pooler.dense.weight"] = model.roberta.pooler.dense.weight
new_stat_dict["roberta.pooler.dense.bias"] = model.roberta.pooler.dense.bias
return new_stat_dict
def update_hf_sd(old_sd, xlmr_path):
x = torch.load(xlmr_path, map_location="cpu")
m = old_sd
d = OrderedDict()
for k, v in m.items():
if k == 'roberta.pooler.dense.weight':
d[k] = x[k].half().clone()
elif k not in ('proj_matrix_fast', 'lm_head.decoder.bias', 'roberta.pooler.dense.weight'):
d[k] = v.data.half().clone()
assert set(d.keys()) == set(x.keys())
for k in d.keys():
assert d[k].size() == x[k].size()
for k in d.keys():
if k != 'roberta.pooler.dense.weight':
assert (d[k].float() - m[k].float()).abs().max().item() <= 1e-4
return d
def convert_pt_to_hf(xlmr_path, inf, logger=None):
if logger:
logger.info("converting pt file at {} to hf file.".format(inf))
sd = convert_cxlm_to_transformers(inf)
return update_hf_sd(sd, xlmr_path)
if __name__ == "__main__":
import os
xlmr_path = "/home/v-zechi/data/unilm/zechi/exp/res/huggingface/hf-ckpt/xlmr-large/pytorch_model.bin"
inf = "/home/v-zechi/data/unilm/zechi/exp/cxlm_exp/dump-ifx94-large/checkpoint_2_200000.pt"
outf = "/home/v-zechi/data/unilm/zechi/usw/res/infoxlm-models/huggingface/infoxlm-large-without-meta/pytorch_model.bin"
assert not os.path.exists(outf)
sd = convert_cxlm_to_transformers(inf)
sd2 = update_hf_sd(sd, xlmr_path)
torch.save(sd2, outf) |