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69e5b1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """Reconstruct a HuggingFace PegasusForConditionalGeneration checkpoint from a
CTranslate2 `model.bin` that has SEPARATE source and target vocabularies.
This is the OpenNMT-py 3.x family (Proxecto Nos). It differs from the shared
sentencepiece OpenNMT-tf models handled by ct2_to_pegasus.py:
* `encoder/embeddings_0/weight` and `decoder/embeddings/weight` are different
tables of different sizes. Pegasus has one vocabulary, so the two tables are
concatenated as [target | source]; encoder input ids are offset by the size
of the target vocabulary and the source half is made unreachable at decode
time with `final_logits_bias = -1e9`;
* position encodings ARE stored in the binary
(`decoder/position_encodings/encodings`), so no formula has to be guessed --
but Pegasus still refuses to save the table, so `_keys_to_ignore_on_save`
must be cleared;
* attention and feed-forward projections carry no bias (`add_qkvbias=False`),
so zeros are written where HuggingFace insists on one.
Usage:
python ct2_to_pegasus_dual.py <ct2_model_dir> <output_hf_dir>
"""
import json
import os
import sys
import numpy as np
import torch
from transformers import PegasusConfig, PegasusForConditionalGeneration
from ct2_reader import read_ct2_model, dequantize
from ct2_to_pegasus import ACTIVATIONS, scalar
NEG = -1e9
MAX_POS = 1024
def inspect_spec(v):
def nlayers(prefix):
return len({int(k.split("/")[1][6:]) for k in v
if k.startswith(prefix + "/layer_") and k.split("/")[1][6:].isdigit()})
tgt_size, d_model = v["decoder/embeddings/weight"].shape
src_size = v["encoder/embeddings_0/weight"].shape[0]
return dict(
encoder_layers=nlayers("encoder"),
decoder_layers=nlayers("decoder"),
source_vocab_size=int(src_size),
target_vocab_size=int(tgt_size),
d_model=int(d_model),
heads=int(scalar(v, ("num_heads", "encoder/num_heads"))),
ffn_dim=int(v["encoder/layer_0/ffn/linear_0/weight"].shape[0]),
pre_norm=bool(scalar(v, ("pre_norm", "encoder/pre_norm"))),
activation=ACTIVATIONS[int(scalar(v, ("activation", "encoder/activation")))],
layernorm_embedding=bool(scalar(v, ("layernorm_embedding",
"encoder/layernorm_embedding"), False)),
relative_position=any(k.endswith("relative_attention_bias") for k in v),
scale_embeddings=bool(scalar(v, ("decoder/scale_embeddings",))),
output_bias="decoder/projection/bias" in v,
stored_positions="decoder/position_encodings/encodings" in v,
attention_bias="encoder/layer_0/self_attention/linear_0/bias" in v,
)
def build_state_dict(v, spec):
g = lambda n: torch.from_numpy(dequantize(v, n).copy())
sd = {}
d = spec["d_model"]
n_tgt = spec["target_vocab_size"]
zero = lambda n: torch.zeros(n)
tgt_emb = g("decoder/embeddings/weight")
src_emb = g("encoder/embeddings_0/weight")
shared = torch.cat([tgt_emb, src_emb], dim=0)
sd["model.shared.weight"] = shared
sd["model.encoder.embed_tokens.weight"] = shared.clone()
sd["model.decoder.embed_tokens.weight"] = shared.clone()
# decoder/projection/weight is aliased to decoder/embeddings/weight
sd["lm_head.weight"] = shared.clone()
bias = torch.full((1, shared.shape[0]), NEG)
bias[0, :n_tgt] = g("decoder/projection/bias") if spec["output_bias"] else 0.0
sd["final_logits_bias"] = bias
pos = g("decoder/position_encodings/encodings")[:MAX_POS]
sd["model.encoder.embed_positions.weight"] = pos.clone()
sd["model.decoder.embed_positions.weight"] = pos.clone()
def ln(dst, src):
sd[dst + ".weight"] = g(src + "/gamma")
sd[dst + ".bias"] = g(src + "/beta")
def lin(dst, src, weight=None, bias=None, n=None):
w = g(src + "/weight") if weight is None else weight
sd[dst + ".weight"] = w
if bias is None:
bias = g(src + "/bias") if src and (src + "/bias") in v else zero(w.shape[0])
sd[dst + ".bias"] = bias
ln("model.encoder.layer_norm", "encoder/layer_norm")
ln("model.decoder.layer_norm", "decoder/layer_norm")
def split_bias(src, lo, hi):
return g(src + "/bias")[lo:hi] if (src + "/bias") in v else zero(hi - lo)
def self_attn(dst, src):
w = g(src + "/linear_0/weight")
for i, part in enumerate(("q_proj", "k_proj", "v_proj")):
lo, hi = i * d, (i + 1) * d
lin(dst + "." + part, None, w[lo:hi], split_bias(src + "/linear_0", lo, hi))
lin(dst + ".out_proj", src + "/linear_1")
for i in range(spec["encoder_layers"]):
s, p = "encoder/layer_%d" % i, "model.encoder.layers.%d" % i
self_attn(p + ".self_attn", s + "/self_attention")
ln(p + ".self_attn_layer_norm", s + "/self_attention/layer_norm")
lin(p + ".fc1", s + "/ffn/linear_0")
lin(p + ".fc2", s + "/ffn/linear_1")
ln(p + ".final_layer_norm", s + "/ffn/layer_norm")
for i in range(spec["decoder_layers"]):
s, p = "decoder/layer_%d" % i, "model.decoder.layers.%d" % i
self_attn(p + ".self_attn", s + "/self_attention")
ln(p + ".self_attn_layer_norm", s + "/self_attention/layer_norm")
a = s + "/attention"
lin(p + ".encoder_attn.q_proj", a + "/linear_0")
kv = g(a + "/linear_1/weight")
lin(p + ".encoder_attn.k_proj", None, kv[:d], split_bias(a + "/linear_1", 0, d))
lin(p + ".encoder_attn.v_proj", None, kv[d:], split_bias(a + "/linear_1", d, 2 * d))
lin(p + ".encoder_attn.out_proj", a + "/linear_2")
ln(p + ".encoder_attn_layer_norm", a + "/layer_norm")
lin(p + ".fc1", s + "/ffn/linear_0")
lin(p + ".fc2", s + "/ffn/linear_1")
ln(p + ".final_layer_norm", s + "/ffn/layer_norm")
return sd
def convert(ct2_dir, out_dir, src_vocab, tgt_vocab, ct2_cfg=None):
os.makedirs(out_dir, exist_ok=True)
model = read_ct2_model(os.path.join(ct2_dir, "model.bin"))
v = model["variables"]
spec = inspect_spec(v)
spec["ct2_spec"] = "%s rev %d, binary_version %d" % (
model["spec"], model["revision"], model["binary_version"])
cfg_json = ct2_cfg or {}
spec["source_eos"] = bool(cfg_json.get("add_source_eos", False))
spec["source_bos"] = bool(cfg_json.get("add_source_bos", False))
spec["decoder_start_token"] = cfg_json.get("decoder_start_token", "<s>")
print("CT2 spec:", json.dumps(spec, indent=2))
assert spec["pre_norm"] and not spec["relative_position"]
assert not spec["layernorm_embedding"]
assert spec["stored_positions"]
assert len(src_vocab) == spec["source_vocab_size"]
assert len(tgt_vocab) == spec["target_vocab_size"]
n_tgt = spec["target_vocab_size"]
cfg = PegasusConfig(
vocab_size=n_tgt + spec["source_vocab_size"], d_model=spec["d_model"],
encoder_layers=spec["encoder_layers"], decoder_layers=spec["decoder_layers"],
encoder_attention_heads=spec["heads"], decoder_attention_heads=spec["heads"],
encoder_ffn_dim=spec["ffn_dim"], decoder_ffn_dim=spec["ffn_dim"],
max_position_embeddings=MAX_POS,
activation_function=spec["activation"],
scale_embedding=spec["scale_embeddings"],
dropout=0.0, attention_dropout=0.0, activation_dropout=0.0,
pad_token_id=tgt_vocab.index("<blank>"),
bos_token_id=tgt_vocab.index("<s>"),
eos_token_id=tgt_vocab.index("</s>"),
decoder_start_token_id=tgt_vocab.index(spec["decoder_start_token"]),
forced_eos_token_id=None, max_length=512, num_beams=4,
tie_word_embeddings=True, static_position_embeddings=True,
)
hf = PegasusForConditionalGeneration(cfg)
sd = build_state_dict(v, spec)
missing, unexpected = hf.load_state_dict(sd, strict=False)
print("missing:", missing, "unexpected:", unexpected)
assert not missing and not unexpected
hf.eval()
for cls in (PegasusForConditionalGeneration,):
cls._keys_to_ignore_on_save = None
hf.save_pretrained(out_dir, safe_serialization=True)
check = PegasusForConditionalGeneration.from_pretrained(out_dir)
err = (check.model.encoder.embed_positions.weight
- hf.model.encoder.embed_positions.weight).abs().max().item()
print("position table round-trip max abs err:", err)
assert err < 1e-6, "position table was not persisted"
json.dump({"source_vocab": src_vocab, "target_vocab": tgt_vocab,
"source_offset": n_tgt, "target_vocab_size": n_tgt},
open(os.path.join(out_dir, "nos_vocab.json"), "w"), ensure_ascii=False)
return spec
if __name__ == "__main__":
d = sys.argv[1]
sv = json.load(open(os.path.join(d, "source_vocabulary.json"), encoding="utf-8"))
tv = json.load(open(os.path.join(d, "target_vocabulary.json"), encoding="utf-8"))
convert(d, sys.argv[2], sv, tv)
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