107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
|
import os
|
||
|
|
import numpy
|
||
|
|
from copy import deepcopy
|
||
|
|
from typing import Dict
|
||
|
|
import torch
|
||
|
|
from flax import traverse_util, serialization
|
||
|
|
|
||
|
|
|
||
|
|
def load_vqgan_torch_params(path: str) -> Dict[str, torch.Tensor]:
|
||
|
|
with open(os.path.join(path, 'flax_model.msgpack'), "rb") as f:
|
||
|
|
params: Dict[str, numpy.ndarray] = serialization.msgpack_restore(f.read())
|
||
|
|
|
||
|
|
P: Dict[str, numpy.ndarray] = traverse_util.flatten_dict(params, sep='.')
|
||
|
|
|
||
|
|
for i in list(P.keys()):
|
||
|
|
j = i
|
||
|
|
if 'up' in i or 'down' in i:
|
||
|
|
j = i.replace('_', '.')
|
||
|
|
j = j.replace('proj.out', 'proj_out')
|
||
|
|
j = j.replace('nin.short', 'nin_short')
|
||
|
|
if 'bias' in i:
|
||
|
|
P[j] = P.pop(i)
|
||
|
|
elif 'scale' in i:
|
||
|
|
j = j.replace('scale', 'weight')
|
||
|
|
P[j] = P.pop(i)
|
||
|
|
elif 'kernel' in i:
|
||
|
|
j = j.replace('kernel', 'weight')
|
||
|
|
P[j] = P.pop(i).transpose(3, 2, 0, 1)
|
||
|
|
|
||
|
|
for i in P:
|
||
|
|
P[i] = torch.tensor(P[i])
|
||
|
|
|
||
|
|
P['embedding.weight'] = P.pop('quantize.embedding.embedding')
|
||
|
|
|
||
|
|
for i in list(P):
|
||
|
|
if i.split('.')[0] in ['encoder', 'quant_conv']:
|
||
|
|
P.pop(i)
|
||
|
|
|
||
|
|
return P
|
||
|
|
|
||
|
|
|
||
|
|
def load_dalle_bart_flax_params(path: str) -> Dict[str, numpy.ndarray]:
|
||
|
|
with open(os.path.join(path, "flax_model.msgpack"), "rb") as f:
|
||
|
|
params = serialization.msgpack_restore(f.read())
|
||
|
|
|
||
|
|
for codec in ['encoder', 'decoder']:
|
||
|
|
k = 'FlaxBart{}Layers'.format(codec.title())
|
||
|
|
P: dict = params['model'][codec]['layers'][k]
|
||
|
|
P['pre_self_attn_layer_norm'] = P.pop('LayerNorm_0')
|
||
|
|
P['self_attn_layer_norm'] = P.pop('LayerNorm_1')
|
||
|
|
P['self_attn'] = P.pop('FlaxBartAttention_0')
|
||
|
|
if codec == 'decoder':
|
||
|
|
P['pre_encoder_attn_layer_norm'] = P.pop('LayerNorm_2')
|
||
|
|
P['encoder_attn_layer_norm'] = P.pop('LayerNorm_3')
|
||
|
|
P['encoder_attn'] = P.pop('FlaxBartAttention_1')
|
||
|
|
P['glu']: dict = P.pop('GLU_0')
|
||
|
|
P['glu']['ln0'] = P['glu'].pop('LayerNorm_0')
|
||
|
|
P['glu']['ln1'] = P['glu'].pop('LayerNorm_1')
|
||
|
|
P['glu']['fc0'] = P['glu'].pop('Dense_0')
|
||
|
|
P['glu']['fc1'] = P['glu'].pop('Dense_1')
|
||
|
|
P['glu']['fc2'] = P['glu'].pop('Dense_2')
|
||
|
|
|
||
|
|
for codec in ['encoder', 'decoder']:
|
||
|
|
layers_params = params['model'][codec].pop('layers')
|
||
|
|
params['model'][codec] = {
|
||
|
|
**params['model'][codec],
|
||
|
|
**layers_params
|
||
|
|
}
|
||
|
|
|
||
|
|
model_params = params.pop('model')
|
||
|
|
params = {**params, **model_params}
|
||
|
|
|
||
|
|
params['decoder']['lm_head'] = params.pop('lm_head')
|
||
|
|
|
||
|
|
return params
|
||
|
|
|
||
|
|
|
||
|
|
def convert_dalle_bart_torch_from_flax_params(
|
||
|
|
params: dict,
|
||
|
|
layer_count: int,
|
||
|
|
is_encoder: bool
|
||
|
|
) -> dict:
|
||
|
|
P = deepcopy(params)
|
||
|
|
P: Dict[str, numpy.ndarray] = traverse_util.flatten_dict(P, sep='.')
|
||
|
|
|
||
|
|
for i in P:
|
||
|
|
P[i] = torch.tensor(P[i])
|
||
|
|
|
||
|
|
for i in list(P):
|
||
|
|
if 'kernel' in i:
|
||
|
|
j = i.replace('kernel', 'weight')
|
||
|
|
P[j] = P.pop(i).transpose(-1, -2)
|
||
|
|
elif 'scale' in i:
|
||
|
|
j = i.replace('scale', 'weight')
|
||
|
|
P[j] = P.pop(i)
|
||
|
|
|
||
|
|
for i in list(P):
|
||
|
|
j = 'FlaxBart{}Layers'.format('Encoder' if is_encoder else 'Decoder')
|
||
|
|
if j in i:
|
||
|
|
for l in range(layer_count):
|
||
|
|
k = i.replace(j, 'layers.' + str(l))
|
||
|
|
P[k] = P[i][l]
|
||
|
|
P.pop(i)
|
||
|
|
|
||
|
|
P['embed_tokens.weight'] = P.pop('embed_tokens.embedding')
|
||
|
|
P['embed_positions.weight'] = P.pop('embed_positions.embedding')
|
||
|
|
return P
|