Spaces:
Build error
Build error
File size: 6,908 Bytes
92740f3 |
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 |
# Copyright (c) 2024 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/mlfoundations/open_flamingo under the MIT license.
# LICENSE is in incl_licenses directory.
import sys
sys.path.append('../')
from typing import Optional
from copy import deepcopy
from transformers import AutoModelForCausalLM, AutoTokenizer
from ms_clap.src.CLAPWrapper import CLAPWrapper
import torch
from torch import nn
try:
from .flamingo import Flamingo
from .flamingo_lm import FlamingoLMMixin
from .utils import extend_instance
except:
from flamingo import Flamingo
from flamingo_lm import FlamingoLMMixin
from utils import extend_instance
class CLAP(nn.Module):
def __init__(self, clap_config):
super(CLAP, self).__init__()
self.method = clap_config["method"]
device_id = f'cuda:{torch.cuda.current_device()}'
if self.method == 'laion-clap':
# https://github.com/LAION-AI/CLAP
if clap_config["model_name"] in ['630k-audioset-best', '630k-best', '630k-audioset-fusion-best', '630k-fusion-best']:
amodel = 'HTSAT-tiny'
elif clap_config["model_name"] in ['music_speech_audioset_epoch_15_esc_89.98']:
amodel = 'HTSAT-base'
else:
raise NotImplementedError
enable_fusion = 'fusion' in clap_config["model_name"].lower()
self.laion_clap = CLAP_Module(enable_fusion=enable_fusion, amodel=amodel, device=device_id)
self.laion_clap.load_ckpt(ckpt=clap_config["checkpoint"])
for param in self.laion_clap.parameters():
param.requires_grad = False
self.laion_clap.eval()
print('loaded laion-clap model: {}'.format(clap_config["checkpoint"]))
elif self.method == 'microsoft-clap':
# https://github.com/microsoft/CLAP
self.ms_clap = CLAPWrapper(
clap_config["checkpoint"],
config_root=clap_config["config_root"],
version=clap_config['model_name'],
use_cuda=True
)
if clap_config['model_name'] in ['2022', '2023']:
for param in self.ms_clap.clap.parameters():
param.requires_grad = False
self.ms_clap.clap.eval()
else:
for param in self.ms_clap.clapcap.parameters():
param.requires_grad = False
self.ms_clap.clapcap.eval()
print('loaded microsoft-clap model: {}'.format(clap_config["checkpoint"]))
else:
raise NotImplementedError
def forward(self, audio_clips):
if len(audio_clips.shape) == 2:
audio_clips = audio_clips.unsqueeze(0)
assert len(audio_clips.shape) == 3
audio_embeds = []
for x in audio_clips:
if self.method == 'laion-clap':
audio_embed = self.laion_clap.get_audio_embedding_from_data(x=x, use_tensor=True)
elif self.method == 'microsoft-clap':
audio_embed = self.ms_clap.get_audio_embeddings_from_clips(x)
audio_embeds.append(audio_embed)
audio_embeds = torch.stack(audio_embeds, dim=0)
audio_embeds.requires_grad = False
return audio_embeds
def create_model_and_transforms(
clap_config: dict,
lang_encoder_path: str,
tokenizer_path: str,
audio_transformer_kwargs: dict,
cross_attn_every_n_layers: int = 1,
use_local_files: bool = False,
decoder_layers_attr_name: str = None,
freeze_lm_embeddings: bool = False,
unfreeze_full_lm: bool = False,
cache_dir: Optional[str] = None,
**flamingo_kwargs,
):
clap = CLAP(clap_config)
text_tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path,
local_files_only=use_local_files,
trust_remote_code=True,
cache_dir=cache_dir,
)
text_tokenizer.add_special_tokens(
{"additional_special_tokens": ["<audio>", "<|endofchunk|>"]}
)
if text_tokenizer.pad_token is None:
text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
if text_tokenizer.sep_token is None:
text_tokenizer.add_special_tokens({"sep_token": "<SEP>"})
lang_encoder = AutoModelForCausalLM.from_pretrained(
lang_encoder_path,
local_files_only=use_local_files,
trust_remote_code=True,
cache_dir=cache_dir,
)
extend_instance(lang_encoder, FlamingoLMMixin)
if decoder_layers_attr_name is None:
decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
lang_encoder.resize_token_embeddings(len(text_tokenizer))
unfreeze_clap = False
model = Flamingo(
clap,
unfreeze_clap,
lang_encoder,
text_tokenizer.encode("<|endofchunk|>")[-1],
text_tokenizer.encode("<audio>")[-1],
text_tokenizer.sep_token_id,
audio_embed_dim=clap_config["audio_embed_dim"],
audio_transformer_kwargs=audio_transformer_kwargs,
cross_attn_every_n_layers=cross_attn_every_n_layers,
**flamingo_kwargs,
)
model.requires_grad_(False)
assert sum(p.numel() for p in model.parameters() if p.requires_grad) == 0
model.audio_transformer.requires_grad_(True)
model.lang_encoder.gated_cross_attn_layers.requires_grad_(True)
if not freeze_lm_embeddings:
model.lang_encoder.get_input_embeddings().requires_grad_(True)
if unfreeze_full_lm:
model.lang_encoder.requires_grad_(True)
if unfreeze_clap:
model.clap.requires_grad_(True)
print("Flamingo model initialized with {:,} trainable parameters (audio transformer has {:,}, LM has {:,})".format(
sum(p.numel() for p in model.parameters() if p.requires_grad),
sum(p.numel() for p in model.audio_transformer.parameters() if p.requires_grad),
sum(p.numel() for p in model.lang_encoder.parameters() if p.requires_grad)
))
return model, text_tokenizer
def _infer_decoder_layers_attr_name(model):
for k in __KNOWN_DECODER_LAYERS_ATTR_NAMES:
if k.lower() in model.__class__.__name__.lower():
return __KNOWN_DECODER_LAYERS_ATTR_NAMES[k]
raise ValueError(
f"We require the attribute name for the nn.ModuleList in the decoder storing the transformer block layers. Please supply this string manually."
)
__KNOWN_DECODER_LAYERS_ATTR_NAMES = {
"opt": "model.decoder.layers",
"gptj": "transformer.h",
"gpt-j": "transformer.h",
"pythia": "gpt_neox.layers",
"llama": "model.layers",
"gptneoxforcausallm": "gpt_neox.layers",
"mpt": "transformer.blocks",
"mosaicgpt": "transformer.blocks",
}
|