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# 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"]
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
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)
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=torch.cuda.is_available()
)
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",
}
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