Update ldm/modules/encoders/modules.py

ldm/modules/encoders/modules.py

@@ -1,582 +1,582 @@
-import torch
-import torch.nn as nn
-from functools import partial
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-from torch.utils.checkpoint import checkpoint
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer
-from importlib_resources import files
-from ldm.modules.encoders.CLAP.utils import read_config_as_args
-from ldm.modules.encoders.CLAP.clap import TextEncoder
-import copy
-from ldm.util import default, count_params
-import pytorch_lightning as pl
-
-class AbstractEncoder(pl.LightningModule):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]# (bsz,1)
-        c = self.embedding(c)
-        return c
-
-
-class TransformerEmbedder(AbstractEncoder):
-    """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
-        super().__init__()
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
-
-    def forward(self, tokens):
-        tokens = tokens.to(self.device)  # meh
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, x):
-        return self(x)
-
-
-class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
-        super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
-        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        self.device = device
-        self.vq_interface = vq_interface
-        self.max_length = max_length
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        return tokens
-
-    @torch.no_grad()
-    def encode(self, text):
-        tokens = self(text)
-        if not self.vq_interface:
-            return tokens
-        return None, None, [None, None, tokens]
-
-    def decode(self, text):
-        return text
-
-
-class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper
-    """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
-        super().__init__()
-        self.use_tknz_fn = use_tokenizer
-        if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
-
-    def forward(self, text):
-        if self.use_tknz_fn:
-            tokens = self.tknz_fn(text)#.to(self.device)
-        else:
-            tokens = text
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, text):
-        # output of length 77
-        return self(text)
-
-
-class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
-        super().__init__()
-        self.n_stages = n_stages
-        assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
-        self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
-        self.remap_output = out_channels is not None
-        if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
-
-    def forward(self,x):
-        for stage in range(self.n_stages):
-            x = self.interpolator(x, scale_factor=self.multiplier)
-
-
-        if self.remap_output:
-            x = self.channel_mapper(x)
-        return x
-
-    def encode(self, x):
-        return self(x)
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-class FrozenT5Embedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-class FrozenFLANEmbedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)# tango的flanT5是不定长度的batch，这里做成定长的batch
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-class FrozenCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text from microsoft"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):# only freeze
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-
-        outputs = self.caption_encoder.base(input_ids=tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return z
-
-class FrozenLAIONCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text from LAION-AI"""
-    def __init__(self, weights_path, freeze=True,sentence=False, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-        # To device
-        from transformers import RobertaTokenizer
-        from ldm.modules.encoders.open_clap import create_model
-        self.sentence = sentence
-
-        model, model_cfg = create_model(
-            'HTSAT-tiny',
-            'roberta',
-            weights_path,
-            enable_fusion=True,
-            fusion_type='aff_2d'
-        )
-
-        del model.audio_branch, model.audio_transform, model.audio_projection
-        self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-        self.model = model
-
-        self.max_length = max_length
-        self.device = device
-        self.to(self.device)
-        if freeze: self.freeze()
-
-        param_num = sum(p.numel() for p in model.parameters())
-        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e6:.3f} M params.')
-
-    def to(self,device):
-        self.model.to(device=device)
-        self.device=device
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.model.parameters():
-            param.requires_grad = False
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt").to(self.device)
-        if self.sentence:
-            z = self.model.get_text_embedding(batch_encoding).unsqueeze(1)
-        else:
-            # text_branch is roberta
-            outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device))
-            z = self.model.text_projection(outputs.last_hidden_state)
-        
-        return z
-    
-class FrozenLAIONCLAPSetenceEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text from LAION-AI"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-        # To device
-        from transformers import RobertaTokenizer
-        from ldm.modules.encoders.open_clap import create_model
-
-
-        model, model_cfg = create_model(
-            'HTSAT-tiny',
-            'roberta',
-            weights_path,
-            enable_fusion=True,
-            fusion_type='aff_2d'
-        )
-
-        del model.audio_branch, model.audio_transform, model.audio_projection
-        self.tokenize = RobertaTokenizer.from_pretrained('roberta-base')
-        self.model = model
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        param_num = sum(p.numel() for p in model.parameters())
-        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.')
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.model.parameters():
-            param.requires_grad = False
-
-    def tokenizer(self, text):
-        result = self.tokenize(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=512,
-            return_tensors="pt",
-        )
-        return result
-
-    def encode(self, text):
-        with torch.no_grad():
-            # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
-            text_data = self.tokenizer(text)# input_ids shape:(b,512)
-            embed = self.model.get_text_embedding(text_data)
-        embed = embed.unsqueeze(1)# (b,1,512)
-        return embed
-
-class FrozenCLAPOrderEmbedder2(AbstractEncoder):# 每个object后面都加上|
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda"):
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        ).to(device)
-        self.max_objs = 10
-        self.max_length = args.text_len
-        self.device = device
-        self.order_to_label = self.build_order_dict()
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-    def build_order_dict(self):
-        order2label = {}
-        num_orders = 10
-        time_stamps = ['start','mid','end']
-        time_num = len(time_stamps)
-        for i in range(num_orders):
-            for j,time_stamp in enumerate(time_stamps):
-                order2label[f'order {i} {time_stamp}'] = i * time_num + j
-        order2label['all'] = num_orders*len(time_stamps)
-        order2label['unknown'] = num_orders*len(time_stamps) + 1
-        return order2label
-
-    def encode(self, text):
-        obj_list,orders_list = [],[]
-        for raw in text:
-            splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>'
-            objs = []
-            orders = []
-            for split in splits:# <obj& order>
-                split = split[1:-1]
-                obj,order = split.split('&')
-                objs.append(obj.strip())
-                try:
-                    orders.append(self.order_to_label[order.strip()])
-                except:
-                    print(order.strip(),raw)
-            assert len(objs) == len(orders)
-            obj_list.append(' | '.join(objs)+' |')# '|' after every word
-            orders_list.append(orders)
-        batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"]
-
-        outputs = self.caption_encoder.base(input_ids=tokens.to(self.device))
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return {'token_embedding':z,'token_ids':tokens,'orders':orders_list}
-    
-class FrozenCLAPOrderEmbedder3(AbstractEncoder):# 相比于FrozenCLAPOrderEmbedder2移除了projection,使用正确的max_len,去除了order仅保留时间。
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda"):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        ).to(device)
-        self.max_objs = 10
-        self.max_length = args.text_len
-        self.device = device
-        self.order_to_label = self.build_order_dict()
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-    def build_order_dict(self):
-        order2label = {}
-        time_stamps = ['all','start','mid','end']
-        for i,time_stamp in enumerate(time_stamps):
-            order2label[time_stamp] = i
-        return order2label
-
-    def encode(self, text):
-        obj_list,orders_list = [],[]
-        for raw in text:
-            splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>'
-            objs = []
-            orders = []
-            for split in splits:# <obj& order>
-                split = split[1:-1]
-                obj,order = split.split('&')
-                objs.append(obj.strip())
-                try:
-                    orders.append(self.order_to_label[order.strip()])
-                except:
-                    print(order.strip(),raw)
-            assert len(objs) == len(orders)
-            obj_list.append(' | '.join(objs)+' |')# '|' after every word
-            orders_list.append(orders)
-        batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"]
-        attn_mask = batch_encoding["attention_mask"]
-        outputs = self.caption_encoder.base(input_ids=tokens.to(self.device))
-        z = outputs.last_hidden_state
-        return {'token_embedding':z,'token_ids':tokens,'orders':orders_list,'attn_mask':attn_mask}
-
-class FrozenCLAPT5Embedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text from microsoft"""
-    def __init__(self, weights_path,t5version="google/flan-t5-large", freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-    
-        self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version)
-        self.t5_transformer = T5EncoderModel.from_pretrained(t5version)
-
-        self.max_length = max_length
-        self.to(device=device)
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder = self.caption_encoder.eval()
-        for param in self.caption_encoder.parameters():
-            param.requires_grad = False
-
-    def to(self,device):
-        self.t5_transformer.to(device)
-        self.caption_encoder.to(device)
-        self.device = device
-
-    def encode(self, text):
-        ori_caption = text['ori_caption']
-        struct_caption = text['struct_caption']
-        # print(ori_caption,struct_caption)
-        clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        ori_tokens = clap_batch_encoding["input_ids"].to(self.device)
-        t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        struct_tokens = t5_batch_encoding["input_ids"].to(self.device)
-        outputs = self.caption_encoder.base(input_ids=ori_tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state
-        return torch.concat([z,z2],dim=1)
-
-
-class FrozenCLAPFLANEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text from microsoft"""
-    def __init__(self, weights_path,t5version="../ldm/modules/encoders/CLAP/t5-v1_1-large", freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yaml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-    
-        self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version)
-        self.t5_transformer = T5EncoderModel.from_pretrained(t5version)
-
-        self.max_length = max_length
-        # self.to(device=device)
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder = self.caption_encoder.eval()
-        for param in self.caption_encoder.parameters():
-            param.requires_grad = False
-
-    def to(self,device):
-        self.t5_transformer.to(device)
-        self.caption_encoder.to(device)
-        self.device = device
-
-    def encode(self, text):
-        ori_caption = text['ori_caption']
-        struct_caption = text['struct_caption']
-        # print(ori_caption,struct_caption)
-        clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        ori_tokens = clap_batch_encoding["input_ids"].to(self.device)
-        t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        struct_tokens = t5_batch_encoding["input_ids"].to(self.device)
-        # if self.caption_encoder.device != ori_tokens.device:
-        # self.to(self.device)
-        outputs = self.caption_encoder.base(input_ids=ori_tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state
-        return torch.concat([z,z2],dim=1)
+import torch
+import torch.nn as nn
+from functools import partial
+from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+from torch.utils.checkpoint import checkpoint
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer
+from importlib_resources import files
+from ldm.modules.encoders.CLAP.utils import read_config_as_args
+from ldm.modules.encoders.CLAP.clap import TextEncoder
+import copy
+from ldm.util import default, count_params
+import pytorch_lightning as pl
+
+class AbstractEncoder(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key='class'):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]# (bsz,1)
+        c = self.embedding(c)
+        return c
+
+
+class TransformerEmbedder(AbstractEncoder):
+    """Some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+        super().__init__()
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
+
+    def forward(self, tokens):
+        tokens = tokens.to(self.device)  # meh
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, x):
+        return self(x)
+
+
+class BERTTokenizer(AbstractEncoder):
+    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+        super().__init__()
+        from transformers import BertTokenizerFast  # TODO: add to reuquirements
+        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+        self.device = device
+        self.vq_interface = vq_interface
+        self.max_length = max_length
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        return tokens
+
+    @torch.no_grad()
+    def encode(self, text):
+        tokens = self(text)
+        if not self.vq_interface:
+            return tokens
+        return None, None, [None, None, tokens]
+
+    def decode(self, text):
+        return text
+
+
+class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper
+    """Uses the BERT tokenizr model and add some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
+                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+        super().__init__()
+        self.use_tknz_fn = use_tokenizer
+        if self.use_tknz_fn:
+            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
+                                              emb_dropout=embedding_dropout)
+
+    def forward(self, text):
+        if self.use_tknz_fn:
+            tokens = self.tknz_fn(text)#.to(self.device)
+        else:
+            tokens = text
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, text):
+        # output of length 77
+        return self(text)
+
+
+class SpatialRescaler(nn.Module):
+    def __init__(self,
+                 n_stages=1,
+                 method='bilinear',
+                 multiplier=0.5,
+                 in_channels=3,
+                 out_channels=None,
+                 bias=False):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.remap_output = out_channels is not None
+        if self.remap_output:
+            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
+            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
+
+    def forward(self,x):
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+class FrozenT5Embedder(AbstractEncoder):
+    """Uses the T5 transformer encoder for text"""
+    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length   # TODO: typical value?
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        #self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+class FrozenFLANEmbedder(AbstractEncoder):
+    """Uses the T5 transformer encoder for text"""
+    def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length   # TODO: typical value?
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        #self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)# tango的flanT5是不定长度的batch，这里做成定长的batch
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+class FrozenCLAPEmbedder(AbstractEncoder):
+    """Uses the CLAP transformer encoder for text from microsoft"""
+    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+
+        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
+        match_params = dict()
+        for key in list(model_state_dict.keys()):
+            if 'caption_encoder' in key:
+                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
+
+        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
+        args = read_config_as_args(config_as_str, is_config_str=True)
+
+        # To device
+        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
+        self.caption_encoder = TextEncoder(
+            args.d_proj, args.text_model, args.transformer_embed_dim
+        )
+
+        self.max_length = max_length
+        self.device = device
+        if freeze: self.freeze()
+
+        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
+
+    def freeze(self):# only freeze
+        self.caption_encoder.base = self.caption_encoder.base.eval()
+        for param in self.caption_encoder.base.parameters():
+            param.requires_grad = False
+
+
+    def encode(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+
+        outputs = self.caption_encoder.base(input_ids=tokens)
+        z = self.caption_encoder.projection(outputs.last_hidden_state)
+        return z
+
+class FrozenLAIONCLAPEmbedder(AbstractEncoder):
+    """Uses the CLAP transformer encoder for text from LAION-AI"""
+    def __init__(self, weights_path, freeze=True,sentence=False, device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+        # To device
+        from transformers import RobertaTokenizer
+        from ldm.modules.encoders.open_clap import create_model
+        self.sentence = sentence
+
+        model, model_cfg = create_model(
+            'HTSAT-tiny',
+            'roberta',
+            weights_path,
+            enable_fusion=True,
+            fusion_type='aff_2d'
+        )
+
+        del model.audio_branch, model.audio_transform, model.audio_projection
+        self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+        self.model = model
+
+        self.max_length = max_length
+        self.device = device
+        self.to(self.device)
+        if freeze: self.freeze()
+
+        param_num = sum(p.numel() for p in model.parameters())
+        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e6:.3f} M params.')
+
+    def to(self,device):
+        self.model.to(device=device)
+        self.device=device
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.model.parameters():
+            param.requires_grad = False
+
+    def encode(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt").to(self.device)
+        if self.sentence:
+            z = self.model.get_text_embedding(batch_encoding).unsqueeze(1)
+        else:
+            # text_branch is roberta
+            outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device))
+            z = self.model.text_projection(outputs.last_hidden_state)
+        
+        return z
+    
+class FrozenLAIONCLAPSetenceEmbedder(AbstractEncoder):
+    """Uses the CLAP transformer encoder for text from LAION-AI"""
+    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+        # To device
+        from transformers import RobertaTokenizer
+        from ldm.modules.encoders.open_clap import create_model
+
+
+        model, model_cfg = create_model(
+            'HTSAT-tiny',
+            'roberta',
+            weights_path,
+            enable_fusion=True,
+            fusion_type='aff_2d'
+        )
+
+        del model.audio_branch, model.audio_transform, model.audio_projection
+        self.tokenize = RobertaTokenizer.from_pretrained('roberta-base')
+        self.model = model
+
+        self.max_length = max_length
+        self.device = device
+        if freeze: self.freeze()
+
+        param_num = sum(p.numel() for p in model.parameters())
+        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.')
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.model.parameters():
+            param.requires_grad = False
+
+    def tokenizer(self, text):
+        result = self.tokenize(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=512,
+            return_tensors="pt",
+        )
+        return result
+
+    def encode(self, text):
+        with torch.no_grad():
+            # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
+            text_data = self.tokenizer(text)# input_ids shape:(b,512)
+            embed = self.model.get_text_embedding(text_data)
+        embed = embed.unsqueeze(1)# (b,1,512)
+        return embed
+
+class FrozenCLAPOrderEmbedder2(AbstractEncoder):# 每个object后面都加上|
+    """Uses the CLAP transformer encoder for text (from huggingface)"""
+    def __init__(self, weights_path, freeze=True, device="cuda"):
+        super().__init__()
+
+        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
+        match_params = dict()
+        for key in list(model_state_dict.keys()):
+            if 'caption_encoder' in key:
+                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
+
+        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
+        args = read_config_as_args(config_as_str, is_config_str=True)
+
+        # To device
+        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
+        self.caption_encoder = TextEncoder(
+            args.d_proj, args.text_model, args.transformer_embed_dim
+        ).to(device)
+        self.max_objs = 10
+        self.max_length = args.text_len
+        self.device = device
+        self.order_to_label = self.build_order_dict()
+        if freeze: self.freeze()
+
+        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
+
+    def freeze(self):
+        self.caption_encoder.base = self.caption_encoder.base.eval()
+        for param in self.caption_encoder.base.parameters():
+            param.requires_grad = False
+
+    def build_order_dict(self):
+        order2label = {}
+        num_orders = 10
+        time_stamps = ['start','mid','end']
+        time_num = len(time_stamps)
+        for i in range(num_orders):
+            for j,time_stamp in enumerate(time_stamps):
+                order2label[f'order {i} {time_stamp}'] = i * time_num + j
+        order2label['all'] = num_orders*len(time_stamps)
+        order2label['unknown'] = num_orders*len(time_stamps) + 1
+        return order2label
+
+    def encode(self, text):
+        obj_list,orders_list = [],[]
+        for raw in text:
+            splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>'
+            objs = []
+            orders = []
+            for split in splits:# <obj& order>
+                split = split[1:-1]
+                obj,order = split.split('&')
+                objs.append(obj.strip())
+                try:
+                    orders.append(self.order_to_label[order.strip()])
+                except:
+                    print(order.strip(),raw)
+            assert len(objs) == len(orders)
+            obj_list.append(' | '.join(objs)+' |')# '|' after every word
+            orders_list.append(orders)
+        batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"]
+
+        outputs = self.caption_encoder.base(input_ids=tokens.to(self.device))
+        z = self.caption_encoder.projection(outputs.last_hidden_state)
+        return {'token_embedding':z,'token_ids':tokens,'orders':orders_list}
+    
+class FrozenCLAPOrderEmbedder3(AbstractEncoder):# 相比于FrozenCLAPOrderEmbedder2移除了projection,使用正确的max_len,去除了order仅保留时间。
+    """Uses the CLAP transformer encoder for text (from huggingface)"""
+    def __init__(self, weights_path, freeze=True, device="cuda"):  # clip-vit-base-patch32
+        super().__init__()
+
+        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
+        match_params = dict()
+        for key in list(model_state_dict.keys()):
+            if 'caption_encoder' in key:
+                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
+
+        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
+        args = read_config_as_args(config_as_str, is_config_str=True)
+
+        # To device
+        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
+        self.caption_encoder = TextEncoder(
+            args.d_proj, args.text_model, args.transformer_embed_dim
+        ).to(device)
+        self.max_objs = 10
+        self.max_length = args.text_len
+        self.device = device
+        self.order_to_label = self.build_order_dict()
+        if freeze: self.freeze()
+
+        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
+
+    def freeze(self):
+        self.caption_encoder.base = self.caption_encoder.base.eval()
+        for param in self.caption_encoder.base.parameters():
+            param.requires_grad = False
+
+    def build_order_dict(self):
+        order2label = {}
+        time_stamps = ['all','start','mid','end']
+        for i,time_stamp in enumerate(time_stamps):
+            order2label[time_stamp] = i
+        return order2label
+
+    def encode(self, text):
+        obj_list,orders_list = [],[]
+        for raw in text:
+            splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>'
+            objs = []
+            orders = []
+            for split in splits:# <obj& order>
+                split = split[1:-1]
+                obj,order = split.split('&')
+                objs.append(obj.strip())
+                try:
+                    orders.append(self.order_to_label[order.strip()])
+                except:
+                    print(order.strip(),raw)
+            assert len(objs) == len(orders)
+            obj_list.append(' | '.join(objs)+' |')# '|' after every word
+            orders_list.append(orders)
+        batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"]
+        attn_mask = batch_encoding["attention_mask"]
+        outputs = self.caption_encoder.base(input_ids=tokens.to(self.device))
+        z = outputs.last_hidden_state
+        return {'token_embedding':z,'token_ids':tokens,'orders':orders_list,'attn_mask':attn_mask}
+
+class FrozenCLAPT5Embedder(AbstractEncoder):
+    """Uses the CLAP transformer encoder for text from microsoft"""
+    def __init__(self, weights_path,t5version="google/flan-t5-large", freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+
+        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
+        match_params = dict()
+        for key in list(model_state_dict.keys()):
+            if 'caption_encoder' in key:
+                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
+
+        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
+        args = read_config_as_args(config_as_str, is_config_str=True)
+
+        self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
+        self.caption_encoder = TextEncoder(
+            args.d_proj, args.text_model, args.transformer_embed_dim
+        )
+    
+        self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version)
+        self.t5_transformer = T5EncoderModel.from_pretrained(t5version)
+
+        self.max_length = max_length
+        self.to(device=device)
+        if freeze: self.freeze()
+
+        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
+
+    def freeze(self):
+        self.caption_encoder = self.caption_encoder.eval()
+        for param in self.caption_encoder.parameters():
+            param.requires_grad = False
+
+    def to(self,device):
+        self.t5_transformer.to(device)
+        self.caption_encoder.to(device)
+        self.device = device
+
+    def encode(self, text):
+        ori_caption = text['ori_caption']
+        struct_caption = text['struct_caption']
+        # print(ori_caption,struct_caption)
+        clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        ori_tokens = clap_batch_encoding["input_ids"].to(self.device)
+        t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        struct_tokens = t5_batch_encoding["input_ids"].to(self.device)
+        outputs = self.caption_encoder.base(input_ids=ori_tokens)
+        z = self.caption_encoder.projection(outputs.last_hidden_state)
+        z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state
+        return torch.concat([z,z2],dim=1)
+
+
+class FrozenCLAPFLANEmbedder(AbstractEncoder):
+    """Uses the CLAP transformer encoder for text from microsoft"""
+    def __init__(self, weights_path,t5version="./ldm/modules/encoders/CLAP/t5-v1_1-large", freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
+        super().__init__()
+
+        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
+        match_params = dict()
+        for key in list(model_state_dict.keys()):
+            if 'caption_encoder' in key:
+                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
+
+        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yaml').read_text()
+        args = read_config_as_args(config_as_str, is_config_str=True)
+
+        self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
+        self.caption_encoder = TextEncoder(
+            args.d_proj, args.text_model, args.transformer_embed_dim
+        )
+    
+        self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version)
+        self.t5_transformer = T5EncoderModel.from_pretrained(t5version)
+
+        self.max_length = max_length
+        # self.to(device=device)
+        if freeze: self.freeze()
+
+        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
+
+    def freeze(self):
+        self.caption_encoder = self.caption_encoder.eval()
+        for param in self.caption_encoder.parameters():
+            param.requires_grad = False
+
+    def to(self,device):
+        self.t5_transformer.to(device)
+        self.caption_encoder.to(device)
+        self.device = device
+
+    def encode(self, text):
+        ori_caption = text['ori_caption']
+        struct_caption = text['struct_caption']
+        # print(ori_caption,struct_caption)
+        clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        ori_tokens = clap_batch_encoding["input_ids"].to(self.device)
+        t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        struct_tokens = t5_batch_encoding["input_ids"].to(self.device)
+        # if self.caption_encoder.device != ori_tokens.device:
+        # self.to(self.device)
+        outputs = self.caption_encoder.base(input_ids=ori_tokens)
+        z = self.caption_encoder.projection(outputs.last_hidden_state)
+        z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state
+        return torch.concat([z,z2],dim=1)