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| import torch | |
| from torch import nn | |
| from torch.utils.checkpoint import checkpoint | |
| import open_clip | |
| class FrozenOpenCLIPEmbedder(nn.Module): | |
| def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, | |
| freeze=True, layer="last"): | |
| super().__init__() | |
| model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) | |
| del model.visual | |
| self.model = model | |
| self.device = device | |
| self.max_length = max_length | |
| if freeze: self.freeze() | |
| self.layer = layer | |
| if self.layer == "last": | |
| self.layer_idx = 0 | |
| elif self.layer == "penultimate": | |
| self.layer_idx = 1 | |
| else: | |
| raise NotImplementedError() | |
| def freeze(self): | |
| self.model = self.model.eval() | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| def forward(self, text): | |
| tokens = open_clip.tokenize(text) | |
| z = self.encode_with_transformer(tokens.to(self.device)) | |
| return z | |
| def encode_with_transformer(self, text): | |
| x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] | |
| x = x + self.model.positional_embedding | |
| x = x.permute(1, 0, 2) # NLD -> LND | |
| x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) | |
| x = x.permute(1, 0, 2) # LND -> NLD | |
| x = self.model.ln_final(x) | |
| return x | |
| def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): | |
| for i, r in enumerate(self.model.transformer.resblocks): | |
| if i == len(self.model.transformer.resblocks) - self.layer_idx: | |
| break | |
| if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): | |
| x = checkpoint(r, x, attn_mask) | |
| else: | |
| x = r(x, attn_mask=attn_mask) | |
| return x | |
| def encode(self, text): | |
| return self(text) |