# take features from the eot embedding (eot_token is the highest number in each sequence) # 结束符"<|endoftext|>"的编号最大,得到该位置的embedding x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection return x """ # positional encoding self.positional_embeddi...
def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]: sot_token = _tokenizer.encoder["<|startoftext|>"] eot_token = _tokenizer.encoder["<|endoftext|>"] all_tokens = [[sot_token] + _tokenizer....
# take features from the eot embedding (eot_token is the highest number in each sequence) x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projectionreturn x clip.tokenize()该函数用于将文本字符串列表转换为可以输入到 CLIP 模型中的标记化张量。此函数处理标记化过程,包括...
0, 2) # NLD -> LNDx = self.transformer(x)x = x.permute(1, 0, 2) # LND -> NLDx = self.ln_final(x).type(self.dtype)# x.shape = [batch_size, n_ctx, transformer.width]# take features from the eot embedding (eot_token is the highest...
# take features from the eot embedding (eot_token is the highest number in each sequence) x=x[torch.arange(x.shape[0]),text.argmax(dim=-1)]@self.text_projection returnx 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 其中文本 transformer 模块的实现如下: ...
(tokens)>context_length:#context_length:77,如果tokens的长度大于context_length,做截断处理或者抛异常iftruncate:tokens=tokens[:context_length]tokens[-1]=eot_tokenelse:raiseRuntimeError(f"Input {texts[i]} is too long for context length {context_length}")result[i,:len(tokens)]=torch.tensor(...
[texts] sot_token = tokenizer.encoder["<|startoftext|>"] eot_token = tokenizer.encoder["<|endoftext|>"] all_tokens = [[sot_token] + tokenizer.encode(text) + [eot_token] for text in texts] result = paddle.zeros((len(all_tokens), context_length), dtype='int64') for i, tokens...
(text_tokens), 77), dtype="int64") sot_token = tokenizer.encoder['<|startoftext|>'] eot_token = tokenizer.encoder['<|endoftext|>'] for i, tokens in enumerate(text_tokens): tokens = [sot_token] + tokens + [eot_token] text_input[i, :len(tokens)] = paddle.to_tensor(tokens) ...
const eotTokens = mx.argmax(x, -1); x = this.embeddings.forward(x); const mask = nn.MultiHeadAttention.createAdditiveCausalMask(N, x.dtype); x = this.encoder.forward(x, mask); const lastHiddenState = this.finalLayerNorm.forward(x); const poolerOutput = lastHiddenState.index(mx.arang...
token_embedding(x) embeddings += self.position_embedding[: x.shape[1]] return embeddings def __call__(self, x: mx.array) -> CLIPTextOutput: B, N = x.shape eot_tokens = mx.argmax(x, axis=-1) x = self._embed(x) mask = nn.MultiHeadAttention.create_additive_causal_mask(N, x...