decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask) 初始化: self.encoder 编码器结构 self.decoder 解码器结构 self.src_embed 源嵌入 self.tgt_embed 汇嵌入 self.generator 生成器 在后续make_model function中通过如下语句调用生成实例: def make_model( src_vocab, tgt_vocab, N=6, d_model=...
"language_model.model.decoder.embed_positions.weight": "pytorch_model-00001-of-00002.bin", "language_model.model.decoder.embed_tokens.weight": "pytorch_model-00001-of-00002.bin", "language_model.model.decoder.final_layer_norm.bias": "pytorch_model-00001-of-00002.bin", "language_model....
embed_size=8,num_hiddens=16,num_layers=2)decoder.eval()X=torch.zeros((4,7),dtype=torch....
decoder.embed_tokens self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: h = w = self.config.vision_config.image_size @@ -653,7 +635,8 @@ def forward( if image_...
device_map["decoder.embed_tokens"] = "cpu" device_map["decoder.embed_positions"] = "cpu" device_map["decoder.layers.0"] = "cpu" device_map["decoder.layers.1"] = "cpu" device_map["decoder.layers.2"] = "cpu" device_map["decoder.layers.3"] = "cpu" model = M2M100ForConditional...
"""Embed positions in tensor.""" masks = sequence_mask(ilens, device=ilens.device)[:, None, :] xs_pad *= self.output_size() ** 0.5 xs_pad = self.embed(xs_pad) # forward encoder1 for layer_idx, encoder_layer in enumerate(self.encoders0): encoder_outs = encoder_layer(...
They are also flexible for different generative tasks, as they can use different types of encoders, decoders, priors, and diffusion processes. However, diffusion models have the drawback of being slower to sample than GANs, as they need to run multiple steps in the denoising process to ...
They are also flexible for different generative tasks, as they can use different types of encoders, decoders, priors, and diffusion processes. However, diffusion models have the drawback of being slower to sample than GANs, as they need to run multiple steps in the denoising process to ...
froma⋅W+b. The decoder layer is applied to each row of the encoder output. This design imposes a strong constraint on the autoencoder: the model is required to reconstruct the input using a decoder function with only a relatively small number of parameters shared across all positions. ...
#create encoder-decoder model encoder = Encoder(input_size, hidden_size, embed_size, num_layers) decoder = Decoder(output_size, hidden_size, embed_size, num_layers) model = Seq2Seq(encoder, decoder, device).to(device) #print model