请问chatglm得generate方法是否支持embedding输入？ about chatglm3 HOT 5 CLOSED

请问，你有任何新的想法吗？我在源文件中找到了‘PrefixEncoder’的类，似乎被用在了P-TuningV2里
定义在：

65: class PrefixEncoder(torch.nn.Module)

使用在：

736: class ChatGLMModel(ChatGLMPreTrainedModel):
789:      def forward():

在这一过程中，官方是将这里的emmbedding作为past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None
送入模型的，不知道能不能帮到你，我也恰好正在研究这里

from chatglm3.

不确定，将跟算法同学进行讨论

from chatglm3.

I'm trying to use GCG with ChatGLM3.

After I read the code carefully, I think generate() actually supports inputs_embeds, which may solve the issue.
I found that input_ids is only used to provide size to create attention_mask and position_ids. When inputs_embeds is passed in, according to the code

if inputs_embeds is None:
    inputs_embeds = self.embedding(input_ids)

the parameter input_ids does not actually affect the inference results?

So in fact, to use inputs_embeds as input, we only needmodel(input_ids, inputs_embeds)

Not sure if my understanding is correct?

And I find, when run model(input_ids=input_ids.unsqueeze(0),inputs_embeds=full_embeds), the output dimensions of ChatGLM3 seem to be different with those of Llama2 or Vicuna? Need to use something like .permute(1, 0, 2)?

Not sure about my understanding, thanks a lot in advance for your support!

from chatglm3.

Following the code below does pass embedding as an input, but when using model.generate(), it will prompt an error:"You passed inputs_embeds to .generate(), but the model class ChatGLMForConditionalGeneration doesn't have its forwarding implemented. See the GPT2 implementation for an example (huggingface/transformers#21405), and feel free to open a PR with it!"

inputs  = tokenizer(MutilTalk_Prompt,padding = 'max_length',max_length = 99)
tensor_input_ids = torch.tensor(inputs['input_ids']+[2])
tensor_input_ids = tensor_input_ids.cuda()
print(tensor_input_ids)
input_embeds = model.transformer.embedding(tensor_input_ids.unsqueeze(0))

outputs = model(input_ids=tensor_input_ids.unsqueeze(0),inputs_embeds=input_embeds)
logits_output = tokenizer.batch_decode(torch.argmax(outputs['logits'], -1).detach().cpu().numpy(), skip_special_tokens=True)
print(logits_output)

#error
outputs = model.generate(input_ids=tensor_input_ids.unsqueeze(0),inputs_embeds=input_embeds)
logits_output = tokenizer.batch_decode(torch.argmax(outputs['logits'], -1).detach().cpu().numpy(), skip_special_tokens=True)
print(logits_output)

from chatglm3.

Following the code below does pass embedding as an input, but when using model.generate(), it will prompt an error:"You passed inputs_embeds to .generate(), but the model class ChatGLMForConditionalGeneration doesn't have its forwarding implemented. See the GPT2 implementation for an example (huggingface/transformers#21405), and feel free to open a PR with it!"

inputs  = tokenizer(MutilTalk_Prompt,padding = 'max_length',max_length = 99)
tensor_input_ids = torch.tensor(inputs['input_ids']+[2])
tensor_input_ids = tensor_input_ids.cuda()
print(tensor_input_ids)
input_embeds = model.transformer.embedding(tensor_input_ids.unsqueeze(0))

outputs = model(input_ids=tensor_input_ids.unsqueeze(0),inputs_embeds=input_embeds)
logits_output = tokenizer.batch_decode(torch.argmax(outputs['logits'], -1).detach().cpu().numpy(), skip_special_tokens=True)
print(logits_output)

#error
outputs = model.generate(input_ids=tensor_input_ids.unsqueeze(0),inputs_embeds=input_embeds)
logits_output = tokenizer.batch_decode(torch.argmax(outputs['logits'], -1).detach().cpu().numpy(), skip_special_tokens=True)
print(logits_output)

Oh, I get what u mean now, actually I do not use generate(), instead I just use model().logits. And in this case it runs well. But output has a different dimension with that of llama2 or vicuna XD

from chatglm3.