## data flow

input_ids = tokenizer.build_chat_input(query, history=history, role=role)

input_ids -> [1, 6]  1:batch_num  6:sequence_length
inputs_embeds -> [6, 1, 4096]  4096:hidden_size
rotary_pos_emb -> [6, 1, 32, 2]  32:pos的编码维度  2:cos+sin

hidden_states = inputs_embeds
for layers : GLMBlock(hidden_states, rotary_pos_emb)
hidden_states = self.final_layernorm(hidden_states)
hidden_states = hidden_states[-1:]
lm_logits = self.output_layer(hidden_states)
lm_logits = lm_logits.transpose(0, 1).contiguous()  -> [1, 1, 65024]

probs = softmax(lm_logits) -> [1, 65024]
next_tokens = torch.multinomial(probs, num_samples=1) 采样  -> [1]  1:batch_num 
input_ids = torch.cat([input_ids, next_tokens)  -> [1, 7]  1:batch_num 

response = tokenizer.decode(outputs)