Add model config json files.

Readme.md

@@ -5,19 +5,23 @@
 
 input_ids = tokenizer.build_chat_input(query, history=history, role=role)
 
-input_ids -> [1, 6]  1:batch_num  6:sequence_length
+for
-inputs_embeds -> [6, 1, 4096]  4096:hidden_size
+  input_ids -> [1, 6]  1:batch_num  6:sequence_length
-rotary_pos_emb -> [6, 1, 32, 2]  32:pos的编码维度  2:cos+sin
+  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
+  hidden_states = inputs_embeds
-for layers : GLMBlock(hidden_states, rotary_pos_emb)
+  for layers : GLMBlock(hidden_states, rotary_pos_emb)
-hidden_states = self.final_layernorm(hidden_states)
+  hidden_states = self.final_layernorm(hidden_states)
-hidden_states = hidden_states[-1:]
+  hidden_states = hidden_states[-1:]
-lm_logits = self.output_layer(hidden_states)
+  lm_logits = self.output_layer(hidden_states)
-lm_logits = lm_logits.transpose(0, 1).contiguous()  -> [1, 1, 65024]
+  lm_logits = lm_logits.transpose(0, 1).contiguous()  -> [1, 1, 65024]
 
-probs = softmax(lm_logits) -> [1, 65024]
+  probs = softmax(lm_logits) -> [1, 65024]
-next_tokens = torch.multinomial(probs, num_samples=1) 采样  -> [1]  1:batch_num 
+  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 
+
+  if next_tokens == eos_token_id 推理结束退出循环
+
+  input_ids = torch.cat([input_ids, next_tokens)  -> [1, 7]  1:batch_num
 
 response = tokenizer.decode(outputs)

chatglm/config.json

@@ -0,0 +1,42 @@
+{
+  "_name_or_path": "THUDM/chatglm3-6b",
+  "model_type": "chatglm",
+  "architectures": [
+    "ChatGLMModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
+    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
+  },
+  "add_bias_linear": false,
+  "add_qkv_bias": true,
+  "apply_query_key_layer_scaling": true,
+  "apply_residual_connection_post_layernorm": false,
+  "attention_dropout": 0.0,
+  "attention_softmax_in_fp32": true,
+  "bias_dropout_fusion": true,
+  "ffn_hidden_size": 13696,
+  "fp32_residual_connection": false,
+  "hidden_dropout": 0.0,
+  "hidden_size": 4096,
+  "kv_channels": 128,
+  "layernorm_epsilon": 1e-05,
+  "multi_query_attention": true,
+  "multi_query_group_num": 2,
+  "num_attention_heads": 32,
+  "num_layers": 28,
+  "original_rope": true,
+  "padded_vocab_size": 65024,
+  "post_layer_norm": true,
+  "rmsnorm": true,
+  "seq_length": 8192,
+  "use_cache": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "tie_word_embeddings": false,
+  "eos_token_id": 2,
+  "pad_token_id": 0
+}

chatglm/configuration.json

@@ -0,0 +1 @@
+{"framework":"Pytorch","task":"chatbot"}

chatglm/modeling_chatglm.py

@@ -700,10 +700,10 @@ class ChatGLMForConditionalGeneration(nn.Module):
             # finished sentences should add a padding token to next
             pad_token = pad_token_id * isFinished
             next_tokens = next_tokens * (1 - isFinished) + pad_token
-            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
 
             isFinished = isFinished | next_tokens.eq(eos_token_id_tensor)
             if isFinished.min() == 1:  # all batch is finish
                 break
 
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
         return input_ids

chatglm/pytorch_model.bin.index.json

@@ -0,0 +1,207 @@
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