Merge main to develop (#309)

* fix(chat): fix stream_chat to return generator (#123)

* fix(configs/7B_sft.py): model dtype float16 to bfloat16 (#302)

* fix(convert2hf.py): fix the rotary_emb.inv_freq KeyError (#299)

---------

Co-authored-by: yingtongxiong <974106207@qq.com>
Co-authored-by: zhjunqin <zhjunqin@users.noreply.github.com>
Co-authored-by: jiangtann <39088437+jiangtann@users.noreply.github.com>

configs/7B_sft.py

@@ -127,7 +127,7 @@ model = dict(
     num_layers=NUM_LAYER,
     mlp_ratio=MLP_RATIO,
     apply_post_layer_norm=False,
-    dtype="torch.float16",  # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32"
+    dtype="torch.bfloat16",  # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32"
     norm_type="rmsnorm",
     layer_norm_epsilon=1e-5,
     use_flash_attn=True,

tools/transformers/convert2hf.py

@@ -38,7 +38,7 @@ def convert2hf(model_config, states_tp_pps):
         current_states["lm_head.weight"] = states.pop("head.weight")
 
         for i in range(model_config["num_layers"]):
-            states.pop(f"blocks.{i}.mixer.rotary_emb.inv_freq")
+            states.pop(f"blocks.{i}.mixer.rotary_emb.inv_freq", None)
 
             wqkv = states.pop(f"blocks.{i}.mixer.Wqkv.weight").reshape(
                 3, model_config["num_attention_heads"], -1, model_config["hidden_size"]

tools/transformers/modeling_internlm.py

@@ -20,6 +20,7 @@
 """ PyTorch InternLM model."""
 import math
 from typing import List, Optional, Tuple, Union
+import threading, queue
 
 import torch
 import torch.utils.checkpoint
@@ -810,23 +811,47 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
                     temperature: float = 0.8,
                     top_p: float = 0.8,
                     **kwargs):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+
+        response_queue = queue.Queue(maxsize=20)
+
         class ChatStreamer(BaseStreamer):
             def __init__(self, tokenizer) -> None:
                 super().__init__()
                 self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ""
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
 
             def put(self, value):
                 if len(value.shape) > 1 and value.shape[0] > 1:
                     raise ValueError("ChatStreamer only supports batch size 1")
                 elif len(value.shape) > 1:
                     value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
                 token = self.tokenizer.decode([value[-1]], skip_special_tokens=True)
                 if token.strip() != "<eoa>":
-                    print(token, end="")
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
 
             def end(self):
-                print("")
+                self.queue.put(None)
 
+        def stream_producer():
             return self.chat(
                 tokenizer=tokenizer,
                 query=query,
@@ -839,6 +864,17 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
                 **kwargs
             )
 
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is not None:
+                    return
+                yield res
+
+        return consumer()
+
 
 @add_start_docstrings(
     """