implement stream generating

src/cli_demo.py

@@ -3,12 +3,13 @@
 # Usage: python cli_demo.py --checkpoint_dir path_to_checkpoint
 
 
-import torch
 from utils import (
     load_pretrained,
     prepare_infer_args,
     get_logits_processor
 )
+from threading import Thread
+from transformers import TextIteratorStreamer
 
 
 def main():
@@ -34,25 +35,32 @@ def main():
         return prompt
 
     format_example = format_example_alpaca if data_args.prompt_template == "alpaca" else format_example_ziya
+    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
 
-    def predict(query, history: list):
+    def predict_and_print(query, history: list):
         input_ids = tokenizer([format_example(query, history)], return_tensors="pt")["input_ids"]
         input_ids = input_ids.to(model.device)
         gen_kwargs = {
+            "input_ids": input_ids,
             "do_sample": True,
             "top_p": 0.7,
             "temperature": 0.95,
             "num_beams": 1,
             "max_new_tokens": 256,
             "repetition_penalty": 1.0,
-            "logits_processor": get_logits_processor()
+            "logits_processor": get_logits_processor(),
+            "streamer": streamer
         }
-        with torch.no_grad():
+        thread = Thread(target=model.generate, kwargs=gen_kwargs)
-            generation_output = model.generate(input_ids=input_ids, **gen_kwargs)
+        thread.start()
-        outputs = generation_output.tolist()[0][len(input_ids[0]):]
+        response = ""
-        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        print("{}: ".format(model_name), end="")
+        for new_text in streamer:
+            print(new_text, end="", flush=True)
+            response += new_text
+        print()
         history = history + [(query, response)]
-        return response, history
+        return history
 
     history = []
     print("欢迎使用 {} 模型，输入内容即可对话，clear清空对话历史，stop终止程序".format(model_name))
@@ -73,8 +81,7 @@ def main():
             print("History has been removed.")
             continue
 
-        response, history = predict(query, history)
+        history = predict_and_print(query, history)
-        print("{}:".format(model_name), response)
 
 
 if __name__ == "__main__":

src/utils/other.py

@@ -52,13 +52,12 @@ class AverageMeter:
 
 
 # Avoid runtime error in model.generate(do_sample=True).
-# Borrowed from: https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/modeling_chatglm.py#L54
 class InvalidScoreLogitsProcessor(LogitsProcessor):
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         if torch.isnan(scores).any() or torch.isinf(scores).any():
             scores.zero_()
-            scores[..., 5] = 5e4
+            scores[:, 0] = 1.0
         return scores
 
 

src/web_demo.py

@@ -3,11 +3,12 @@
 # Usage: python web_demo.py --checkpoint_dir path_to_checkpoint
 
 
-import torch
 import mdtex2html
 import gradio as gr
 
+from threading import Thread
 from utils import load_pretrained, prepare_infer_args, get_logits_processor
+from transformers import TextIteratorStreamer
 from transformers.utils.versions import require_version
 
 
@@ -83,6 +84,7 @@ def format_example_ziya(query, history):
 
 
 format_example = format_example_alpaca if data_args.prompt_template == "alpaca" else format_example_ziya
+streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
 
 
 def predict(input, chatbot, max_length, top_p, temperature, history):
@@ -97,12 +99,14 @@ def predict(input, chatbot, max_length, top_p, temperature, history):
         "num_beams": 1,
         "max_length": max_length,
         "repetition_penalty": 1.0,
-        "logits_processor": get_logits_processor()
+        "logits_processor": get_logits_processor(),
+        "streamer": streamer
     }
-    with torch.no_grad():
+    thread = Thread(target=model.generate, kwargs=gen_kwargs)
-        generation_output = model.generate(input_ids=input_ids, **gen_kwargs)
+    thread.start()
-    outputs = generation_output.tolist()[0][len(input_ids[0]):]
+    response = ""
-    response = tokenizer.decode(outputs, skip_special_tokens=True)
+    for new_text in streamer:
+        response += new_text
         history = history + [(input, response)]
         chatbot[-1] = (parse_text(input), parse_text(response))
         yield chatbot, history
@@ -129,7 +133,7 @@ with gr.Blocks() as demo:
                 submitBtn = gr.Button("Submit", variant="primary")
         with gr.Column(scale=1):
             emptyBtn = gr.Button("Clear History")
-            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
+            max_length = gr.Slider(0, 2048, value=1024, step=1.0, label="Maximum length", interactive=True)
             top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
             temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)