fix inference, add prompt template

src/api_demo.py

@@ -23,7 +23,9 @@ from fastapi import FastAPI, Request
 from utils import (
     load_pretrained,
     prepare_infer_args,
-    get_logits_processor
+    get_logits_processor,
+    prompt_template_alpaca,
+    prompt_template_ziya
 )
 
 
@@ -96,23 +98,6 @@ async def create_item(request: Request):
 if __name__ == "__main__":
     model_args, data_args, finetuning_args = prepare_infer_args()
     model, tokenizer = load_pretrained(model_args, finetuning_args)
-
+    format_example = prompt_template_alpaca if data_args.prompt_template == "alpaca" else prompt_template_ziya
-    def format_example_alpaca(query, history):
-        prompt = "Below is an instruction that describes a task. "
-        prompt += "Write a response that appropriately completes the request.\n"
-        prompt += "Instruction:\n"
-        for old_query, response in history:
-            prompt += "Human: {}\nAssistant: {}\n".format(old_query, response)
-        prompt += "Human: {}\nAssistant:".format(query)
-        return prompt
-
-    def format_example_ziya(query, history):
-        prompt = ""
-        for old_query, response in history:
-            prompt += "<human>: {}\n<bot>: {}\n".format(old_query, response)
-        prompt += "<human>: {}\n<bot>:".format(query)
-        return prompt
-
-    format_example = format_example_alpaca if data_args.prompt_template == "alpaca" else format_example_ziya
 
     uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)

src/cli_demo.py

@@ -6,7 +6,9 @@
 from utils import (
     load_pretrained,
     prepare_infer_args,
-    get_logits_processor
+    get_logits_processor,
+    prompt_template_alpaca,
+    prompt_template_ziya
 )
 from threading import Thread
 from transformers import TextIteratorStreamer
@@ -18,23 +20,7 @@ def main():
     model_name = "BLOOM" if "bloom" in model_args.model_name_or_path else "LLaMA"
     model, tokenizer = load_pretrained(model_args, finetuning_args)
 
-    def format_example_alpaca(query, history):
+    format_example = prompt_template_alpaca if data_args.prompt_template == "alpaca" else prompt_template_ziya
-        prompt = "Below is an instruction that describes a task. "
-        prompt += "Write a response that appropriately completes the request.\n"
-        prompt += "Instruction:\n"
-        for old_query, response in history:
-            prompt += "Human: {}\nAssistant: {}\n".format(old_query, response)
-        prompt += "Human: {}\nAssistant:".format(query)
-        return prompt
-
-    def format_example_ziya(query, history):
-        prompt = ""
-        for old_query, response in history:
-            prompt += "<human>: {}\n<bot>: {}\n".format(old_query, response)
-        prompt += "<human>: {}\n<bot>:".format(query)
-        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_and_print(query, history: list):

src/utils/__init__.py

@@ -14,4 +14,6 @@ from .seq2seq import ComputeMetrics, Seq2SeqPeftTrainer
 from .pairwise import PairwiseDataCollatorWithPadding, PairwisePeftTrainer
 from .ppo import PPOPeftTrainer
 
+from .template import prompt_template_alpaca, prompt_template_ziya
+
 from .other import get_logits_processor, plot_loss

src/utils/common.py

@@ -37,6 +37,11 @@ from .config import (
     FinetuningArguments
 )
 
+from .template import (
+    prompt_template_alpaca,
+    prompt_template_ziya
+)
+
 from .other import (
     get_logger,
     load_trainable_params,
@@ -224,6 +229,7 @@ def load_pretrained(
 
     if not is_trainable:
         model.requires_grad_(False) # fix all model params
+        model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
 
     print_trainable_params(model)
 
@@ -395,39 +401,19 @@ def preprocess_data(
 
     column_names = list(dataset.column_names)
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
+    prompt_template = prompt_template_alpaca if data_args.prompt_template == "alpaca" else prompt_template_ziya
 
     # support question with a single answer or multiple answers
-    def format_example_alpaca(examples):
+    def format_example(examples):
-        for i in range(len(examples["prompt"])):
-            if examples["prompt"][i] and examples["response"][i]:
-                query, answer = examples["prompt"][i], examples["response"][i]
-                if examples["query"][i]:
-                    query += "\n" + examples["query"][i]
-                prompt = "Below is an instruction that describes a task. "
-                prompt += "Write a response that appropriately completes the request.\n"
-                prompt += "Instruction:\n" + prefix
-                if examples["history"][i]:
-                    for old_query, response in examples["history"][i]:
-                        prompt += "Human: {}\nAssistant: {}\n".format(old_query, response)
-                prompt += "Human: {}\nAssistant: ".format(query)
-                yield prompt, answer
-
-    def format_example_ziya(examples):
         for i in range(len(examples["prompt"])):
             if examples["prompt"][i] and examples["response"][i]:
                 query, answer = examples["prompt"][i], examples["response"][i]
                 if examples["query"][i]:
                     query += "\n" + examples["query"][i]
-                prompt = ""
+                prompt = prompt_template(query, examples["history"][i])
-                if examples["history"][i]:
-                    for old_query, response in examples["history"][i]:
-                        prompt += "<human>: {}\n<bot>: {}\n".format(old_query, response)
-                prompt += "<human>: {}\n<bot>:".format(query)
                 prompt = prefix + prompt
                 yield prompt, answer
 
-    format_example = format_example_alpaca if data_args.prompt_template == "alpaca" else format_example_ziya
-
     def preprocess_pretrain_dataset(examples):
         # build grouped texts with format `<s> X1 X2 X3 ...` (without </s>)
         text_ids = tokenizer(examples["prompt"])["input_ids"]

src/utils/template.py

@@ -0,0 +1,16 @@
+def prompt_template_alpaca(query, history=None):
+    prompt = ""
+    if history:
+        for old_query, response in history:
+            prompt += "Human:{}\nAssistant:{}\n".format(old_query, response)
+    prompt += "Human:{}\nAssistant:".format(query)
+    return prompt
+
+
+def prompt_template_ziya(query, history=None):
+    prompt = ""
+    if history:
+        for old_query, response in history:
+            prompt += "<human>:{}\n<bot>:{}\n".format(old_query, response)
+    prompt += "<human>:{}\n<bot>:".format(query)
+    return prompt

src/web_demo.py

@@ -7,7 +7,14 @@ import mdtex2html
 import gradio as gr
 
 from threading import Thread
-from utils import load_pretrained, prepare_infer_args, get_logits_processor
+from utils import (
+    load_pretrained,
+    prepare_infer_args,
+    get_logits_processor,
+    prompt_template_alpaca,
+    prompt_template_ziya
+)
+
 from transformers import TextIteratorStreamer
 from transformers.utils.versions import require_version
 
@@ -18,26 +25,7 @@ require_version("gradio>=3.30.0", "To fix: pip install gradio>=3.30.0")
 model_args, data_args, finetuning_args = prepare_infer_args()
 model, tokenizer = load_pretrained(model_args, finetuning_args)
 
-
+format_example = prompt_template_alpaca if data_args.prompt_template == "alpaca" else prompt_template_ziya
-def format_example_alpaca(query, history):
-    prompt = "Below is an instruction that describes a task. "
-    prompt += "Write a response that appropriately completes the request.\n"
-    prompt += "Instruction:\n"
-    for old_query, response in history:
-        prompt += "Human: {}\nAssistant: {}\n".format(old_query, response)
-    prompt += "Human: {}\nAssistant:".format(query)
-    return prompt
-
-
-def format_example_ziya(query, history):
-    prompt = ""
-    for old_query, response in history:
-        prompt += "<human>: {}\n<bot>: {}\n".format(old_query, response)
-    prompt += "<human>: {}\n<bot>:".format(query)
-    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)