Merge pull request #6 from backend-developers-ltd/optimize-and-fix-pr…

…ompt-generation

Optimize and fix prompt generation

.github/workflows/smoke_test.yml

@@ -28,7 +28,7 @@ jobs:
         run: | 
           cd src/compute_horde_prompt_gen
 
-          python3 run.py --output_folder_path output/ --model_name mock --number_of_batches 5 --number_of_prompts_per_batch 20 --uuids uuid1,uuid2,uuid3,uuid4,uuid5
+          python3 run.py --output_folder_path output/ --model_name mock --number_of_prompts_per_batch 20 --uuids uuid1,uuid2,uuid3,uuid4,uuid5
 
           echo -e "\ngenerated batches:"
           ls

src/compute_horde_prompt_gen/prompt.py

@@ -4,17 +4,17 @@
 
 class PromptGeneratingPrompt:
     def random_select(self, arr: list[str], num: int = 5) -> str:
-        random.shuffle(arr)
-        return ", ".join(arr[:num]) + ", etc"
+        return random.choices(arr, k=num)
 
-    def generate_prompt(self, short=True) -> str:
-        themes = self.random_select(THEMES, num=3)
+    def random_select_str(self, arr: list[str], num: int = 5) -> str:
+        choices = self.random_select(arr, num)
+        return ", ".join(choices) + ", etc"
 
+    def generate_prompt(self, short=True) -> str:
         if short:
-            return (
-                f"Generate a list of 10 questions or instruct tasks related to the themes of {themes}. "
-                f"Output each prompt on a new line without any extra commentary or special characters."
-            )
+            theme = self.random_select(THEMES, num=1)[0]
+            return f"{theme}"
+        themes = self.random_select_str(THEMES, num=3)
 
         relevance_level = random.randint(5, 20)
         complexity_level = random.randint(5, 20)
@@ -34,5 +34,5 @@ def generate_prompt(self, short=True) -> str:
 
     def generate_role(self, short=True) -> str:
         if short:
-            return "You are a prompt engineer tasked with prompts of varying complexity to test the capabilities of a new language model."
+            return "You are my friend helping me study. I say theme and you ask me question about it. Quiestions have to be short but open. Write only question and nothing more."
         return "You are a prompt engineer tasked with prompts of varying complexity to test the capabilities of a new language model. For each prompt, consider what aspect of the language model's capabilities it is designed to test and ensure that the set of prompts covers a broad spectrum of potential use cases for the language model. Only output the prompts, one per line without any extra commentary. Do not use any special characters or formatting, numbering or styling in the output."

src/compute_horde_prompt_gen/run.py

@@ -2,6 +2,7 @@
 import os
 import logging
 import argparse
+from collections import deque
 
 from prompt import PromptGeneratingPrompt
 from model import MockModel, Llama3, Phi3
@@ -19,7 +20,10 @@ def generate_prompts(
     max_new_tokens: int = 2000,
     temperature: float = 1.0,
     filepath: str = "prompts.txt",
+    leftover_prompts: deque = None,
 ):
+    if leftover_prompts is None:
+        leftover_prompts = deque()
     prompt_generator = PromptGeneratingPrompt()
 
     i = -1
@@ -38,22 +42,27 @@ def generate_prompts(
         )
 
         seconds_taken = (datetime.datetime.now() - start_ts).total_seconds()
-        log.info(f"{i=} generation took {seconds_taken:.2f}s")
 
         new_prompts = []
         for j, sequence in enumerate(sequences):
             generated_prompts = parse_output(sequence)
-            log.debug(f"{i=} sequence={j} {generated_prompts=} from {sequence=}")
-
-            log.info(f"{i=} sequence={j} generated {len(generated_prompts)} prompts")
             new_prompts.extend(generated_prompts)
 
         # check_prompts_quality(new_prompts)
 
         # remove any duplicates
         new_prompts = list(set(new_prompts))
+        log.info(
+            f"{i=} generation took {seconds_taken:.2f}s; generated {len(new_prompts)} prompts"
+        )
 
-        if total_prompts - len(new_prompts) < 0:
+        # Use leftover prompts from previous batch if available
+        while leftover_prompts and len(new_prompts) < total_prompts:
+            new_prompts.append(leftover_prompts.popleft())
+
+        if len(new_prompts) > total_prompts:
+            # Save extra prompts for next batch
+            leftover_prompts.extend(new_prompts[total_prompts:])
             new_prompts = new_prompts[:total_prompts]
 
         total_prompts -= len(new_prompts)
@@ -62,6 +71,8 @@ def generate_prompts(
         if total_prompts == 0:
             break
 
+    return leftover_prompts
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Generate prompts")
@@ -74,19 +85,19 @@ def generate_prompts(
     parser.add_argument(
         "--batch_size",
         type=int,
-        default=20,
+        default=262,  # on A6000 we want 240 prompts generated in single file, but not all results are valid
         help="Batch size - number of prompts given as input per generation request",
     )
     parser.add_argument(
         "--num_return_sequences",
         type=int,
-        default=5,
+        default=1,  # better to generate as many as possible prompts on different themes
         help="Number of return sequences outputted for each prompt given as input",
     )
     parser.add_argument(
         "--max_new_tokens",
         type=int,
-        default=500,
+        default=40,  # 40 new tokens is enough for reasonable length prompt - 30 caused too much cut off prompts
         help="Max new tokens",
     )
     parser.add_argument(
@@ -108,16 +119,10 @@ def generate_prompts(
         default="./saved_models/",
         help="Path to load the model and tokenizer from",
     )
-    parser.add_argument(
-        "--number_of_batches",
-        type=int,
-        default=None,
-        help="Number of batches to generate",
-    )
     parser.add_argument(
         "--number_of_prompts_per_batch",
         type=int,
-        required=True,
+        default=240,
         help="Number of prompts per uuid batch",
     )
     parser.add_argument(
@@ -137,11 +142,6 @@ def generate_prompts(
 
     uuids = args.uuids.split(",")
 
-    if args.number_of_batches:
-        assert (
-            len(uuids) == args.number_of_batches
-        ), "Number of uuids should be equal to number of batches requested"
-
     model_path = os.path.join(args.model_path, args.model_name)
     if args.model_name == "mock":
         model = MockModel()
@@ -158,16 +158,18 @@ def generate_prompts(
     else:
         raise ValueError(f"Invalid model name: {args.model_name}")
 
+    leftover_prompts = None
     for uuid in uuids:
         start_ts = datetime.datetime.now()
-        generate_prompts(
+        leftover_prompts = generate_prompts(
             model,
             total_prompts=args.number_of_prompts_per_batch,
             batch_size=args.batch_size,
             num_return_sequences=args.num_return_sequences,
             max_new_tokens=args.max_new_tokens,
             temperature=args.temperature,
             filepath=os.path.join(args.output_folder_path, f"prompts_{uuid}.txt"),
+            leftover_prompts=leftover_prompts,
         )
         seconds_taken = (datetime.datetime.now() - start_ts).total_seconds()
         log.info(

src/compute_horde_prompt_gen/utils.py

@@ -8,22 +8,34 @@
 
 def clean_line(line: str) -> str:
     line = line.strip()
+    head, sep, tail = line.partition("<|")
+    if head:
+        line = head.strip()
+    else:
+        # if we started with a tag we assume that inside we find our prompt
+        line = tail.partition("|>")[2].partition("<|")[0].strip()
     # remove list numbering if present
     line = re.sub(r"^\s*\d+\.?\s*", "", line)
+    # strip quotations
+    line = line.strip("\"'")
     return line
 
 
 def parse_output(output: str) -> list[str]:
     # split into lines and clean them
     lines = output.split("\n")
-    lines = [clean_line(line) for line in lines]
+    for line in lines:
+        cleaned_line = clean_line(line)
+        # we skip if line is too short or too long and not ends with ?
+        # in most cases it would be just first line
+        if (
+            len(cleaned_line) > 10
+            and len(cleaned_line) < 300
+            and cleaned_line.endswith("?")
+        ):
+            return [cleaned_line]
 
-    # filter out null lines or prompts that are too short or long
-    lines = [line for line in lines if (len(line) > 10 and len(line) < 300)]
-
-    # skip first line as that's frequently broken (i.e. "Here are the prompts:")
-    # skip last line as it might not be comletely generated
-    return lines[1:-1]
+    return []
 
 
 def check_prompts_quality(prompts: list[str]):