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Chat Finetuning Data Prep

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Chat Finetuning Data Prep

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Summary

Data preparation and analysis for chat model fine-tuning This notebook serves as a tool to preprocess and analyze the chat dataset used for fine-tuning a chat model. It checks for format errors, provides basic statistics, and estimates token counts for fine-tuning costs. The method shown here corresponds to the current fine-tuning method for gpt-3.5-turbo. S…

What This Teaches

  • How to prepare data or workflows for model adaptation and fine-tuning.

Implementation Use Cases

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Data preparation and analysis for chat model fine-tuning

This notebook serves as a tool to preprocess and analyze the chat dataset used for fine-tuning a chat model. It checks for format errors, provides basic statistics, and estimates token counts for fine-tuning costs. The method shown here corresponds to the current fine-tuning method for gpt-3.5-turbo. See legacy fine-tuning for models like babbage-002 and davinci-002.

import json
import tiktoken # for token counting
import numpy as np
from collections import defaultdict

Data loading

We first load the chat dataset from an example JSONL file.

data_path = "data/toy_chat_fine_tuning.jsonl"
 
# Load the dataset
with open(data_path, 'r', encoding='utf-8') as f:
    dataset = [json.loads(line) for line in f]
 
# Initial dataset stats
print("Num examples:", len(dataset))
print("First example:")
for message in dataset[0]["messages"]:
    print(message)

Format validation

We can perform a variety of error checks to validate that each conversation in the dataset adheres to the format expected by the fine-tuning API. Errors are categorized based on their nature for easier debugging.

  1. Data Type Check: Checks whether each entry in the dataset is a dictionary (dict). Error type: data_type.
  2. Presence of Message List: Checks if a messages list is present in each entry. Error type: missing_messages_list.
  3. Message Keys Check: Validates that each message in the messages list contains the keys role and content. Error type: message_missing_key.
  4. Unrecognized Keys in Messages: Logs if a message has keys other than role, content, weight, function_call, and name. Error type: message_unrecognized_key.
  5. Role Validation: Ensures the role is one of “system”, “user”, or “assistant”. Error type: unrecognized_role.
  6. Content Validation: Verifies that content has textual data and is a string. Error type: missing_content.
  7. Assistant Message Presence: Checks that each conversation has at least one message from the assistant. Error type: example_missing_assistant_message.

The code below performs these checks, and outputs counts for each type of error found are printed. This is useful for debugging and ensuring the dataset is ready for the next steps.

# Format error checks
format_errors = defaultdict(int)
 
for ex in dataset:
    if not isinstance(ex, dict):
        format_errors["data_type"] += 1
        continue
 
    messages = ex.get("messages", None)
    if not messages:
        format_errors["missing_messages_list"] += 1
        continue
 
    for message in messages:
        if "role" not in message or "content" not in message:
            format_errors["message_missing_key"] += 1
 
        if any(k not in ("role", "content", "name", "function_call", "weight") for k in message):
            format_errors["message_unrecognized_key"] += 1
 
        if message.get("role", None) not in ("system", "user", "assistant", "function"):
            format_errors["unrecognized_role"] += 1
 
        content = message.get("content", None)
        function_call = message.get("function_call", None)
 
        if (not content and not function_call) or not isinstance(content, str):
            format_errors["missing_content"] += 1
 
    if not any(message.get("role", None) == "assistant" for message in messages):
        format_errors["example_missing_assistant_message"] += 1
 
if format_errors:
    print("Found errors:")
    for k, v in format_errors.items():
        print(f"{k}: {v}")
else:
    print("No errors found")

Token Counting Utilities

Lets define a few helpful utilities to be used in the rest of the notebook.

encoding = tiktoken.get_encoding("cl100k_base")
 
# not exact!
# simplified from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3
    return num_tokens
 
def num_assistant_tokens_from_messages(messages):
    num_tokens = 0
    for message in messages:
        if message["role"] == "assistant":
            num_tokens += len(encoding.encode(message["content"]))
    return num_tokens
 
def print_distribution(values, name):
    print(f"\n#### Distribution of {name}:")
    print(f"min / max: {min(values)}, {max(values)}")
    print(f"mean / median: {np.mean(values)}, {np.median(values)}")
    print(f"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}")

Data Warnings and Token Counts

With some lightweight analysis we can identify potential issues in the dataset, like missing messages, and provide statistical insights into message and token counts.

  1. Missing System/User Messages: Counts the number of conversations missing a “system” or “user” message. Such messages are critical for defining the assistant’s behavior and initiating the conversation.
  2. Number of Messages Per Example: Summarizes the distribution of the number of messages in each conversation, providing insight into dialogue complexity.
  3. Total Tokens Per Example: Calculates and summarizes the distribution of the total number of tokens in each conversation. Important for understanding fine-tuning costs.
  4. Tokens in Assistant’s Messages: Calculates the number of tokens in the assistant’s messages per conversation and summarizes this distribution. Useful for understanding the assistant’s verbosity.
  5. Token Limit Warnings: Checks if any examples exceed the maximum token limit (16,385 tokens), as such examples will be truncated during fine-tuning, potentially resulting in data loss.
# Warnings and tokens counts
n_missing_system = 0
n_missing_user = 0
n_messages = []
convo_lens = []
assistant_message_lens = []
 
for ex in dataset:
    messages = ex["messages"]
    if not any(message["role"] == "system" for message in messages):
        n_missing_system += 1
    if not any(message["role"] == "user" for message in messages):
        n_missing_user += 1
    n_messages.append(len(messages))
    convo_lens.append(num_tokens_from_messages(messages))
    assistant_message_lens.append(num_assistant_tokens_from_messages(messages))
 
print("Num examples missing system message:", n_missing_system)
print("Num examples missing user message:", n_missing_user)
print_distribution(n_messages, "num_messages_per_example")
print_distribution(convo_lens, "num_total_tokens_per_example")
print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
n_too_long = sum(l > 16385 for l in convo_lens)
print(f"\n{n_too_long} examples may be over the 16,385 token limit, they will be truncated during fine-tuning")

Cost Estimation

In this final section, we estimate the total number of tokens that will be used for fine-tuning, which allows us to approximate the cost. It is worth noting that the duration of the fine-tuning jobs will also increase with the token count.

# Pricing and default n_epochs estimate
MAX_TOKENS_PER_EXAMPLE = 16385
 
TARGET_EPOCHS = 3
MIN_TARGET_EXAMPLES = 100
MAX_TARGET_EXAMPLES = 25000
MIN_DEFAULT_EPOCHS = 1
MAX_DEFAULT_EPOCHS = 25
 
n_epochs = TARGET_EPOCHS
n_train_examples = len(dataset)
if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
    n_epochs = min(MAX_DEFAULT_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
    n_epochs = max(MIN_DEFAULT_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)
 
n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)
print(f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training")
print(f"By default, you'll train for {n_epochs} epochs on this dataset")
print(f"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens")

See https://openai.com/pricing to estimate total costs.

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