mirror of
https://github.com/rasbt/LLMs-from-scratch.git
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248 lines
7.4 KiB
Python
248 lines
7.4 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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import json
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import os
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import psutil
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import urllib
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import torch
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from tqdm import tqdm
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from torch.utils.data import Dataset
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def download_and_load_file(file_path, url):
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if not os.path.exists(file_path):
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with urllib.request.urlopen(url) as response:
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text_data = response.read().decode("utf-8")
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with open(file_path, "w", encoding="utf-8") as file:
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file.write(text_data)
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# The book originally contained this unnecessary "else" clause:
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# else:
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# with open(file_path, "r", encoding="utf-8") as file:
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# text_data = file.read()
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with open(file_path, "r", encoding="utf-8") as file:
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data = json.load(file)
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return data
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def format_input(entry):
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instruction_text = (
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f"Below is an instruction that describes a task. "
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f"Write a response that appropriately completes the request."
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f"\n\n### Instruction:\n{entry['instruction']}"
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)
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input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
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return instruction_text + input_text
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class InstructionDataset(Dataset):
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def __init__(self, data, tokenizer):
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self.data = data
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# Pre-tokenize texts
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self.encoded_texts = []
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for entry in data:
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instruction_plus_input = format_input(entry)
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response_text = f"\n\n### Response:\n{entry['output']}"
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full_text = instruction_plus_input + response_text
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self.encoded_texts.append(
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tokenizer.encode(full_text)
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)
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def __getitem__(self, index):
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return self.encoded_texts[index]
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def __len__(self):
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return len(self.data)
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def custom_collate_draft_1(
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batch,
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pad_token_id=50256,
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device="cpu"
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):
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# Find the longest sequence in the batch
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# and increase the max length by +1, which will add one extra
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# padding token below
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batch_max_length = max(len(item)+1 for item in batch)
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# Pad and prepare inputs
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inputs_lst = []
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for item in batch:
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new_item = item.copy()
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# Add an <|endoftext|> token
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new_item += [pad_token_id]
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# Pad sequences to batch_max_length
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padded = (
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new_item + [pad_token_id] *
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(batch_max_length - len(new_item))
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)
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# Via padded[:-1], we remove the extra padded token
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# that has been added via the +1 setting in batch_max_length
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# (the extra padding token will be relevant in later codes)
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inputs = torch.tensor(padded[:-1])
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inputs_lst.append(inputs)
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# Convert list of inputs to tensor and transfer to target device
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inputs_tensor = torch.stack(inputs_lst).to(device)
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return inputs_tensor
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def custom_collate_draft_2(
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batch,
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pad_token_id=50256,
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device="cpu"
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):
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# Find the longest sequence in the batch
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batch_max_length = max(len(item)+1 for item in batch)
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# Pad and prepare inputs
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inputs_lst, targets_lst = [], []
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for item in batch:
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new_item = item.copy()
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# Add an <|endoftext|> token
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new_item += [pad_token_id]
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# Pad sequences to max_length
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padded = (
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new_item + [pad_token_id] *
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(batch_max_length - len(new_item))
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)
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inputs = torch.tensor(padded[:-1]) # Truncate the last token for inputs
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targets = torch.tensor(padded[1:]) # Shift +1 to the right for targets
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inputs_lst.append(inputs)
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targets_lst.append(targets)
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# Convert list of inputs to tensor and transfer to target device
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inputs_tensor = torch.stack(inputs_lst).to(device)
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targets_tensor = torch.stack(targets_lst).to(device)
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return inputs_tensor, targets_tensor
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def custom_collate_fn(
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batch,
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pad_token_id=50256,
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ignore_index=-100,
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allowed_max_length=None,
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device="cpu"
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):
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# Find the longest sequence in the batch
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batch_max_length = max(len(item)+1 for item in batch)
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# Pad and prepare inputs and targets
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inputs_lst, targets_lst = [], []
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for item in batch:
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new_item = item.copy()
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# Add an <|endoftext|> token
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new_item += [pad_token_id]
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# Pad sequences to max_length
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padded = (
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new_item + [pad_token_id] *
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(batch_max_length - len(new_item))
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)
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inputs = torch.tensor(padded[:-1]) # Truncate the last token for inputs
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targets = torch.tensor(padded[1:]) # Shift +1 to the right for targets
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# New: Replace all but the first padding tokens in targets by ignore_index
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mask = targets == pad_token_id
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indices = torch.nonzero(mask).squeeze()
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if indices.numel() > 1:
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targets[indices[1:]] = ignore_index
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# New: Optionally truncate to maximum sequence length
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if allowed_max_length is not None:
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inputs = inputs[:allowed_max_length]
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targets = targets[:allowed_max_length]
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inputs_lst.append(inputs)
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targets_lst.append(targets)
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# Convert list of inputs and targets to tensors and transfer to target device
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inputs_tensor = torch.stack(inputs_lst).to(device)
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targets_tensor = torch.stack(targets_lst).to(device)
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return inputs_tensor, targets_tensor
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def check_if_running(process_name):
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running = False
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for proc in psutil.process_iter(["name"]):
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if process_name in proc.info["name"]:
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running = True
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break
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return running
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def query_model(
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prompt,
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model="llama3",
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url="http://localhost:11434/api/chat"
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):
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# Create the data payload as a dictionary
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data = {
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"model": model,
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"messages": [
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{"role": "user", "content": prompt}
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],
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"options": { # Settings below are required for deterministic responses
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"seed": 123,
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"temperature": 0,
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"num_ctx": 2048
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}
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}
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# Convert the dictionary to a JSON formatted string and encode it to bytes
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payload = json.dumps(data).encode("utf-8")
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# Create a request object, setting the method to POST and adding necessary headers
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request = urllib.request.Request(
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url,
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data=payload,
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method="POST"
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)
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request.add_header("Content-Type", "application/json")
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# Send the request and capture the response
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response_data = ""
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with urllib.request.urlopen(request) as response:
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# Read and decode the response
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while True:
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line = response.readline().decode("utf-8")
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if not line:
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break
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response_json = json.loads(line)
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response_data += response_json["message"]["content"]
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return response_data
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def generate_model_scores(json_data, json_key, model="llama3"):
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scores = []
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for entry in tqdm(json_data, desc="Scoring entries"):
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prompt = (
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f"Given the input `{format_input(entry)}` "
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f"and correct output `{entry['output']}`, "
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f"score the model response `{entry[json_key]}`"
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f" on a scale from 0 to 100, where 100 is the best score. "
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f"Respond with the integer number only."
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)
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score = query_model(prompt, model)
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try:
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scores.append(int(score))
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except ValueError:
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print(f"Could not convert score: {score}")
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continue
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return scores
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