mirror of
https://github.com/karpathy/nanochat.git
synced 2026-02-10 04:29:50 +00:00
refactor: migrate from os.path to pathlib.Path across codebase
Converted all path operations to use pathlib.Path instead of os.path module.
This modernizes the codebase and fixes all 135 ruff PTH violations.
Changes:
- Replace os.path.join() with Path / operator
- Replace os.path.exists() with Path.exists()
- Replace os.makedirs() with Path.mkdir()
- Replace open() with Path.open() where appropriate
- Replace os.remove() with Path.unlink()
- Replace os.getcwd() with Path.cwd()
- Replace os.path.expanduser("~") with Path.home()
- Add type hints for Path parameters in function signatures
All path objects are now created at first occurrence and propagated
through the codebase, eliminating unnecessary string conversions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Tsvika Shapira
parent
886b409e75
commit
b1925368f9
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@ -30,14 +30,14 @@ NOTE: For more details see this discussion: https://github.com/karpathy/nanochat
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"""
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import requests
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import json
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import os
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import copy
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import random
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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from nanochat.common import get_base_dir
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api_key = open("openroutertoken.txt", "r", encoding="utf-8").read().strip()
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api_key = Path("openroutertoken.txt").open("r", encoding="utf-8").read().strip()
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url = "https://openrouter.ai/api/v1/chat/completions"
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headers = {
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@ -45,7 +45,7 @@ headers = {
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"Content-Type": "application/json"
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}
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readme = open("README.md", "r", encoding="utf-8").read().strip()
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readme = Path("README.md").open("r", encoding="utf-8").read().strip()
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prompt = r"""
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I want to generate synthetic data for an LLM to teach it about its identity. Here is the identity I want:
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@ -346,10 +346,10 @@ def generate_conversation(idx: int):
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num_conversations = 1000
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num_workers = 4
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output_file = os.path.join(get_base_dir(), "identity_conversations.jsonl")
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output_file = get_base_dir() / "identity_conversations.jsonl"
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# Wipe the file clean first to reset it
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if os.path.exists(output_file):
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os.remove(output_file)
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if output_file.exists():
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output_file.unlink()
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print(f"Saving to {output_file}")
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# Use ThreadPoolExecutor to generate conversations in parallel
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@ -372,7 +372,7 @@ with ThreadPoolExecutor(max_workers=num_workers) as executor:
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assert message['role'] == expected_role, f"Message {i} has role {message['role']} but should be {expected_role}"
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# If all looks good, write the messages to file
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with open(output_file, 'a') as f:
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with output_file.open('a') as f:
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f.write(json.dumps(messages) + '\n')
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completed_count += 1
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print(f"✓ Saved conversation {completed_count}/{num_conversations}")
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@ -13,8 +13,8 @@ training latency.
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NOTE: This file is meant only as reference/documentation of the
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dataset preparation and it is not used during the project runtime.
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"""
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import os
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import time
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from pathlib import Path
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from datasets import load_dataset
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import pyarrow.parquet as pq
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@ -34,8 +34,8 @@ ndocs = len(ds) # total number of documents to process
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print(f"Total number of documents: {ndocs}")
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# Repackage into parquet files
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output_dir = "/home/ubuntu/.cache/nanochat/base_data"
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os.makedirs(output_dir, exist_ok=True)
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output_dir = Path("/home/ubuntu/.cache/nanochat/base_data")
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output_dir.mkdir(parents=True, exist_ok=True)
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# Write to parquet files
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chars_per_shard = 250_000_000
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@ -53,11 +53,11 @@ for doc in ds:
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collected_enough_chars = shard_characters >= chars_per_shard
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docs_multiple_of_row_group_size = len(shard_docs) % row_group_size == 0
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if collected_enough_chars and docs_multiple_of_row_group_size: # leads to ~100MB of text (compressed)
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shard_path = os.path.join(output_dir, f"shard_{shard_index:05d}.parquet")
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shard_path = output_dir / f"shard_{shard_index:05d}.parquet"
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shard_table = pa.Table.from_pydict({"text": shard_docs})
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pq.write_table(
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shard_table,
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shard_path,
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str(shard_path),
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row_group_size=row_group_size,
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use_dictionary=False, # this is usually used for categorical data
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compression="zstd", # Valid values: {‘NONE’, ‘SNAPPY’, ‘GZIP’, ‘BROTLI’, ‘LZ4’, ‘ZSTD’}
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@ -3,9 +3,9 @@ Utilities for saving and loading model/optim/state checkpoints.
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"""
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import os
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import re
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import glob
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import json
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import logging
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from pathlib import Path
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import torch
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from nanochat.common import get_base_dir
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@ -22,35 +22,35 @@ def log0(message):
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def save_checkpoint(checkpoint_dir, step, model_data, optimizer_data, meta_data, rank=0):
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if rank == 0:
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os.makedirs(checkpoint_dir, exist_ok=True)
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checkpoint_dir.mkdir(parents=True, exist_ok=True)
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# Save the model state parameters
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model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt")
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model_path = checkpoint_dir / f"model_{step:06d}.pt"
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torch.save(model_data, model_path)
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logger.info(f"Saved model parameters to: {model_path}")
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# Save the metadata dict as json
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meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json")
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with open(meta_path, "w", encoding="utf-8") as f:
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meta_path = checkpoint_dir / f"meta_{step:06d}.json"
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with meta_path.open("w", encoding="utf-8") as f:
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json.dump(meta_data, f, indent=2)
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logger.info(f"Saved metadata to: {meta_path}")
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# Note that optimizer state is sharded across ranks, so each rank must save its own.
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if optimizer_data is not None:
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os.makedirs(checkpoint_dir, exist_ok=True)
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optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}_rank{rank:d}.pt")
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checkpoint_dir.mkdir(parents=True, exist_ok=True)
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optimizer_path = checkpoint_dir / f"optim_{step:06d}_rank{rank:d}.pt"
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torch.save(optimizer_data, optimizer_path)
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logger.info(f"Saved optimizer state to: {optimizer_path}")
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def load_checkpoint(checkpoint_dir, step, device, load_optimizer=False, rank=0):
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# Load the model state
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model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt")
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model_path = checkpoint_dir / f"model_{step:06d}.pt"
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model_data = torch.load(model_path, map_location=device)
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# Load the optimizer state if requested
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optimizer_data = None
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if load_optimizer:
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optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}_rank{rank:d}.pt")
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optimizer_path = checkpoint_dir / f"optim_{step:06d}_rank{rank:d}.pt"
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optimizer_data = torch.load(optimizer_path, map_location=device)
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# Load the metadata
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meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json")
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with open(meta_path, "r", encoding="utf-8") as f:
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meta_path = checkpoint_dir / f"meta_{step:06d}.json"
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with meta_path.open("r", encoding="utf-8") as f:
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meta_data = json.load(f)
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return model_data, optimizer_data, meta_data
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@ -96,7 +96,7 @@ def build_model(checkpoint_dir, step, device, phase):
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def find_largest_model(checkpoints_dir):
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# attempt to guess the model tag: take the biggest model available
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model_tags = [f for f in os.listdir(checkpoints_dir) if os.path.isdir(os.path.join(checkpoints_dir, f))]
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model_tags = [f.name for f in checkpoints_dir.iterdir() if f.is_dir()]
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if not model_tags:
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raise FileNotFoundError(f"No checkpoints found in {checkpoints_dir}")
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# 1) normally all model tags are of the form d<number>, try that first:
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@ -110,16 +110,16 @@ def find_largest_model(checkpoints_dir):
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candidates.sort(key=lambda x: x[0], reverse=True)
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return candidates[0][1]
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# 2) if that failed, take the most recently updated model:
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model_tags.sort(key=lambda x: os.path.getmtime(os.path.join(checkpoints_dir, x)), reverse=True)
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model_tags.sort(key=lambda x: (checkpoints_dir / x).stat().st_mtime, reverse=True)
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return model_tags[0]
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def find_last_step(checkpoint_dir):
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# Look into checkpoint_dir and find model_<step>.pt with the highest step
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checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "model_*.pt"))
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checkpoint_files = list(checkpoint_dir.glob("model_*.pt"))
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if not checkpoint_files:
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raise FileNotFoundError(f"No checkpoints found in {checkpoint_dir}")
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last_step = int(max(os.path.basename(f).split("_")[-1].split(".")[0] for f in checkpoint_files))
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last_step = int(max(f.name.split("_")[-1].split(".")[0] for f in checkpoint_files))
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return last_step
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# -----------------------------------------------------------------------------
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@ -130,7 +130,7 @@ def load_model_from_dir(checkpoints_dir, device, phase, model_tag=None, step=Non
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# guess the model tag by defaulting to the largest model
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model_tag = find_largest_model(checkpoints_dir)
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log0(f"No model tag provided, guessing model tag: {model_tag}")
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checkpoint_dir = os.path.join(checkpoints_dir, model_tag)
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checkpoint_dir = checkpoints_dir / model_tag
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if step is None:
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# guess the step by defaulting to the last step
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step = find_last_step(checkpoint_dir)
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@ -148,5 +148,5 @@ def load_model(source, *args, **kwargs):
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"rl": "chatrl_checkpoints",
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}[source]
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base_dir = get_base_dir()
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checkpoints_dir = os.path.join(base_dir, model_dir)
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checkpoints_dir = Path(base_dir) / model_dir
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return load_model_from_dir(checkpoints_dir, *args, **kwargs)
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@ -6,6 +6,7 @@ import os
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import re
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import logging
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import urllib.request
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from pathlib import Path
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import torch
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import torch.distributed as dist
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from filelock import FileLock
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@ -50,12 +51,12 @@ logger = logging.getLogger(__name__)
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def get_base_dir():
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# co-locate nanochat intermediates with other cached data in ~/.cache (by default)
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if os.environ.get("NANOCHAT_BASE_DIR"):
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nanochat_dir = os.environ.get("NANOCHAT_BASE_DIR")
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nanochat_dir = Path(os.environ.get("NANOCHAT_BASE_DIR"))
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else:
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home_dir = os.path.expanduser("~")
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cache_dir = os.path.join(home_dir, ".cache")
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nanochat_dir = os.path.join(cache_dir, "nanochat")
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os.makedirs(nanochat_dir, exist_ok=True)
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home_dir = Path.home()
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cache_dir = home_dir / ".cache"
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nanochat_dir = cache_dir / "nanochat"
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nanochat_dir.mkdir(parents=True, exist_ok=True)
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return nanochat_dir
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def download_file_with_lock(url, filename, postprocess_fn=None):
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@ -64,10 +65,10 @@ def download_file_with_lock(url, filename, postprocess_fn=None):
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Uses a lock file to prevent concurrent downloads among multiple ranks.
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"""
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base_dir = get_base_dir()
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file_path = os.path.join(base_dir, filename)
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lock_path = file_path + ".lock"
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file_path = base_dir / filename
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lock_path = Path(str(file_path) + ".lock")
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if os.path.exists(file_path):
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if file_path.exists():
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return file_path
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with FileLock(lock_path):
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@ -75,7 +76,7 @@ def download_file_with_lock(url, filename, postprocess_fn=None):
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# All other ranks block until it is released
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# Recheck after acquiring lock
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if os.path.exists(file_path):
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if file_path.exists():
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return file_path
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# Download the content as bytes
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@ -84,7 +85,7 @@ def download_file_with_lock(url, filename, postprocess_fn=None):
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content = response.read() # bytes
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# Write to local file
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with open(file_path, 'wb') as f:
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with file_path.open('wb') as f:
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f.write(content)
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print(f"Downloaded to {file_path}")
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@ -17,6 +17,7 @@ comes up with a better simple Python solution I am all ears.
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import os
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import sys
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from ast import literal_eval
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from pathlib import Path
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def print0(s="",**kwargs):
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ddp_rank = int(os.environ.get('RANK', 0))
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@ -27,11 +28,12 @@ for arg in sys.argv[1:]:
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if '=' not in arg:
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# assume it's the name of a config file
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assert not arg.startswith('--')
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config_file = arg
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config_file = Path(arg)
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print0(f"Overriding config with {config_file}:")
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with open(config_file) as f:
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with config_file.open() as f:
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print0(f.read())
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exec(open(config_file).read())
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with config_file.open() as f:
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exec(f.read())
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else:
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# assume it's a --key=value argument
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assert arg.startswith('--')
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@ -7,9 +7,9 @@ This file contains utilities for:
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For details of how the dataset was prepared, see `repackage_data_reference.py`.
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"""
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import os
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import argparse
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import time
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from pathlib import Path
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import requests
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import pyarrow.parquet as pq
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from multiprocessing import Pool
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@ -24,20 +24,20 @@ BASE_URL = "https://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle/re
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MAX_SHARD = 1822 # the last datashard is shard_01822.parquet
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index_to_filename = lambda index: f"shard_{index:05d}.parquet" # format of the filenames
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base_dir = get_base_dir()
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DATA_DIR = os.path.join(base_dir, "base_data")
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os.makedirs(DATA_DIR, exist_ok=True)
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DATA_DIR = base_dir / "base_data"
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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# -----------------------------------------------------------------------------
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# These functions are useful utilities to other modules, can/should be imported
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def list_parquet_files(data_dir=None):
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def list_parquet_files(data_dir = None):
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""" Looks into a data dir and returns full paths to all parquet files. """
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data_dir = DATA_DIR if data_dir is None else data_dir
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parquet_files = sorted([
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f for f in os.listdir(data_dir)
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if f.endswith('.parquet') and not f.endswith('.tmp')
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f.name for f in data_dir.iterdir()
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if f.name.endswith('.parquet') and not f.name.endswith('.tmp')
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])
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parquet_paths = [os.path.join(data_dir, f) for f in parquet_files]
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parquet_paths = [data_dir / f for f in parquet_files]
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return parquet_paths
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def parquets_iter_batched(split, start=0, step=1):
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@ -62,8 +62,8 @@ def download_single_file(index):
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# Construct the local filepath for this file and skip if it already exists
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filename = index_to_filename(index)
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filepath = os.path.join(DATA_DIR, filename)
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if os.path.exists(filepath):
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filepath = DATA_DIR / filename
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if filepath.exists():
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print(f"Skipping {filepath} (already exists)")
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return True
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@ -78,23 +78,23 @@ def download_single_file(index):
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response = requests.get(url, stream=True, timeout=30)
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response.raise_for_status()
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# Write to temporary file first
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temp_path = filepath + f".tmp"
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with open(temp_path, 'wb') as f:
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temp_path = Path(str(filepath) + ".tmp")
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with temp_path.open('wb') as f:
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for chunk in response.iter_content(chunk_size=1024 * 1024): # 1MB chunks
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if chunk:
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f.write(chunk)
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# Move temp file to final location
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os.rename(temp_path, filepath)
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temp_path.rename(filepath)
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print(f"Successfully downloaded {filename}")
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return True
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except (requests.RequestException, IOError) as e:
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print(f"Attempt {attempt}/{max_attempts} failed for {filename}: {e}")
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# Clean up any partial files
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for path in [filepath + f".tmp", filepath]:
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if os.path.exists(path):
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for path in [Path(str(filepath) + ".tmp"), filepath]:
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if path.exists():
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try:
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os.remove(path)
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path.unlink()
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except:
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pass
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# Try a few times with exponential backoff: 2^attempt seconds
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@ -30,6 +30,7 @@ import platform
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import signal
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import tempfile
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Optional
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# -----------------------------------------------------------------------------
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@ -123,7 +124,7 @@ def chdir(root):
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if root == ".":
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yield
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return
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cwd = os.getcwd()
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cwd = Path.cwd()
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os.chdir(root)
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try:
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yield
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@ -9,6 +9,7 @@ import subprocess
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import socket
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import datetime
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import platform
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from pathlib import Path
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import psutil
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import torch
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@ -79,7 +80,7 @@ def get_system_info():
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# User and environment
|
||||
info['user'] = os.environ.get('USER', 'unknown')
|
||||
info['nanochat_base_dir'] = os.environ.get('NANOCHAT_BASE_DIR', 'out')
|
||||
info['working_dir'] = os.getcwd()
|
||||
info['working_dir'] = Path.cwd()
|
||||
|
||||
return info
|
||||
|
||||
|
|
@ -169,8 +170,9 @@ Generated: {timestamp}
|
|||
|
||||
# count dependencies via uv.lock
|
||||
uv_lock_lines = 0
|
||||
if os.path.exists('uv.lock'):
|
||||
with open('uv.lock', 'r', encoding='utf-8') as f:
|
||||
uv_lock_path = Path('uv.lock')
|
||||
if uv_lock_path.exists():
|
||||
with uv_lock_path.open('r', encoding='utf-8') as f:
|
||||
uv_lock_lines = len(f.readlines())
|
||||
|
||||
header += f"""
|
||||
|
|
@ -233,15 +235,15 @@ class Report:
|
|||
"""Maintains a bunch of logs, generates a final markdown report."""
|
||||
|
||||
def __init__(self, report_dir):
|
||||
os.makedirs(report_dir, exist_ok=True)
|
||||
report_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.report_dir = report_dir
|
||||
|
||||
def log(self, section, data):
|
||||
"""Log a section of data to the report."""
|
||||
slug = slugify(section)
|
||||
file_name = f"{slug}.md"
|
||||
file_path = os.path.join(self.report_dir, file_name)
|
||||
with open(file_path, "w", encoding="utf-8") as f:
|
||||
file_path = self.report_dir / file_name
|
||||
with file_path.open("w", encoding="utf-8") as f:
|
||||
f.write(f"## {section}\n")
|
||||
f.write(f"timestamp: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
|
||||
for item in data:
|
||||
|
|
@ -267,16 +269,16 @@ class Report:
|
|||
def generate(self):
|
||||
"""Generate the final report."""
|
||||
report_dir = self.report_dir
|
||||
report_file = os.path.join(report_dir, "report.md")
|
||||
report_file = report_dir / "report.md"
|
||||
print(f"Generating report to {report_file}")
|
||||
final_metrics = {} # the most important final metrics we'll add as table at the end
|
||||
start_time = None
|
||||
end_time = None
|
||||
with open(report_file, "w", encoding="utf-8") as out_file:
|
||||
with report_file.open("w", encoding="utf-8") as out_file:
|
||||
# write the header first
|
||||
header_file = os.path.join(report_dir, "header.md")
|
||||
if os.path.exists(header_file):
|
||||
with open(header_file, "r", encoding="utf-8") as f:
|
||||
header_file = report_dir / "header.md"
|
||||
if header_file.exists():
|
||||
with header_file.open("r", encoding="utf-8") as f:
|
||||
header_content = f.read()
|
||||
out_file.write(header_content)
|
||||
start_time = extract_timestamp(header_content, "Run started:")
|
||||
|
|
@ -289,11 +291,11 @@ class Report:
|
|||
print(f"Warning: {header_file} does not exist. Did you forget to run `nanochat reset`?")
|
||||
# process all the individual sections
|
||||
for file_name in EXPECTED_FILES:
|
||||
section_file = os.path.join(report_dir, file_name)
|
||||
if not os.path.exists(section_file):
|
||||
section_file = report_dir / file_name
|
||||
if not section_file.exists():
|
||||
print(f"Warning: {section_file} does not exist, skipping")
|
||||
continue
|
||||
with open(section_file, "r", encoding="utf-8") as in_file:
|
||||
with section_file.open("r", encoding="utf-8") as in_file:
|
||||
section = in_file.read()
|
||||
# Extract timestamp from this section (the last section's timestamp will "stick" as end_time)
|
||||
if "rl" not in file_name:
|
||||
|
|
@ -362,18 +364,18 @@ class Report:
|
|||
"""Reset the report."""
|
||||
# Remove section files
|
||||
for file_name in EXPECTED_FILES:
|
||||
file_path = os.path.join(self.report_dir, file_name)
|
||||
if os.path.exists(file_path):
|
||||
os.remove(file_path)
|
||||
file_path = self.report_dir / file_name
|
||||
if file_path.exists():
|
||||
file_path.unlink()
|
||||
# Remove report.md if it exists
|
||||
report_file = os.path.join(self.report_dir, "report.md")
|
||||
if os.path.exists(report_file):
|
||||
os.remove(report_file)
|
||||
report_file = self.report_dir / "report.md"
|
||||
if report_file.exists():
|
||||
report_file.unlink()
|
||||
# Generate and write the header section with start timestamp
|
||||
header_file = os.path.join(self.report_dir, "header.md")
|
||||
header_file = self.report_dir / "header.md"
|
||||
header = generate_header()
|
||||
start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
with open(header_file, "w", encoding="utf-8") as f:
|
||||
with header_file.open("w", encoding="utf-8") as f:
|
||||
f.write(header)
|
||||
f.write(f"Run started: {start_time}\n\n---\n\n")
|
||||
print(f"Reset report and wrote header to {header_file}")
|
||||
|
|
@ -392,7 +394,7 @@ def get_report():
|
|||
from nanochat.common import get_base_dir, get_dist_info
|
||||
ddp, ddp_rank, ddp_local_rank, ddp_world_size = get_dist_info()
|
||||
if ddp_rank == 0:
|
||||
report_dir = os.path.join(get_base_dir(), "report")
|
||||
report_dir = get_base_dir() / "report"
|
||||
return Report(report_dir)
|
||||
else:
|
||||
return DummyReport()
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ Two implementations are available:
|
|||
2) Our own RustBPE Tokenizer for training and tiktoken for efficient inference
|
||||
"""
|
||||
|
||||
import os
|
||||
import copy
|
||||
from functools import lru_cache
|
||||
|
||||
|
|
@ -51,8 +50,8 @@ class HuggingFaceTokenizer:
|
|||
@classmethod
|
||||
def from_directory(cls, tokenizer_dir):
|
||||
# init from a local directory on disk (e.g. "out/tokenizer")
|
||||
tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
|
||||
tokenizer = HFTokenizer.from_file(tokenizer_path)
|
||||
tokenizer_path = tokenizer_dir / "tokenizer.json"
|
||||
tokenizer = HFTokenizer.from_file(str(tokenizer_path))
|
||||
return cls(tokenizer)
|
||||
|
||||
@classmethod
|
||||
|
|
@ -141,9 +140,9 @@ class HuggingFaceTokenizer:
|
|||
|
||||
def save(self, tokenizer_dir):
|
||||
# save the tokenizer to disk
|
||||
os.makedirs(tokenizer_dir, exist_ok=True)
|
||||
tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
|
||||
self.tokenizer.save(tokenizer_path)
|
||||
tokenizer_dir.mkdir(parents=True, exist_ok=True)
|
||||
tokenizer_path = tokenizer_dir / "tokenizer.json"
|
||||
self.tokenizer.save(str(tokenizer_path))
|
||||
print(f"Saved tokenizer to {tokenizer_path}")
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
|
|
@ -183,8 +182,8 @@ class RustBPETokenizer:
|
|||
|
||||
@classmethod
|
||||
def from_directory(cls, tokenizer_dir):
|
||||
pickle_path = os.path.join(tokenizer_dir, "tokenizer.pkl")
|
||||
with open(pickle_path, "rb") as f:
|
||||
pickle_path = tokenizer_dir / "tokenizer.pkl"
|
||||
with pickle_path.open("rb") as f:
|
||||
enc = pickle.load(f)
|
||||
return cls(enc, "<|bos|>")
|
||||
|
||||
|
|
@ -249,9 +248,9 @@ class RustBPETokenizer:
|
|||
|
||||
def save(self, tokenizer_dir):
|
||||
# save the encoding object to disk
|
||||
os.makedirs(tokenizer_dir, exist_ok=True)
|
||||
pickle_path = os.path.join(tokenizer_dir, "tokenizer.pkl")
|
||||
with open(pickle_path, "wb") as f:
|
||||
tokenizer_dir.mkdir(parents=True, exist_ok=True)
|
||||
pickle_path = tokenizer_dir / "tokenizer.pkl"
|
||||
with pickle_path.open("wb") as f:
|
||||
pickle.dump(self.enc, f)
|
||||
print(f"Saved tokenizer encoding to {pickle_path}")
|
||||
|
||||
|
|
@ -382,7 +381,7 @@ class RustBPETokenizer:
|
|||
def get_tokenizer():
|
||||
from nanochat.common import get_base_dir
|
||||
base_dir = get_base_dir()
|
||||
tokenizer_dir = os.path.join(base_dir, "tokenizer")
|
||||
tokenizer_dir = base_dir / "tokenizer"
|
||||
# return HuggingFaceTokenizer.from_directory(tokenizer_dir)
|
||||
return RustBPETokenizer.from_directory(tokenizer_dir)
|
||||
|
||||
|
|
@ -390,9 +389,9 @@ def get_token_bytes(device="cpu"):
|
|||
import torch
|
||||
from nanochat.common import get_base_dir
|
||||
base_dir = get_base_dir()
|
||||
tokenizer_dir = os.path.join(base_dir, "tokenizer")
|
||||
token_bytes_path = os.path.join(tokenizer_dir, "token_bytes.pt")
|
||||
assert os.path.exists(token_bytes_path), f"Token bytes not found at {token_bytes_path}? It gets written by tok_train.py"
|
||||
with open(token_bytes_path, "rb") as f:
|
||||
tokenizer_dir = base_dir / "tokenizer"
|
||||
token_bytes_path = tokenizer_dir / "token_bytes.pt"
|
||||
assert token_bytes_path.exists(), f"Token bytes not found at {token_bytes_path}? It gets written by tok_train.py"
|
||||
with token_bytes_path.open("rb") as f:
|
||||
token_bytes = torch.load(f, map_location=device)
|
||||
return token_bytes
|
||||
|
|
|
|||
|
|
@ -9,7 +9,6 @@ torchrun --nproc_per_node=8 -m scripts.base_eval
|
|||
|
||||
The script will print the CORE metric to the console.
|
||||
"""
|
||||
import os
|
||||
import csv
|
||||
import time
|
||||
import json
|
||||
|
|
@ -19,6 +18,7 @@ import random
|
|||
import zipfile
|
||||
import tempfile
|
||||
from contextlib import nullcontext
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
|
|
@ -37,12 +37,12 @@ def place_eval_bundle(file_path):
|
|||
# here file_path is the path to the eval_bundle.zip file
|
||||
# we need to unzip it and place it in the base directory
|
||||
base_dir = get_base_dir()
|
||||
eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
|
||||
eval_bundle_dir = base_dir / "eval_bundle"
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
with zipfile.ZipFile(file_path, 'r') as zip_ref:
|
||||
zip_ref.extractall(tmpdir)
|
||||
extracted_bundle_dir = os.path.join(tmpdir, "eval_bundle")
|
||||
shutil.move(extracted_bundle_dir, eval_bundle_dir)
|
||||
extracted_bundle_dir = Path(tmpdir) / "eval_bundle"
|
||||
shutil.move(str(extracted_bundle_dir), str(eval_bundle_dir))
|
||||
print0(f"Placed eval_bundle directory at {eval_bundle_dir}")
|
||||
|
||||
def evaluate_model(model, tokenizer, device, max_per_task=-1):
|
||||
|
|
@ -52,20 +52,20 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
|
|||
"""
|
||||
# Load config and task metadata
|
||||
base_dir = get_base_dir()
|
||||
eval_bundle_dir = os.path.join(base_dir, "eval_bundle")
|
||||
eval_bundle_dir = base_dir / "eval_bundle"
|
||||
# Download the eval bundle to disk (and unzip if needed)
|
||||
if not os.path.exists(eval_bundle_dir):
|
||||
if not eval_bundle_dir.exists():
|
||||
download_file_with_lock(EVAL_BUNDLE_URL, "eval_bundle.zip", postprocess_fn=place_eval_bundle)
|
||||
config_path = os.path.join(eval_bundle_dir, "core.yaml")
|
||||
data_base_path = os.path.join(eval_bundle_dir, "eval_data")
|
||||
eval_meta_data = os.path.join(eval_bundle_dir, "eval_meta_data.csv")
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config_path = eval_bundle_dir / "core.yaml"
|
||||
data_base_path = eval_bundle_dir / "eval_data"
|
||||
eval_meta_data = eval_bundle_dir / "eval_meta_data.csv"
|
||||
with config_path.open('r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
tasks = config['icl_tasks']
|
||||
|
||||
# Load random baseline values from eval metadata
|
||||
random_baselines = {}
|
||||
with open(eval_meta_data, 'r', encoding='utf-8') as f:
|
||||
with eval_meta_data.open('r', encoding='utf-8') as f:
|
||||
reader = csv.DictReader(f)
|
||||
for row in reader:
|
||||
task_name = row['Eval Task']
|
||||
|
|
@ -87,8 +87,8 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
|
|||
print0(f"Evaluating: {label} ({task_meta['num_fewshot']}-shot, type: {task_meta['task_type']})... ", end='')
|
||||
|
||||
# Load data for this task
|
||||
data_path = os.path.join(data_base_path, task_meta['dataset_uri'])
|
||||
with open(data_path, 'r', encoding='utf-8') as f:
|
||||
data_path = data_base_path / task_meta['dataset_uri']
|
||||
with data_path.open('r', encoding='utf-8') as f:
|
||||
data = [json.loads(line.strip()) for line in f]
|
||||
|
||||
# shuffle the data because in many cases it appears ordered but we want
|
||||
|
|
@ -179,12 +179,12 @@ def main():
|
|||
centered_results = {}
|
||||
if ddp_rank == 0:
|
||||
base_dir = get_base_dir()
|
||||
output_csv_path = os.path.join(base_dir, "base_eval", f"{model_slug}.csv")
|
||||
os.makedirs(os.path.dirname(output_csv_path), exist_ok=True)
|
||||
output_csv_path = base_dir / "base_eval" / f"{model_slug}.csv"
|
||||
output_csv_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
results = out["results"]
|
||||
centered_results = out["centered_results"]
|
||||
core_metric = out["core_metric"]
|
||||
with open(output_csv_path, 'w', encoding='utf-8', newline='') as f:
|
||||
with output_csv_path.open('w', encoding='utf-8', newline='') as f:
|
||||
f.write(f"{'Task':<35}, {'Accuracy':<10}, {'Centered':<10}\n")
|
||||
for label in results:
|
||||
f.write(f"{label:<35}, {results[label]:<10.6f}, {centered_results[label]:<10.6f}\n")
|
||||
|
|
@ -193,7 +193,7 @@ def main():
|
|||
print0("="*80)
|
||||
print0(f"Model: {model_name}")
|
||||
print0("="*80)
|
||||
with open(output_csv_path, 'r', encoding='utf-8') as f:
|
||||
with output_csv_path.open('r', encoding='utf-8') as f:
|
||||
print0(f.read())
|
||||
|
||||
# Log to report
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ Loads a checkpoint, and:
|
|||
Example run as:
|
||||
torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
|
||||
"""
|
||||
import os
|
||||
from contextlib import nullcontext
|
||||
from pathlib import Path
|
||||
import torch
|
||||
from nanochat.checkpoint_manager import load_model
|
||||
from nanochat.common import compute_init, print0, compute_cleanup, autodetect_device_type
|
||||
|
|
@ -22,7 +22,8 @@ split_tokens = 20*524288 # number of tokens to evaluate per split
|
|||
model_tag = None # optional model tag for the output directory name
|
||||
model_step = None # optional model step for the output directory name
|
||||
device_type = "" # cuda|cpu|mps (empty => autodetect)
|
||||
exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
|
||||
with (Path('nanochat') / 'configurator.py').open() as f:
|
||||
exec(f.read()) # overrides from command line or config file
|
||||
|
||||
# Load the base model and the tokenizer
|
||||
device_type = autodetect_device_type() if device_type == "" else device_type
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ import os
|
|||
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
|
||||
import time
|
||||
from contextlib import nullcontext
|
||||
from pathlib import Path
|
||||
|
||||
import wandb
|
||||
import torch
|
||||
|
|
@ -64,7 +65,9 @@ save_every = -1 # every how many steps to save model checkpoints (-1 = disable,
|
|||
model_tag = "" # optionally override the model tag for the output checkpoint directory name
|
||||
# now allow CLI to override the settings via the configurator lol
|
||||
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
|
||||
exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
|
||||
configurator_path = Path('nanochat') / 'configurator.py'
|
||||
with configurator_path.open() as f:
|
||||
exec(f.read()) # overrides from command line or config file
|
||||
user_config = {k: globals()[k] for k in config_keys} # will be useful for logging
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
|
@ -120,7 +123,7 @@ model.init_weights()
|
|||
# If we are resuming, overwrite the model parameters with those of the checkpoint
|
||||
base_dir = get_base_dir()
|
||||
output_dirname = model_tag if model_tag else f"d{depth}" # e.g. d12
|
||||
checkpoint_dir = os.path.join(base_dir, "base_checkpoints", output_dirname)
|
||||
checkpoint_dir = base_dir / "base_checkpoints" / output_dirname
|
||||
resuming = resume_from_step != -1
|
||||
if resuming:
|
||||
print0(f"Resuming optimization from step {resume_from_step}")
|
||||
|
|
@ -167,7 +170,7 @@ if resuming:
|
|||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Initialize the DataLoaders for train/val
|
||||
tokens_dir = os.path.join(base_dir, "tokenized_data")
|
||||
tokens_dir = base_dir / "tokenized_data"
|
||||
dataloader_resume_state_dict = None if not resuming else meta_data["dataloader_state_dict"]
|
||||
train_loader = tokenizing_distributed_data_loader_with_state(device_batch_size, max_seq_len, split="train", device=device, resume_state_dict=dataloader_resume_state_dict)
|
||||
build_val_loader = lambda: tokenizing_distributed_data_loader(device_batch_size, max_seq_len, split="val", device=device)
|
||||
|
|
|
|||
|
|
@ -16,8 +16,8 @@ python -m scripts.chat_rl
|
|||
torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=default
|
||||
"""
|
||||
|
||||
import os
|
||||
import itertools
|
||||
from pathlib import Path
|
||||
import wandb
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
|
@ -48,7 +48,9 @@ eval_every = 60 # every how many steps to evaluate the model for val pass@k
|
|||
eval_examples = 400 # number of examples used for evaluating pass@k
|
||||
# now allow CLI to override the settings via the configurator lol
|
||||
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
|
||||
exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
|
||||
configurator_path = Path('nanochat') / 'configurator.py'
|
||||
with configurator_path.open() as f:
|
||||
exec(f.read()) # overrides from command line or config file
|
||||
user_config = {k: globals()[k] for k in config_keys} # will be useful for logging
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
|
@ -307,7 +309,7 @@ for step in range(num_steps):
|
|||
base_dir = get_base_dir()
|
||||
depth = model.config.n_layer
|
||||
model_tag = f"d{depth}" # base the model tag on the depth of the base model
|
||||
checkpoint_dir = os.path.join(base_dir, "chatrl_checkpoints", model_tag)
|
||||
checkpoint_dir = base_dir / "chatrl_checkpoints" / model_tag
|
||||
model_config_kwargs = model.config.__dict__ # slightly naughty, abusing the simplicity of GPTConfig, TODO nicer
|
||||
save_checkpoint(
|
||||
checkpoint_dir,
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft
|
|||
import os
|
||||
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
|
||||
|
||||
from pathlib import Path
|
||||
import wandb
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
|
@ -57,7 +58,9 @@ eval_metrics_every = 200
|
|||
eval_metrics_max_problems = 1024
|
||||
# now allow CLI to override the settings via the configurator lol
|
||||
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
|
||||
exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
|
||||
configurator_path = Path('nanochat') / 'configurator.py'
|
||||
with configurator_path.open() as f:
|
||||
exec(f.read()) # overrides from command line or config file
|
||||
user_config = {k: globals()[k] for k in config_keys} # possibly useful for logging
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
|
@ -80,7 +83,7 @@ engine = Engine(model, tokenizer) # will be used for inline model evaluation onl
|
|||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Task data mixture we'll train on
|
||||
identity_conversations_filepath = os.path.join(get_base_dir(), "identity_conversations.jsonl")
|
||||
identity_conversations_filepath = get_base_dir() / "identity_conversations.jsonl"
|
||||
train_ds = TaskMixture([
|
||||
ARC(subset="ARC-Easy", split="train"), # 2.3K rows
|
||||
ARC(subset="ARC-Challenge", split="train"), # 1.1K rows
|
||||
|
|
@ -251,7 +254,7 @@ if master_process:
|
|||
base_dir = get_base_dir()
|
||||
depth = model.config.n_layer
|
||||
model_tag = f"d{depth}" # base the model tag on the depth of the base model
|
||||
checkpoint_dir = os.path.join(base_dir, "chatsft_checkpoints", model_tag)
|
||||
checkpoint_dir = base_dir / "chatsft_checkpoints" / model_tag
|
||||
model_config_kwargs = model.config.__dict__ # slightly naughty, abusing the simplicity of GPTConfig, TODO nicer
|
||||
save_checkpoint(
|
||||
checkpoint_dir,
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ Abuse Prevention:
|
|||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
import torch
|
||||
import asyncio
|
||||
import logging
|
||||
|
|
@ -242,8 +242,8 @@ app.add_middleware(
|
|||
@app.get("/")
|
||||
async def root():
|
||||
"""Serve the chat UI."""
|
||||
ui_html_path = os.path.join("nanochat", "ui.html")
|
||||
with open(ui_html_path, "r", encoding="utf-8") as f:
|
||||
ui_html_path = Path("nanochat") / "ui.html"
|
||||
with ui_html_path.open("r", encoding="utf-8") as f:
|
||||
html_content = f.read()
|
||||
# Replace the API_URL to use the same origin
|
||||
html_content = html_content.replace(
|
||||
|
|
@ -256,8 +256,8 @@ async def root():
|
|||
@app.get("/logo.svg")
|
||||
async def logo():
|
||||
"""Serve the NanoChat logo for favicon and header."""
|
||||
logo_path = os.path.join("nanochat", "logo.svg")
|
||||
return FileResponse(logo_path, media_type="image/svg+xml")
|
||||
logo_path = Path("nanochat") / "logo.svg"
|
||||
return FileResponse(str(logo_path), media_type="image/svg+xml")
|
||||
|
||||
async def generate_stream(
|
||||
worker: Worker,
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ from collections import deque
|
|||
import os
|
||||
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
|
||||
import time
|
||||
from pathlib import Path
|
||||
import wandb
|
||||
import torch
|
||||
from contextlib import nullcontext
|
||||
|
|
@ -49,7 +50,9 @@ eval_tokens = 20*524288
|
|||
total_batch_size = 524288
|
||||
dry_run = 0 # dry_run=1 is for experiments: we will log to wandb but we won't write checkpoints or report
|
||||
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
|
||||
exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
|
||||
configurator_path = Path('nanochat') / 'configurator.py'
|
||||
with configurator_path.open() as f:
|
||||
exec(f.read()) # overrides from command line or config file
|
||||
user_config = {k: globals()[k] for k in config_keys} # possibly useful for logging
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
|
@ -94,7 +97,7 @@ for opt in optimizers:
|
|||
|
||||
# Midtraining data mixture and DataLoader
|
||||
base_dir = get_base_dir()
|
||||
identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
|
||||
identity_conversations_filepath = base_dir / "identity_conversations.jsonl"
|
||||
train_dataset = TaskMixture([
|
||||
SmolTalk(split="train"), # 460K rows of general conversations
|
||||
MMLU(subset="auxiliary_train", split="train"), # 100K rows of multiple choice problems drawn from ARC, MC_TEST, OBQA, RACE
|
||||
|
|
@ -208,7 +211,7 @@ while True:
|
|||
# save checkpoint at the end of the run (only on master process)
|
||||
if master_process and last_step and not dry_run:
|
||||
output_dirname = f"d{depth}" # e.g. d12
|
||||
checkpoint_dir = os.path.join(base_dir, "mid_checkpoints", output_dirname)
|
||||
checkpoint_dir = base_dir / "mid_checkpoints" / output_dirname
|
||||
save_checkpoint(
|
||||
checkpoint_dir,
|
||||
step,
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
Train a tokenizer using the HuggingFace Tokenizers library.
|
||||
In the style of GPT-4 tokenizer.
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import torch
|
||||
|
|
@ -54,7 +53,7 @@ print(f"Training time: {train_time:.2f}s")
|
|||
# -----------------------------------------------------------------------------
|
||||
# Save the tokenizer to disk
|
||||
base_dir = get_base_dir()
|
||||
tokenizer_dir = os.path.join(base_dir, "tokenizer")
|
||||
tokenizer_dir = base_dir / "tokenizer"
|
||||
tokenizer.save(tokenizer_dir)
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
|
|
@ -85,8 +84,8 @@ for token_id in range(vocab_size):
|
|||
id_bytes = len(token_str.encode("utf-8")) # number of bytes that make up this token
|
||||
token_bytes.append(id_bytes)
|
||||
token_bytes = torch.tensor(token_bytes, dtype=torch.int32, device='cpu')
|
||||
token_bytes_path = os.path.join(tokenizer_dir, "token_bytes.pt")
|
||||
with open(token_bytes_path, "wb") as f:
|
||||
token_bytes_path = tokenizer_dir / "token_bytes.pt"
|
||||
with token_bytes_path.open("wb") as f:
|
||||
torch.save(token_bytes, f)
|
||||
print(f"Saved token_bytes to {token_bytes_path}")
|
||||
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ CustomJSON task for loading conversations from JSONL files.
|
|||
Each line in the JSONL file should be a JSON array of messages.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
from pathlib import Path
|
||||
from tasks.common import Task
|
||||
|
||||
class CustomJSON(Task):
|
||||
|
|
@ -16,23 +16,23 @@ class CustomJSON(Task):
|
|||
|
||||
def __init__(self, filepath, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.filepath = filepath
|
||||
self.filepath = Path(filepath)
|
||||
self.conversations = []
|
||||
|
||||
# Load all conversations from the JSONL file
|
||||
if not os.path.exists(filepath):
|
||||
if not self.filepath.exists():
|
||||
# Helpful error message due to recent change. Will be removed in the future.
|
||||
print("-" * 80)
|
||||
print(f"Warning: File {filepath} does not exist")
|
||||
print(f"Warning: File {self.filepath} does not exist")
|
||||
print("HINT (Oct 21 2025)")
|
||||
print("If you recently did a git pull and suddely see this, it might be due to the new addition of identity conversations")
|
||||
print("See this discussion for more details: https://github.com/karpathy/nanochat/discussions/139")
|
||||
print("Quick fix: simply run the following command to download the file and you're done:")
|
||||
print(f"curl -L -o {filepath} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl")
|
||||
print(f"curl -L -o {self.filepath} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl")
|
||||
print("-" * 80)
|
||||
|
||||
else:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
with self.filepath.open('r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line: # skip empty lines
|
||||
|
|
|
|||
|
|
@ -121,7 +121,7 @@ class SpellingBee(Task):
|
|||
self.split = split
|
||||
filename = WORD_LIST_URL.split("/")[-1]
|
||||
word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
|
||||
with open(word_list_path, 'r', encoding='utf-8') as f:
|
||||
with word_list_path.open('r', encoding='utf-8') as f:
|
||||
words = [line.strip() for line in f]
|
||||
self.words = words
|
||||
|
||||
|
|
@ -240,7 +240,7 @@ class SimpleSpelling(Task):
|
|||
self.split = split
|
||||
filename = WORD_LIST_URL.split("/")[-1]
|
||||
word_list_path = download_file_with_lock(WORD_LIST_URL, filename)
|
||||
with open(word_list_path, 'r', encoding='utf-8') as f:
|
||||
with word_list_path.open('r', encoding='utf-8') as f:
|
||||
words = [line.strip() for line in f]
|
||||
rng = random.Random(42)
|
||||
rng.shuffle(words) # use a different word order than the SpellingBee task
|
||||
|
|
|
|||
|
|
@ -428,24 +428,23 @@ class HuggingFaceTokenizer:
|
|||
@pytest.fixture(scope="module")
|
||||
def enwik8_path():
|
||||
"""Fixture to download and cache enwik8 dataset."""
|
||||
import os
|
||||
import zipfile
|
||||
from nanochat.common import get_base_dir
|
||||
base_dir = get_base_dir()
|
||||
# download and unzip enwik8 to .cache directory
|
||||
enwik8_url = "https://mattmahoney.net/dc/enwik8.zip"
|
||||
enwik8_local_path = os.path.join(base_dir, "enwik8")
|
||||
enwik8_local_path_zip = os.path.join(base_dir, "enwik8.zip")
|
||||
if not os.path.exists(enwik8_local_path):
|
||||
enwik8_local_path = base_dir / "enwik8"
|
||||
enwik8_local_path_zip = base_dir / "enwik8.zip"
|
||||
if not enwik8_local_path.exists():
|
||||
print(f"Downloading enwik8 to {enwik8_local_path_zip}")
|
||||
import requests
|
||||
response = requests.get(enwik8_url)
|
||||
with open(enwik8_local_path_zip, "wb") as f:
|
||||
with enwik8_local_path_zip.open("wb") as f:
|
||||
f.write(response.content)
|
||||
with zipfile.ZipFile(enwik8_local_path_zip, "r") as zip_ref:
|
||||
zip_ref.extractall(base_dir)
|
||||
print(f"Unzipped enwik8 to {enwik8_local_path}")
|
||||
os.remove(enwik8_local_path_zip)
|
||||
enwik8_local_path_zip.unlink()
|
||||
print(f"Removed {enwik8_local_path_zip}")
|
||||
else:
|
||||
print(f"Using existing enwik8 at {enwik8_local_path}")
|
||||
|
|
@ -455,13 +454,13 @@ def enwik8_path():
|
|||
@pytest.fixture(scope="module")
|
||||
def enwik8_small(enwik8_path):
|
||||
"""Fixture providing 100KB of enwik8 for quick tests."""
|
||||
with open(enwik8_path, "r", encoding="utf-8") as f:
|
||||
with enwik8_path.open("r", encoding="utf-8") as f:
|
||||
return f.read(100_000)
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def enwik8_large(enwik8_path):
|
||||
"""Fixture providing 10MB of enwik8 for performance tests."""
|
||||
with open(enwik8_path, "r", encoding="utf-8") as f:
|
||||
with enwik8_path.open("r", encoding="utf-8") as f:
|
||||
return f.read(10**7)
|
||||
|
||||
def time_function(func, *args, **kwargs):
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user