Jason Matthew
cc5c54574d
Three independent improvements to extract_corpus_parallel: 1. Token-aware chunking. Replaces `chunk_size=20` static packing with a greedy packer keyed on `token_budget` (default 60_000), grouped by parent directory so related artefacts share a chunk. Pass `token_budget=None` to fall back to fixed-count packing. 2. Optional tiktoken (added to the [kimi] extra). When available, `_estimate_file_tokens` uses cl100k_base for accurate counts; without it, the existing chars/4 heuristic kicks in. Kimi-K2 ships a tiktoken-based tokenizer so estimates against Moonshot are very close to truth. 3. True parallelism. The function name said "parallel" but the body was a sequential for-loop. Now uses ThreadPoolExecutor capped at `max_concurrency` (default 4 — conservative against provider rate limits). `on_chunk_done(idx, total, result)` still fires once per chunk with the original submission idx so progress UIs work unchanged. `max_concurrency=1` skips the pool to preserve sequential semantics. Plus failure tolerance: a chunk raising is now caught, logged to stderr, and the run continues. Other chunks' results merge as normal. On a 162-file repo (~125k words), the same work that took ~36 min sequential under the old code finishes in ~7 min.
278 lines
9.9 KiB
Python
278 lines
9.9 KiB
Python
"""Tests for token-aware chunking and parallel chunk execution in graphify.llm."""
|
||
import time
|
||
from pathlib import Path
|
||
from unittest.mock import patch
|
||
|
||
import pytest
|
||
|
||
|
||
@pytest.fixture(autouse=False)
|
||
def no_tokenizer():
|
||
"""Force the chars/4 fallback so packing math is deterministic regardless
|
||
of whether tiktoken is installed in the test environment. tiktoken's BPE
|
||
compresses repeated/synthetic content heavily, which would make pack-size
|
||
assertions tied to specific input sizes flaky."""
|
||
from graphify import llm
|
||
with patch.object(llm, "_TOKENIZER", None):
|
||
yield
|
||
|
||
|
||
# ---- Token-aware packing -----------------------------------------------------
|
||
|
||
def test_pack_chunks_packs_small_files_together(tmp_path):
|
||
"""Many small files should land in a single chunk, not one chunk per file."""
|
||
from graphify.llm import _pack_chunks_by_tokens
|
||
|
||
files = []
|
||
for i in range(20):
|
||
f = tmp_path / f"small_{i}.py"
|
||
f.write_text("x = 1\n") # ~6 bytes => ~1 token
|
||
files.append(f)
|
||
|
||
chunks = _pack_chunks_by_tokens(files, token_budget=10_000)
|
||
assert len(chunks) == 1
|
||
assert sorted(chunks[0]) == sorted(files)
|
||
|
||
|
||
def test_pack_chunks_starts_new_chunk_when_budget_would_overflow(tmp_path, no_tokenizer):
|
||
"""When the next file would push the chunk past the budget, start a new chunk.
|
||
|
||
With chars/4 fallback: each 10,000-char file = (10000+80)/4 = 2520 tokens.
|
||
Budget 6000 fits two (5040 < 6000) but not three (7560 > 6000).
|
||
Five files → 2/2/1 = three chunks.
|
||
"""
|
||
from graphify.llm import _pack_chunks_by_tokens
|
||
|
||
files = []
|
||
for i in range(5):
|
||
f = tmp_path / f"file_{i}.py"
|
||
f.write_text("x" * 10_000)
|
||
files.append(f)
|
||
|
||
chunks = _pack_chunks_by_tokens(files, token_budget=6_000)
|
||
sizes = [len(c) for c in chunks]
|
||
assert sizes == [2, 2, 1], f"expected [2, 2, 1], got {sizes}"
|
||
assert sum(sizes) == 5 # all files accounted for
|
||
|
||
|
||
def test_pack_chunks_groups_by_directory(tmp_path):
|
||
"""Files in the same directory should land in the same chunk when they fit."""
|
||
from graphify.llm import _pack_chunks_by_tokens
|
||
|
||
dir_a = tmp_path / "a"
|
||
dir_b = tmp_path / "b"
|
||
dir_a.mkdir()
|
||
dir_b.mkdir()
|
||
|
||
a1 = dir_a / "x.py"; a1.write_text("a")
|
||
a2 = dir_a / "y.py"; a2.write_text("a")
|
||
b1 = dir_b / "x.py"; b1.write_text("b")
|
||
b2 = dir_b / "y.py"; b2.write_text("b")
|
||
|
||
# Big budget — everything fits in one chunk in principle, but the order
|
||
# within the chunk should keep dir_a's files contiguous and dir_b's
|
||
# contiguous (not interleaved).
|
||
chunks = _pack_chunks_by_tokens([a1, b1, a2, b2], token_budget=1_000_000)
|
||
assert len(chunks) == 1
|
||
chunk = chunks[0]
|
||
a_indices = [i for i, p in enumerate(chunk) if p.parent == dir_a]
|
||
b_indices = [i for i, p in enumerate(chunk) if p.parent == dir_b]
|
||
assert a_indices == sorted(a_indices)
|
||
assert b_indices == sorted(b_indices)
|
||
# all of one directory comes before all of the other
|
||
assert max(a_indices) < min(b_indices) or max(b_indices) < min(a_indices)
|
||
|
||
|
||
def test_pack_chunks_oversized_file_gets_its_own_chunk(tmp_path, no_tokenizer):
|
||
"""A file larger than the budget can't be split — it goes alone in a chunk."""
|
||
from graphify.llm import _pack_chunks_by_tokens
|
||
|
||
big = tmp_path / "big.py"; big.write_text("x" * 200_000) # ~50k tokens (cap-bound)
|
||
small = tmp_path / "small.py"; small.write_text("x")
|
||
|
||
chunks = _pack_chunks_by_tokens([big, small], token_budget=1_000)
|
||
sizes = [len(c) for c in chunks]
|
||
# big should be alone in its own chunk; small in its own (no other file
|
||
# to share with)
|
||
assert sizes == [1, 1]
|
||
|
||
|
||
def test_pack_chunks_rejects_non_positive_budget(tmp_path):
|
||
from graphify.llm import _pack_chunks_by_tokens
|
||
|
||
f = tmp_path / "x.py"; f.write_text("a")
|
||
with pytest.raises(ValueError):
|
||
_pack_chunks_by_tokens([f], token_budget=0)
|
||
|
||
|
||
# ---- Tokenizer fallback ------------------------------------------------------
|
||
|
||
def test_estimate_file_tokens_uses_tiktoken_when_available(tmp_path):
|
||
"""When tiktoken is installed, the estimator should call into it for
|
||
accurate counts rather than the chars/4 heuristic."""
|
||
from graphify import llm
|
||
|
||
f = tmp_path / "sample.py"
|
||
text = "def hello():\n return 'world'\n" * 50 # ~1500 chars
|
||
f.write_text(text)
|
||
|
||
# Force the tokenizer to be a mock that records calls and returns a known
|
||
# token list, so we can assert the tiktoken path is taken.
|
||
fake_encoder = type("E", (), {"encode": staticmethod(lambda s: [0] * 999)})()
|
||
with patch.object(llm, "_TOKENIZER", fake_encoder):
|
||
n = llm._estimate_file_tokens(f)
|
||
assert n == 999 + (llm._PER_FILE_OVERHEAD_CHARS // llm._CHARS_PER_TOKEN)
|
||
|
||
|
||
def test_estimate_file_tokens_falls_back_to_chars_when_no_tokenizer(tmp_path):
|
||
"""Without tiktoken installed, the estimator falls back to chars/4."""
|
||
from graphify import llm
|
||
|
||
f = tmp_path / "sample.py"
|
||
f.write_text("x" * 1_000) # 1000 bytes
|
||
|
||
with patch.object(llm, "_TOKENIZER", None):
|
||
n = llm._estimate_file_tokens(f)
|
||
# 1000 chars + 80 overhead = 1080 / 4 = 270 tokens
|
||
assert n == (1000 + llm._PER_FILE_OVERHEAD_CHARS) // llm._CHARS_PER_TOKEN
|
||
|
||
|
||
# ---- Parallel execution ------------------------------------------------------
|
||
|
||
def _stub_chunk_result(file_count: int, idx: int) -> dict:
|
||
"""Build a deterministic fake extraction result for a chunk."""
|
||
return {
|
||
"nodes": [{"id": f"chunk_{idx}_node_{i}"} for i in range(file_count)],
|
||
"edges": [],
|
||
"hyperedges": [],
|
||
"input_tokens": 100 * file_count,
|
||
"output_tokens": 50 * file_count,
|
||
}
|
||
|
||
|
||
def test_corpus_parallel_runs_chunks_concurrently(tmp_path):
|
||
"""With max_concurrency > 1, total wall time should be ~max(chunk times),
|
||
not the sum. Each stub extraction sleeps; we assert wall time."""
|
||
from graphify.llm import extract_corpus_parallel
|
||
|
||
files = []
|
||
for i in range(8):
|
||
f = tmp_path / f"f{i}.py"; f.write_text("x")
|
||
files.append(f)
|
||
|
||
def slow_extract(chunk, **kwargs):
|
||
time.sleep(0.3)
|
||
return _stub_chunk_result(len(chunk), 0)
|
||
|
||
with patch("graphify.llm.extract_files_direct", side_effect=slow_extract):
|
||
t0 = time.time()
|
||
# Force 4 chunks of 2 files each by setting a tight token budget.
|
||
result = extract_corpus_parallel(
|
||
files, backend="kimi", token_budget=None, chunk_size=2, max_concurrency=4
|
||
)
|
||
elapsed = time.time() - t0
|
||
|
||
# 4 chunks × 0.3s sequential = 1.2s. Parallel with 4 workers should land near 0.3-0.5s.
|
||
assert elapsed < 1.0, f"expected parallel speedup, took {elapsed:.2f}s"
|
||
assert len(result["nodes"]) == 8
|
||
|
||
|
||
def test_corpus_parallel_sequential_when_max_concurrency_is_one(tmp_path):
|
||
"""max_concurrency=1 should run sequentially (no thread pool)."""
|
||
from graphify.llm import extract_corpus_parallel
|
||
|
||
files = []
|
||
for i in range(3):
|
||
f = tmp_path / f"f{i}.py"; f.write_text("x")
|
||
files.append(f)
|
||
|
||
call_order = []
|
||
|
||
def record(chunk, **kwargs):
|
||
call_order.append(tuple(p.name for p in chunk))
|
||
return _stub_chunk_result(len(chunk), len(call_order))
|
||
|
||
with patch("graphify.llm.extract_files_direct", side_effect=record):
|
||
extract_corpus_parallel(
|
||
files, backend="kimi", token_budget=None, chunk_size=1, max_concurrency=1
|
||
)
|
||
|
||
# Sequential => we see calls in submission order
|
||
assert call_order == [("f0.py",), ("f1.py",), ("f2.py",)]
|
||
|
||
|
||
def test_corpus_parallel_continues_after_chunk_failure(tmp_path, capsys):
|
||
"""A single chunk raising should be logged but not abort the run.
|
||
Other chunks' results should still be merged."""
|
||
from graphify.llm import extract_corpus_parallel
|
||
|
||
files = []
|
||
for i in range(4):
|
||
f = tmp_path / f"f{i}.py"; f.write_text("x")
|
||
files.append(f)
|
||
|
||
call_count = {"n": 0}
|
||
|
||
def maybe_fail(chunk, **kwargs):
|
||
call_count["n"] += 1
|
||
if call_count["n"] == 2:
|
||
raise RuntimeError("simulated API error")
|
||
return _stub_chunk_result(len(chunk), call_count["n"])
|
||
|
||
with patch("graphify.llm.extract_files_direct", side_effect=maybe_fail):
|
||
result = extract_corpus_parallel(
|
||
files, backend="kimi", token_budget=None, chunk_size=1, max_concurrency=1
|
||
)
|
||
|
||
# 4 chunks dispatched, 1 failed → 3 chunks contributed nodes
|
||
assert len(result["nodes"]) == 3
|
||
err = capsys.readouterr().err
|
||
assert "failed" in err and "simulated API error" in err
|
||
|
||
|
||
def test_corpus_parallel_legacy_mode_when_token_budget_is_none(tmp_path):
|
||
"""token_budget=None should fall back to legacy fixed-count chunking."""
|
||
from graphify.llm import extract_corpus_parallel
|
||
|
||
files = []
|
||
for i in range(45):
|
||
f = tmp_path / f"f{i}.py"; f.write_text("x")
|
||
files.append(f)
|
||
|
||
chunks_seen = []
|
||
|
||
def record(chunk, **kwargs):
|
||
chunks_seen.append(len(chunk))
|
||
return _stub_chunk_result(len(chunk), len(chunks_seen))
|
||
|
||
with patch("graphify.llm.extract_files_direct", side_effect=record):
|
||
extract_corpus_parallel(
|
||
files, backend="kimi", token_budget=None, chunk_size=20, max_concurrency=1
|
||
)
|
||
|
||
# 45 files / chunk_size=20 = 3 chunks of 20, 20, 5
|
||
assert chunks_seen == [20, 20, 5]
|
||
|
||
|
||
def test_corpus_parallel_token_budget_default_packs_files(tmp_path):
|
||
"""With the default token_budget, many tiny files pack into one chunk."""
|
||
from graphify.llm import extract_corpus_parallel
|
||
|
||
files = []
|
||
for i in range(50):
|
||
f = tmp_path / f"f{i}.py"; f.write_text("x = 1\n")
|
||
files.append(f)
|
||
|
||
chunks_seen = []
|
||
|
||
def record(chunk, **kwargs):
|
||
chunks_seen.append(len(chunk))
|
||
return _stub_chunk_result(len(chunk), len(chunks_seen))
|
||
|
||
with patch("graphify.llm.extract_files_direct", side_effect=record):
|
||
extract_corpus_parallel(files, backend="kimi", max_concurrency=1)
|
||
|
||
# 50 tiny files at default 60k token budget should pack into 1 chunk
|
||
assert len(chunks_seen) == 1
|
||
assert chunks_seen[0] == 50
|