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from typing import Any
import polars as pl
import pytest
from sglang.srt.debug_utils.comparator.bundle_matcher import (
TensorBundleInfo,
TensorFileInfo,
_rows_to_tensor_infos,
match_bundles,
)
from sglang.srt.debug_utils.comparator.utils import Pair
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=15, suite="default", nightly=True)
def _make_row(
*,
name: str,
step: int = 0,
rank: int = 0,
layer_id: int | None = None,
filename: str | None = None,
) -> dict[str, Any]:
if filename is None:
layer_part: str = f"___layer_id={layer_id}" if layer_id is not None else ""
filename = f"name={name}___step={step}___rank={rank}{layer_part}.pt"
row: dict[str, Any] = {
"name": name,
"step": step,
"rank": rank,
"filename": filename,
}
if layer_id is not None:
row["layer_id"] = layer_id
return row
def _make_df(rows: list[dict[str, Any]]) -> pl.DataFrame:
if not rows:
return pl.DataFrame(rows)
all_keys: set[str] = set()
for row in rows:
all_keys.update(row.keys())
normalized: list[dict[str, Any]] = [
{k: row.get(k, None) for k in all_keys} for row in rows
]
return pl.DataFrame(normalized)
class TestMatchBundles:
def test_single_tensor_single_step(self) -> None:
target_df: pl.DataFrame = _make_df([_make_row(name="t_a")])
baseline_df: pl.DataFrame = _make_df([_make_row(name="t_a")])
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename"},
)
assert len(results) == 1
assert len(results[0].x) == 1
assert len(results[0].y) == 1
assert results[0].y[0].name == "t_a"
def test_multiple_names_separate_bundles(self) -> None:
target_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a"),
_make_row(name="t_b"),
]
)
baseline_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a"),
_make_row(name="t_b"),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename"},
)
assert len(results) == 2
result_names: list[str] = [r.y[0].name for r in results]
assert "t_a" in result_names
assert "t_b" in result_names
def test_skip_rank_groups_across_ranks(self) -> None:
target_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a", rank=0),
_make_row(name="t_a", rank=1),
]
)
baseline_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a", rank=0),
_make_row(name="t_a", rank=1),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename", "rank"},
)
assert len(results) == 1
assert len(results[0].y) == 2
def test_baseline_missing_tensor(self) -> None:
target_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a"),
_make_row(name="t_extra"),
]
)
baseline_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a"),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename"},
)
assert len(results) == 2
extra_pair: Pair[TensorBundleInfo] = [
r for r in results if r.y[0].name == "t_extra"
][0]
assert extra_pair.x == []
def test_empty_target_returns_empty(self) -> None:
target_df: pl.DataFrame = _make_df([])
baseline_df: pl.DataFrame = _make_df([_make_row(name="t_a")])
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename"},
)
assert results == []
def test_skip_step_groups_across_steps(self) -> None:
target_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a", step=0),
_make_row(name="t_a", step=1),
]
)
baseline_df: pl.DataFrame = _make_df(
[
_make_row(name="t_a", step=0),
_make_row(name="t_a", step=1),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys={"filename", "step"},
)
assert len(results) == 1
assert len(results[0].y) == 2
class TestMatchBundlesPipelineParallel:
"""Tests verifying that PP works correctly with the existing matching logic."""
LOGICAL_SKIP_KEYS: set[str] = {"filename", "rank", "dump_index", "recompute_status"}
def test_same_layer_id_different_ranks_match(self) -> None:
"""SGLang PP=2 rank 0 (layers 0-31) vs Megatron PP=4 rank 2 (layers 16-31):
layer_id=20 should match regardless of world rank."""
target_df: pl.DataFrame = _make_df(
[_make_row(name="hidden", rank=0, layer_id=20)]
)
baseline_df: pl.DataFrame = _make_df(
[_make_row(name="hidden", rank=2, layer_id=20)]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys=self.LOGICAL_SKIP_KEYS,
)
assert len(results) == 1
assert len(results[0].x) == 1
assert len(results[0].y) == 1
def test_layer_id_none_non_layer_tensors_match(self) -> None:
"""Non-layer tensors (embedding, lm_head) have no layer_id.
They should match across different PP ranks."""
target_df: pl.DataFrame = _make_df([_make_row(name="embed_tokens", rank=0)])
baseline_df: pl.DataFrame = _make_df([_make_row(name="embed_tokens", rank=0)])
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys=self.LOGICAL_SKIP_KEYS,
)
assert len(results) == 1
assert len(results[0].x) == 1
assert len(results[0].y) == 1
def test_different_pp_sizes_layer_and_non_layer_bundles(self) -> None:
"""SGLang PP=2 TP=2 (4 ranks) vs Megatron PP=4 TP=2 (8 ranks).
Layer tensors match by (name, layer_id); non-layer tensors match by name.
All ranks are grouped into the same bundle when rank is skipped."""
target_df: pl.DataFrame = _make_df(
[
# SGLang: pp_stage=0 has ranks 0,1 (TP=2)
_make_row(name="hidden", rank=0, layer_id=20),
_make_row(name="hidden", rank=1, layer_id=20),
# SGLang: embedding on pp_stage=0
_make_row(name="embed_tokens", rank=0),
_make_row(name="embed_tokens", rank=1),
# SGLang: lm_head on pp_stage=1, ranks 2,3
_make_row(name="lm_head", rank=2),
_make_row(name="lm_head", rank=3),
]
)
baseline_df: pl.DataFrame = _make_df(
[
# Megatron: pp_stage=1 has ranks 2,3 for layer 20
_make_row(name="hidden", rank=2, layer_id=20),
_make_row(name="hidden", rank=3, layer_id=20),
# Megatron: embedding on pp_stage=0
_make_row(name="embed_tokens", rank=0),
_make_row(name="embed_tokens", rank=1),
# Megatron: lm_head on pp_stage=3, ranks 6,7
_make_row(name="lm_head", rank=6),
_make_row(name="lm_head", rank=7),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys=self.LOGICAL_SKIP_KEYS,
)
assert len(results) == 3
names_to_pairs: dict[str, Pair[TensorBundleInfo]] = {}
for pair in results:
key: str = pair.y[0].name
layer_suffix: str = ""
if "layer_id" in target_df.columns:
row_match = [
r
for r in target_df.to_dicts()
if r["filename"] == pair.y[0].filename
]
if row_match and row_match[0].get("layer_id") is not None:
layer_suffix = f"_{row_match[0]['layer_id']}"
names_to_pairs[key + layer_suffix] = pair
assert len(names_to_pairs["hidden_20"].x) == 2
assert len(names_to_pairs["hidden_20"].y) == 2
assert len(names_to_pairs["embed_tokens"].x) == 2
assert len(names_to_pairs["embed_tokens"].y) == 2
assert len(names_to_pairs["lm_head"].x) == 2
assert len(names_to_pairs["lm_head"].y) == 2
def test_unmatched_layer_id_creates_empty_baseline(self) -> None:
"""If target has a layer_id that baseline doesn't, the baseline side
should be empty (not incorrectly matched to a different layer)."""
target_df: pl.DataFrame = _make_df(
[
_make_row(name="hidden", rank=0, layer_id=10),
_make_row(name="hidden", rank=0, layer_id=20),
]
)
baseline_df: pl.DataFrame = _make_df(
[
_make_row(name="hidden", rank=0, layer_id=10),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys=self.LOGICAL_SKIP_KEYS,
)
assert len(results) == 2
matched: list[Pair[TensorBundleInfo]] = [r for r in results if r.x]
unmatched: list[Pair[TensorBundleInfo]] = [r for r in results if not r.x]
assert len(matched) == 1
assert len(unmatched) == 1
def test_pp1_vs_pp_gt1_matches_by_layer_id(self) -> None:
"""PP=1 (all layers on 1 rank) vs PP>1 (layers split across ranks).
Should match correctly by layer_id regardless of rank."""
target_df: pl.DataFrame = _make_df(
[
# PP=1: all on rank 0
_make_row(name="hidden", rank=0, layer_id=0),
_make_row(name="hidden", rank=0, layer_id=1),
]
)
baseline_df: pl.DataFrame = _make_df(
[
# PP=2: layer 0 on rank 0, layer 1 on rank 1
_make_row(name="hidden", rank=0, layer_id=0),
_make_row(name="hidden", rank=1, layer_id=1),
]
)
results: list[Pair[TensorBundleInfo]] = match_bundles(
dfs=Pair(x=baseline_df, y=target_df),
skip_keys=self.LOGICAL_SKIP_KEYS,
)
assert len(results) == 2
for pair in results:
assert len(pair.x) == 1
assert len(pair.y) == 1
class TestRowsToTensorInfos:
def test_filters_extra_columns(self) -> None:
rows: list[dict[str, Any]] = [
{"filename": "a.pt", "name": "t_a", "step": 0, "rank": 7}
]
infos: list[TensorFileInfo] = _rows_to_tensor_infos(rows)
assert len(infos) == 1
assert infos[0] == TensorFileInfo(filename="a.pt", name="t_a", step=0)
def test_empty_rows(self) -> None:
infos: list[TensorFileInfo] = _rows_to_tensor_infos([])
assert infos == []
if __name__ == "__main__":
sys.exit(pytest.main([__file__]))
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