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from __future__ import annotations
import json
import logging
import sys
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
import torch
# Add scripts/ to the import path the same way the benchmark script does.
sys.path.insert(0, str(Path(__file__).resolve().parents[3] / "scripts"))
from benchmark import ( # type: ignore[import-not-found]
_MetricCapture,
_SyntheticLoader,
collect_provenance,
compare_benchmarks,
compute_aggregate_throughput,
compute_statistics,
create_synthetic_batch,
select_measured_profiles,
shutdown_dataloader_workers,
)
# ---------------------------------------------------------------------------
# compute_statistics
# ---------------------------------------------------------------------------
class TestComputeStatistics:
def test_basic(self):
values = [10.0, 20.0, 30.0, 40.0, 50.0]
result = compute_statistics(values)
assert result["mean"] == pytest.approx(30.0)
assert result["min"] == 10.0
assert result["max"] == 50.0
assert result["median"] == 30.0
assert result["count"] == 5
assert result["std"] > 0
def test_single_value(self):
result = compute_statistics([42.0])
assert result["mean"] == 42.0
assert result["std"] == 0.0
assert result["min"] == 42.0
assert result["max"] == 42.0
assert result["median"] == 42.0
assert result["count"] == 1
def test_identical_values(self):
result = compute_statistics([5.0, 5.0, 5.0])
assert result["mean"] == 5.0
assert result["std"] == 0.0
def test_compute_aggregate_throughput_is_time_weighted():
profiles = [
{"step_ms": 1000.0, "tokens_per_s": 100.0},
{"step_ms": 3000.0, "tokens_per_s": 300.0},
]
result = compute_aggregate_throughput(profiles)
assert result["measured_time_s"] == 4.0
assert result["rank0_tokens"] == 1000.0
assert result["effective_rank0_tokens_per_s"] == 250.0
def test_shutdown_dataloader_workers():
loader = MagicMock()
iterator = loader._iterator
shutdown_dataloader_workers(loader)
iterator._shutdown_workers.assert_called_once_with()
assert loader._iterator is None
# ---------------------------------------------------------------------------
# collect_provenance
# ---------------------------------------------------------------------------
class TestCollectProvenance:
@patch("benchmark.torch")
@patch("benchmark.subprocess.run")
def test_keys_present(self, mock_run, mock_torch):
mock_run.return_value = MagicMock(returncode=0, stdout="abc123\n")
mock_torch.cuda.is_available.return_value = False
mock_torch.cuda.device_count.return_value = 0
mock_torch.__version__ = "2.9.0"
mock_torch.version.cuda = "12.4"
result = collect_provenance()
expected_keys = {
"git_sha",
"timestamp",
"hostname",
"python_version",
"pytorch_version",
"cuda_version",
"speculators_version",
"transformers_version",
"gpu_info",
"num_gpus",
}
assert set(result.keys()) == expected_keys
assert result["git_sha"] == "abc123"
assert result["num_gpus"] == 0
@patch("benchmark.torch")
@patch("benchmark.subprocess.run")
def test_git_failure(self, mock_run, mock_torch):
mock_run.return_value = MagicMock(returncode=128, stdout="")
mock_torch.cuda.is_available.return_value = False
mock_torch.cuda.device_count.return_value = 0
mock_torch.__version__ = "2.9.0"
mock_torch.version.cuda = None
result = collect_provenance()
assert result["git_sha"] == "unknown"
assert result["cuda_version"] == "none"
# ---------------------------------------------------------------------------
# create_synthetic_batch
# ---------------------------------------------------------------------------
class TestCreateSyntheticBatch:
def test_shapes(self):
seq_len = 128
hidden_size = 64
num_layers = 3
batch = create_synthetic_batch(
total_seq_len=seq_len,
hidden_size=hidden_size,
num_target_layers=num_layers,
device="cpu",
)
assert batch["hidden_states"].shape == (
1,
seq_len,
num_layers * hidden_size,
)
assert batch["input_ids"].shape == (1, seq_len)
assert batch["verifier_last_hidden_states"].shape == (
1,
seq_len,
hidden_size,
)
assert batch["loss_mask"].shape == (1, seq_len)
assert batch["position_ids"].shape == (1, seq_len)
assert batch["document_ids"].shape == (1, seq_len)
def test_dtypes(self):
batch = create_synthetic_batch(
total_seq_len=64,
hidden_size=32,
num_target_layers=2,
dtype=torch.bfloat16,
device="cpu",
)
assert batch["hidden_states"].dtype == torch.bfloat16
assert batch["verifier_last_hidden_states"].dtype == torch.bfloat16
assert batch["input_ids"].dtype == torch.long
assert batch["loss_mask"].dtype == torch.bool
assert batch["position_ids"].dtype == torch.long
assert batch["document_ids"].dtype == torch.long
def test_position_ids_start_at_one(self):
batch = create_synthetic_batch(
total_seq_len=10,
hidden_size=16,
num_target_layers=1,
device="cpu",
)
assert batch["position_ids"][0, 0].item() == 1
assert batch["position_ids"][0, -1].item() == 10
def test_document_ids_all_zero(self):
batch = create_synthetic_batch(
total_seq_len=10,
hidden_size=16,
num_target_layers=1,
device="cpu",
)
assert (batch["document_ids"] == 0).all()
def test_all_keys_present(self):
batch = create_synthetic_batch(
total_seq_len=8,
hidden_size=16,
num_target_layers=1,
device="cpu",
)
expected_keys = {
"hidden_states",
"input_ids",
"verifier_last_hidden_states",
"loss_mask",
"position_ids",
"document_ids",
}
assert set(batch.keys()) == expected_keys
# ---------------------------------------------------------------------------
# _MetricCapture
# ---------------------------------------------------------------------------
class TestMetricCapture:
def test_captures_profile_dicts(self):
capture = _MetricCapture()
profile = {"step_ms": 45.0, "fwd_ms": 20.0}
record = logging.LogRecord(
name="speculators.metrics",
level=logging.INFO,
pathname="",
lineno=0,
msg={"train": {}, "profile": profile, "epoch": 0},
args=None,
exc_info=None,
)
capture.emit(record)
assert len(capture.profiles) == 1
assert capture.profiles[0] is profile
def test_ignores_records_without_profile(self):
capture = _MetricCapture()
record = logging.LogRecord(
name="speculators.metrics",
level=logging.INFO,
pathname="",
lineno=0,
msg={"train": {}, "epoch": 0},
args=None,
exc_info=None,
)
capture.emit(record)
assert len(capture.profiles) == 0
def test_ignores_none_profile(self):
capture = _MetricCapture()
record = logging.LogRecord(
name="speculators.metrics",
level=logging.INFO,
pathname="",
lineno=0,
msg={"train": {}, "profile": None, "epoch": 0},
args=None,
exc_info=None,
)
capture.emit(record)
assert len(capture.profiles) == 0
def test_ignores_non_dict_messages(self):
capture = _MetricCapture()
record = logging.LogRecord(
name="speculators.metrics",
level=logging.INFO,
pathname="",
lineno=0,
msg="some string message",
args=None,
exc_info=None,
)
capture.emit(record)
assert len(capture.profiles) == 0
def test_captures_multiple(self):
capture = _MetricCapture()
for i in range(5):
record = logging.LogRecord(
name="speculators.metrics",
level=logging.INFO,
pathname="",
lineno=0,
msg={"profile": {"step_ms": float(i)}, "train": {}},
args=None,
exc_info=None,
)
capture.emit(record)
assert len(capture.profiles) == 5
assert capture.profiles[3]["step_ms"] == 3.0
# ---------------------------------------------------------------------------
# _SyntheticLoader
# ---------------------------------------------------------------------------
class TestSyntheticLoader:
def test_len(self):
batch = {"x": torch.zeros(1)}
loader = _SyntheticLoader(batch, num_steps=7)
assert len(loader) == 7
def test_iter_yields_correct_count(self):
batch = {"x": torch.zeros(1)}
loader = _SyntheticLoader(batch, num_steps=3)
batches = list(loader)
assert len(batches) == 3
def test_iter_yields_same_batch(self):
batch = {"x": torch.tensor([1.0, 2.0])}
loader = _SyntheticLoader(batch, num_steps=3)
for b in loader:
assert b is batch
def test_batch_sampler_has_set_epoch(self):
batch = {"x": torch.zeros(1)}
loader = _SyntheticLoader(batch, num_steps=1)
assert hasattr(loader.batch_sampler, "set_epoch")
loader.batch_sampler.set_epoch(5)
# ---------------------------------------------------------------------------
# Warmup / measured split
# ---------------------------------------------------------------------------
class TestWarmupMeasuredSplit:
"""Tests for the profile slicing logic used in run_benchmark."""
def test_discard_warmup(self):
warmup_steps = 3
all_profiles = [{"step_ms": float(i)} for i in range(13)]
measured = select_measured_profiles(all_profiles, warmup_steps, 10)
assert len(measured) == 10
assert measured[0]["step_ms"] == 3.0
def test_exact_boundary(self):
warmup_steps = 5
all_profiles = [{"step_ms": float(i)} for i in range(5)]
with pytest.raises(RuntimeError, match="dataset exhausted"):
select_measured_profiles(all_profiles, warmup_steps, 1)
def test_zero_warmup(self):
warmup_steps = 0
all_profiles = [{"step_ms": float(i)} for i in range(10)]
measured = select_measured_profiles(all_profiles, warmup_steps, 10)
assert len(measured) == 10
assert measured[0]["step_ms"] == 0.0
def test_insufficient_profiles_raises(self):
all_profiles = [{"step_ms": float(i)} for i in range(5)]
with pytest.raises(RuntimeError, match="got 5, requested 15"):
select_measured_profiles(all_profiles, 10, 5)
def test_extra_profiles_are_not_measured(self):
all_profiles = [{"step_ms": float(i)} for i in range(20)]
measured = select_measured_profiles(all_profiles, 3, 5)
assert [profile["step_ms"] for profile in measured] == [3.0, 4.0, 5.0, 6.0, 7.0]
# ---------------------------------------------------------------------------
# compare_benchmarks
# ---------------------------------------------------------------------------
def _make_result(
step_ms_mean=45.0,
step_ms_std=1.0,
peak_alloc=2048.0,
git_sha="aaa",
gpu_name="H100",
speculator_type="eagle3",
):
"""Create a minimal benchmark result dict for testing."""
timing = {}
for key in (
"step_ms",
"fwd_ms",
"bwd_ms",
"opt_ms",
"fetch_ms",
"tokens_per_s",
):
timing[key] = {
"mean": step_ms_mean,
"std": step_ms_std,
"min": step_ms_mean - 2,
"max": step_ms_mean + 2,
"median": step_ms_mean,
"count": 50,
}
return {
"benchmark_version": "1.0",
"provenance": {
"git_sha": git_sha,
"gpu_info": [{"name": gpu_name, "total_memory_gb": 80.0}],
},
"config": {
"speculator_type": speculator_type,
"hidden_size": 4096,
"total_seq_len": 8192,
"num_gpus_used": 1,
"fsdp_shard": False,
"optimizer": "muon",
"hidden_states_dtype": "bfloat16",
},
"memory": {
"peak_allocated_mb": peak_alloc,
"peak_reserved_mb": peak_alloc + 1024,
},
"timing": timing,
}
class TestCompareBenchmarks:
def test_basic_compare(self, tmp_path, capsys):
baseline = _make_result(step_ms_mean=50.0, git_sha="aaa111")
candidate = _make_result(step_ms_mean=45.0, git_sha="bbb222")
baseline["aggregate"] = {"effective_rank0_tokens_per_s": 1000.0}
candidate["aggregate"] = {"effective_rank0_tokens_per_s": 1200.0}
baseline_path = tmp_path / "baseline.json"
candidate_path = tmp_path / "candidate.json"
baseline_path.write_text(json.dumps(baseline))
candidate_path.write_text(json.dumps(candidate))
compare_benchmarks(str(baseline_path), str(candidate_path))
output = capsys.readouterr().out
assert "aaa111" in output
assert "bbb222" in output
assert "step_ms" in output
assert "-5.00" in output or "-10.0%" in output
assert "1000.00 -> 1200.00" in output
def test_comparability_warning_gpu(self, tmp_path, capsys):
baseline = _make_result(gpu_name="H100")
candidate = _make_result(gpu_name="A100")
baseline_path = tmp_path / "b.json"
candidate_path = tmp_path / "c.json"
baseline_path.write_text(json.dumps(baseline))
candidate_path.write_text(json.dumps(candidate))
compare_benchmarks(str(baseline_path), str(candidate_path))
output = capsys.readouterr().out
assert "GPU" in output
assert "H100" in output
assert "A100" in output
def test_comparability_warning_config(self, tmp_path, capsys):
baseline = _make_result(speculator_type="eagle3")
candidate = _make_result(speculator_type="dflash")
baseline_path = tmp_path / "b.json"
candidate_path = tmp_path / "c.json"
baseline_path.write_text(json.dumps(baseline))
candidate_path.write_text(json.dumps(candidate))
compare_benchmarks(str(baseline_path), str(candidate_path))
output = capsys.readouterr().out
assert "Speculator type" in output
def test_memory_delta(self, tmp_path, capsys):
baseline = _make_result(peak_alloc=2000.0)
candidate = _make_result(peak_alloc=1800.0)
baseline_path = tmp_path / "b.json"
candidate_path = tmp_path / "c.json"
baseline_path.write_text(json.dumps(baseline))
candidate_path.write_text(json.dumps(candidate))
compare_benchmarks(str(baseline_path), str(candidate_path))
output = capsys.readouterr().out
assert "peak_allocated_mb" in output
assert "-200.0" in output
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