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import sys
import unittest
import os
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
WORKSPACE = ROOT.parent
os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
os.environ.setdefault("XDG_CACHE_HOME", "/tmp")
sys.path.insert(0, str(ROOT))
sys.path.insert(0, str(WORKSPACE / "pp-scheduler"))
from pp_scheduler import ( # noqa: E402
BigMac,
BigMacVPP,
DistTrainEncoder1F1B,
DistTrain1F1B,
GPipe,
OpType,
UnifiedBigMacVPP,
VPP1F1BSchedule,
)
from pp_simulator import OpDurationSpec, PipelineSimulator # noqa: E402
from web.app import run_comparison # noqa: E402
class PipelineSimulatorTest(unittest.TestCase):
def test_gpipe_fixed_duration_result_is_valid(self):
scheduler = GPipe(pp_size=2, num_microbatches=4)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(
{
OpType.FORWARD: OpDurationSpec(mean=1.0),
OpType.BACKWARD: OpDurationSpec(mean=2.0),
},
seed=1,
)
simulator.validate_result(result)
self.assertGreater(result.summary["makespan"], 0.0)
self.assertEqual(result.metadata["op_count"], 16)
def test_vpp_dependencies_are_valid(self):
scheduler = VPP1F1BSchedule(pp_size=2, vpp_size=2, num_microbatches=4)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(
{
"F": OpDurationSpec(mean=1.0, variance=0.01),
"B": OpDurationSpec(mean=2.0, variance=0.01),
},
seed=2,
)
simulator.validate_result(result)
self.assertEqual(result.metadata["vpp_size"], 2)
def test_chrome_trace_export(self):
scheduler = GPipe(pp_size=2, num_microbatches=2)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(
{
OpType.FORWARD: OpDurationSpec(mean=1.0),
OpType.BACKWARD: OpDurationSpec(mean=2.0),
}
)
trace = result.to_chrome_trace_dict()
complete_events = [event for event in trace["traceEvents"] if event["ph"] == "X"]
thread_name_events = [
event
for event in trace["traceEvents"]
if event["ph"] == "M" and event["name"] == "thread_name"
]
self.assertEqual(len(complete_events), result.metadata["op_count"])
self.assertEqual(len(thread_name_events), 2)
self.assertIn("summary", trace)
self.assertEqual(complete_events[0]["pid"], 1)
self.assertIn(complete_events[0]["tid"], {1, 2})
self.assertIn("batch_id", complete_events[0]["args"])
self.assertIn("dep_reasons", complete_events[0]["args"])
def test_chrome_trace_keeps_pp4_vpp2_f3_c0_and_chunk_categories(self):
scheduler = UnifiedBigMacVPP(pp_size=4, vpp_size=2, num_microbatches=8, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(_default_specs())
trace = result.to_chrome_trace_dict()
complete_events = [event for event in trace["traceEvents"] if event["ph"] == "X"]
f3_c0_events = [event for event in complete_events if event["name"] == "F3-C0"]
self.assertEqual(len(f3_c0_events), 4)
self.assertEqual({event["args"]["pp_rank"] for event in f3_c0_events}, {0, 1, 2, 3})
f0_c0 = next(event for event in complete_events if event["name"] == "F0-C0")
f0_c1 = next(event for event in complete_events if event["name"] == "F0-C1")
self.assertEqual(f0_c0["cat"], "F/chunk_a")
self.assertEqual(f0_c1["cat"], "F/chunk_b")
self.assertNotEqual(f0_c0["cat"], f0_c1["cat"])
def test_perfetto_compat_trace_uses_unique_event_names(self):
scheduler = UnifiedBigMacVPP(pp_size=4, vpp_size=2, num_microbatches=8, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(_default_specs())
trace = result.to_chrome_trace_dict(perfetto_compat=True)
complete_events = [event for event in trace["traceEvents"] if event["ph"] == "X"]
f3_c0_events = [
event
for event in complete_events
if event["args"]["label"] == "F3-C0"
]
self.assertEqual(len(f3_c0_events), 4)
self.assertEqual(
{event["name"] for event in f3_c0_events},
{
"rank0/F/chunk_a/mb3",
"rank1/F/chunk_a/mb3",
"rank2/F/chunk_a/mb3",
"rank3/F/chunk_a/mb3",
},
)
def test_monte_carlo_report_statistics(self):
scheduler = GPipe(pp_size=2, num_microbatches=2)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
report = simulator.monte_carlo(
{
OpType.FORWARD: OpDurationSpec(mean=1.0, variance=0.01),
OpType.BACKWARD: OpDurationSpec(mean=2.0, variance=0.01),
},
num_trials=5,
seed=11,
validate=True,
)
self.assertEqual(report.metadata["num_trials"], 5)
self.assertEqual(len(report.trial_summaries), 5)
self.assertEqual(report.statistics["makespan"]["count"], 5)
self.assertIn("0", report.statistics["rank_utilization"])
self.assertIn("F", report.statistics["op_type_time"])
def test_monte_carlo_fixed_duration_has_zero_makespan_std(self):
scheduler = GPipe(pp_size=2, num_microbatches=2)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
report = simulator.monte_carlo(
{
OpType.FORWARD: OpDurationSpec(mean=1.0),
OpType.BACKWARD: OpDurationSpec(mean=2.0),
},
num_trials=3,
seed=3,
)
self.assertEqual(report.statistics["makespan"]["std"], 0.0)
self.assertEqual(report.statistics["makespan"]["min"], report.statistics["makespan"]["max"])
def test_monte_carlo_rejects_non_positive_trial_count(self):
scheduler = GPipe(pp_size=2, num_microbatches=2)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
with self.assertRaises(ValueError):
simulator.monte_carlo(
{
OpType.FORWARD: OpDurationSpec(mean=1.0),
OpType.BACKWARD: OpDurationSpec(mean=2.0),
},
num_trials=0,
)
def test_bigmac_llm_forward_waits_for_ve_unit(self):
scheduler = BigMac(pp_size=2, num_microbatches=4, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
target = _find_op(simulator, rank=0, op_type="F", microbatch_id=0, chunk_id=None)
vf0 = _find_op(simulator, rank=0, op_type="VF", microbatch_id=0, chunk_id=None)
vf1 = _find_op(simulator, rank=1, op_type="VF", microbatch_id=1, chunk_id=None)
reasons = simulator.graph.dependency_reasons[target.id]
self.assertIn("ve_forward_ready", reasons[vf0.id])
self.assertNotIn(vf1.id, reasons)
def test_bigmac_llm_forward_waits_only_for_matching_ve_forward(self):
scheduler = BigMac(
pp_size=4,
num_microbatches=16,
ve_forward_limit=3,
check_ve_forward_limit=False,
)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
f12 = _find_op(simulator, rank=0, op_type="F", microbatch_id=12, chunk_id=None)
vf12 = _find_op(simulator, rank=0, op_type="VF", microbatch_id=12, chunk_id=None)
peer_vfs = [
_find_op(simulator, rank=rank, op_type="VF", microbatch_id=12 + rank, chunk_id=None)
for rank in range(1, 4)
]
reasons = simulator.graph.dependency_reasons[f12.id]
self.assertIn("ve_forward_ready", reasons[vf12.id])
for peer_vf in peer_vfs:
self.assertNotIn(peer_vf.id, reasons)
def test_bigmac_low_ve_forward_limit_exposes_bubbles(self):
scheduler = BigMac(
pp_size=4,
num_microbatches=16,
ve_forward_limit=2,
check_ve_forward_limit=False,
)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(_default_specs(), seed=23)
simulator.validate_result(result)
idle_counts = {
rank: sum(1 for op in ops if op.op_type == OpType.IDLE)
for rank, ops in scheduler.schedules.items()
}
self.assertGreater(sum(idle_counts.values()), 0)
def test_bigmac_low_ve_forward_limit_interleaves_ve_backward_before_next_ve_forward(self):
scheduler = BigMac(
pp_size=4,
num_microbatches=16,
ve_forward_limit=3,
check_ve_forward_limit=False,
)
scheduler.generate_schedule()
rank0_ops = [
str(op)
for op in scheduler.schedules[0]
if op.op_type != OpType.IDLE
]
self.assertLess(rank0_ops.index("B8"), rank0_ops.index("VB0"))
self.assertEqual(rank0_ops[rank0_ops.index("VB0") + 1], "VF12")
self.assertLess(rank0_ops.index("VF12"), rank0_ops.index("B9"))
def test_bigmac_vpp_chunk_wrap_dependencies(self):
scheduler = BigMacVPP(pp_size=2, vpp_size=2, num_microbatches=4, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
f_rank0_chunk1 = _find_op(simulator, rank=0, op_type="F", microbatch_id=0, chunk_id=1)
f_rank1_chunk0 = _find_op(simulator, rank=1, op_type="F", microbatch_id=0, chunk_id=0)
b_rank1_chunk0 = _find_op(simulator, rank=1, op_type="B", microbatch_id=0, chunk_id=0)
b_rank0_chunk1 = _find_op(simulator, rank=0, op_type="B", microbatch_id=0, chunk_id=1)
reasons = simulator.graph.dependency_reasons
self.assertIn("vpp_forward_from_previous_chunk", reasons[f_rank0_chunk1.id][f_rank1_chunk0.id])
self.assertIn("vpp_backward_from_next_chunk", reasons[b_rank1_chunk0.id][b_rank0_chunk1.id])
def test_unified_generator_dependencies_use_matching_microbatch(self):
scheduler = UnifiedBigMacVPP(pp_size=2, vpp_size=2, num_microbatches=4, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
gf0 = _find_op(simulator, rank=0, op_type="GF", microbatch_id=0, chunk_id=1)
gb0 = _find_op(simulator, rank=0, op_type="GB", microbatch_id=0, chunk_id=0)
b_last = _find_op(simulator, rank=1, op_type="B", microbatch_id=0, chunk_id=1)
final_f0 = _find_op(simulator, rank=1, op_type="F", microbatch_id=0, chunk_id=1)
final_f1 = _find_op(simulator, rank=1, op_type="F", microbatch_id=1, chunk_id=1)
gb1 = _find_op(simulator, rank=1, op_type="GB", microbatch_id=1, chunk_id=0)
reasons = simulator.graph.dependency_reasons
self.assertIn("llm_output_ready", reasons[gf0.id][final_f0.id])
self.assertNotIn(final_f1.id, reasons[gf0.id])
self.assertIn("generator_backward_consumes_forward", reasons[gb0.id][gf0.id])
self.assertNotIn(gb1.id, reasons[gb0.id])
self.assertIn("generator_backward_ready", reasons[b_last.id][gb0.id])
self.assertNotIn(gb1.id, reasons[b_last.id])
def test_unified_generator_backward_does_not_wait_for_peer_generator_forward(self):
scheduler = UnifiedBigMacVPP(pp_size=4, vpp_size=2, num_microbatches=8, ve_forward_limit=3)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
gb4 = _find_op(simulator, rank=0, op_type="GB", microbatch_id=4, chunk_id=0)
gf4 = _find_op(simulator, rank=0, op_type="GF", microbatch_id=4, chunk_id=1)
peer_gfs = [
_find_op(simulator, rank=rank, op_type="GF", microbatch_id=4 + rank, chunk_id=1)
for rank in range(1, 4)
]
reasons = simulator.graph.dependency_reasons[gb4.id]
self.assertIn("generator_backward_consumes_forward", reasons[gf4.id])
for peer_gf in peer_gfs:
self.assertNotIn(peer_gf.id, reasons)
def test_bigmac_ve_backward_waits_only_for_matching_llm_backward(self):
scheduler = BigMac(
pp_size=4,
num_microbatches=32,
ve_forward_limit=3,
check_ve_forward_limit=False,
)
scheduler.generate_schedule()
simulator = PipelineSimulator.from_scheduler(scheduler)
vb30 = _find_op(simulator, rank=2, op_type="VB", microbatch_id=30, chunk_id=None)
b30 = _find_op(simulator, rank=0, op_type="B", microbatch_id=30, chunk_id=None)
peer_bs = [
_find_op(simulator, rank=0, op_type="B", microbatch_id=mb, chunk_id=None)
for mb in (28, 29, 31)
]
reasons = simulator.graph.dependency_reasons[vb30.id]
self.assertIn("llm_input_gradient_ready", reasons[b30.id])
for peer_b in peer_bs:
self.assertNotIn(peer_b.id, reasons)
def test_disttrain_1f1b_layout_and_dependencies(self):
scheduler = DistTrain1F1B(pp_size=4, num_microbatches=8)
scheduler.generate_schedule()
self.assertEqual(scheduler.pp_size, 6)
self.assertEqual(scheduler.llm_pp_size, 4)
self.assertEqual(scheduler.encoder_stage, 0)
self.assertEqual(scheduler.generator_stage, 5)
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(_default_specs(), seed=5)
simulator.validate_result(result)
vf0 = _find_op(simulator, rank=0, op_type="VF", microbatch_id=0, chunk_id=None)
f0_llm0 = _find_op(simulator, rank=1, op_type="F", microbatch_id=0, chunk_id=None)
f0_llm_last = _find_op(simulator, rank=4, op_type="F", microbatch_id=0, chunk_id=None)
gf0 = _find_op(simulator, rank=5, op_type="GF", microbatch_id=0, chunk_id=None)
gb0 = _find_op(simulator, rank=5, op_type="GB", microbatch_id=0, chunk_id=None)
b0_llm_last = _find_op(simulator, rank=4, op_type="B", microbatch_id=0, chunk_id=None)
b0_llm0 = _find_op(simulator, rank=1, op_type="B", microbatch_id=0, chunk_id=None)
vb0 = _find_op(simulator, rank=0, op_type="VB", microbatch_id=0, chunk_id=None)
reasons = simulator.graph.dependency_reasons
self.assertIn("encoder_forward_ready", reasons[f0_llm0.id][vf0.id])
self.assertIn("llm_output_ready", reasons[gf0.id][f0_llm_last.id])
self.assertIn("generator_backward_consumes_forward", reasons[gb0.id][gf0.id])
self.assertIn("generator_backward_ready", reasons[b0_llm_last.id][gb0.id])
self.assertIn("llm_input_gradient_ready", reasons[vb0.id][b0_llm0.id])
gf1 = _find_op(simulator, rank=5, op_type="GF", microbatch_id=1, chunk_id=None)
self.assertNotIn(gf1.id, reasons[gb0.id])
def test_disttrain_1f1b_activation_limits(self):
scheduler = DistTrain1F1B(pp_size=4, num_microbatches=8)
scheduler.generate_schedule()
forward_ops = {OpType.VEFORWARD, OpType.FORWARD, OpType.GENFORWARD}
backward_ops = {OpType.VEBACKWARD, OpType.BACKWARD, OpType.GENBACKWARD}
peaks = {}
for rank, ops in scheduler.schedules.items():
live = 0
peak = 0
for op in ops:
if op.op_type in forward_ops:
live += 1
peak = max(peak, live)
elif op.op_type in backward_ops:
live -= 1
peaks[rank] = peak
self.assertLessEqual(peak, scheduler.memory_limits[rank])
self.assertEqual(live, 0)
self.assertEqual(peaks[5], 1)
self.assertEqual(peaks[4], 2)
def test_disttrain_uses_uniform_module_durations(self):
scheduler = DistTrain1F1B(pp_size=4, num_microbatches=8)
scheduler.generate_schedule()
forward_ops = {OpType.VEFORWARD, OpType.FORWARD, OpType.GENFORWARD}
backward_ops = {OpType.VEBACKWARD, OpType.BACKWARD, OpType.GENBACKWARD}
for ops in scheduler.schedules.values():
for op in ops:
if op.op_type in forward_ops:
self.assertEqual(op.duration, scheduler.forward_duration)
elif op.op_type in backward_ops:
self.assertEqual(op.duration, scheduler.backward_duration)
rank4_ops = [
(op.op_type, op.microbatch_id)
for op in scheduler.schedules[4]
if op.op_type is not OpType.IDLE
]
self.assertLess(
rank4_ops.index((OpType.BACKWARD, 0)),
rank4_ops.index((OpType.FORWARD, 2)),
)
def test_disttrain_encoder_1f1b_layout_and_dependencies(self):
scheduler = DistTrainEncoder1F1B(pp_size=4, num_microbatches=8)
scheduler.generate_schedule()
self.assertEqual(scheduler.pp_size, 5)
self.assertEqual(scheduler.llm_pp_size, 4)
self.assertEqual(scheduler.encoder_stage, 0)
self.assertIsNone(scheduler.generator_stage)
self.assertFalse(scheduler.pipeline_layout["has_generator"])
all_op_types = {op.op_type for ops in scheduler.schedules.values() for op in ops}
self.assertNotIn(OpType.GENFORWARD, all_op_types)
self.assertNotIn(OpType.GENBACKWARD, all_op_types)
simulator = PipelineSimulator.from_scheduler(scheduler)
result = simulator.simulate(_default_specs(), seed=11)
simulator.validate_result(result)
vf0 = _find_op(simulator, rank=0, op_type="VF", microbatch_id=0, chunk_id=None)
f0_llm0 = _find_op(simulator, rank=1, op_type="F", microbatch_id=0, chunk_id=None)
f0_llm_last = _find_op(simulator, rank=4, op_type="F", microbatch_id=0, chunk_id=None)
b0_llm_last = _find_op(simulator, rank=4, op_type="B", microbatch_id=0, chunk_id=None)
b0_llm0 = _find_op(simulator, rank=1, op_type="B", microbatch_id=0, chunk_id=None)
vb0 = _find_op(simulator, rank=0, op_type="VB", microbatch_id=0, chunk_id=None)
reasons = simulator.graph.dependency_reasons
self.assertIn("encoder_forward_ready", reasons[f0_llm0.id][vf0.id])
self.assertIn("backward_consumes_forward_activation", reasons[b0_llm_last.id][f0_llm_last.id])
self.assertNotIn("generator_backward_ready", {
reason
for dep_reasons in reasons[b0_llm_last.id].values()
for reason in dep_reasons
})
self.assertIn("llm_input_gradient_ready", reasons[vb0.id][b0_llm0.id])
def test_compare_uses_shared_duration_per_rank_op_microbatch_chunk(self):
payload = {
"scheduler_a": "GPipe",
"scheduler_b": "Schedule1F1B",
"workload": {
"pp_size": 2,
"vpp_size": 1,
"num_microbatches": 4,
"ve_forward_limit": 3,
"include_encoder": False,
"include_generator": False,
},
"duration_specs": {
"F": {"mean": 1.0, "variance": 0.1},
"B": {"mean": 2.0, "variance": 0.1},
},
"seed": 17,
}
response = run_comparison(payload)
result_a = response["result_a"]
result_b = response["result_b"]
for rank in (0, 1):
a_op = _find_result_op(result_a, rank=rank, op_type="F", microbatch_id=0, chunk_id=None)
b_op = _find_result_op(result_b, rank=rank, op_type="F", microbatch_id=0, chunk_id=None)
self.assertEqual(a_op["duration"], b_op["duration"])
rank0 = _find_result_op(result_a, rank=0, op_type="F", microbatch_id=0, chunk_id=None)
rank1 = _find_result_op(result_a, rank=1, op_type="F", microbatch_id=0, chunk_id=None)
self.assertNotEqual(rank0["duration"], rank1["duration"])
def test_compare_rejects_incompatible_scheduler_shape(self):
payload = {
"scheduler_a": "GPipe",
"scheduler_b": "BigMac",
"workload": {
"pp_size": 2,
"vpp_size": 1,
"num_microbatches": 4,
"ve_forward_limit": 3,
"include_encoder": False,
"include_generator": False,
},
"duration_specs": _default_web_specs(),
"seed": 17,
}
with self.assertRaises(ValueError):
run_comparison(payload)
def test_compare_can_allow_vpp_and_non_vpp_with_llm_duration_scale(self):
payload = {
"scheduler_a": "GPipe",
"scheduler_b": "VPP1F1BSchedule",
"workload": {
"pp_size": 2,
"vpp_size": 2,
"num_microbatches": 4,
"ve_forward_limit": 3,
"include_encoder": False,
"include_generator": False,
},
"duration_specs": _default_web_specs(),
"seed": 17,
}
response = run_comparison(payload)
self.assertTrue(response["comparison"]["workload"]["allow_cross_vpp"])
self.assertEqual(response["result_b"]["metadata"]["vpp_size"], 2)
non_vpp_f = _find_result_op(
response["result_a"],
rank=0,
op_type="F",
microbatch_id=0,
chunk_id=None,
)
vpp_f_c0 = _find_result_op(
response["result_b"],
rank=0,
op_type="F",
microbatch_id=0,
chunk_id=0,
)
vpp_f_c1 = _find_result_op(
response["result_b"],
rank=0,
op_type="F",
microbatch_id=0,
chunk_id=1,
)
self.assertEqual(vpp_f_c0["duration"], non_vpp_f["duration"] / 2)
self.assertEqual(vpp_f_c1["duration"], non_vpp_f["duration"] / 2)
def test_compare_adjusts_disttrain_stage_budget_and_llm_duration_scale(self):
payload = {
"scheduler_a": "BigMac",
"scheduler_b": "DistTrainEncoder1F1B",
"workload": {
"pp_size": 4,
"vpp_size": 1,
"num_microbatches": 8,
"ve_forward_limit": 3,
"include_encoder": True,
"include_generator": False,
},
"duration_specs": _default_web_specs(),
"seed": 17,
}
response = run_comparison(payload)
disttrain = response["result_b"]
self.assertEqual(disttrain["metadata"]["pp_size"], 4)
self.assertEqual(disttrain["metadata"]["pipeline_layout"]["llm_pp_size"], 3)
bigmac_f = _find_result_op(
response["result_a"],
rank=1,
op_type="F",
microbatch_id=0,
chunk_id=None,
)
disttrain_f = _find_result_op(
disttrain,
rank=1,
op_type="F",
microbatch_id=0,
chunk_id=None,
)
self.assertAlmostEqual(disttrain_f["duration"], bigmac_f["duration"] * 4 / 3)
def _find_op(simulator, *, rank, op_type, microbatch_id, chunk_id):
for op in simulator.plan.ops_by_rank[rank]:
if (
op.op_type == op_type
and op.microbatch_id == microbatch_id
and op.chunk_id == chunk_id
):
return op
raise AssertionError(
f"missing op rank={rank} op_type={op_type} "
f"microbatch_id={microbatch_id} chunk_id={chunk_id}"
)
def _find_result_op(result, *, rank, op_type, microbatch_id, chunk_id):
for op in result["ops"]:
if (
op["rank"] == rank
and op["op_type"] == op_type
and op["microbatch_id"] == microbatch_id
and op["chunk_id"] == chunk_id
):
return op
raise AssertionError(
f"missing result op rank={rank} op_type={op_type} "
f"microbatch_id={microbatch_id} chunk_id={chunk_id}"
)
def _default_specs():
return {
"F": OpDurationSpec(mean=1.0),
"B": OpDurationSpec(mean=2.0),
"VF": OpDurationSpec(mean=0.8),
"VB": OpDurationSpec(mean=0.9),
"GF": OpDurationSpec(mean=0.6),
"GB": OpDurationSpec(mean=0.7),
}
def _default_web_specs():
return {
"F": {"mean": 1.0, "variance": 0.0},
"B": {"mean": 2.0, "variance": 0.0},
"VF": {"mean": 0.8, "variance": 0.0},
"VB": {"mean": 0.9, "variance": 0.0},
"GF": {"mean": 0.6, "variance": 0.0},
"GB": {"mean": 0.7, "variance": 0.0},
}
if __name__ == "__main__":
unittest.main()
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