mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-07-21 17:57:24 -05:00
Vijay Janapa Reddi
02c37be63a
Move the simulator out of core/ into a new engine/ package: solver, engine,
evaluation, results, pipeline, dse, walls, resolver_factory, explainers,
config, calibration, and the evaluatable scenarios bundle. core/ now holds
only primitives (units, types, provenance, registry, validation, exceptions).
No core primitive imports an engine module; the dependency runs one way.
Also relocate the appendix_lineage audit gate (a used lineage auditor, NOT
vestigial — kept, not deleted) from core/ to tools/, beside its consumer
audit_provenance.py.
Consumers repointed mlsysim.core.<mod> -> mlsysim.engine.<mod> (and the
from-core-import-calibration form): package internals (cli, sim, solvers,
viz), tests, runnable examples, paper scripts, and 9 book chapters. Pure
reorg, zero functional change.
482 passed; import clean (no cycles); vol1 (engine.calibration) and vol2
(engine.solver) chapters render with 0 {python} leaks, 0 tracebacks.
Deferred to P10: mlsysim/docs prose, generated docs/api/* stubs, tutorial
slides, paper.tex, README — propagate to website/slides/paper.
70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
# tests/test_engine.py
|
|
# Engine-level tests — covers the core Engine.solve() API.
|
|
#
|
|
# Note: Comprehensive solver tests are in test_solver_suite.py (TestSingleNodeModel).
|
|
# This file tests Engine-specific behavior not covered there.
|
|
|
|
import pytest
|
|
from mlsysim.engine.engine import Engine
|
|
from mlsysim.hardware.registry import Hardware
|
|
from mlsysim.models.registry import Models
|
|
from mlsysim.engine import calibration as cal
|
|
|
|
|
|
def test_engine_energy_proportional():
|
|
"""Engine energy uses the energy-proportional model: P = TDP * (idle_fraction + dynamic_fraction * MFU).
|
|
|
|
For memory-bound workloads, MFU can reach 1.0 (the clamped ceiling) because
|
|
achieved_flops/peak_flops exceeds 1 when latency is dominated by memory time.
|
|
We verify the model applies correctly by checking energy > 0 and that the
|
|
energy-proportional formula is consistent.
|
|
"""
|
|
resnet = Models.Vision.ResNet50
|
|
a100 = Hardware.Cloud.A100
|
|
|
|
perf = Engine.solve(resnet, a100, batch_size=1)
|
|
|
|
# Energy should always be positive
|
|
assert perf.energy.to("J").magnitude > 0
|
|
# Energy = TDP * (idle_fraction + dynamic_fraction * MFU) * latency
|
|
expected = (a100.tdp * (cal.ENERGY_IDLE_FRACTION + cal.ENERGY_DYNAMIC_FRACTION * perf.mfu) * perf.latency.to("s")).to("J").magnitude
|
|
assert perf.energy.to("J").magnitude == pytest.approx(expected, rel=0.01)
|
|
|
|
|
|
def test_engine_energy_per_inference_property():
|
|
"""PerformanceProfile should expose energy_per_inference."""
|
|
resnet = Models.Vision.ResNet50
|
|
a100 = Hardware.Cloud.A100
|
|
perf = Engine.solve(resnet, a100, batch_size=1)
|
|
assert hasattr(perf, "energy_per_inference")
|
|
assert perf.energy_per_inference.magnitude > 0
|
|
|
|
|
|
def test_engine_input_validation():
|
|
"""Engine should reject invalid inputs with clear errors."""
|
|
resnet = Models.Vision.ResNet50
|
|
a100 = Hardware.Cloud.A100
|
|
|
|
with pytest.raises(ValueError, match="efficiency"):
|
|
Engine.solve(resnet, a100, batch_size=1, efficiency=50.0)
|
|
|
|
with pytest.raises(ValueError, match="efficiency"):
|
|
Engine.solve(resnet, a100, batch_size=1, efficiency=-0.1)
|
|
|
|
with pytest.raises(ValueError, match="batch_size"):
|
|
Engine.solve(resnet, a100, batch_size=0)
|
|
|
|
|
|
def test_engine_handles_model_size_only_workloads():
|
|
"""Registry workloads with model_size but no parameter count should still lower."""
|
|
perf = Engine.solve(Models.Recommendation.DLRM, Hardware.Cloud.H200, batch_size=1)
|
|
assert perf.feasible is True
|
|
assert perf.memory_footprint.to("GB").magnitude > 0
|
|
|
|
|
|
def test_nvl72_fp16_does_not_use_fp8_peak_silently():
|
|
"""GB200 NVL72 exposes FP8/FP4 peaks, but FP16 should not alias to FP8."""
|
|
perf_fp16 = Engine.solve(Models.Vision.ResNet50, Hardware.Cloud.GB200_NVL72, precision="fp16")
|
|
perf_fp8 = Engine.solve(Models.Vision.ResNet50, Hardware.Cloud.GB200_NVL72, precision="fp8")
|
|
assert perf_fp16.peak_flops_actual < perf_fp8.peak_flops_actual
|