mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-07-21 00:23:30 -05:00
Vijay Janapa Reddi
ff8c5c0f83
- Added 'make docs', 'make docs-preview', and 'make audit' targets to Makefile for easier local development. - Added comprehensive README.md to the vscode-ext workbench extension. - Refactored test_engine.py to dynamically import calibration constants rather than hardcoding physics assumptions, ensuring tests don't break if base parameters are tuned. - Fixed a registry path alias in philosophy.qmd caught by the doc drift linter.
70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
# tests/test_engine.py
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# Engine-level tests — covers the core Engine.solve() API.
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#
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# Note: Comprehensive solver tests are in test_solver_suite.py (TestSingleNodeModel).
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# This file tests Engine-specific behavior not covered there.
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import pytest
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from mlsysim.core.engine import Engine
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from mlsysim.hardware.registry import Hardware
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from mlsysim.models.registry import Models
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from mlsysim.core import calibration as cal
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def test_engine_energy_proportional():
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"""Engine energy uses the energy-proportional model: P = TDP * (idle_fraction + dynamic_fraction * MFU).
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For memory-bound workloads, MFU can reach 1.0 (the clamped ceiling) because
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achieved_flops/peak_flops exceeds 1 when latency is dominated by memory time.
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We verify the model applies correctly by checking energy > 0 and that the
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energy-proportional formula is consistent.
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"""
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resnet = Models.Vision.ResNet50
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a100 = Hardware.Cloud.A100
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perf = Engine.solve(resnet, a100, batch_size=1)
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# Energy should always be positive
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assert perf.energy.to("J").magnitude > 0
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# Energy = TDP * (idle_fraction + dynamic_fraction * MFU) * latency
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expected = (a100.tdp * (cal.ENERGY_IDLE_FRACTION + cal.ENERGY_DYNAMIC_FRACTION * perf.mfu) * perf.latency.to("s")).to("J").magnitude
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assert perf.energy.to("J").magnitude == pytest.approx(expected, rel=0.01)
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def test_engine_energy_per_inference_property():
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"""PerformanceProfile should expose energy_per_inference."""
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resnet = Models.Vision.ResNet50
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a100 = Hardware.Cloud.A100
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perf = Engine.solve(resnet, a100, batch_size=1)
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assert hasattr(perf, "energy_per_inference")
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assert perf.energy_per_inference.magnitude > 0
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def test_engine_input_validation():
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"""Engine should reject invalid inputs with clear errors."""
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resnet = Models.Vision.ResNet50
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a100 = Hardware.Cloud.A100
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with pytest.raises(ValueError, match="efficiency"):
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Engine.solve(resnet, a100, batch_size=1, efficiency=50.0)
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with pytest.raises(ValueError, match="efficiency"):
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Engine.solve(resnet, a100, batch_size=1, efficiency=-0.1)
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with pytest.raises(ValueError, match="batch_size"):
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Engine.solve(resnet, a100, batch_size=0)
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def test_engine_handles_model_size_only_workloads():
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"""Registry workloads with model_size but no parameter count should still lower."""
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perf = Engine.solve(Models.Recommendation.DLRM, Hardware.Cloud.H200, batch_size=1)
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assert perf.feasible is True
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assert perf.memory_footprint.to("GB").magnitude > 0
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def test_nvl72_fp16_does_not_use_fp8_peak_silently():
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"""GB200 NVL72 exposes FP8/FP4 peaks, but FP16 should not alias to FP8."""
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perf_fp16 = Engine.solve(Models.Vision.ResNet50, Hardware.Cloud.GB200_NVL72, precision="fp16")
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perf_fp8 = Engine.solve(Models.Vision.ResNet50, Hardware.Cloud.GB200_NVL72, precision="fp8")
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assert perf_fp16.peak_flops_actual < perf_fp8.peak_flops_actual
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