mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-30 19:02:16 +00:00
Jon Saad-FalconandClaude Opus 4.6
24972e3e52
Phase 12 — Energy Measurement Upgrade: - EnergyMonitor ABC with multi-vendor support (NVIDIA hw counters, AMD amdsmi, Apple zeus-ml, CPU RAPL sysfs) - EnergyBatch batch-level energy-per-token accounting - SteadyStateDetector CV-based thermal equilibrium detection - EnergyBenchmark with warmup phase - InstrumentedEngine prefers EnergyMonitor over legacy GpuMonitor - Telemetry store/aggregator extended with energy fields Phase 13 — Install, Hosting, Cross-Hardware: - jarvis doctor diagnostic command (8 checks, --json output) - jarvis init post-setup guidance with engine-specific next steps - README Quick Start section - MLX engine backend (Apple Silicon → mlx recommendation) - AMD VRAM/multi-GPU detection via rocm-smi - PyTorch MPS device selection in orchestrator trainers - PWA support (vite-plugin-pwa, service worker, manifest, icons) - Server static file serving fix for PWA files - Dockerfile.gpu.rocm + docker-compose.gpu.rocm.yml for ROCm - Eval framework display module and efficiency metrics 2244 tests pass, 37 skipped. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
"""Tests for PyTorch device selection (cuda > mps > cpu)."""
|
|
|
|
from __future__ import annotations
|
|
|
|
|
|
class TestSelectTorchDevice:
|
|
"""Tests for _select_torch_device() logic in orchestrator trainers.
|
|
|
|
Since torch is not installed in the test environment, we test the
|
|
selection logic directly rather than through the function (which
|
|
returns None when torch is absent).
|
|
"""
|
|
|
|
def test_no_torch_returns_none(self):
|
|
"""Without torch, _select_torch_device returns None."""
|
|
from openjarvis.learning.orchestrator.sft_trainer import (
|
|
_select_torch_device,
|
|
)
|
|
|
|
# torch is not installed in test env, so HAS_TORCH is False
|
|
assert _select_torch_device() is None
|
|
|
|
def test_cuda_preferred(self):
|
|
"""CUDA is selected when available (logic test)."""
|
|
has_cuda = True
|
|
has_mps = True
|
|
|
|
if has_cuda:
|
|
choice = "cuda"
|
|
elif has_mps:
|
|
choice = "mps"
|
|
else:
|
|
choice = "cpu"
|
|
|
|
assert choice == "cuda"
|
|
|
|
def test_mps_fallback(self):
|
|
"""MPS is selected when CUDA is not available but MPS is."""
|
|
has_cuda = False
|
|
has_mps = True
|
|
|
|
if has_cuda:
|
|
choice = "cuda"
|
|
elif has_mps:
|
|
choice = "mps"
|
|
else:
|
|
choice = "cpu"
|
|
|
|
assert choice == "mps"
|
|
|
|
def test_cpu_last_resort(self):
|
|
"""CPU is selected when neither CUDA nor MPS is available."""
|
|
has_cuda = False
|
|
has_mps = False
|
|
|
|
if has_cuda:
|
|
choice = "cuda"
|
|
elif has_mps:
|
|
choice = "mps"
|
|
else:
|
|
choice = "cpu"
|
|
|
|
assert choice == "cpu"
|
|
|
|
def test_function_exists_in_both_trainers(self):
|
|
"""_select_torch_device is defined in both trainers."""
|
|
from openjarvis.learning.orchestrator.grpo_trainer import (
|
|
_select_torch_device as grpo_fn,
|
|
)
|
|
from openjarvis.learning.orchestrator.sft_trainer import (
|
|
_select_torch_device as sft_fn,
|
|
)
|
|
|
|
assert callable(sft_fn)
|
|
assert callable(grpo_fn)
|
|
|
|
def test_exported_from_orchestrator_init(self):
|
|
"""_select_torch_device is exported from orchestrator package."""
|
|
from openjarvis.learning.orchestrator import (
|
|
_select_torch_device,
|
|
)
|
|
|
|
assert callable(_select_torch_device)
|