diff --git a/.github/actions/setup-python-env/action.yml b/.github/actions/setup-python-env/action.yml index ad4a274..6feb8b5 100644 --- a/.github/actions/setup-python-env/action.yml +++ b/.github/actions/setup-python-env/action.yml @@ -26,5 +26,5 @@ runs: cache-suffix: ${{ matrix.python-version }} - name: Install Python dependencies - run: uv sync --frozen + run: uv sync --frozen --extra pytorch shell: bash diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fcbdfcf..b123b20 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -29,7 +29,7 @@ jobs: runs-on: self-hosted strategy: matrix: - python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13", "3.14"] fail-fast: false defaults: run: @@ -44,7 +44,11 @@ jobs: python-version: ${{ matrix.python-version }} - name: Run tests - run: uv run python -m pytest tests --cov --cov-config=pyproject.toml --cov-report=xml + run: uv run --frozen --extra pytorch python -m pytest tests --cov --cov-config=pyproject.toml --cov-report=xml + env: + # Ray 2.58+ otherwise creates worker environments from the bare + # `uv run` command and drops the optional PyTorch dependencies. + RAY_ENABLE_UV_RUN_RUNTIME_ENV: "0" # - name: Check typing # run: uv run mypy diff --git a/AGENTS.md b/AGENTS.md index 11a3127..e515e29 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -13,14 +13,15 @@ This repo uses `uv` for dependency management (creates `.venv/` and maintains `u - `make install`: create/sync env and install `pre-commit` hooks. - `make check`: verify lockfile consistency and run `pre-commit` (ruff/black/isort, etc.). - `make test`: run unit tests with coverage (`pytest --cov`, emits `coverage.xml`). -- `tox`: run tests across supported Python versions and run `mypy` (matches CI expectations). +- `tox`: run tests across supported Python versions. `mypy` is currently disabled but its dependency and configuration + are retained for future re-enablement. - `make docs` / `make docs-test`: serve or build MkDocs docs. - `make build`: build a wheel into `dist/` (for release preparation). ## Coding Style & Naming Conventions - Python 3.9+; 4-space indentation; line length target is 120. - Formatting/linting is enforced via `pre-commit`: Black + isort + Ruff (auto-fix enabled). -- Prefer explicit names and type hints; `mypy` is part of the `tox` run and untyped defs are disallowed. +- Prefer explicit names and type hints; static type checking is currently disabled and may be re-enabled later. ## Development Principles - 如无必要，勿增实体: do not add new concepts, state, helpers, layers, or dependencies unless they are needed to solve the concrete problem. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b394772..9175b40 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -65,7 +65,7 @@ cd TinyExp Then, install and activate the environment with: ```bash -uv sync +make install-pytorch ``` 4. Install pre-commit to run linters/formatters at commit time: diff --git a/Makefile b/Makefile index 53616b0..0d2ca15 100644 --- a/Makefile +++ b/Makefile @@ -1,9 +1,17 @@ .PHONY: install -install: ## Install the virtual environment and install the pre-commit hooks +install: ## Install core dependencies while preserving machine-selected optional packages @echo "🚀 Creating virtual environment using uv" - @uv sync + @uv sync --locked --inexact @uv run pre-commit install +.PHONY: install-pytorch +install-pytorch: ## Install the default PyPI PyTorch, TorchVision, and Accelerate builds + @uv sync --locked --extra pytorch + +.PHONY: install-without-pytorch +install-without-pytorch: ## Install core dependencies while preserving machine-selected accelerator packages + @uv sync --locked --no-extra pytorch --inexact + .PHONY: check check: ## Run code quality tools. @echo "🚀 Checking lock file consistency with 'pyproject.toml'" @@ -16,7 +24,7 @@ check: ## Run code quality tools. .PHONY: test test: ## Test the code with pytest @echo "🚀 Testing code: Running pytest" - @uv run python -m pytest --cov --cov-config=pyproject.toml --cov-report=xml + @RAY_ENABLE_UV_RUN_RUNTIME_ENV=0 uv run python -m pytest --cov --cov-config=pyproject.toml --cov-report=xml .PHONY: build build: clean-build ## Build wheel file diff --git a/README.md b/README.md index 6c71cc9..b0e54a8 100644 --- a/README.md +++ b/README.md @@ -51,7 +51,7 @@ For a longer explanation, see [`docs/philosophy.md`](docs/philosophy.md). ### Option A: Install with pip and use import-based entrypoint ```bash -pip install tinyexp +pip install "tinyexp[pytorch]" ``` ```python @@ -71,7 +71,7 @@ python your_exp.py dataloader_cfg.train_batch_size_per_device=16 ```bash git clone https://github.com/HKUST-SAIL/tinyexp.git cd tinyexp -make install +make install-pytorch uv run python tinyexp/examples/mnist_exp.py ``` @@ -149,6 +149,11 @@ tinyexp-run-with-redis -- python your_exp.py redis_cfg.redis_cache_enabled=true another Redis process, startup fails without shutting down or taking ownership of that server. Connect to externally managed Redis directly through `redis_cfg` instead of wrapping the command with `tinyexp-run-with-redis`. +For multi-node training, the helper's Redis lifecycle follows the local command: each wrapper stops the Redis +resources it owns as soon as its child exits. If one node fails, the whole distributed training job is expected to +fail and restart; the helper does not keep Redis alive for a global finish barrier or implement heartbeat/lease-based +failure coordination. The external launcher or supervisor owns whole-job restart and termination. + ## Example Experiments - MNIST baseline: [`tinyexp/examples/mnist_exp.py`](tinyexp/examples/mnist_exp.py) @@ -181,12 +186,14 @@ model preparation, reduction, synchronization, and cleanup methods. ## Development -Install environment and hooks: +Install the core environment and hooks: ```bash make install ``` +`make install` installs the core environment and hooks without selecting or removing optional accelerator packages. For the default PyPI stack, run `make install-pytorch` before `make test`. On a machine with a preselected CUDA, ROCm, or vendor PyTorch build, `make install` (or the more explicit `make install-without-pytorch`) preserves that environment; install `torch`, `torchvision`, and `accelerate` together according to that machine's package index/backend, then use ordinary `uv run`. Because the PyTorch packages are optional and ordinary `uv run` does not remove extraneous packages by default, no repeated `--no-sync` flag is needed. Avoid `uv sync` without `--inexact` in that environment. + Run checks: ```bash diff --git a/docs/running-modes.md b/docs/running-modes.md index 120766f..f5a8238 100644 --- a/docs/running-modes.md +++ b/docs/running-modes.md @@ -40,10 +40,32 @@ make install Or install the published package with: ```bash -pip install tinyexp +pip install "tinyexp[pytorch]" ``` -TinyExp declares PyTorch, Ray, Accelerate, and their Python-level dependencies, but it does not choose a CUDA-specific PyTorch wheel or manage GPU drivers. Follow the PyTorch installation guidance for the target machine when a GPU build is required. +TinyExp keeps Ray and its Python-level dependencies in the core installation. PyTorch, TorchVision, and Accelerate are provided by the optional `pytorch` extra. This avoids pretending that Python package metadata can choose a compatible CPU, CUDA, ROCm, or vendor-specific build for every machine. The universal `uv.lock` records Python- and operating-system-aware resolutions for the extra; accelerator runtime selection remains machine-specific. + +For the default PyPI PyTorch build, use: + +```bash +make install-pytorch +# or: pip install "tinyexp[pytorch]" +``` + +This convenience extra resolves the default PyPI builds recorded in `uv.lock`; it is not a universal CUDA/driver compatibility choice. For a machine that needs a machine-specific CUDA, ROCm, or vendor build, install the base project without the extra, then install all accelerator packages together using the target machine's instructions: + +```bash +make install-without-pytorch +# Install torch/torchvision from the machine-specific index/build, while +# keeping PyPI available for accelerate and other regular packages. +uv pip install --python .venv/bin/python torch torchvision accelerate \ + --index --default-index https://pypi.org/simple +# Or use uv's PyTorch backend selector where supported, e.g.: +# uv pip install --python .venv/bin/python torch torchvision accelerate --torch-backend cu126 +uv run python -c "import torch; print(torch.__version__, torch.cuda.is_available())" +``` + +Python 3.14 requires PyTorch/TorchVision builds that publish compatible `cp314` wheels; this is an ABI requirement, not a CUDA-version requirement. The PyTorch packages are optional, so ordinary `uv run` keeps a machine-selected accelerator build in place and does not require a repeated `--no-sync` flag. Use `make install` (which is intentionally non-exact) on such a machine, and avoid exact `uv sync` without `--inexact` afterward. Check the active environment before launching: @@ -93,13 +115,17 @@ Requirements: - The `ray` Python package must import successfully. - The machine must have enough resources for every worker bundle. -- `ray_cfg.ray_num_worker` must be `-1` or a positive integer. `-1` fills the available CPU or GPU capacity. +- `ray_cfg.ray_num_worker` must be `-1` or a positive integer. `-1` sizes workers from the CPU/GPU resources + available when the run starts. - `ray_cfg.ray_num_cpus_per_worker` must be positive. - `ray_cfg.ray_num_gpus_per_worker` must be non-negative. - `ray_cfg.ray_placement_timeout_s` controls how long TinyExp waits for the placement group; the default is 120 seconds. -- Requests that exceed the cluster's total CPU or GPU capacity fail before placement starts. If the total capacity is sufficient but currently busy, placement waits up to the configured timeout. +- Requests that exceed the cluster's total CPU or GPU capacity fail before placement starts. If the total capacity is + sufficient but currently busy, placement waits up to the configured timeout; a timeout reports total and currently + available CPU/GPU resources. - GPU workers require a CUDA-enabled PyTorch installation and visible GPUs. - TinyExp reads the placement-group bundle-to-node topology before creating Ray worker actors, then derives `RANK` and `LOCAL_RANK` from that topology. Ray workers must be homogeneous: every participating node must host the same number of workers, so every node has the same local-rank range. When bundles are interleaved across nodes, global ranks are reassigned so ranks remain contiguous within each node (for example, node-local workers receive `0..N-1`, then the next node receives the following range). +- For multi-worker runs, TinyExp starts a zero-CPU TCPStore actor in placement-group bundle 0. The actor binds and holds a dynamic port before worker creation, and all ranks connect to it as clients. The Ray head therefore does not need to host rank 0 or provide a GPU. If `RAY_ADDRESS` already points to a reachable Ray cluster, `ray.init()` can attach to that cluster. Otherwise, Ray starts a local runtime. @@ -241,12 +267,14 @@ Multi-node requirements: - `ray` and Python executables must be available on every node. Use `--ray-bin` and `--python-bin` when they are not on `PATH`. - The helper owns the Ray runtime on each participating node: it runs `ray stop --force` before startup and during cleanup. Do not use it on a node whose existing Ray runtime must remain active. +- In multi-node mode, `--node-rank` must be in `[0, --node-count)`, `--ray-port` must be a fixed port in `1..65535`, and `--wait-timeout` bounds Ray head/node readiness. - Each node must use compatible Python, TinyExp, PyTorch, Ray, and experiment dependency versions. - Custom experiment modules must be importable on every node. The helper does not distribute source code or create environments. - Dataset paths used by a scheduled worker must exist on that worker, either through a shared filesystem or equivalent per-node data layout. - The head address must be reachable from every worker and must not resolve to loopback for a multi-node job. - Firewalls and security groups must allow Ray's head port and Ray's node-to-node runtime traffic. The head port defaults to `6379`; choose an unused `--ray-port` when that port is occupied. The selected head port must also be outside Ray's configured worker port range. Dashboard, metrics, client, and other Ray runtime ports may also need explicit network policy. - Aggregate Ray cluster resources must satisfy the requested placement group. A job waits if resources exist in theory but cannot be placed with the requested bundle shape or placement strategy. +- The Ray head may be CPU-only. GPU worker bundles and the distributed TCPStore are placed on eligible worker nodes according to the resolved placement group. When `--node-count=1`, the helper executes the command unchanged and does not start a static Ray cluster. A command with `launcher=ray` may still start a local Ray runtime itself. @@ -264,9 +292,25 @@ Redis and W&B requirements are independent of the four launch styles. - Enabling Redis cache requires a reachable Redis service. - TinyExp-managed standalone Redis requires `redis-server` on the host that starts it. - TinyExp-managed Redis Cluster also requires `redis-cli` on the coordinating host. +- Ray-managed Redis Cluster startup and readiness use `redis_cfg.redis_cluster_startup_timeout_s` (30 seconds by + default). The `tinyexp-run-with-redis` wrapper uses its `--wait-timeout` option for the multi-node registration, + cluster creation, and readiness deadline. - The ResNet example enables Redis cache by default. Set `redis_cfg.redis_cache_enabled=false` when Redis is not installed or desired. - Enabling W&B requires suitable credentials and network access, or an explicitly configured offline mode. +### Redis helper lifecycle in multi-node jobs + +`tinyexp-run-with-redis` is a per-process wrapper, not a long-lived Redis service manager. It starts Redis resources +for the command it launches and stops the resources owned by that wrapper as soon as the child command exits, whether +the child succeeds or fails. In multi-node mode, the HTTP rendezvous is used only to register nodes and create the +Redis Cluster during startup; it is not a finish barrier. + +When a multi-node training process fails, the distributed training job is considered failed and must be restarted as +a whole by the external process supervisor or launcher. The supervisor must terminate the remaining wrappers; their +signal handlers stop their child processes and destroy the Redis resources they own. TinyExp deliberately does not add +heartbeat, lease, or a global terminal-state protocol here: Redis exists only for the lifetime of the command on each +node, and a failed multi-node job is expected to be torn down rather than kept alive for coordination. + Check Redis system commands when cache management is enabled: ```bash diff --git a/pyproject.toml b/pyproject.toml index a963f2d..1d21727 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,23 +17,42 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Topic :: Software Development :: Libraries :: Python Modules", ] dependencies = [ "loguru>=0.7.3", - "numpy", + # NumPy releases before 2.3.2 do not publish CPython 3.14 wheels. + "numpy; python_version < '3.14'", + "numpy>=2.3.2; python_version >= '3.14'", "psutil", "tabulate", "hydra-core", "omegaconf", - "ray", + # Ray 2.53+ does not publish a CPython 3.9 wheel. Keep the older + # interpreter resolution installable while using the current line for + # Python versions that have compatible wheels, including 3.14. + "ray<2.52; python_version < '3.10'", + "ray; python_version >= '3.10'", + # Ray pulls rpds-py through jsonschema; use a release with CPython 3.14 wheels. + "rpds-py>=0.26; python_version >= '3.14'", "tqdm", - "torch", - "torchvision", "redis", "wandb", "loguru", +] + +[project.optional-dependencies] +pytorch = [ "accelerate", + # Python 3.14 requires releases that publish cp314 wheels. The CPU/CUDA/ + # ROCm/vendor runtime is selected separately for each target machine. + "torch; python_version < '3.14'", + "torch>=2.13; python_version >= '3.14'", + "torchvision; python_version < '3.14'", + "torchvision>=0.28; python_version >= '3.14'", + # TorchVision pulls Pillow; use a release with CPython 3.14 wheels. + "pillow>=11.3; python_version >= '3.14'", ] [project.urls] @@ -80,15 +99,16 @@ include = [ -[tool.mypy] -files = ["tinyexp"] -disallow_untyped_defs = true -disallow_any_unimported = true -no_implicit_optional = true -check_untyped_defs = true -warn_return_any = true -warn_unused_ignores = true -show_error_codes = true +# [tool.mypy] +# Temporarily disabled. Uncomment this section when mypy is enabled again. +# files = ["tinyexp"] +# disallow_untyped_defs = true +# disallow_any_unimported = true +# no_implicit_optional = true +# check_untyped_defs = true +# warn_return_any = true +# warn_unused_ignores = true +# show_error_codes = true [tool.pytest.ini_options] testpaths = ["tests"] diff --git a/tests/conftest.py b/tests/conftest.py index 3ef2070..c945380 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -11,10 +11,12 @@ def ray_session(): if not ray.is_initialized(): runtime_env = { "working_dir": ".", - "excludes": ["*.md", "data/", "tests/", ".git/", ".venv/", "output/", "outputs/", "site/"], + "excludes": ["data/", "tests/", ".git/", ".venv/", "output/", "outputs/", "site/"], } try: - ray.init(runtime_env=runtime_env) + # Force a fresh local runtime instead of auto-connecting to a + # stale cluster address left by an interrupted Ray test run. + ray.init(address="local", runtime_env=runtime_env) except Exception as exc: pytest.skip(f"Ray is not available in this environment: {exc}") diff --git a/tests/dataset/test_sampler.py b/tests/dataset/test_sampler.py index 42f9e53..27806a4 100644 --- a/tests/dataset/test_sampler.py +++ b/tests/dataset/test_sampler.py @@ -3,6 +3,7 @@ from types import SimpleNamespace import pytest +from torch.utils.data import DataLoader, Dataset from tinyexp.dataset.fake_dataloader import HoldOnesampleDataLoader from tinyexp.dataset.sampler import InfiniteSampler @@ -83,6 +84,64 @@ def test_infinite_sampler_no_shuffle_multi_worker_slices() -> None: assert first_six == [1, 3, 1, 3, 1, 3] +def test_infinite_sampler_reuses_one_dataloader_iterator_across_epochs() -> None: + class IndexDataset(Dataset[int]): + def __len__(self) -> int: + return 4 + + def __getitem__(self, index: int) -> int: + return index + + class CountingInfiniteSampler(InfiniteSampler): + iterator_count = 0 + + def __iter__(self): + type(self).iterator_count += 1 + return super().__iter__() + + sampler = CountingInfiniteSampler(size=4, shuffle=False, seed=0) + dataloader = DataLoader(IndexDataset(), batch_size=2, sampler=sampler, num_workers=0) + iterator = iter(dataloader) + + epochs = [[next(iterator).tolist() for _ in range(len(dataloader))] for _ in range(3)] + + assert CountingInfiniteSampler.iterator_count == 1 + assert epochs == [ + [[0, 1], [2, 3]], + [[0, 1], [2, 3]], + [[0, 1], [2, 3]], + ] + + +def test_infinite_sampler_resumes_at_epoch_before_iterator_creation() -> None: + class IndexDataset(Dataset[int]): + def __len__(self) -> int: + return 4 + + def __getitem__(self, index: int) -> int: + return index + + sampler = InfiniteSampler(size=4, shuffle=True, seed=17) + dataloader = DataLoader(IndexDataset(), batch_size=2, sampler=sampler, num_workers=0) + iterator = iter(dataloader) + first_epoch = [next(iterator).tolist() for _ in range(len(dataloader))] + second_epoch = [next(iterator).tolist() for _ in range(len(dataloader))] + + resumed_sampler = InfiniteSampler(size=4, shuffle=True, seed=17) + resumed_sampler.set_epoch(1) + resumed_dataloader = DataLoader( + IndexDataset(), + batch_size=2, + sampler=resumed_sampler, + num_workers=0, + ) + resumed_iterator = iter(resumed_dataloader) + resumed_epoch = [next(resumed_iterator).tolist() for _ in range(len(resumed_dataloader))] + + assert first_epoch != second_epoch + assert resumed_epoch == second_epoch + + def test_infinite_sampler_set_epoch_continues_fixed_stream() -> None: expected_sampler = InfiniteSampler(size=10, shuffle=True, seed=17) expected_stream = iter(expected_sampler) diff --git a/tests/examples/test_mnist_exp_run.py b/tests/examples/test_mnist_exp_run.py index 079e206..667004f 100644 --- a/tests/examples/test_mnist_exp_run.py +++ b/tests/examples/test_mnist_exp_run.py @@ -68,6 +68,29 @@ def fake_evaluate(*, accelerator, logger, module_or_module_path, val_dataloader= assert destroy_calls == [True] +def test_mnist_run_destroys_accelerator_when_workload_fails( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + exp = Exp(output_root=str(tmp_path), exp_name="mnist_failure") + events: list[str] = [] + + class DummyAccelerator: + rank = 0 + + def destroy(self) -> None: + events.append("destroy") + + accelerator = DummyAccelerator() + monkeypatch.setattr(exp.accelerator_cfg, "build_accelerator", lambda: accelerator) + monkeypatch.setattr(exp, "_run", lambda _accelerator: (_ for _ in ()).throw(RuntimeError("boom"))) + + with pytest.raises(RuntimeError, match="boom"): + exp.run() + + assert events == ["destroy"] + + def test_mnist_val_dataloader_partitions_without_padding(tmp_path: Path, monkeypatch) -> None: dataset = torch.utils.data.TensorDataset(torch.arange(10), torch.arange(10)) monkeypatch.setattr("tinyexp.examples.mnist_exp.datasets.MNIST", lambda *args, **kwargs: dataset) diff --git a/tests/examples/test_pi_exp_run.py b/tests/examples/test_pi_exp_run.py index 51b4f4a..163b9f5 100644 --- a/tests/examples/test_pi_exp_run.py +++ b/tests/examples/test_pi_exp_run.py @@ -40,3 +40,29 @@ def destroy(self) -> None: assert events == ["destroy"] assert "pi ~= 3.140000 (error=0.000000, samples=10000000)" in capsys.readouterr().out + + +def test_pi_run_destroys_accelerator_when_workload_fails( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + events: list[str] = [] + + class DummyAccelerator: + rank = 0 + world_size = 1 + is_main_process = True + + def destroy(self) -> None: + events.append("destroy") + + accelerator = DummyAccelerator() + exp = pi_exp.Exp(output_root=str(tmp_path), exp_name="pi_failure") + + monkeypatch.setattr("tinyexp.tiny_engine.accelerator.CPUAccelerator", lambda: accelerator) + monkeypatch.setattr(exp, "_run", lambda _accelerator: (_ for _ in ()).throw(RuntimeError("boom"))) + + with pytest.raises(RuntimeError, match="boom"): + exp.run() + + assert events == ["destroy"] diff --git a/tests/examples/test_resnet_exp_run.py b/tests/examples/test_resnet_exp_run.py index 02d3520..eb6f6a2 100644 --- a/tests/examples/test_resnet_exp_run.py +++ b/tests/examples/test_resnet_exp_run.py @@ -165,6 +165,47 @@ def __len__(self): assert captured["root"] == "/imagenet/train" +@pytest.mark.parametrize("num_workers", [0, 2]) +def test_resnet_dataloaders_match_persistent_workers_to_num_workers( + monkeypatch, + num_workers: int, +) -> None: + class FakeDataset: + def __len__(self) -> int: + return 2 + + def __getitem__(self, index: int) -> tuple[int, int]: + return index, index + + monkeypatch.setattr( + "tinyexp.examples.resnet_exp.datasets.ImageFolder", + lambda root, transform=None: FakeDataset(), + ) + monkeypatch.setattr( + "tinyexp.examples.resnet_exp.LocalCachedImageFolder", + lambda root, transform=None: FakeDataset(), + ) + monkeypatch.setattr( + "tinyexp.examples.resnet_exp.transform_template_imagenet", + lambda **kwargs: None, + ) + + dataloader_cfg = ResNetExp.DataloaderCfg( + data_root="/imagenet", + train_data_worker_per_gpu=num_workers, + val_data_worker_per_gpu=num_workers, + ) + accelerator = SimpleNamespace(rank=0, world_size=1) + redis_cfg = SimpleNamespace(redis_cache_enabled=False) + + train_dataloader = dataloader_cfg.build_train_dataloader(accelerator, redis_cfg) + val_dataloader = dataloader_cfg.build_val_dataloader(accelerator) + + expected = num_workers > 0 + assert train_dataloader.persistent_workers is expected + assert val_dataloader.persistent_workers is expected + + def test_resnet_run_val_mode_requires_resume_from(tmp_path: Path, monkeypatch) -> None: exp = ResNetExp(output_root=str(tmp_path), exp_name="resnet_val", mode="val", resume_from="") dummy_accelerator = SimpleNamespace( @@ -251,6 +292,29 @@ def fake_evaluate(*, accelerator, logger, module_or_module_path, val_dataloader= assert called["val_dataloader"] is None +def test_resnet_run_destroys_accelerator_when_workload_fails( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + exp = ResNetExp(output_root=str(tmp_path), exp_name="resnet_failure") + events: list[str] = [] + + class DummyAccelerator: + rank = 0 + + def destroy(self) -> None: + events.append("destroy") + + accelerator = DummyAccelerator() + monkeypatch.setattr(exp.accelerator_cfg, "build_accelerator", lambda: accelerator) + monkeypatch.setattr(exp, "_run", lambda _accelerator: (_ for _ in ()).throw(RuntimeError("boom"))) + + with pytest.raises(RuntimeError, match="boom"): + exp.run() + + assert events == ["destroy"] + + def test_resnet_train_saves_last_and_best_checkpoints(tmp_path: Path, monkeypatch) -> None: exp = ResNetExp(output_root=str(tmp_path), exp_name="resnet_train") @@ -361,6 +425,70 @@ def backward(self, loss): assert [item["global_step"] for item in saved] == [1, 1, 2] +def test_resnet_train_positions_sampler_once_before_creating_iterator( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + exp = ResNetExp(output_root=str(tmp_path), exp_name="resnet_train", max_train_epochs=2) + events: list[object] = [] + + class DummySampler: + def set_epoch(self, epoch: int) -> None: + events.append(("set_epoch", epoch)) + + class DummyDataLoader: + batch_size = 2 + sampler = DummySampler() + + def __len__(self) -> int: + return 1 + + def __iter__(self): + events.append("iter") + while True: + yield torch.randn(2, 2), torch.tensor([0, 1]) + + class DummyAccelerator: + rank = 0 + device = "cpu" + is_main_process = True + world_size = 1 + + def prepare(self, module, optimizer): + return module, optimizer + + def unwrap_model(self, module): + return module + + def backward(self, loss): + loss.backward() + + train_dataloader = DummyDataLoader() + monkeypatch.setattr( + exp.dataloader_cfg, + "build_train_dataloader", + lambda accelerator, redis_cfg: train_dataloader, + ) + monkeypatch.setattr(exp.dataloader_cfg, "build_val_dataloader", lambda accelerator: []) + monkeypatch.setattr(exp.module_cfg, "build_module", lambda: nn.Linear(2, 2)) + monkeypatch.setattr( + exp.optimizer_cfg, + "build_optimizer", + lambda module, dataloader, accelerator: torch.optim.SGD(module.parameters(), lr=0.1), + ) + monkeypatch.setattr(exp, "_evaluate", lambda **kwargs: 0.5) + monkeypatch.setattr(exp.checkpoint_cfg, "save_checkpoint", lambda **kwargs: None) + + exp._train( + accelerator=DummyAccelerator(), + logger=SimpleNamespace(info=lambda *args, **kwargs: None), + cfg_dict={}, + run_dir=str(tmp_path / "resnet_train"), + ) + + assert events == [("set_epoch", 0), "iter"] + + def test_resnet_train_stops_at_max_train_steps(tmp_path: Path, monkeypatch) -> None: exp = ResNetExp(output_root=str(tmp_path), exp_name="resnet_train", max_train_steps=1) diff --git a/tests/scripts/test_run_with_ray_cluster.py b/tests/scripts/test_run_with_ray_cluster.py index 28ed490..273aa38 100644 --- a/tests/scripts/test_run_with_ray_cluster.py +++ b/tests/scripts/test_run_with_ray_cluster.py @@ -4,8 +4,11 @@ import subprocess import sys from pathlib import Path +from types import SimpleNamespace from unittest.mock import patch +import pytest + import tinyexp.cli.run_with_ray_cluster as ray_cluster ROOT = Path(__file__).resolve().parents[2] @@ -72,3 +75,154 @@ def test_run_with_ray_cluster_multi_node_requires_head_addr() -> None: assert result.returncode == 2 assert "--head-addr is required" in result.stderr + + +def test_parse_args_rejects_node_rank_out_of_range() -> None: + with pytest.raises(SystemExit) as exc_info: + ray_cluster.parse_args( + [ + "--node-count", + "2", + "--node-rank", + "2", + "--head-addr", + "10.0.0.1", + "--", + "echo", + "unused", + ] + ) + + assert exc_info.value.code == 2 + + +@pytest.mark.parametrize("ray_port", ["0", "65536"]) +def test_parse_args_rejects_invalid_ray_port(ray_port: str) -> None: + with pytest.raises(SystemExit) as exc_info: + ray_cluster.parse_args( + [ + "--node-count", + "2", + "--head-addr", + "10.0.0.1", + "--ray-port", + ray_port, + "--", + "echo", + "unused", + ] + ) + + assert exc_info.value.code == 2 + + +def test_parse_args_rejects_zero_wait_timeout() -> None: + with pytest.raises(SystemExit) as exc_info: + ray_cluster.parse_args( + [ + "--node-count", + "2", + "--head-addr", + "10.0.0.1", + "--wait-timeout", + "0", + "--", + "echo", + "unused", + ] + ) + + assert exc_info.value.code == 2 + + +def test_ray_alive_count_passes_timeout_and_handles_timeout(monkeypatch: pytest.MonkeyPatch) -> None: + captured: dict[str, object] = {} + + def fake_run(command: list[str], **kwargs: object) -> None: + captured["timeout"] = kwargs["timeout"] + raise subprocess.TimeoutExpired(command, kwargs["timeout"]) # type: ignore[arg-type] + + monkeypatch.setattr(ray_cluster.subprocess, "run", fake_run) + + assert ray_cluster.ray_alive_count("python", "10.0.0.1:6379", {}, timeout_s=1.25) is None + assert captured["timeout"] == 1.25 + + +def test_wait_for_head_uses_remaining_timeout(monkeypatch: pytest.MonkeyPatch) -> None: + clock = {"now": 0.0} + timeouts: list[float] = [] + sleeps: list[float] = [] + + def monotonic() -> float: + return clock["now"] + + def sleep(seconds: float) -> None: + sleeps.append(seconds) + clock["now"] += seconds + + def fake_run_quiet(command: list[str], *, timeout_s: float = 5.0) -> int: + timeouts.append(timeout_s) + return 1 + + monkeypatch.setattr( + ray_cluster, + "time", + SimpleNamespace(monotonic=monotonic, sleep=sleep), + ) + monkeypatch.setattr(ray_cluster, "run_quiet", fake_run_quiet) + + assert ray_cluster.wait_for_head("ray", "10.0.0.1:6379", 3) is False + assert timeouts == [3.0, 1.0] + assert sleeps == [2, 1.0] + + +def test_run_head_uses_remaining_timeout_and_bounds_sleep(monkeypatch: pytest.MonkeyPatch) -> None: + clock = {"now": 0.0} + timeouts: list[float] = [] + sleeps: list[float] = [] + + def monotonic() -> float: + return clock["now"] + + def sleep(seconds: float) -> None: + sleeps.append(seconds) + clock["now"] += seconds + + def fake_alive_count( + python_bin: str, + ray_address: str, + env: dict[str, str], + *, + timeout_s: float, + ) -> None: + timeouts.append(timeout_s) + return None + + args = SimpleNamespace( + node_count=2, + head_addr="10.0.0.1", + head_node_ip="", + ray_port=6379, + dashboard_port=8265, + metrics_port=8080, + include_dashboard="false", + client_port=None, + wait_timeout=3, + command=["echo", "unused"], + ) + + monkeypatch.setattr( + ray_cluster, + "time", + SimpleNamespace(monotonic=monotonic, sleep=sleep), + ) + monkeypatch.setattr(ray_cluster, "ray_alive_count", fake_alive_count) + monkeypatch.setattr( + ray_cluster.subprocess, + "run", + lambda *command, **kwargs: SimpleNamespace(returncode=0), + ) + + assert ray_cluster.run_head(args, "ray", "python", "10.0.0.1:6379", {}) == 1 + assert timeouts == [3.0, 1.0] + assert sleeps == [2, 1.0] diff --git a/tests/scripts/test_run_with_redis.py b/tests/scripts/test_run_with_redis.py index 11e2616..0f4f156 100644 --- a/tests/scripts/test_run_with_redis.py +++ b/tests/scripts/test_run_with_redis.py @@ -1,9 +1,14 @@ from __future__ import annotations +import json import signal +import socket import subprocess import sys +import threading from pathlib import Path +from types import SimpleNamespace +from urllib.request import ProxyHandler, Request, build_opener import pytest @@ -13,6 +18,24 @@ ROOT = Path(__file__).resolve().parents[2] +def _free_port() -> int: + with socket.socket() as sock: + sock.bind(("127.0.0.1", 0)) + return int(sock.getsockname()[1]) + + +def _post_register(port: int, host: str, timeout_s: float = 3.0) -> dict[str, object]: + request = Request( + f"http://127.0.0.1:{port}/register", + data=json.dumps({"host": host, "ports": [7000, 7001]}).encode(), + headers={"Content-Type": "application/json"}, + method="POST", + ) + opener = build_opener(ProxyHandler({})) + with opener.open(request, timeout=timeout_s) as response: + return json.loads(response.read()) + + def _write_demo_exp(tmp_path: Path) -> Path: exp_file = tmp_path / "demo_exp.py" exp_file.write_text( @@ -308,6 +331,147 @@ def fake_popen(args, **kwargs): assert captured["popen_kwargs"]["start_new_session"] is True +def test_rendezvous_server_reports_cluster_create_timeout( + monkeypatch: pytest.MonkeyPatch, +) -> None: + port = _free_port() + command_calls: list[dict[str, object]] = [] + + def fake_run(command, **kwargs): # type: ignore[no-untyped-def] + command_calls.append(kwargs) + raise subprocess.TimeoutExpired(command, kwargs["timeout"]) + + monkeypatch.setattr(run_with_redis.subprocess, "run", fake_run) + server = run_with_redis.start_rendezvous_server( + "127.0.0.1", + port, + world_size=2, + redis_cli_path="/fake/redis-cli", + timeout_s=1.0, + ) + assert server is not None + + try: + assert _post_register(port, "node-a")["status"] == "waiting" + response = _post_register(port, "node-b") + finally: + server.shutdown() + server.server_close() + + assert response["status"] == "error" + assert "timed out" in str(response["message"]) + assert len(command_calls) == 1 + assert 0 < float(command_calls[0]["timeout"]) <= 1.0 + + +def test_rendezvous_server_does_not_hold_lock_during_cluster_startup( + monkeypatch: pytest.MonkeyPatch, +) -> None: + port = _free_port() + command_started = threading.Event() + release_command = threading.Event() + second_response: dict[str, object] = {} + + def slow_run(command, **kwargs): # type: ignore[no-untyped-def] + command_started.set() + assert release_command.wait(timeout=3) + return SimpleNamespace(returncode=0, stdout="", stderr="") + + monkeypatch.setattr(run_with_redis.subprocess, "run", slow_run) + monkeypatch.setattr(run_with_redis, "wait_for_redis_cluster", lambda *args, **kwargs: True) + server = run_with_redis.start_rendezvous_server( + "127.0.0.1", + port, + world_size=2, + redis_cli_path="/fake/redis-cli", + timeout_s=5.0, + ) + assert server is not None + + try: + assert _post_register(port, "node-a")["status"] == "waiting" + second_thread = threading.Thread( + target=lambda: second_response.update(_post_register(port, "node-b", timeout_s=5.0)), + daemon=True, + ) + second_thread.start() + assert command_started.wait(timeout=2) + + third_response = _post_register(port, "node-c", timeout_s=1.0) + assert third_response["status"] == "waiting" + + release_command.set() + second_thread.join(timeout=3) + assert not second_thread.is_alive() + assert second_response["status"] == "ready" + finally: + release_command.set() + server.shutdown() + server.server_close() + + +@pytest.mark.parametrize("child_exit_code", [0, 7]) +def test_multi_node_wrapper_cleans_up_immediately_after_child_exit( + tmp_path: Path, + monkeypatch, + child_exit_code: int, +) -> None: + exp_file = _write_demo_exp(tmp_path) + events: list[str] = [] + + class FakeLifecycle: + def __init__(self) -> None: + self._started_nodes = [("10.0.0.1", 7010)] + + @property + def started_nodes(self) -> list[tuple[str, int]]: + return self._started_nodes + + def close(self) -> None: + events.append("close") + + class FakeProcess: + pid = 12345 + + def wait(self) -> int: + events.append("child-exit") + return child_exit_code + + def fake_start_rendezvous_redis_cluster(*args, **kwargs): + events.append("redis-start") + return "10.0.0.1", [7010] + + def fake_popen(*args, **kwargs): + events.append("child-start") + return FakeProcess() + + def unexpected_finish_barrier(*args, **kwargs): + pytest.fail("multi-node wrapper must not wait for a finish barrier after child exit") + + monkeypatch.setattr(run_with_redis, "_RedisLifecycle", FakeLifecycle) + monkeypatch.setattr(run_with_redis, "start_rendezvous_redis_cluster", fake_start_rendezvous_redis_cluster) + monkeypatch.setattr(run_with_redis.subprocess, "Popen", fake_popen) + monkeypatch.setattr(run_with_redis, "wait_for_rendezvous_finish", unexpected_finish_barrier, raising=False) + + exit_code = main( + [ + "--node-count", + "2", + "--node-rank", + "0", + "--head-addr", + "10.0.0.1", + "--", + "python", + str(exp_file), + "redis_cfg.redis_cluster_ports=[7010]", + ] + ) + + assert exit_code == child_exit_code + assert events == ["redis-start", "child-start", "child-exit", "close"] + + def test_main_restores_signal_handlers_after_child_exit(tmp_path: Path, monkeypatch) -> None: exp_file = _write_demo_exp(tmp_path) previous_handlers = { diff --git a/tests/test_ray_multinode_integration.py b/tests/test_ray_multinode_integration.py new file mode 100644 index 0000000..5580449 --- /dev/null +++ b/tests/test_ray_multinode_integration.py @@ -0,0 +1,170 @@ +from __future__ import annotations + +import errno +import json +import os +import socket +import subprocess +import sys +from contextlib import suppress +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +RESULT_PREFIX = "RAY_MULTINODE_RESULT=" + + +def _run_multinode_probe() -> None: + import ray + import torch + from ray.cluster_utils import Cluster + from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy + + from tinyexp.tiny_engine.accelerator import CPUAccelerator + from tinyexp.utils.ray_utils import ( + get_num_worker_options, + get_placement_group, + get_placement_group_node_ids, + start_ray_rendezvous_store, + ) + + cluster = Cluster() + pg = None + rendezvous_actor = None + workers = [] + try: + cluster.add_node(num_cpus=1, num_gpus=0, include_dashboard=False) + gpu_node = cluster.add_node(num_cpus=2, num_gpus=2) + ray.init(address=cluster.address) + cluster.wait_for_nodes() + + pg = get_placement_group( + num_worker=2, + num_gpus_per_worker=1, + num_cpus_per_worker=1, + strategy="PACK", + timeout_s=10, + ) + node_ids = get_placement_group_node_ids(pg, 2) + assert set(node_ids) == {gpu_node.node_id} + + rendezvous_actor, master_addr, master_port = start_ray_rendezvous_store( + pg, + world_size=2, + timeout_s=10, + ) + nodes_by_id = {str(node["NodeID"]): node for node in ray.nodes() if node["Alive"]} + assert master_addr == nodes_by_id[node_ids[0]]["NodeManagerAddress"] + + def try_bind_rendezvous_port(address: str, port: int) -> int | None: + with socket.socket() as sock: + try: + sock.bind((address, port)) + except OSError as exc: + return exc.errno + return None + + bind_probe = ray.remote(num_cpus=0)(try_bind_rendezvous_port) + bind_errno = ray.get( + bind_probe.options( + scheduling_strategy=PlacementGroupSchedulingStrategy( + placement_group=pg, + placement_group_bundle_index=0, + ) + ).remote(master_addr, master_port), + timeout=10, + ) + assert bind_errno == errno.EADDRINUSE + + options_list = get_num_worker_options( + pg, + num_worker=2, + gpu_ratio=1, + num_cpus_per_worker=1, + master_addr=master_addr, + master_port=master_port, + node_ids=node_ids, + ) + + @ray.remote + class Worker: + def run(self) -> dict[str, object]: + accelerator = CPUAccelerator() + value = torch.tensor(float(accelerator.rank + 1)) + reduced = accelerator.reduce_sum(value).item() + result = { + "rank": accelerator.rank, + "sum": reduced, + "node_id": ray.get_runtime_context().get_node_id(), + "master_addr": accelerator.master_addr, + "master_port": accelerator.master_port, + "agent_store": os.environ.get("TORCHELASTIC_USE_AGENT_STORE"), + } + accelerator.destroy() + return result + + workers = [Worker.options(**options).remote() for options in options_list] + results = ray.get([worker.run.remote() for worker in workers], timeout=30) + results.sort(key=lambda result: int(result["rank"])) + + assert [result["rank"] for result in results] == [0, 1] + assert [result["sum"] for result in results] == [3.0, 3.0] + assert {result["node_id"] for result in results} == {gpu_node.node_id} + assert {result["master_addr"] for result in results} == {master_addr} + assert {result["master_port"] for result in results} == {master_port} + assert {result["agent_store"] for result in results} == {"True"} + + print( + RESULT_PREFIX + + json.dumps( + { + "bundle_node_ids": node_ids, + "gpu_node_id": gpu_node.node_id, + "master_addr": master_addr, + "master_port": master_port, + "port_reserved": True, + "results": results, + }, + sort_keys=True, + ), + flush=True, + ) + finally: + for worker in workers: + with suppress(Exception): + ray.kill(worker, no_restart=True) + if rendezvous_actor is not None: + with suppress(Exception): + ray.kill(rendezvous_actor, no_restart=True) + if pg is not None and ray.is_initialized(): + with suppress(Exception): + ray.util.remove_placement_group(pg) + if ray.is_initialized(): + ray.shutdown() + cluster.shutdown() + + +def test_cpu_only_head_with_logical_gpu_workers_uses_reserved_worker_rendezvous() -> None: + env = os.environ.copy() + existing_pythonpath = env.get("PYTHONPATH") + env["PYTHONPATH"] = str(ROOT) if not existing_pythonpath else f"{ROOT}{os.pathsep}{existing_pythonpath}" + + result = subprocess.run( # noqa: S603 + [sys.executable, str(Path(__file__).resolve()), "--probe"], + cwd=ROOT, + env=env, + text=True, + capture_output=True, + check=False, + timeout=120, + ) + + assert result.returncode == 0, f"stdout:\n{result.stdout}\n\nstderr:\n{result.stderr}" + result_lines = [line for line in result.stdout.splitlines() if line.startswith(RESULT_PREFIX)] + assert len(result_lines) == 1 + payload = json.loads(result_lines[0][len(RESULT_PREFIX) :]) + assert payload["port_reserved"] is True + assert set(payload["bundle_node_ids"]) == {payload["gpu_node_id"]} + + +if __name__ == "__main__": + _run_multinode_probe() diff --git a/tests/test_tinyexp.py b/tests/test_tinyexp.py index 40bfdfa..bc2e240 100644 --- a/tests/test_tinyexp.py +++ b/tests/test_tinyexp.py @@ -19,6 +19,11 @@ class SubCfg: b: int = 2 +@dataclass +class _MappingCfgExp(TinyExp): + options: dict[str, object] = field(default_factory=lambda: {"base": 1}) + + @dataclass class _StoreAndRunExpCfg(TinyExp): check_exp_class: str = f"{__name__}._StoreAndRunExpCfg" @@ -104,6 +109,21 @@ def test_set_cfg_overrides_nested(monkeypatch: pytest.MonkeyPatch) -> None: assert exp.b == 4 +def test_set_cfg_overrides_mapping_field(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("RANK", "1") + exp = _MappingCfgExp() + + cfg = OmegaConf.create({"options": {"base": 3, "extra": {"enabled": True}}}) + exp.set_cfg(cfg) + + assert exp.options == {"base": 3, "extra": {"enabled": True}} + assert isinstance(exp.options, dict) + assert exp.overrided_cfg["options"] == { + "value": {"base": 3, "extra": {"enabled": True}}, + "original": {"base": 1}, + } + + def test_set_cfg_unknown_key_raises(monkeypatch: pytest.MonkeyPatch) -> None: monkeypatch.setenv("RANK", "1") exp = _CfgExp() diff --git a/tests/test_tinyexp_artifacts.py b/tests/test_tinyexp_artifacts.py index 2d98769..425e69d 100644 --- a/tests/test_tinyexp_artifacts.py +++ b/tests/test_tinyexp_artifacts.py @@ -143,6 +143,32 @@ def test_checkpoint_cfg_rng_state_restores_continuation() -> None: assert torch.equal(resumed[2], expected[2]) +def test_checkpoint_cfg_loads_checkpoint_with_rng_state_on_torch_26_plus(tmp_path: Path) -> None: + checkpoint_cfg = CheckpointCfgMixin.CheckpointCfg() + random.seed(7) + np.random.seed(7) + torch.manual_seed(7) + rng_state = checkpoint_cfg.capture_rng_state() + checkpoint_path = checkpoint_cfg.save_checkpoint( + run_dir=str(tmp_path), + name=checkpoint_cfg.last_ckpt_name, + extra_state={"rng_state": rng_state}, + ) + + expected = (random.random(), np.random.random(), torch.rand(3)) # noqa: S311 + random.random() # noqa: S311 + np.random.random() + torch.rand(3) + + checkpoint = checkpoint_cfg.load_checkpoint(checkpoint_path) + checkpoint_cfg.restore_rng_state(checkpoint["extra_state"]["rng_state"]) + resumed = (random.random(), np.random.random(), torch.rand(3)) # noqa: S311 + + assert resumed[0] == expected[0] + assert resumed[1] == expected[1] + assert torch.equal(resumed[2], expected[2]) + + def test_checkpoint_cfg_atomic_save_preserves_previous_checkpoint_on_failure( tmp_path: Path, monkeypatch: pytest.MonkeyPatch ) -> None: diff --git a/tests/tine_engine/test_cpu_accelerator.py b/tests/tine_engine/test_cpu_accelerator.py index c09c96d..97e68ef 100644 --- a/tests/tine_engine/test_cpu_accelerator.py +++ b/tests/tine_engine/test_cpu_accelerator.py @@ -1,9 +1,16 @@ +from contextlib import suppress + import ray import torch from torch import nn from tinyexp.tiny_engine.accelerator import CPUAccelerator -from tinyexp.utils.ray_utils import get_num_worker_options, get_placement_group, get_placement_group_node_ids +from tinyexp.utils.ray_utils import ( + get_num_worker_options, + get_placement_group, + get_placement_group_node_ids, + start_ray_rendezvous_store, +) @ray.remote @@ -33,6 +40,8 @@ class TestCPUAcceleratorWithRay: def test_ddp_accelerator(self, ray_session): num_worker = 2 pg = None + rendezvous_actor = None + worker_group = [] try: # This utility correctly sets up env vars and placement groups. pg = get_placement_group( @@ -41,10 +50,17 @@ def test_ddp_accelerator(self, ray_session): num_cpus_per_worker=2, # Each worker gets 2 CPUs ) node_ids = get_placement_group_node_ids(pg, num_worker) + rendezvous_actor, master_addr, master_port = start_ray_rendezvous_store( + pg, + num_worker, + timeout_s=30, + ) options_list = get_num_worker_options( pg, num_worker=num_worker, gpu_ratio=0.0, + master_addr=master_addr, + master_port=master_port, node_ids=node_ids, ) # Create the remote actors. @@ -52,13 +68,22 @@ def test_ddp_accelerator(self, ray_session): # Run the test method on all workers and wait for them to complete. run_futures = [worker.test_reduce_sum.remote() for worker in worker_group] - results = ray.get(run_futures) + results = ray.get(run_futures, timeout=60) # Verify that all tests passed. assert all(results) - model_results = ray.get([worker.test_model_parameters_are_synchronized.remote() for worker in worker_group]) + model_results = ray.get( + [worker.test_model_parameters_are_synchronized.remote() for worker in worker_group], + timeout=60, + ) assert model_results == [1.0] * num_worker finally: + for worker in worker_group: + with suppress(Exception): + ray.kill(worker, no_restart=True) + if rendezvous_actor is not None: + with suppress(Exception): + ray.kill(rendezvous_actor, no_restart=True) if pg: ray.util.remove_placement_group(pg) diff --git a/tests/tine_engine/test_ddp_accelerator.py b/tests/tine_engine/test_ddp_accelerator.py index 064ec5d..b756698 100644 --- a/tests/tine_engine/test_ddp_accelerator.py +++ b/tests/tine_engine/test_ddp_accelerator.py @@ -5,7 +5,12 @@ import torch from tinyexp.tiny_engine.accelerator import DDPAccelerator -from tinyexp.utils.ray_utils import get_num_worker_options, get_placement_group, get_placement_group_node_ids +from tinyexp.utils.ray_utils import ( + get_num_worker_options, + get_placement_group, + get_placement_group_node_ids, + start_ray_rendezvous_store, +) @ray.remote @@ -49,21 +54,38 @@ def test_ddp_accelerator(self, ray_session): cpus_per_actor = 1 worker_group = [] + rendezvous_actors = [] try: + rendezvous_actor1, master_addr1, master_port1 = start_ray_rendezvous_store( + pg, + num_workers, + timeout_s=30, + ) + rendezvous_actors.append(rendezvous_actor1) options_list1 = get_num_worker_options( pg, num_workers, gpu_ratio=gpu_per_actor, num_cpus_per_worker=cpus_per_actor, + master_addr=master_addr1, + master_port=master_port1, node_ids=node_ids, ) worker_group1 = [DDPAcceleratorProxy.options(**options).remote() for options in options_list1] + rendezvous_actor2, master_addr2, master_port2 = start_ray_rendezvous_store( + pg, + num_workers, + timeout_s=30, + ) + rendezvous_actors.append(rendezvous_actor2) options_list2 = get_num_worker_options( pg, num_workers, gpu_ratio=gpu_per_actor, num_cpus_per_worker=cpus_per_actor, + master_addr=master_addr2, + master_port=master_port2, node_ids=node_ids, ) worker_group2 = [DDPAcceleratorProxy.options(**options).remote() for options in options_list2] @@ -77,4 +99,7 @@ def test_ddp_accelerator(self, ray_session): for worker in worker_group: with suppress(Exception): ray.kill(worker, no_restart=True) + for rendezvous_actor in rendezvous_actors: + with suppress(Exception): + ray.kill(rendezvous_actor, no_restart=True) ray.util.remove_placement_group(pg) diff --git a/tests/tine_engine/test_ddp_accelerator_unit.py b/tests/tine_engine/test_ddp_accelerator_unit.py index 51ce048..298579e 100644 --- a/tests/tine_engine/test_ddp_accelerator_unit.py +++ b/tests/tine_engine/test_ddp_accelerator_unit.py @@ -13,3 +13,23 @@ def test_ddp_accelerator_requires_cuda() -> None: with pytest.raises(CudaNotAvailableError): DDPAccelerator() + + +def test_ddp_accelerator_single_process_skips_process_group_and_ddp( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr(torch.cuda, "is_available", lambda: True) + monkeypatch.setattr(torch.cuda, "current_device", lambda: 0) + monkeypatch.setattr(torch.cuda, "set_device", lambda device: None) + monkeypatch.setattr(torch.distributed, "is_initialized", lambda: False) + monkeypatch.setenv("WORLD_SIZE", "1") + monkeypatch.setenv("RANK", "0") + + accelerator = DDPAccelerator() + model = torch.nn.Linear(2, 1) + monkeypatch.setattr(model, "to", lambda device: model) + + assert accelerator.device == torch.device("cuda", 0) + assert accelerator._process_group_initialized is False + assert accelerator.prepare_model(model) is model + accelerator.destroy() diff --git a/tests/utils/test_ray_utils_unit.py b/tests/utils/test_ray_utils_unit.py index ff81ebb..e0d7642 100644 --- a/tests/utils/test_ray_utils_unit.py +++ b/tests/utils/test_ray_utils_unit.py @@ -16,6 +16,7 @@ get_num_worker_options, get_placement_group, get_placement_group_node_ids, + start_ray_rendezvous_store, ) @@ -34,18 +35,35 @@ def test_ray_cfg_run_uses_explicit_resources_without_dataloader_cfg( } ) captured: dict[str, object] = {} + init_calls: list[None] = [] + ray_state = {"initialized": False} shutdown_kwargs: list[dict[str, object]] = [] - monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.init", lambda: None) + def fake_init() -> None: + init_calls.append(None) + ray_state["initialized"] = True + + def fake_shutdown(**kwargs: object) -> None: + shutdown_kwargs.append(kwargs) + ray_state["initialized"] = False + + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.init", fake_init) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.remote", lambda exp_class: object()) monkeypatch.setattr( "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", lambda: {"CPU": 3.0, "GPU": 0.0}, ) - monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.available_resources", + lambda: pytest.fail("explicit worker sizing should not query available resources"), + ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.is_initialized", + lambda: ray_state["initialized"], + ) monkeypatch.setattr( "tinyexp.exp_mixins.basic_mixins.ray.shutdown", - lambda **kwargs: shutdown_kwargs.append(kwargs), + fake_shutdown, ) def stop_after_resource_resolution(**kwargs): # type: ignore[no-untyped-def] @@ -64,7 +82,56 @@ def stop_after_resource_resolution(**kwargs): # type: ignore[no-untyped-def] assert captured["num_cpus_per_worker"] == 3 assert captured["num_gpus_per_worker"] == 0 assert captured["timeout_s"] == 7.0 + assert init_calls == [None] assert shutdown_kwargs == [{"_exiting_interpreter": True}] + assert ray_state["initialized"] is False + + +def test_ray_cfg_run_reuses_external_ray_runtime( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cfg = OmegaConf.create( + { + "ray_cfg": { + "ray_num_worker": 1, + "ray_num_cpus_per_worker": 1, + "ray_num_gpus_per_worker": 0, + "ray_placement_strategy": "PACK", + } + } + ) + shutdown_calls: list[dict[str, object]] = [] + + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.init", + lambda: pytest.fail("ray.init should not be called for an external runtime"), + ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.is_initialized", + lambda: True, + ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.shutdown", + lambda **kwargs: shutdown_calls.append(kwargs), + ) + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.remote", lambda exp_class: object()) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", + lambda: {"CPU": 1.0, "GPU": 0.0}, + ) + + def fail_placement_group(**kwargs: object) -> None: + raise RuntimeError("run failed") # noqa: TRY003 + + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.get_placement_group", + fail_placement_group, + ) + + with pytest.raises(RuntimeError, match="run failed"): + RayCfgMixin.RayCfg.run(object, cfg) + + assert shutdown_calls == [] def test_ray_cfg_run_rejects_invalid_ray_worker_count_without_starting_ray( @@ -88,7 +155,7 @@ def test_ray_cfg_run_rejects_invalid_ray_worker_count_without_starting_ray( RayCfgMixin.RayCfg.run(object, cfg) -def test_ray_cfg_run_resolves_auto_worker_count_from_cluster_gpus( +def test_ray_cfg_run_resolves_auto_worker_count_from_available_gpus( monkeypatch: pytest.MonkeyPatch, ) -> None: cfg = OmegaConf.create( @@ -110,16 +177,56 @@ def stop_after_resolution(exp_class): # type: ignore[no-untyped-def] "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", lambda: {"CPU": 8.0, "GPU": 2.0}, ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.available_resources", + lambda: {"CPU": 8.0, "GPU": 1.0}, + ) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.shutdown", lambda **kwargs: None) with pytest.raises(RuntimeError, match="resolved"): RayCfgMixin.RayCfg.run(object, cfg) - assert cfg.ray_cfg.ray_num_worker == 2 + assert cfg.ray_cfg.ray_num_worker == 1 -def test_ray_cfg_run_caps_auto_worker_count_by_cpu_and_gpu_capacity( +def test_ray_cfg_run_uses_total_resources_for_explicit_worker_count( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cfg = OmegaConf.create( + { + "ray_cfg": { + "ray_num_worker": 2, + "ray_num_cpus_per_worker": 1, + "ray_num_gpus_per_worker": 0, + } + } + ) + calls: list[str] = [] + + def stop_after_resolution(exp_class): # type: ignore[no-untyped-def] + raise RuntimeError("resolved") + + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.init", lambda: None) + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.remote", stop_after_resolution) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", + lambda: (calls.append("total"), {"CPU": 2.0, "GPU": 0.0})[1], + ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.available_resources", + lambda: (calls.append("available"), {"CPU": 0.0, "GPU": 0.0})[1], + ) + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.shutdown", lambda **kwargs: None) + + with pytest.raises(RuntimeError, match="resolved"): + RayCfgMixin.RayCfg.run(object, cfg) + + assert calls == ["total"] + + +def test_ray_cfg_run_caps_auto_worker_count_by_available_cpu_and_gpu_capacity( monkeypatch: pytest.MonkeyPatch, ) -> None: cfg = OmegaConf.create( @@ -141,16 +248,20 @@ def stop_after_resolution(exp_class): # type: ignore[no-untyped-def] "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", lambda: {"CPU": 8.0, "GPU": 4.0}, ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.available_resources", + lambda: {"CPU": 3.0, "GPU": 2.0}, + ) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.shutdown", lambda **kwargs: None) with pytest.raises(RuntimeError, match="resolved"): RayCfgMixin.RayCfg.run(object, cfg) - assert cfg.ray_cfg.ray_num_worker == 2 + assert cfg.ray_cfg.ray_num_worker == 1 -def test_ray_cfg_run_resolves_auto_worker_count_from_cluster_cpus_for_cpu_workers( +def test_ray_cfg_run_resolves_auto_worker_count_from_available_cpus_for_cpu_workers( monkeypatch: pytest.MonkeyPatch, ) -> None: cfg = OmegaConf.create( @@ -172,13 +283,17 @@ def stop_after_resolution(exp_class): # type: ignore[no-untyped-def] "tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", lambda: {"CPU": 8.0, "GPU": 0.0}, ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.available_resources", + lambda: {"CPU": 4.0, "GPU": 0.0}, + ) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.shutdown", lambda **kwargs: None) with pytest.raises(RuntimeError, match="resolved"): RayCfgMixin.RayCfg.run(object, cfg) - assert cfg.ray_cfg.ray_num_worker == 4 + assert cfg.ray_cfg.ray_num_worker == 2 @pytest.mark.parametrize( @@ -274,6 +389,7 @@ def test_ray_cfg_run_rejects_missing_cluster_gpus( ) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.init", lambda: None) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.cluster_resources", lambda: {"CPU": 8.0}) + monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.available_resources", lambda: {"CPU": 8.0}) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.is_initialized", lambda: True) monkeypatch.setattr("tinyexp.exp_mixins.basic_mixins.ray.shutdown", lambda **kwargs: None) @@ -311,6 +427,18 @@ def test_build_worker_env_vars_omits_ifname_by_default( assert "GLOO_SOCKET_IFNAME" not in env_vars +def test_build_worker_env_vars_enables_torchelastic_agent_store_for_multiple_workers() -> None: + env_vars = _build_worker_env_vars( + num_worker=2, + rank=0, + local_rank=0, + master_addr="10.0.0.1", + master_port=12345, + ) + + assert env_vars["TORCHELASTIC_USE_AGENT_STORE"] == "True" + + def test_build_ray_worker_env_vars_uses_actual_node_local_ranks( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -330,6 +458,7 @@ def test_build_ray_worker_env_vars_uses_actual_node_local_ranks( "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK", + "TORCHELASTIC_USE_AGENT_STORE", } ] * 4 assert [(env["RANK"], env["LOCAL_RANK"]) for env in env_vars] == [ @@ -437,9 +566,30 @@ def test_get_num_worker_options_uses_final_topology_env_for_each_bundle() -> Non for option in options_list ] == [("0", "0"), ("2", "0"), ("1", "1"), ("3", "1")] assert all( - set(option["runtime_env"]["env_vars"]) == {"WORLD_SIZE", "RANK", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"} + set(option["runtime_env"]["env_vars"]) + == { + "WORLD_SIZE", + "RANK", + "MASTER_ADDR", + "MASTER_PORT", + "LOCAL_RANK", + "TORCHELASTIC_USE_AGENT_STORE", + } for option in options_list ) + assert all(option["runtime_env"]["env_vars"]["TORCHELASTIC_USE_AGENT_STORE"] == "True" for option in options_list) + + +def test_get_num_worker_options_requires_managed_endpoint_for_multiple_workers() -> None: + placement_group = SimpleNamespace(bundle_specs=[{"CPU": 2, "GPU": 0}]) + + with pytest.raises(ValueError, match="start_ray_rendezvous_store"): + get_num_worker_options( + placement_group, + num_worker=2, + gpu_ratio=0.0, + node_ids=["node-a", "node-a"], + ) def test_ray_cfg_run_resolves_topology_before_constructing_worker_actors( @@ -456,6 +606,7 @@ def test_ray_cfg_run_resolves_topology_before_constructing_worker_actors( } ) events: list[object] = [] + rendezvous_actor = object() class FakeWorker: def __init__(self, env_vars: dict[str, str]) -> None: @@ -500,9 +651,20 @@ def options(self, **options: object) -> FakeConfiguredActor: ["node-a", "node-b", "node-a", "node-b"], )[1], ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.start_ray_rendezvous_store", + lambda pg, world_size, timeout_s: ( + events.append(("rendezvous", world_size, timeout_s)), + (rendezvous_actor, "10.0.0.1", 12345), + )[1], + ) monkeypatch.setattr( "tinyexp.exp_mixins.basic_mixins.get_network_config", - lambda: ("10.0.0.1", 12345), + lambda master_node_id: pytest.fail("multi-worker Ray run should use the rendezvous store"), + ) + monkeypatch.setattr( + "tinyexp.exp_mixins.basic_mixins.ray.kill", + lambda actor, no_restart: events.append(("kill", actor, no_restart)), ) monkeypatch.setattr( "tinyexp.exp_mixins.basic_mixins.ray.util.remove_placement_group", @@ -512,14 +674,18 @@ def options(self, **options: object) -> FakeConfiguredActor: RayCfgMixin.RayCfg.run(object, cfg) topology_index = events.index(("topology",)) + rendezvous_index = events.index(("rendezvous", 4, 120.0)) actor_events = [event for event in events if event[0] == "actor"] - assert topology_index < events.index(actor_events[0]) + assert topology_index < rendezvous_index < events.index(actor_events[0]) assert [(event[1]["RANK"], event[1]["LOCAL_RANK"]) for event in actor_events] == [ ("0", "0"), ("2", "0"), ("1", "1"), ("3", "1"), ] + assert {event[1]["MASTER_ADDR"] for event in actor_events} == {"10.0.0.1"} + assert {event[1]["TORCHELASTIC_USE_AGENT_STORE"] for event in actor_events} == {"True"} + assert ("kill", rendezvous_actor, True) in events def test_get_network_config_uses_public_ray_ip_api( @@ -533,6 +699,124 @@ def test_get_network_config_uses_public_ray_ip_api( assert 0 < master_port <= 65535 +def test_get_network_config_uses_requested_ray_node_address( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.nodes", + lambda: [ + { + "Alive": True, + "NodeID": "node-head", + "NodeManagerAddress": "10.0.0.1", + }, + { + "Alive": True, + "NodeID": "node-worker", + "NodeManagerAddress": "10.0.0.2", + }, + ], + ) + + master_addr, master_port = get_network_config("node-worker") + + assert master_addr == "10.0.0.2" + assert 0 < master_port <= 65535 + + +def test_get_network_config_rejects_missing_ray_node( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr("tinyexp.utils.ray_utils.ray.nodes", lambda: []) + + with pytest.raises(RuntimeError, match="node-worker.*not alive or is missing"): + get_network_config("node-worker") + + +def test_start_ray_rendezvous_store_uses_bundle_zero( + monkeypatch: pytest.MonkeyPatch, +) -> None: + captured = {} + + class FakeEndpointMethod: + def remote(self) -> str: + return "endpoint-ref" + + class FakeActor: + get_endpoint = FakeEndpointMethod() + + actor = FakeActor() + + class FakeConfiguredActorClass: + def remote(self, *args: object) -> FakeActor: + captured["actor_args"] = args + return actor + + class FakeRemoteActorClass: + def options(self, **options: object) -> FakeConfiguredActorClass: + captured["options"] = options + return FakeConfiguredActorClass() + + def fake_remote(**kwargs: object): # type: ignore[no-untyped-def] + captured["remote_kwargs"] = kwargs + return lambda actor_cls: FakeRemoteActorClass() + + monkeypatch.setattr("tinyexp.utils.ray_utils.ray.remote", fake_remote) + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.get", + lambda ref, **kwargs: ("10.0.0.2", 23456), + ) + + result = start_ray_rendezvous_store(object(), world_size=2, timeout_s=7.0) + + strategy = captured["options"]["scheduling_strategy"] + assert captured["remote_kwargs"] == {"num_cpus": 0} + assert captured["actor_args"] == (2, 7.0) + assert strategy.placement_group_bundle_index == 0 + assert result == (actor, "10.0.0.2", 23456) + + +def test_start_ray_rendezvous_store_timeout_kills_actor( + monkeypatch: pytest.MonkeyPatch, +) -> None: + killed = [] + + class FakeEndpointMethod: + def remote(self) -> str: + return "endpoint-ref" + + class FakeActor: + get_endpoint = FakeEndpointMethod() + + actor = FakeActor() + + class FakeConfiguredActorClass: + def remote(self, *args: object) -> FakeActor: + return actor + + class FakeRemoteActorClass: + def options(self, **options: object) -> FakeConfiguredActorClass: + return FakeConfiguredActorClass() + + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.remote", + lambda **kwargs: lambda actor_cls: FakeRemoteActorClass(), + ) + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.get", + lambda ref, **kwargs: (_ for _ in ()).throw(ray.exceptions.GetTimeoutError("timed out")), + ) + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.kill", + lambda actor_handle, no_restart: killed.append((actor_handle, no_restart)), + ) + + with pytest.raises(TimeoutError, match="rendezvous store timed out.*bundle 0"): + start_ray_rendezvous_store(object(), world_size=2, timeout_s=3.5) + + assert killed == [(actor, True)] + + def test_get_placement_group_defaults_to_pack(monkeypatch: pytest.MonkeyPatch) -> None: captured = {} @@ -561,6 +845,7 @@ def test_get_placement_group_timeout_removes_group( monkeypatch: pytest.MonkeyPatch, ) -> None: removed = [] + events = [] class FakePlacementGroup: def ready(self): @@ -574,10 +859,26 @@ def fake_get(ref, **kwargs): # type: ignore[no-untyped-def] monkeypatch.setattr("tinyexp.utils.ray_utils.ray.get", fake_get) monkeypatch.setattr( "tinyexp.utils.ray_utils.ray.util.remove_placement_group", - lambda pg: removed.append(pg), + lambda pg: (removed.append(pg), events.append("remove")), + ) + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.cluster_resources", + lambda: (events.append("total"), {"CPU": 8.0, "GPU": 2.0})[1], + ) + monkeypatch.setattr( + "tinyexp.utils.ray_utils.ray.available_resources", + lambda: (events.append("available"), {"CPU": 1.0, "GPU": 0.0})[1], ) - with pytest.raises(TimeoutError, match="workers=2.*GPU/worker=0.5.*strategy=SPREAD"): + with pytest.raises( + TimeoutError, + match=( + r"workers=2.*GPU/worker=0.5.*strategy=SPREAD.*" + r"requested CPU=6.*requested GPU=1.0.*" + r"total CPU=8.0.*total GPU=2.0.*" + r"available CPU=1.0.*available GPU=0.0" + ), + ): get_placement_group( num_worker=2, num_gpus_per_worker=0.5, @@ -587,6 +888,7 @@ def fake_get(ref, **kwargs): # type: ignore[no-untyped-def] ) assert len(removed) == 1 + assert events == ["total", "available", "remove"] def test_get_placement_group_error_removes_group( @@ -614,7 +916,7 @@ def ready(self): assert len(removed) == 1 -def test_get_placement_group_pins_first_bundle_to_head_when_ray_initialized( +def test_get_placement_group_does_not_pin_first_bundle_to_head_when_ray_initialized( monkeypatch: pytest.MonkeyPatch, ) -> None: captured = {} @@ -640,7 +942,7 @@ def fake_placement_group(bundles, strategy): assert captured == { "bundles": [ - {"CPU": 3, "GPU": 1, "node:__internal_head__": 0.001}, + {"CPU": 3, "GPU": 1}, {"CPU": 3, "GPU": 1}, ], "strategy": "PACK", diff --git a/tests/utils/test_redis_utils_unit.py b/tests/utils/test_redis_utils_unit.py index 93c0b40..7a872ba 100644 --- a/tests/utils/test_redis_utils_unit.py +++ b/tests/utils/test_redis_utils_unit.py @@ -1,16 +1,19 @@ from __future__ import annotations +import subprocess from types import SimpleNamespace from typing import Any from unittest.mock import Mock import pytest +import redis from tinyexp.utils.redis_utils import ( RayRedisClusterManager, RedisClientManager, RedisClusterManager, RedisClusterStartupError, + wait_for_redis_cluster, ) @@ -141,6 +144,105 @@ def set(self, key, value) -> bool: # type: ignore[no-untyped-def] assert manager._redis_client_for_key("train:sample:0") is manager._redis_client_for_key("train:sample:0") +def test_redis_client_manager_safe_operations_ignore_cluster_exceptions() -> None: + cluster_exception = getattr(redis.exceptions, "RedisClusterException", redis.exceptions.RedisError) + + class BrokenClusterClient: + def get(self, key: Any) -> None: + raise cluster_exception() # type: ignore[misc] + + def set(self, key: Any, value: Any) -> None: + raise cluster_exception() # type: ignore[misc] + + manager = RedisClientManager.__new__(RedisClientManager) + manager.redis_clients = [BrokenClusterClient()] + + assert manager.safe_get("key") is None + assert manager.safe_set("key", b"value") is False + + +def test_wait_for_redis_cluster_retries_constructor_failures( + monkeypatch: pytest.MonkeyPatch, +) -> None: + calls = [] + + def failing_constructor(**_kwargs: Any) -> None: + calls.append(True) + raise redis.exceptions.RedisClusterException("cluster is not reachable") # noqa: TRY003 + + monkeypatch.setattr("tinyexp.utils.redis_utils.redis.RedisCluster", failing_constructor) + + assert wait_for_redis_cluster("10.0.0.1", [7000, 7001, 7002], timeout_s=1.05) is False + assert len(calls) >= 2 + + +def test_wait_for_redis_cluster_recovers_from_cluster_exception( + monkeypatch: pytest.MonkeyPatch, +) -> None: + calls = [] + closed = [] + + class FakeRedisCluster: + def __init__(self, **_kwargs: Any) -> None: + calls.append(True) + + def ping(self) -> None: + if len(calls) == 1: + raise redis.exceptions.RedisClusterException("slots are not covered") # noqa: TRY003 + + def set(self, key: str, value: str, ex: int) -> None: + pass + + def get(self, key: str) -> bytes: + return b"ok" + + def close(self) -> None: + closed.append(True) + + monkeypatch.setattr("tinyexp.utils.redis_utils.redis.RedisCluster", FakeRedisCluster) + + assert wait_for_redis_cluster("10.0.0.1", [7000, 7001, 7002], timeout_s=2) is True + assert len(calls) == 2 + assert len(closed) == 2 + + +def test_ray_redis_cluster_manager_create_cluster_uses_timeout( + monkeypatch: pytest.MonkeyPatch, +) -> None: + captured: dict[str, Any] = {} + + def fake_run(command, **kwargs): # type: ignore[no-untyped-def] + captured["command"] = command + captured["kwargs"] = kwargs + return SimpleNamespace(returncode=0, stdout="", stderr="") + + monkeypatch.setattr("tinyexp.utils.redis_utils.subprocess.run", fake_run) + + RayRedisClusterManager._create_cluster( + "/usr/bin/redis-cli", + [{"host": "10.0.0.1", "ports": [7000, 7001, 7002]}], + timeout_s=4.5, + ) + + assert captured["kwargs"]["timeout"] == 4.5 + + +def test_ray_redis_cluster_manager_create_cluster_timeout_is_reported( + monkeypatch: pytest.MonkeyPatch, +) -> None: + def fake_run(*args, **kwargs): # type: ignore[no-untyped-def] + raise subprocess.TimeoutExpired(cmd=args[0], timeout=1.5) + + monkeypatch.setattr("tinyexp.utils.redis_utils.subprocess.run", fake_run) + + with pytest.raises(RedisClusterStartupError, match="cluster create timed out after 1.5s"): + RayRedisClusterManager._create_cluster( + "/usr/bin/redis-cli", + [{"host": "10.0.0.1", "ports": [7000, 7001, 7002]}], + timeout_s=1.5, + ) + + def test_redis_cluster_manager_validates_inputs() -> None: with pytest.raises(ValueError, match="ports must not be empty"): RedisClusterManager(ports=[], max_memory_per_port=1.0) @@ -155,6 +257,21 @@ def test_redis_cluster_manager_validates_inputs() -> None: RedisClusterManager(ports=[7000], max_memory_per_port=0.0) +def test_redis_cluster_manager_validates_cluster_bus_port_range() -> None: + with pytest.raises(ValueError, match="Invalid Redis Cluster bus port 65536.*data port 55536"): + RedisClusterManager(ports=[55536], max_memory_per_port=1.0, cluster_enabled=True) + + RedisClusterManager(ports=[55535], max_memory_per_port=1.0, cluster_enabled=True) + + +def test_redis_cluster_manager_rejects_data_bus_port_conflicts() -> None: + with pytest.raises( + ValueError, + match="Redis Cluster data/bus port conflict: data port 7000 derives bus port 17000", + ): + RedisClusterManager(ports=[7000, 17000], max_memory_per_port=1.0, cluster_enabled=True) + + def test_redis_cluster_manager_converts_gb_to_bytes() -> None: mgr = RedisClusterManager(ports=[7000], max_memory_per_port=0.5) assert mgr.max_memory_per_port_gb == 0.5 @@ -348,11 +465,18 @@ def __init__(self, **kwargs: Any) -> None: def ping(self) -> None: pass + def set(self, key: str, value: str, ex: int) -> None: + captured.setdefault("readiness_values", {})[key] = value + + def get(self, key: str) -> bytes: + return b"ok" + def close(self) -> None: pass - def fake_run(command, check, capture_output, text): # type: ignore[no-untyped-def] + def fake_run(command, check, capture_output, text, **kwargs): # type: ignore[no-untyped-def] captured["commands"].append(command) + captured["command_kwargs"] = kwargs return SimpleNamespace(returncode=0, stdout="", stderr="") monkeypatch.setattr("tinyexp.utils.redis_utils.shutil.which", lambda _cmd: "/usr/bin/redis-cli") @@ -389,6 +513,7 @@ def fake_run(command, check, capture_output, text): # type: ignore[no-untyped-d "--cluster-yes", ] ] + assert captured["command_kwargs"] == {"timeout": 30.0} assert startup_host == "10.0.0.1" assert startup_ports == [7000, 7001, 7002] assert world_size == 2 diff --git a/tinyexp/__init__.py b/tinyexp/__init__.py index c18fa47..07185db 100644 --- a/tinyexp/__init__.py +++ b/tinyexp/__init__.py @@ -4,6 +4,7 @@ import os import sys +from collections.abc import Mapping from dataclasses import dataclass, field from typing import Any, Optional @@ -34,6 +35,28 @@ ] +def _patch_argparse_help_for_python314() -> None: + """Allow Hydra's lazy help object to work with Python 3.14 argparse.""" + if sys.version_info < (3, 14): + return + + import argparse + + original_get_help_string = argparse.HelpFormatter._get_help_string + if getattr(original_get_help_string, "_tinyexp_python314_compat", False): + return + + def get_help_string(self, action): + help_string = original_get_help_string(self, action) + return help_string if isinstance(help_string, str) else str(help_string) + + get_help_string._tinyexp_python314_compat = True + argparse.HelpFormatter._get_help_string = get_help_string + + +_patch_argparse_help_for_python314() + + @dataclass class _HydraConfig(HydraConf): """ @@ -115,10 +138,24 @@ def set_cfg(self, cfg_hydra, cfg_object=None): cfg_object = self for key, value in cfg_hydra.items(): if hasattr(cfg_object, key): - if isinstance(value, (DictConfig, dict)): - # If the value is a dictionary, recursively set attributes - sub_object = getattr(cfg_object, key) - self.set_cfg(value, sub_object) + sub_object = getattr(cfg_object, key) + if isinstance(value, (DictConfig, Mapping)): + if isinstance(sub_object, Mapping): + # Hydra has already merged and validated mapping values; store the resolved plain dict. + new_value = ( + dict(OmegaConf.to_container(value, resolve=True)) + if isinstance(value, DictConfig) + else dict(value) + ) + if sub_object != new_value: + self.overrided_cfg[key] = { + "value": new_value, + "original": sub_object, + } + setattr(cfg_object, key, new_value) + else: + # Nested config objects still receive recursive attribute updates. + self.set_cfg(value, sub_object) else: # Otherwise, set the attribute directly ori_value = getattr(cfg_object, key, None) diff --git a/tinyexp/cli/run_with_ray_cluster.py b/tinyexp/cli/run_with_ray_cluster.py index 6e4d935..e045d61 100644 --- a/tinyexp/cli/run_with_ray_cluster.py +++ b/tinyexp/cli/run_with_ray_cluster.py @@ -48,6 +48,13 @@ def positive_int(value: str) -> int: return parsed +def port(value: str) -> int: + parsed = uint(value) + if parsed < 1 or parsed > 65535: + raise argparse.ArgumentTypeError(f"must be in 1..65535: {value}") # noqa: TRY003 + return parsed + + def parse_args(argv: list[str]) -> argparse.Namespace: parser = argparse.ArgumentParser( prog="tinyexp-run-with-ray-cluster", @@ -66,7 +73,7 @@ def parse_args(argv: list[str]) -> argparse.Namespace: default="", help="Node IP passed to ray start --head. Default: head addr.", ) - parser.add_argument("--ray-port", type=uint, default=6379, help="Ray head port. Default: 6379.") + parser.add_argument("--ray-port", type=port, default=6379, help="Ray head port. Default: 6379.") parser.add_argument( "--ray-bin", default="", @@ -79,7 +86,7 @@ def parse_args(argv: list[str]) -> argparse.Namespace: ) parser.add_argument( "--wait-timeout", - type=uint, + type=positive_int, default=600, help="Startup wait timeout. Default: 600.", ) @@ -118,6 +125,8 @@ def parse_args(argv: list[str]) -> argparse.Namespace: args.command = args.command[1:] if not args.command: parser.error("missing command.") + if args.node_rank >= args.node_count: + parser.error(f"--node-rank must be < --node-count, got {args.node_rank} >= {args.node_count}") if args.node_count > 1 and not args.head_addr: parser.error("--head-addr is required for multi-node Ray jobs.") return args @@ -142,7 +151,7 @@ def prepend_no_proxy(env: dict[str, str], head_addr: str) -> None: env["no_proxy"] = f"{prefix},{env.get('no_proxy', '')}" -def run_quiet(args: list[str], env: dict[str, str] | None = None) -> int: +def run_quiet(args: list[str], env: dict[str, str] | None = None, *, timeout_s: float = 5.0) -> int: try: return subprocess.run( # noqa: S603 args, @@ -150,7 +159,7 @@ def run_quiet(args: list[str], env: dict[str, str] | None = None) -> int: stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=False, - timeout=5, + timeout=timeout_s, ).returncode except subprocess.TimeoutExpired: return 1 @@ -170,16 +179,26 @@ def handle_sigterm(signum: int, _frame: object) -> None: raise SystemExit(128 + signum) -def ray_alive_count(python_bin: str, ray_address: str, env: dict[str, str]) -> int | None: +def ray_alive_count( + python_bin: str, + ray_address: str, + env: dict[str, str], + *, + timeout_s: float = 5.0, +) -> int | None: check_env = {**env, "RAY_ADDRESS": ray_address} - result = subprocess.run( # noqa: S603 - [python_bin, "-c", ALIVE_NODE_COUNT_CODE], - env=check_env, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.DEVNULL, - check=False, - ) + try: + result = subprocess.run( # noqa: S603 + [python_bin, "-c", ALIVE_NODE_COUNT_CODE], + env=check_env, + text=True, + stdout=subprocess.PIPE, + stderr=subprocess.DEVNULL, + check=False, + timeout=timeout_s, + ) + except subprocess.TimeoutExpired: + return None for line in reversed(result.stdout.splitlines()): stripped = line.strip() if stripped.isdigit(): @@ -189,13 +208,24 @@ def ray_alive_count(python_bin: str, ray_address: str, env: dict[str, str]) -> i def wait_for_head(ray_bin: str, ray_address: str, wait_timeout: int) -> bool: deadline = time.monotonic() + wait_timeout - while time.monotonic() < deadline: - if run_quiet([ray_bin, "status", f"--address={ray_address}"]) == 0: + while True: + remaining = deadline - time.monotonic() + if remaining <= 0: + return False + if ( + run_quiet( + [ray_bin, "status", f"--address={ray_address}"], + timeout_s=remaining, + ) + == 0 + ): log(f"Ray head is reachable at {ray_address}") return True log(f"waiting for Ray head at {ray_address}") - time.sleep(2) - return False + remaining = deadline - time.monotonic() + if remaining <= 0: + return False + time.sleep(min(2, remaining)) def run_head( @@ -226,14 +256,25 @@ def run_head( deadline = time.monotonic() + args.wait_timeout alive_count: int | None = 0 - while time.monotonic() < deadline: - alive_count = ray_alive_count(python_bin, ray_address, env) + while True: + remaining = deadline - time.monotonic() + if remaining <= 0: + break + alive_count = ray_alive_count( + python_bin, + ray_address, + env, + timeout_s=remaining, + ) if alive_count is not None and alive_count >= args.node_count: log(f"Ray cluster has {alive_count}/{args.node_count} alive nodes") log(f"running command with RAY_ADDRESS={ray_address}: {' '.join(args.command)}") return subprocess.run(args.command, env=env, check=False).returncode # noqa: S603 log(f"waiting for Ray nodes: {alive_count or 0}/{args.node_count} alive") - time.sleep(2) + remaining = deadline - time.monotonic() + if remaining <= 0: + break + time.sleep(min(2, remaining)) print( f"Error: timed out waiting for {args.node_count} Ray nodes at {ray_address}; " diff --git a/tinyexp/cli/run_with_redis.py b/tinyexp/cli/run_with_redis.py index 8f04ce4..c4dbb0d 100644 --- a/tinyexp/cli/run_with_redis.py +++ b/tinyexp/cli/run_with_redis.py @@ -5,6 +5,8 @@ standalone Redis for single-node jobs or a Redis Cluster for multi-node jobs, injects the final Redis connection settings back into the child command as Hydra overrides, waits for the child command, and then stops Redis processes it owns. +In multi-node mode, the HTTP rendezvous is used only during startup; each +wrapper cleans up its locally owned Redis as soon as its child command exits. For multi-node jobs, pass ``--node-count``, ``--node-rank``, and ``--head-addr`` or provide matching StepFun env/Hydra values through ``NODE_RANK`` and @@ -35,11 +37,10 @@ from pathlib import Path from typing import Any, cast -import redis from omegaconf import OmegaConf from tinyexp.exp_mixins import RedisCfgMixin -from tinyexp.utils.redis_utils import RedisClusterManager +from tinyexp.utils.redis_utils import RedisClusterManager, wait_for_redis_cluster REDIS_RENDEZVOUS_PORT = 26379 REDIS_RENDEZVOUS_TIMEOUT_S = 600 @@ -276,13 +277,6 @@ def main(argv: list[str]) -> int: start_new_session=True, ) exit_code = CHILD_PROCESS.wait() - if startup_node is not None and world_size > 1: - wait_for_rendezvous_finish( - head_addr=head_addr, - rendezvous_port=args.rendezvous_port, - node_id=redis_node_id(lifecycle.started_nodes), - timeout_s=args.wait_timeout, - ) return exit_code finally: CHILD_PROCESS = None @@ -377,7 +371,17 @@ def start_rendezvous_redis_cluster( ): return None - server = start_rendezvous_server(head_addr, rendezvous_port, world_size, redis_cli_path) if node_rank == 0 else None + server = ( + start_rendezvous_server( + head_addr, + rendezvous_port, + world_size, + redis_cli_path, + timeout_s=timeout_s, + ) + if node_rank == 0 + else None + ) if node_rank == 0: if server is None: return None @@ -412,20 +416,36 @@ def resolve_node_host(head_addr: str, rendezvous_port: int) -> str: def start_rendezvous_server( # noqa: C901 - head_addr: str, rendezvous_port: int, world_size: int, redis_cli_path: str | None + head_addr: str, + rendezvous_port: int, + world_size: int, + redis_cli_path: str | None, + timeout_s: float = REDIS_RENDEZVOUS_TIMEOUT_S, ) -> ThreadingHTTPServer | None: + if timeout_s <= 0: + raise ValueError("rendezvous timeout must be greater than 0") # noqa: TRY003 + nodes: dict[str, tuple[str, list[int]]] = {} - finished_nodes: set[str] = set() lock = threading.Lock() ready: dict[str, Any] = {} error = "" + creation_in_progress = False + startup_deadline = time.monotonic() + timeout_s + + def response_locked() -> dict[str, Any]: + if error: + return {"status": "error", "message": error} + if ready: + return {"status": "ready", **ready} + return { + "status": "waiting", + "registered": len(nodes), + "world_size": world_size, + } class Handler(BaseHTTPRequestHandler): - def do_POST(self) -> None: - nonlocal error - if self.path == "/finish": - self.handle_finish() - return + def do_POST(self) -> None: # noqa: C901 + nonlocal creation_in_progress, error if self.path != "/register": self.send_error(404) return @@ -441,78 +461,83 @@ def do_POST(self) -> None: self.send_error(400, f"invalid registration payload: {exc}") return + node_key = f"{host}:{','.join(str(port) for port in worker_ports)}" + creation_request: tuple[list[str], str, list[int], float] | None = None + response: dict[str, Any] | None = None + with lock: - nodes[f"{host}:{','.join(str(port) for port in worker_ports)}"] = ( - host, - worker_ports, - ) - if not ready and not error and len(nodes) >= world_size: - addresses = [ - f"{node_host}:{port}" for node_host, node_ports in nodes.values() for port in node_ports - ] - create_cmd = [ - redis_cli_path or "redis-cli", - "--cluster", - "create", - *addresses, - "--cluster-replicas", - "0", - "--cluster-yes", - ] - result = subprocess.run(create_cmd, check=False, capture_output=True, text=True) # noqa: S603 - if result.returncode != 0: - error = f"redis-cli cluster create failed: {result.stderr or result.stdout}" + nodes[node_key] = (host, worker_ports) + if error or ready or creation_in_progress or len(nodes) < world_size: + response = response_locked() + else: + remaining = startup_deadline - time.monotonic() + if remaining <= 0: + error = f"Redis Cluster startup timed out after {timeout_s}s" + response = response_locked() else: + creation_in_progress = True startup_host, startup_ports = next(iter(nodes.values())) - if wait_for_redis_cluster(startup_host, startup_ports): - ready.update( - { - "startup_host": startup_host, - "startup_ports": startup_ports, - } - ) + addresses = [ + f"{node_host}:{port}" for node_host, node_ports in nodes.values() for port in node_ports + ] + creation_request = (addresses, startup_host, startup_ports, remaining) + + if creation_request is not None: + addresses, startup_host, startup_ports, command_timeout = creation_request + creation_error: str | None = None + creation_result: tuple[str, list[int]] | None = None + create_cmd = [ + redis_cli_path or "redis-cli", + "--cluster", + "create", + *addresses, + "--cluster-replicas", + "0", + "--cluster-yes", + ] + try: + result = subprocess.run( # noqa: S603 + create_cmd, + check=False, + capture_output=True, + text=True, + timeout=command_timeout, + ) + if result.returncode != 0: + creation_error = f"redis-cli cluster create failed: {result.stderr or result.stdout}" + else: + readiness_timeout = startup_deadline - time.monotonic() + if readiness_timeout <= 0: + creation_error = f"Redis Cluster startup timed out after {timeout_s}s" + elif wait_for_redis_cluster( + startup_host, + startup_ports, + timeout_s=readiness_timeout, + ): + creation_result = (startup_host, startup_ports) else: - error = f"Redis Cluster did not become ready at {startup_host}:{startup_ports}" - - if error: - response: dict[str, Any] = {"status": "error", "message": error} - elif ready: - response = {"status": "ready", **ready} - else: - response = { - "status": "waiting", - "registered": len(nodes), - "world_size": world_size, - } + creation_error = f"Redis Cluster did not become ready at {startup_host}:{startup_ports}" + except subprocess.TimeoutExpired as exc: + creation_error = f"redis-cli cluster create timed out after {command_timeout:.3f}s" + print(f"{creation_error}: {exc}", file=sys.stderr) + except Exception as exc: + creation_error = f"Redis Cluster startup failed: {exc}" + + with lock: + creation_in_progress = False + if creation_result is not None and creation_error is None: + ready.update( + { + "startup_host": creation_result[0], + "startup_ports": creation_result[1], + } + ) + else: + error = creation_error or "Redis Cluster startup failed" + response = response_locked() - self.write_json(response) - - def handle_finish(self) -> None: - try: - length = int(self.headers.get("Content-Length", "0")) - payload = json.loads(self.rfile.read(length)) - node_id = str(payload["node_id"]) - if not node_id: - self.send_error(400, "invalid finish payload") - return - except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc: - self.send_error(400, f"invalid finish payload: {exc}") - return - - with lock: - finished_nodes.add(node_id) - if len(finished_nodes) >= world_size: - response = { - "status": "done", - "finished": len(finished_nodes), - "world_size": world_size, - } - else: - response = { - "status": "waiting", - "finished": len(finished_nodes), - "world_size": world_size, - } + if response is None: + response = {"status": "error", "message": "Redis Cluster startup did not produce a response"} self.write_json(response) def write_json(self, response: dict[str, Any]) -> None: @@ -556,6 +581,9 @@ def register_redis_node( payload = json.dumps({"host": node_host, "ports": ports}).encode() deadline = time.monotonic() + timeout_s while time.monotonic() < deadline: + remaining = deadline - time.monotonic() + if remaining <= 0: + break try: request = urllib.request.Request( # noqa: S310 url, @@ -563,10 +591,10 @@ def register_redis_node( headers={"Content-Type": "application/json"}, method="POST", ) - with urllib.request.urlopen(request, timeout=5) as response: # noqa: S310 + with urllib.request.urlopen(request, timeout=min(5.0, remaining)) as response: # noqa: S310 result = json.loads(response.read()) except (OSError, urllib.error.URLError, TimeoutError, json.JSONDecodeError): - time.sleep(1) + time.sleep(min(1.0, max(0.0, deadline - time.monotonic()))) continue if result["status"] == "ready": @@ -583,81 +611,12 @@ def register_redis_node( f"Redis rendezvous waiting: {result['registered']}/{result['world_size']} workers registered", flush=True, ) - time.sleep(1) + time.sleep(min(1.0, max(0.0, deadline - time.monotonic()))) print(f"Redis rendezvous timed out after {timeout_s}s", file=sys.stderr) return None -def redis_node_id(started_nodes: list[tuple[str, int]]) -> str: - return ";".join(f"{host}:{port}" for host, port in sorted(started_nodes)) - - -def wait_for_rendezvous_finish(*, head_addr: str, rendezvous_port: int, node_id: str, timeout_s: int) -> bool: - if not node_id: - return True - url = f"http://{head_addr}:{rendezvous_port}/finish" - payload = json.dumps({"node_id": node_id}).encode() - deadline = time.monotonic() + timeout_s - while time.monotonic() < deadline: - try: - request = urllib.request.Request( # noqa: S310 - url, - data=payload, - headers={"Content-Type": "application/json"}, - method="POST", - ) - with urllib.request.urlopen(request, timeout=5) as response: # noqa: S310 - result = json.loads(response.read()) - except (OSError, urllib.error.URLError, TimeoutError, json.JSONDecodeError): - time.sleep(1) - continue - if result["status"] == "done": - print("Redis rendezvous finish barrier complete", flush=True) - return True - print( - f"Redis rendezvous finish waiting: {result['finished']}/{result['world_size']} workers finished", - flush=True, - ) - time.sleep(1) - print(f"Redis rendezvous finish barrier timed out after {timeout_s}s", file=sys.stderr) - return False - - -def wait_for_redis_cluster(startup_host: str, startup_ports: list[int], timeout_s: int = 30) -> bool: - deadline = time.monotonic() + timeout_s - last_error: Exception | None = None - while time.monotonic() < deadline: - cluster = redis.RedisCluster( - startup_nodes=[redis.cluster.ClusterNode(startup_host, int(port)) for port in startup_ports], - decode_responses=False, - socket_connect_timeout=2, - socket_timeout=2, - ) - try: - cluster.ping() - ready = True - for index in range(16): - key = f"tinyexp:redis-cluster-readiness:{index}" - cluster.set(key, "ok", ex=60) - if cluster.get(key) != b"ok": - ready = False - break - except redis.exceptions.RedisError as exc: - last_error = exc - time.sleep(1) - else: - if ready: - return True - last_error = RuntimeError("readiness value mismatch") - time.sleep(1) - finally: - with suppress(Exception): - cluster.close() - print(f"Redis Cluster readiness check failed: {last_error}", file=sys.stderr) - return False - - def signal_process_group(process: subprocess.Popen[Any], signum: int) -> None: pid = getattr(process, "pid", None) killpg = getattr(os, "killpg", None) diff --git a/tinyexp/dataset/sampler.py b/tinyexp/dataset/sampler.py index 5be03e7..4eda6e0 100644 --- a/tinyexp/dataset/sampler.py +++ b/tinyexp/dataset/sampler.py @@ -9,19 +9,60 @@ class InfiniteSampler(Sampler[int]): - """Sample an infinite, rank-sharded stream of dataset indices. - - The global stream is ``shuffle(range(size))`` repeated forever when - ``shuffle`` is true, or ``range(size)`` repeated forever otherwise. Each - rank consumes ``global_stream[rank::world_size]``. This is a continuous - stream sampler; ``set_epoch`` only positions that stream at a logical - epoch boundary for training loops that use epochs. - - ``drop_last`` affects only the logical epoch length returned by ``len``. - It does not stop the infinite stream or remove samples permanently. With - a non-divisible ``size``, ``drop_last=False`` gives each rank the ceiling - number of samples per logical epoch, while ``drop_last=True`` gives the - floor number. + """Yield an infinite, deterministic, rank-sharded stream of indices. + + Stream model: + + - With ``shuffle=True``, the global stream is an endless sequence of + shuffled ``range(size)`` permutations generated from ``seed``. With + ``shuffle=False``, it is ``range(size)`` repeated forever. + - Rank ``r`` consumes ``global_stream[r::world_size]``. Distributed ranks + therefore need the same resolved seed to consume disjoint positions from + one global stream. + - The iterator never raises ``StopIteration``. Training code must impose a + finite step count. Create one iterator and keep consuming it across + logical epochs; there is no need to recreate it or call ``set_epoch`` at + every epoch. + + Logical epoch and length: + + - ``len(sampler)`` is a nominal per-rank epoch length used for bookkeeping; + it is not the finite length of the iterator. + - This sampler's ``drop_last`` only selects ``floor(size / world_size)`` + instead of ``ceil(size / world_size)`` for that nominal length. It does + not truncate the stream or permanently discard indices. The floor can be + zero when ``size < world_size``. + - When ``size`` is not divisible by ``world_size``, a nominal epoch is not + guaranteed to contain every dataset index exactly once. With + ``drop_last=False`` it may cross a permutation boundary; with + ``drop_last=True`` the remainder continues into a later logical epoch. + - ``DataLoader(drop_last=...)`` is separate from this sampler option. + Because the source iterator is infinite, standard DataLoader auto- + batching keeps yielding full batches forever. ``len(dataloader)`` is + merely computed from ``len(sampler)`` with batch rounding. Consuming + ``len(dataloader)`` batches can therefore consume more or fewer than + ``len(sampler)`` indices per rank when the batch size does not divide the + nominal sampler length. + + Resume contract: + + - Resume is supported at logical epoch boundaries only. Call + ``set_epoch(epoch)`` before ``iter(sampler)`` or ``iter(dataloader)``. + Calling it afterwards has no effect on that existing iterator. + - Exact sampler-index continuation requires each completed logical epoch to + have consumed exactly ``len(sampler)`` indices per rank. + - Dataset size and index semantics, resolved seed, ``shuffle``, sampler + ``drop_last``, world size, rank assignment, batch size, DataLoader + ``drop_last``, batch sampler, and steps per epoch must remain unchanged. + - If the batching loop consumes a different number of indices, the method + restores only the nominal epoch. Boundary indices may repeat or be + skipped relative to an uninterrupted run, while subsequent sampling + still follows the configured stream. + - This sampler does not capture model or transform RNG, Python/NumPy/Torch + RNG, DataLoader worker RNG, worker-local dataset state, or prefetched + batches. Full training resume is therefore epoch-level and statistical, + not bit-exact. Mid-epoch or iteration-level resume is intentionally out + of scope. """ def __init__( @@ -37,12 +78,14 @@ def __init__( Args: size: The positive number of samples in the underlying dataset. shuffle: Whether to shuffle each repeated dataset permutation. - seed: The initial shuffle seed. ``None`` creates a random seed - once when the sampler is constructed. Distributed callers - should pass the same explicit seed to every rank. + seed: The initial shuffle seed. ``None`` resolves a random seed + once when this sampler is constructed. Distributed callers + must pass the same explicit seed to every rank, or synchronize + the resolved seed themselves. drop_last: Whether a logical epoch uses the floor rather than the - ceiling number of samples per rank. This only affects ``len`` - and the position selected by ``set_epoch``. + ceiling number of samples per rank. This sampler-level option + only affects ``len`` and the position selected by + ``set_epoch``; it is independent of DataLoader ``drop_last``. accelerator: Optional object providing ``rank`` and ``world_size``. """ if not isinstance(size, int) or isinstance(size, bool) or size < 1: @@ -71,8 +114,13 @@ def __init__( def set_epoch(self, epoch: int) -> None: """Position the continuous stream at the start of logical ``epoch``. - This is epoch-level positioning only. The sampler deliberately does - not expose step-level resume state. + The position is ``epoch * len(self) * world_size`` in the global + stream. This only affects iterators created after the call. The sampler + deliberately does not expose iteration-level state; exact index + continuation depends on the consumption and configuration contract + documented on :class:`InfiniteSampler`. Positioning is implemented by + replaying and skipping the deterministic stream, so a large epoch + offset may add startup work when the new iterator is first consumed. """ epoch = int(epoch) if epoch < 0: diff --git a/tinyexp/examples/mnist_exp.py b/tinyexp/examples/mnist_exp.py index b0e5412..7ead904 100644 --- a/tinyexp/examples/mnist_exp.py +++ b/tinyexp/examples/mnist_exp.py @@ -159,6 +159,12 @@ def build_lr_scheduler(self, optimizer): # ------------------------------ bellowing is the execution part --------------------- # def run(self) -> None: accelerator = self.accelerator_cfg.build_accelerator() + try: + self._run(accelerator) + finally: + accelerator.destroy() + + def _run(self, accelerator: AcceleratorProtocol) -> None: run_dir = self.get_run_dir() logger = self.logger_cfg.build_logger(save_dir=run_dir, distributed_rank=accelerator.rank) cfg_dict = self.print_cfg(logger) @@ -181,8 +187,6 @@ def run(self) -> None: else: raise NotImplementedError(f"Mode {self.mode} is not implemented") - accelerator.destroy() - def _evaluate(self, accelerator, logger, module_or_module_path, val_dataloader=None) -> float: if isinstance(module_or_module_path, str): module = Net() @@ -230,6 +234,8 @@ def _train(self, accelerator, logger, cfg_dict, run_dir: str) -> None: # noqa: train_dataloader = self.dataloader_cfg.build_train_dataloader(accelerator) val_dataloader = self.dataloader_cfg.build_val_dataloader(accelerator) ori_module = self.module_cfg.build_module() + # Keep the optimizer attached to the final device-side parameters. + ori_module.to(accelerator.device) ori_optimizer = self.optimizer_cfg.build_optimizer(ori_module, train_dataloader, accelerator) lr_scheduler = self.lr_scheduler_cfg.build_lr_scheduler(ori_optimizer) diff --git a/tinyexp/examples/pi_exp.py b/tinyexp/examples/pi_exp.py index 74a9085..1675050 100644 --- a/tinyexp/examples/pi_exp.py +++ b/tinyexp/examples/pi_exp.py @@ -18,6 +18,7 @@ from tinyexp import TinyExp, store_and_run_exp from tinyexp.exp_mixins import LoggerCfgMixin, RayCfgMixin +from tinyexp.tiny_engine.accelerator import AcceleratorProtocol @dataclass(repr=False) @@ -51,6 +52,12 @@ def run(self) -> None: from tinyexp.tiny_engine.accelerator import CPUAccelerator accelerator = CPUAccelerator() + try: + self._run(accelerator) + finally: + accelerator.destroy() + + def _run(self, accelerator: AcceleratorProtocol) -> None: logger = self.logger_cfg.build_logger(save_dir=self.get_run_dir(), distributed_rank=accelerator.rank) self.print_cfg(logger) @@ -61,8 +68,6 @@ def run(self) -> None: flush=True, ) - accelerator.destroy() - def _estimate_pi(self, accelerator) -> float: generator = torch.Generator().manual_seed(self.pi_cfg.seed + accelerator.rank) samples_per_rank = self.pi_cfg.total_samples // accelerator.world_size diff --git a/tinyexp/examples/resnet_exp.py b/tinyexp/examples/resnet_exp.py index 2189552..f381295 100644 --- a/tinyexp/examples/resnet_exp.py +++ b/tinyexp/examples/resnet_exp.py @@ -271,7 +271,7 @@ def build_train_dataloader(self, accelerator, redis_cfg): "num_workers": self.train_data_worker_per_gpu, "pin_memory": True, "sampler": sampler, - "persistent_workers": True, # Keep workers alive for multiple epochs + "persistent_workers": self.train_data_worker_per_gpu > 0, } train_dataloader = torch.utils.data.DataLoader(ds_train, **train_kwargs) # from tinyexp.dataset.fake_dataloader import HoldOnesampleDataLoader @@ -290,7 +290,7 @@ def build_val_dataloader(self, accelerator): "batch_size": self.val_batch_size_per_device, "num_workers": self.val_data_worker_per_gpu, "pin_memory": True, - "persistent_workers": True, + "persistent_workers": self.val_data_worker_per_gpu > 0, } val_dataloader = torch.utils.data.DataLoader(ds_val, **val_kwargs) return val_dataloader @@ -299,6 +299,12 @@ def build_val_dataloader(self, accelerator): def run(self) -> None: accelerator = self.accelerator_cfg.build_accelerator() + try: + self._run(accelerator) + finally: + accelerator.destroy() + + def _run(self, accelerator: AcceleratorProtocol) -> None: run_dir = self.get_run_dir() logger = self.logger_cfg.build_logger(save_dir=run_dir, distributed_rank=accelerator.rank) cfg_dict = self.print_cfg(logger) @@ -321,8 +327,6 @@ def run(self) -> None: else: raise NotImplementedError(f"Mode {self.mode} is not implemented") - accelerator.destroy() - def _evaluate(self, accelerator, logger, module_or_module_path, val_dataloader=None) -> None: if isinstance(module_or_module_path, str): module: nn.Module = self.module_cfg.build_module() @@ -370,6 +374,8 @@ def _train(self, accelerator, logger, cfg_dict, run_dir: str) -> None: # noqa: train_dataloader = self.dataloader_cfg.build_train_dataloader(accelerator, self.redis_cfg) val_dataloader = self.dataloader_cfg.build_val_dataloader(accelerator) ori_module = self.module_cfg.build_module() + # Keep the optimizer attached to the final device-side parameters. + ori_module.to(accelerator.device) ori_optimizer = self.optimizer_cfg.build_optimizer(ori_module, train_dataloader, accelerator) module, optimizer = accelerator.prepare(ori_module, ori_optimizer) lr_scheduler = self.lr_scheduler_cfg.build_lr_scheduler(optimizer) @@ -406,8 +412,6 @@ def _train(self, accelerator, logger, cfg_dict, run_dir: str) -> None: # noqa: train_iter = iter(train_dataloader) for global_epoch in range(start_epoch, self.max_train_epochs): - if global_epoch != start_epoch and train_sampler is not None and hasattr(train_sampler, "set_epoch"): - train_sampler.set_epoch(global_epoch) module.train() epoch_start_time = time.time() diff --git a/tinyexp/exp_mixins/basic_mixins.py b/tinyexp/exp_mixins/basic_mixins.py index 9430615..2a671e0 100644 --- a/tinyexp/exp_mixins/basic_mixins.py +++ b/tinyexp/exp_mixins/basic_mixins.py @@ -9,7 +9,6 @@ import numpy as np import ray -import torch from loguru._logger import Logger from omegaconf import DictConfig from omegaconf.listconfig import ListConfig @@ -26,6 +25,7 @@ get_num_worker_options, get_placement_group, get_placement_group_node_ids, + start_ray_rendezvous_store, ) @@ -109,18 +109,22 @@ def run(cls, exp_class: type[Any], experiment_cfg: DictConfig) -> None: # noqa: if ray_cfg.ray_num_worker < -1 or ray_cfg.ray_num_worker == 0: raise InvalidWorkerCountError(ray_cfg.ray_num_worker) - ray.init() + owns_ray_runtime = not ray.is_initialized() + if owns_ray_runtime: + ray.init() pg = None redis_manager = None + rendezvous_actor = None worker_group = [] try: cluster_resources = ray.cluster_resources() + sizing_resources = ray.available_resources() if ray_cfg.ray_num_worker == -1 else cluster_resources ray_num_worker = _resolve_ray_num_worker( ray_cfg.ray_num_worker, num_cpus_per_worker, num_gpus_per_worker, - cluster_resources, + sizing_resources, ) if ray_cfg.ray_num_worker == -1: ray_cfg.ray_num_worker = ray_num_worker @@ -152,8 +156,15 @@ def run(cls, exp_class: type[Any], experiment_cfg: DictConfig) -> None: # noqa: strategy=ray_cfg.ray_placement_strategy, timeout_s=placement_timeout_s, ) - master_addr, master_port = get_network_config() worker_node_ids = get_placement_group_node_ids(pg, ray_cfg.ray_num_worker) + if ray_cfg.ray_num_worker > 1: + rendezvous_actor, master_addr, master_port = start_ray_rendezvous_store( + pg, + ray_cfg.ray_num_worker, + placement_timeout_s, + ) + else: + master_addr, master_port = get_network_config(worker_node_ids[0]) options_list = get_num_worker_options( pg, ray_cfg.ray_num_worker, @@ -181,6 +192,10 @@ def run(cls, exp_class: type[Any], experiment_cfg: DictConfig) -> None: # noqa: ray.get([worker.set_cfg.remote(experiment_cfg) for worker in worker_group]) ray.get([worker.run.remote() for worker in worker_group]) finally: + if rendezvous_actor is not None: + with suppress(Exception): + ray.kill(rendezvous_actor, no_restart=True) + if pg is not None: with suppress(Exception): ray.util.remove_placement_group(pg) @@ -189,7 +204,7 @@ def run(cls, exp_class: type[Any], experiment_cfg: DictConfig) -> None: # noqa: with suppress(Exception): redis_manager.stop() - if ray.is_initialized(): + if owns_ray_runtime and ray.is_initialized(): with suppress(Exception): # Drain Ray's asynchronous worker logs before tearing down the runtime. ray.shutdown(_exiting_interpreter=True) @@ -234,6 +249,7 @@ class RedisCfg: default_factory=lambda: ListConfig([7000, 7001, 7002, 7003, 7004, 7005]) ) redis_cache_max_memory: int = 160 # Maximum memory is 160GB, according to the ImageNet dataset size + redis_cluster_startup_timeout_s: float = 30.0 # 1: standalone Redis. In Ray launches, this starts one local Redis per alive Ray node. # -1: Ray-managed Redis Cluster using the alive Ray nodes. @@ -266,6 +282,8 @@ def save_checkpoint( exp_class: str = "", extra_state: Optional[dict[str, Any]] = None, ) -> str: + import torch + save_path = Path(run_dir) / name save_path.parent.mkdir(parents=True, exist_ok=True) @@ -362,6 +380,8 @@ def _load_required_state( @staticmethod def capture_rng_state() -> dict[str, Any]: + import torch + state: dict[str, Any] = { "python": random.getstate(), "numpy": np.random.get_state(), @@ -373,6 +393,8 @@ def capture_rng_state() -> dict[str, Any]: @staticmethod def restore_rng_state(state: dict[str, Any]) -> None: + import torch + random.setstate(state["python"]) np.random.set_state(state["numpy"]) torch.set_rng_state(state["torch"]) @@ -390,7 +412,12 @@ def load_checkpoint( strict: bool = True, map_location=None, ) -> dict[str, Any]: - checkpoint = self._validate_checkpoint_payload(path, torch.load(path, map_location=map_location)) + import torch + + checkpoint = self._validate_checkpoint_payload( + path, + torch.load(path, map_location=map_location, weights_only=False), + ) self._load_required_state( checkpoint, model=model, diff --git a/tinyexp/tiny_engine/accelerator/ddp_accelerator.py b/tinyexp/tiny_engine/accelerator/ddp_accelerator.py index 5fde232..fb44e9d 100644 --- a/tinyexp/tiny_engine/accelerator/ddp_accelerator.py +++ b/tinyexp/tiny_engine/accelerator/ddp_accelerator.py @@ -13,8 +13,19 @@ def __init__(self): super().__init__() if not torch.cuda.is_available(): raise CudaNotAvailableError() - self._init_process_group() - self._process_group_initialized = True # Mark that the process group has been initialized + + # Select the concrete local CUDA device before initializing NCCL. + # Ray workers may expose only one GPU, in which case the visible + # device is local index 0 even when LOCAL_RANK is non-zero. + if self.device.index is None: + self.device = torch.device("cuda", torch.cuda.current_device()) + torch.cuda.set_device(self.device) + + # Match Accelerate's normal single-process behavior: no process group + # and no DDP wrapper when there is only one worker. + if self.world_size > 1 and not dist.is_initialized(): + self._init_process_group() + self._process_group_initialized = True self.sync_gradients = True # currently not support accumulate gradient def _init_process_group(self): @@ -48,13 +59,17 @@ def prepare(self, model, optimizer=None): def prepare_model(self, module): module.to(self.device) - wrapped_module = nn.parallel.DistributedDataParallel( + if self.world_size < 2: + return module + + device_index = self.device.index + if device_index is None: + device_index = torch.cuda.current_device() + return nn.parallel.DistributedDataParallel( module, - # device_ids=[self.local_rank], - # find_unused_parameters=self.find_unused_parameters, - # broadcast_buffers=self.broadcast_buffers, + device_ids=[device_index], + output_device=device_index, ) - return wrapped_module def prepare_optimizer(self, optimizer): # refer to: https://github.com/pytorch/pytorch/issues/8741 diff --git a/tinyexp/utils/ray_utils.py b/tinyexp/utils/ray_utils.py index a80447b..7c56f66 100644 --- a/tinyexp/utils/ray_utils.py +++ b/tinyexp/utils/ray_utils.py @@ -5,6 +5,7 @@ from collections import Counter from collections.abc import Sequence from contextlib import suppress +from datetime import timedelta from typing import Any, Optional import ray @@ -14,11 +15,6 @@ from .redis_utils import RayRedisClusterManager -_RAY_HEAD_NODE_RESOURCE = "node:__internal_head__" -# Ray node resources are capacity-1 logical labels. A tiny fractional request pins -# bundle 0 to the head node without meaningfully consuming CPU/GPU resources. -_RAY_NODE_RESOURCE_PIN = 0.001 - def _maybe_start_ray_redis_cache( cfg: DictConfig, @@ -54,21 +50,30 @@ def get_placement_group( ): """Create and return a placement group for worker allocation.""" bundles = [{"CPU": num_cpus_per_worker, "GPU": num_gpus_per_worker} for _ in range(num_worker)] - cluster_resources = ray.cluster_resources() if ray.is_initialized() else {} - if num_worker > 1 and _RAY_HEAD_NODE_RESOURCE in cluster_resources: - # PyTorch env:// starts TCPStore on rank 0 at MASTER_ADDR. get_network_config() - # uses the Ray head address, so rank 0's bundle must be scheduled on the head. - bundles[0][_RAY_HEAD_NODE_RESOURCE] = _RAY_NODE_RESOURCE_PIN pg = placement_group(bundles=bundles, strategy=strategy) try: ray.get(pg.ready(), timeout=timeout_s) except ray.exceptions.GetTimeoutError as exc: + try: + total_resources = ray.cluster_resources() + except Exception: + total_resources = {} + try: + available_resources = ray.available_resources() + except Exception: + available_resources = {} with suppress(Exception): ray.util.remove_placement_group(pg) raise TimeoutError( # noqa: TRY003 f"Ray placement group timed out after {timeout_s}s: " f"workers={num_worker}, CPU/worker={num_cpus_per_worker}, " - f"GPU/worker={num_gpus_per_worker}, strategy={strategy}" + f"GPU/worker={num_gpus_per_worker}, strategy={strategy}; " + f"requested CPU={num_worker * num_cpus_per_worker}, " + f"requested GPU={num_worker * num_gpus_per_worker}; " + f"total CPU={total_resources.get('CPU', 'unknown')}, " + f"total GPU={total_resources.get('GPU', 'unknown')}; " + f"available CPU={available_resources.get('CPU', 'unknown')}, " + f"available GPU={available_resources.get('GPU', 'unknown')}" ) from exc except Exception: with suppress(Exception): @@ -77,6 +82,55 @@ def get_placement_group( return pg +class _RayRendezvousStoreActor: + def __init__(self, world_size: int, timeout_s: float) -> None: + import torch.distributed as dist + + self._master_addr = ray.util.get_node_ip_address() + self._store = dist.TCPStore( + host_name=self._master_addr, + port=0, + world_size=world_size, + is_master=True, + timeout=timedelta(seconds=timeout_s), + wait_for_workers=False, + ) + + def get_endpoint(self) -> tuple[str, int]: + return self._master_addr, int(self._store.port) + + +def start_ray_rendezvous_store( + pg: Any, + world_size: int, + timeout_s: float, +) -> tuple[Any, str, int]: + """Start a TCPStore server in placement-group bundle 0 and return its endpoint.""" + if world_size <= 1: + raise ValueError("Ray rendezvous store requires world_size greater than 1") # noqa: TRY003 + + remote_actor_cls = ray.remote(num_cpus=0)(_RayRendezvousStoreActor) + actor = remote_actor_cls.options( + scheduling_strategy=PlacementGroupSchedulingStrategy( + placement_group=pg, + placement_group_bundle_index=0, + ) + ).remote(world_size, timeout_s) + try: + master_addr, master_port = ray.get(actor.get_endpoint.remote(), timeout=timeout_s) + except ray.exceptions.GetTimeoutError as exc: + with suppress(Exception): + ray.kill(actor, no_restart=True) + raise TimeoutError( # noqa: TRY003 + f"Ray rendezvous store timed out after {timeout_s}s while starting on placement-group bundle 0" + ) from exc + except Exception: + with suppress(Exception): + ray.kill(actor, no_restart=True) + raise + return actor, str(master_addr), int(master_port) + + def get_worker_options( gpu_ratio: float, num_cpus: int, @@ -113,6 +167,8 @@ def _build_worker_env_vars( } if os.getenv("GLOO_SOCKET_IFNAME"): env_vars["GLOO_SOCKET_IFNAME"] = os.environ["GLOO_SOCKET_IFNAME"] + if num_worker > 1: + env_vars["TORCHELASTIC_USE_AGENT_STORE"] = "True" return env_vars @@ -181,9 +237,21 @@ def build_ray_worker_env_vars( return env_vars -def get_network_config(): - """Get network configuration for distributed setup.""" - master_addr = ray.util.get_node_ip_address() +def get_network_config(master_node_id: str | None = None) -> tuple[str, int]: + """Get the distributed master address and an available port.""" + if master_node_id is None: + master_addr = ray.util.get_node_ip_address() + else: + master_node = next( + (node for node in ray.nodes() if node.get("Alive", True) and str(node.get("NodeID")) == master_node_id), + None, + ) + if master_node is None: + raise RuntimeError(f"Ray master node {master_node_id} is not alive or is missing") # noqa: TRY003 + master_addr = master_node.get("NodeManagerAddress") + if not isinstance(master_addr, str) or not master_addr: + raise RuntimeError(f"Ray master node {master_node_id} is missing NodeManagerAddress") # noqa: TRY003 + with socket.socket() as sock: sock.bind(("", 0)) master_port = sock.getsockname()[1] @@ -205,8 +273,12 @@ def get_num_worker_options( if num_cpus_per_worker is None: num_cpus_per_worker = pg.bundle_specs[0].get("CPU", 0) + if num_worker > 1 and (master_addr is None or master_port is None): + raise ValueError( # noqa: TRY003 + "multi-worker Ray runs require master_addr/master_port from start_ray_rendezvous_store()" + ) if master_addr is None or master_port is None: - master_addr, master_port = get_network_config() + master_addr, master_port = get_network_config(node_ids[0]) runtime_envs = build_ray_worker_env_vars( num_worker=num_worker, node_ids=node_ids, diff --git a/tinyexp/utils/redis_utils.py b/tinyexp/utils/redis_utils.py index d0d0393..f6448f0 100644 --- a/tinyexp/utils/redis_utils.py +++ b/tinyexp/utils/redis_utils.py @@ -5,6 +5,7 @@ import shutil import socket import subprocess +import sys import tempfile import time import zlib @@ -17,6 +18,8 @@ import redis from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy +_REDIS_CLUSTER_EXCEPTION = getattr(redis.exceptions, "RedisClusterException", redis.exceptions.RedisError) + class RedisClusterConfigError(ValueError): """Invalid arguments to :class:`RedisClusterManager` (e.g. ports, memory).""" @@ -104,7 +107,7 @@ def safe_get(self, key: Any) -> Any | None: return None try: return redis_client.get(key) - except redis.exceptions.RedisError: + except (redis.exceptions.RedisError, _REDIS_CLUSTER_EXCEPTION): return None def safe_set(self, key: Any, value: Any) -> bool: @@ -113,7 +116,7 @@ def safe_set(self, key: Any, value: Any) -> bool: return False try: return bool(redis_client.set(key, value)) - except redis.exceptions.RedisError: + except (redis.exceptions.RedisError, _REDIS_CLUSTER_EXCEPTION): return False def __del__(self) -> None: @@ -123,6 +126,55 @@ def __del__(self) -> None: self.redis_clients = [] +def wait_for_redis_cluster(startup_host: str, startup_ports: list[int], timeout_s: float = 30.0) -> bool: + """Wait for a Redis Cluster to accept requests within a bounded deadline.""" + deadline = time.monotonic() + timeout_s + last_error: Exception | None = None + while time.monotonic() < deadline: + cluster: Any | None = None + ready = False + attempt_timeout = min(2.0, max(0.05, deadline - time.monotonic())) + try: + cluster = redis.RedisCluster( + startup_nodes=[redis.cluster.ClusterNode(startup_host, int(port)) for port in startup_ports], + decode_responses=False, + socket_connect_timeout=attempt_timeout, + socket_timeout=attempt_timeout, + ) + cluster.ping() + ready = True + for index in range(16): + if time.monotonic() >= deadline: + last_error = TimeoutError("Redis Cluster readiness deadline expired") + ready = False + break + key = f"tinyexp:redis-cluster-readiness:{index}" + cluster.set(key, "ok", ex=60) + if cluster.get(key) != b"ok": + last_error = RuntimeError("readiness value mismatch") + ready = False + break + except Exception as exc: + if not isinstance(exc, (redis.exceptions.RedisError, _REDIS_CLUSTER_EXCEPTION)): + raise + last_error = exc + finally: + if cluster is not None: + with contextlib.suppress(Exception): + cluster.close() + + if ready: + return True + + remaining = deadline - time.monotonic() + if remaining <= 0: + break + time.sleep(min(1.0, remaining)) + + print(f"Redis Cluster readiness check failed: {last_error}", file=sys.stderr) + return False + + class RedisClusterManager: def __init__( self, @@ -160,6 +212,27 @@ def __init__( if port <= 0 or port > 65535: raise RedisClusterConfigError(f"Invalid port {port!r}, expected 1..65535") # noqa: TRY003 + cluster_enabled = bool(cluster_enabled) + if cluster_enabled: + data_ports = set(normalized_ports) + bus_ports = [port + 10000 for port in normalized_ports] + for data_port, bus_port in zip(normalized_ports, bus_ports): + if bus_port <= 0 or bus_port > 65535: + raise RedisClusterConfigError( # noqa: TRY003 + f"Invalid Redis Cluster bus port {bus_port!r} derived from data port {data_port!r}, " + "expected 1..65535" + ) + conflicts = [ + (data_port, bus_port) + for data_port, bus_port in zip(normalized_ports, bus_ports) + if bus_port in data_ports + ] + if conflicts: + conflict_text = ", ".join( + f"data port {data_port} derives bus port {bus_port}" for data_port, bus_port in conflicts + ) + raise RedisClusterConfigError(f"Redis Cluster data/bus port conflict: {conflict_text}") # noqa: TRY003 + if max_memory_per_port <= 0: raise RedisClusterConfigError( # noqa: TRY003 f"max_memory_per_port must be > 0 GB, got {max_memory_per_port!r}" @@ -476,7 +549,7 @@ def __init__(self, redis_cfg: Any, *, log_dir: str | Path | None = None) -> None self.log_dir = Path(log_dir) if log_dir is not None else None self._actors: list[Any] = [] - def start(self, *, cluster_enabled: bool | None = None) -> tuple[str, list[int], int]: + def start(self, *, cluster_enabled: bool | None = None) -> tuple[str, list[int], int]: # noqa: C901 if not ray.is_initialized(): raise RedisClusterStartupError("Ray must be initialized before starting Ray-managed Redis.") # noqa: TRY003 @@ -487,6 +560,10 @@ def start(self, *, cluster_enabled: bool | None = None) -> tuple[str, list[int], if cluster_enabled is None: cluster_enabled = len(alive_nodes) > 1 + startup_timeout_s = float(getattr(self.redis_cfg, "redis_cluster_startup_timeout_s", 30.0)) + if cluster_enabled and startup_timeout_s <= 0: + raise RedisClusterStartupError("redis_cluster_startup_timeout_s must be greater than 0") # noqa: TRY003 + ports = [int(port) for port in self.redis_cfg.redis_cluster_ports] total_redis_shards = len(ports) * len(alive_nodes) if cluster_enabled and total_redis_shards < 3: @@ -530,8 +607,8 @@ def start(self, *, cluster_enabled: bool | None = None) -> tuple[str, list[int], startup_ports = [int(port) for port in redis_nodes[0]["ports"]] if cluster_enabled: self._validate_node_hosts(redis_nodes) - self._create_cluster(redis_cli_path, redis_nodes) - self._check_cluster(startup_host, startup_ports) + self._create_cluster(redis_cli_path, redis_nodes, timeout_s=startup_timeout_s) + self._check_cluster(startup_host, startup_ports, timeout_s=startup_timeout_s) except Exception: self.stop() raise @@ -556,40 +633,44 @@ def _validate_node_hosts(redis_nodes: list[dict[str, Any]]) -> None: ) @staticmethod - def _create_cluster(redis_cli_path: str, redis_nodes: list[dict[str, Any]]) -> None: + def _create_cluster( + redis_cli_path: str, + redis_nodes: list[dict[str, Any]], + *, + timeout_s: float = 30.0, + ) -> None: addresses = [f"{node['host']}:{port}" for node in redis_nodes for port in node["ports"]] - result = subprocess.run( - [ - redis_cli_path, - "--cluster", - "create", - *addresses, - "--cluster-replicas", - "0", - "--cluster-yes", - ], - check=False, - capture_output=True, - text=True, - ) + try: + result = subprocess.run( + [ + redis_cli_path, + "--cluster", + "create", + *addresses, + "--cluster-replicas", + "0", + "--cluster-yes", + ], + check=False, + capture_output=True, + text=True, + timeout=timeout_s, + ) + except subprocess.TimeoutExpired as exc: + raise RedisClusterStartupError( # noqa: TRY003 + f"redis-cli cluster create timed out after {timeout_s}s" + ) from exc if result.returncode != 0: raise RedisClusterStartupError( # noqa: TRY003 f"redis-cli cluster create failed with code {result.returncode}: {result.stderr or result.stdout}" ) @staticmethod - def _check_cluster(startup_host: str, startup_ports: list[int]) -> None: - cluster = redis.RedisCluster( - startup_nodes=[redis.cluster.ClusterNode(startup_host, int(port)) for port in startup_ports], - decode_responses=False, - socket_connect_timeout=2, - socket_timeout=2, - ) - try: - cluster.ping() - finally: - with contextlib.suppress(Exception): - cluster.close() + def _check_cluster(startup_host: str, startup_ports: list[int], *, timeout_s: float = 30.0) -> None: + if not wait_for_redis_cluster(startup_host, startup_ports, timeout_s=timeout_s): + raise RedisClusterStartupError( # noqa: TRY003 + f"Redis Cluster did not become ready at {startup_host}:{startup_ports}" + ) def get_db_sizes(self) -> list[dict[str, Any]]: dbsize_refs = [actor.get_db_sizes.remote() for actor in self._actors] diff --git a/tox.ini b/tox.ini index a75c913..8ef5f9b 100644 --- a/tox.ini +++ b/tox.ini @@ -1,6 +1,6 @@ [tox] skipsdist = true -envlist = py39, py310, py311, py312, py313 +envlist = py39, py310, py311, py312, py313, py314 [gh-actions] python = @@ -9,11 +9,14 @@ python = 3.11: py311 3.12: py312 3.13: py313 + 3.14: py314 [testenv] passenv = PYTHON_VERSION allowlist_externals = uv +setenv = + RAY_ENABLE_UV_RUN_RUNTIME_ENV = 0 commands = - uv sync --python {envpython} - uv run python -m pytest --doctest-modules tests --cov --cov-config=pyproject.toml --cov-report=xml - mypy + uv sync --locked --python {envpython} --extra pytorch + uv run --frozen --extra pytorch python -m pytest --doctest-modules tests --cov --cov-config=pyproject.toml --cov-report=xml + # mypy diff --git a/uv.lock b/uv.lock index 9f3bd14..75aaea1 100644 --- a/uv.lock +++ b/uv.lock @@ -2,11 +2,13 @@ version = 1 revision = 3 requires-python = ">=3.9, <4.0" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux'", - "python_full_version >= '3.12' and sys_platform != 'linux'", + "python_full_version >= 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