mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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### What does this PR do? Type of change: refactor (no functional change) **[1/2] of a split. Merge this first; #2514 is [2/2] and is based on this branch.** Three example scripts had each reimplemented the same MLflow wiring: the flags, the `$USER/<tool>/<model>-<variant>` experiment convention, the params/tags/artifacts a run uploads, and the open/close dance with its status. The copies had already drifted — only `hf_ptq` wrote a provenance pointer, only `vllm_serve` republished the resolved URI — and every new tracked script meant another copy. What a script records is now one declarative `Tool` record, **declared in the script itself, beside the flags it reads**: ```python # examples/megatron_bridge/quantize.py QUANTIZE = Tool( name="megatron_bridge_quantize", tracks="Track this run on an MLflow server, uploading the command, the resolved recipe, ...", variant_help="recipe name, or --quant_cfg if no --recipe", variant=lambda args: Path(args.recipe).stem if args.recipe else (args.quant_cfg or "none"), model=lambda args: args.hf_model_name_or_path, checkpoint=lambda args: args.export_megatron_path, texts=lambda args: resolved_recipe_texts(args.recipe), outputs=lambda args: {"summary/quant_summary.txt": Path(args.export_megatron_path) / ".quant_summary.txt"}, ) ``` `tracked_run` takes that record and runs the whole thing, so a script adds tracking in three lines: `add_mlflow_args(parser, TOOL)`, `resolve_mlflow_args(args, parser, TOOL)`, and `with mlflow_run(args, TOOL):`. The shared module knows no script's flags. `examples/hf_ptq`, `examples/vllm_serve` and `examples/megatron_bridge/quantize.py` move onto it. Three helpers fall away as redundant (`track_run`, `checkpoint_run_tags`, and `hf_ptq`'s two flag pass-throughs). ### Usage No user-facing change. The flags, their spellings and the experiment naming are exactly as before; a script author now writes a `Tool` instead of four functions. ### Testing - `tests/unit/torch/utils/test_mlflow.py`, `tests/examples/hf_ptq/test_hf_ptq_args.py`, `tests/examples/vllm_serve/test_vllm_mlflow_utils.py` — **179 pass**. - `tests/examples/megatron_bridge` in `nvcr.io/nvidia/nemo:26.08` (the only lane that runs it), which drives `quantize.py` for real: **34 passed**, locally and in this PR's `megatron` lane. - `pre-commit run --files <changed>`: all hooks pass. - The four suites shared four copies of a stand-in for the `mlflow` module, which had drifted — one recorded artifacts as a list, another as a dict, a third made `log_artifact` a no-op, so a test asserting on an upload asserted nothing. They now share one `tests/_test_utils/mlflow.py`, which also emulates the fluent API's habit of opening a run when none is active. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ — `track_run` and `checkpoint_run_tags` are removed, but neither shipped in a release (0.47.0's `__all__` is `MlflowRunLogger`, `command_text`, `current_user`, `default_experiment_name`, `validate_tracking_uri`, all unchanged here). Three deliberate behaviour changes, each in shared code and each tested: - The `source_checkpoint_path` tag resolves to an absolute path where it recorded the raw argument, which a chain of runs needs to join on the pair. `run_tags` is shared, so this applies to every script that writes the tag — `hf_ptq` **and** `megatron_bridge/quantize.py`, for a local `--hf_model_name_or_path`. A source that names no directory, such as a Hub `org/name` id, is still recorded as given. - `MlflowRunLogger.track()` — which *did* ship in 0.47.0 — records a block ending in `SystemExit(0)` as `FINISHED` where it recorded `FAILED`, since a script that ends by calling `sys.exit()` rather than returning has still finished. - `.experiment.json`'s `tracking_uri` and the `run_url` built from it drop a trailing `/` from the tracking URI, so the link is `https://host/#/...` rather than `https://host//#/...`. Only reachable by constructing `MlflowRunLogger` directly; every CLI path already stripped the slash in `resolve_tracking_uri`. - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A — no user-visible change; the entry is in [2/2]. - Did you get Claude approval on this PR?: several rounds; re-requested on this head. ### Additional Information Split out of #2514. This half is the enabling refactor with no behaviour change; #2514 is the feature it unlocks and is based on this branch. At ~605 changed lines of core logic it is over the ~500 guideline; the owner accepted a two-PR split rather than three, and everything #2514 alone consumes — `split_tracking_credentials`, `log_active_run_experiment_json`, `MlflowRunLogger._reattach` — lands there rather than here. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
195 lines
6.3 KiB
Python
195 lines
6.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import platform
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import sys
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from pathlib import Path
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import pytest
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import torch
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import torch.distributed as dist
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from _test_utils.fs_utils import assert_unmodified_tree
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from _test_utils.mlflow import FakeMlflow, pin_tracking_env
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from _test_utils.torch.distributed.utils import init_process
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import modelopt.torch.opt as mto
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@pytest.fixture(scope="session")
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def verbose(request):
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return request.config.getoption("verbose")
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def pytest_addoption(parser):
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parser.addoption(
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"--run-manual",
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action="store_true",
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default=False,
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help="Run manual tests",
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)
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parser.addoption(
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"--run-release",
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action="store_true",
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default=False,
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help="Run release tests",
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)
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# Default per-test `call` wall-clock cap (seconds) by top-level tests/ subdirectory
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# Every collectible test group must be listed here else collection errors occur
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# A test can override its cap by adding ``@pytest.mark.timeout(...)``
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_DEFAULT_TIMEOUT = {
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"examples": int(os.environ.get("MODELOPT_QA_TEST_TIMEOUT", 300)),
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"gpu": 120,
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"gpu_megatron": 120,
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"gpu_trtllm": 60,
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"gpu_vllm": 60,
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"regression": 180,
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"unit": 120 if platform.system() == "Windows" else 60,
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}
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def pytest_collection_modifyitems(config, items):
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"""Skip flag-gated tests and apply a default per-test timeout based on the test directory."""
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skip_marks = [
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("manual", "--run-manual"),
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("release", "--run-release"),
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]
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for mark_name, option_name in skip_marks:
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if not config.getoption(option_name):
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skipper = pytest.mark.skip(reason=f"Only run when {option_name} is given")
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for item in items:
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if mark_name in item.keywords:
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item.add_marker(skipper)
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tests_root = Path(__file__).parent
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for item in items:
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if item.get_closest_marker("timeout") is not None or not item.path.is_relative_to(
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tests_root
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):
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continue
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# First path component under tests/ is the group dir (unit, gpu, examples, ...).
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# Crash loudly (rather than silently skip) if a group has no configured default, so a
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# newly added tests/<group>/ must be given an explicit timeout in the mapping above.
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group = item.path.relative_to(tests_root).parts[0]
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if group not in _DEFAULT_TIMEOUT:
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raise pytest.UsageError(
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f"tests/{group}/ has no default timeout; add '{group}' to "
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"_DEFAULT_TIMEOUT in tests/conftest.py."
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)
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item.add_marker(pytest.mark.timeout(_DEFAULT_TIMEOUT[group]))
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# General Fixtures #################################################################################
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@pytest.fixture
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def skip_on_windows():
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if platform.system() == "Windows":
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pytest.skip("Skipping on Windows")
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@pytest.fixture(scope="session")
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def num_gpus():
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return torch.cuda.device_count()
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@pytest.fixture(scope="session")
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def cuda_capability():
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if not torch.cuda.is_available():
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pytest.skip("CUDA is not available")
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return torch.cuda.get_device_capability()
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@pytest.fixture
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def distributed_setup_size_1():
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init_process(rank=0, size=1, backend="nccl")
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yield
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dist.destroy_process_group()
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@pytest.fixture
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def need_2_gpus():
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if torch.cuda.device_count() < 2:
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pytest.skip("Need at least 2 GPUs to run this test")
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@pytest.fixture
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def need_4_gpus():
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if torch.cuda.device_count() < 4:
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pytest.skip("Need at least 4 GPUs to run this test")
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@pytest.fixture
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def need_8_gpus():
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if torch.cuda.device_count() < 8:
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pytest.skip("Need at least 8 GPUs to run this test")
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@pytest.fixture(scope="module")
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def set_torch_dtype(request):
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orig_dtype = torch.get_default_dtype()
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torch.set_default_dtype(request.param)
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yield
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torch.set_default_dtype(orig_dtype)
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@pytest.fixture(scope="session", autouse=True)
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def enable_hf_checkpointing():
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mto.enable_huggingface_checkpointing()
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@pytest.fixture(scope="session")
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def project_root_path(request: pytest.FixtureRequest) -> Path:
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"""Fixture providing the project root path for tests."""
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return Path(request.config.rootpath)
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# Transformers Models Fixtures #####################################################################
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@pytest.fixture
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def tiny_tokenizer():
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"""Real tiny HF tokenizer (vocab=128) shared across unit and gpu test lanes."""
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# Lazy import: transformers_models.py runs ``pytest.importorskip("transformers")``
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# at module load, which we don't want to trigger at conftest import time.
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from _test_utils.torch.transformers_models import get_tiny_tokenizer
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return get_tiny_tokenizer()
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@pytest.fixture(scope="session")
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def tiny_wan22_path(tmp_path_factory):
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"""Tiny Wan 2.2 pipeline dir, built once per session (the build is the expensive part).
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Shared by the gpu sparse-attention tests and the diffusers example tests.
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"""
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# Lazy import for the same reason as ``tiny_tokenizer``: diffusers_models.py pulls in
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# transformers at module load.
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from _test_utils.torch.diffusers_models import create_tiny_wan22_pipeline_dir
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pipeline_dir = create_tiny_wan22_pipeline_dir(tmp_path_factory.mktemp("tiny_wan22"))
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with assert_unmodified_tree(pipeline_dir) as path:
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yield str(path)
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@pytest.fixture
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def fake_mlflow(monkeypatch):
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"""Stand in for the ``mlflow`` module; see ``_test_utils.mlflow.FakeMlflow``."""
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# A suite that takes the fake without also importing clean_env would otherwise read the
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# developer's own $MLFLOW_TRACKING_URI and flip the branch under test.
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pin_tracking_env(monkeypatch)
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fake = FakeMlflow()
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monkeypatch.setitem(sys.modules, "mlflow", fake)
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return fake
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