diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..cc644a4 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,39 @@ +name: Tests + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] + + steps: + - name: Checkout + uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v8.1.0 + with: + python-version: ${{ matrix.python-version }} + enable-cache: true + + - name: Cache native builds + uses: actions/cache@v5.0.5 + with: + path: krum/native + key: native-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('krum/native/**/*.cpp', 'krum/native/**/*.cu', 'krum/native/**/*.hpp', 'uv.lock') }} + + - name: Install dependencies + run: uv sync --extra dev + + - name: Check imports (no deprecation warnings) + run: | + uv run python -W error::DeprecationWarning -W error::PendingDeprecationWarning -c "from krum import tools, experiments, aggregators, attacks" + uv run python -W error::DeprecationWarning -W error::PendingDeprecationWarning -c "import krum.tools, krum.experiments, krum.aggregators, krum.attacks" diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 3fd9035..01b2b35 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -1,9 +1,7 @@ name: Build and Deploy Documentation on: - push: - branches: - - main + workflow_dispatch: permissions: contents: read @@ -32,7 +30,7 @@ jobs: python-version-file: "pyproject.toml" - name: Install dependencies - run: uv sync --group docs + run: uv sync --extra dev - name: Build documentation run: uv run sphinx-build -b html docs docs/_build/html diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..684a602 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,25 @@ +name: Publish + +on: + push: + tags: + - "v*" + +jobs: + pypi: + name: Build & Publish to PyPI + runs-on: ubuntu-latest + permissions: + id-token: write # Required for trusted publishing (recommended) + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Build package + run: uv build + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index e85af9a..8017938 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ # Except directories, placeholders, gitignores, LICENSE, Markdown files and Python sources !*/ +!krum/* !.placeholder !.gitignore !LICENSE @@ -10,10 +11,13 @@ !*.py !*.toml !.python-version +!uv.lock +!.pre-commit-config.yaml # Except documentation files docs/_build !*.rst +!Makefile !*.html !*.css !*.js @@ -24,4 +28,4 @@ docs/_build !*.yml # IDE stuff -.idea +.idea \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c0d739b --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,8 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.12 + hooks: + - id: ruff + args: [--fix] + files: ^krum/ + - id: ruff-format diff --git a/.python-version b/.python-version index 4db6d7d..f02be28 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ ->=3.10 +>=3.10, <3.15 diff --git a/README.md b/README.md index 8767ecf..51cc3e5 100644 --- a/README.md +++ b/README.md @@ -13,19 +13,46 @@ This project implements various Byzantine-resilient Gradient Aggregation Rules ( - **Extensible**: Easy to add new aggregation rules or attacks. - **High-performance**: Optional native C++ backend for Krum. +## Supported Python versions + +This project supports Python **3.10 through 3.14**. + ## Installation -This project uses `uv` for dependency management. +### From PyPI + +```bash +pip install krum +``` + +With `uv` (Recommended): ```bash -# Clone the repository -git clone https://github.com/your-username/krum.git +uv pip install krum +# or directly in a uv project +uv add krum +``` + +### From source + +For development or if you want to modify the source, clone the repository and install in editable mode with the development dependencies: + +```bash +git clone https://github.com/calicarpa/krum.git cd krum +pip install -e ".[dev]" +``` + +With `uv` (Recommended): -# Install dependencies -uv pip install -e . +```bash +git clone https://github.com/calicarpa/krum.git +cd krum +uv sync --extra dev ``` +This installs all linting, type-checking, and documentation tools. + ## Usage You can run training simulations using `train.py`. The script accepts numerous arguments to configure the experiment. @@ -71,6 +98,45 @@ uv run python train.py \ - *in progress* +## Contributing + +### Linting, formatting, and type-checking + +This project uses [Ruff](https://docs.astral.sh/ruff/) for unified linting and formatting, and [ty](https://github.com/astral-sh/ty) for type-checking. + +Run the formatter and linter: + +```bash +ruff format . +ruff check --fix . +``` + +Run the type checker: + +```bash +ty check +``` + +### Pre-commit hooks + +Install pre-commit hooks to block non-compliant commits: + +```bash +pre-commit install +``` + +### Documentation + +Build the documentation locally: + +```bash +cd docs +make html # +make watch # +make serve # +make clean # +``` + ## License -See [LICENSE](LICENSE) for details. +MIT License — see [LICENSE](LICENSE). diff --git a/docs/Makefile b/docs/Makefile index 0fbf5de..75555e4 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -1,22 +1,25 @@ # Makefile for Krum documentation -.PHONY: help html clean servedocs live watch +.PHONY: help html build clean serve watch help: @echo "Available targets:" @echo " html - Build HTML documentation" + @echo " build - Build HTML documentation (alias for html)" @echo " clean - Remove generated files" - @echo " servedocs - Build and serve static documentation" + @echo " serve - Build and serve static documentation" @echo " watch - Watch for changes and serve (auto-rebuild)" html: uv run sphinx-build -b html . _build/html +build: html + clean: rm -rf _build -servedocs: +serve: uv run sphinx-build -b html . _build/html && uv run python -m http.server 8000 -d _build/html watch: - uv run sphinx-autobuild -b html . _build/html --port 8000 --host 127.0.0.1 \ No newline at end of file + uv run sphinx-autobuild -b html . _build/html --port 8000 --host 127.0.0.1 diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 6f13958..e95fe1a 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -16,4 +16,9 @@ .bd-toc .page-toc + .sidebar-secondary-item { display: none; +} + +/* Contributions Avatar */ +.sphinx-contributors img { + border-radius: 50%; } \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 4516185..8b36b46 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,11 +6,16 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information +import importlib import os import sys sys.path.insert(0, os.path.abspath("..")) +# Make krum submodules available as top-level imports for autodoc compatibility +for _mod in ("aggregators", "attacks", "experiments", "native", "tools"): + sys.modules[_mod] = importlib.import_module(f"krum.{_mod}") + project = "Krum, the Library" copyright = "2026" author = "Peva BLANCHARD, Arthur DANJOU, El-Mahdi EL-MHAMDI, Sébastien ROUAULT, Mohammed Ammar SAID" @@ -30,9 +35,10 @@ "sphinx.ext.intersphinx", "sphinx_favicon", "sphinx_togglebutton", - "sphinx_contributors" + "sphinx_contributors", ] + def linkcode_resolve(domain, info): """Return a URL to the source code on GitHub for the given object.""" if domain != "py": @@ -90,9 +96,7 @@ def linkcode_resolve(domain, info): # Use MathJax to render math in HTML -mathjax_path = ( - "https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js?config=TeX-AMS-MML_HTMLorMML" -) +mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js?config=TeX-AMS-MML_HTMLorMML" exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] @@ -158,7 +162,7 @@ def linkcode_resolve(domain, info): { "title": "How to add a custom dataset", "url": "how-to/add-custom-dataset", - } + }, ], }, { diff --git a/docs/contributors.rst b/docs/contributors.rst index 0fecb75..9f8e84c 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -3,10 +3,12 @@ Contributors The following people have contributed to Krum: -.. container:: rounded-image - .. contributors:: calicarpa/krum - :avatars: +.. contributors:: calicarpa/krum + :avatars: + :names: We are open to all contributions. Whether it is a bug fix, a new feature, or an improvement to the documentation, feel free to open a pull request or start a discussion on GitHub. +To learn more about how to contribute, please refer to the `contribution guidelines `_. + Thank you to everyone who has helped improve this project. diff --git a/docs/how-to/add-dataset.rst b/docs/how-to/add-dataset.rst index 6401991..9147297 100644 --- a/docs/how-to/add-dataset.rst +++ b/docs/how-to/add-dataset.rst @@ -72,7 +72,7 @@ batching automatically. ) Step 3 — Export in ``__all__`` ------------------------------ +------------------------------ List the builder function in ``__all__`` so the loader can discover it. diff --git a/histogram.py b/histogram.py index 8318a49..e1051dd 100644 --- a/histogram.py +++ b/histogram.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file histogram.py # @author Sébastien Rouault @@ -13,17 +12,16 @@ # Plot the histogram of per-worker norm/variance estimations across the steps. ### -import tools - -import aggregators - import atexit import json -import matplotlib.pyplot as plt import pathlib -import pandas import threading +import matplotlib.pyplot as plt +import pandas as pd + +from krum import aggregators, tools + # Change common font for the default LaTeX one plt.rcParams["font.family"] = "Latin Modern Roman" plt.rcParams["font.size"] = 16 @@ -35,10 +33,10 @@ # Common GTK main loop try: - import gi + import gi # type: ignore[import-not-found] gi.require_version("Gtk", "3.0") - from gi.repository import Gtk, GLib + from gi.repository import GLib, Gtk # type: ignore[import-not-found] gtk_lazy_lock = threading.Lock() gtk_lazy_main = None @@ -65,14 +63,16 @@ def gtk_main(): gtk_lazy_main = thread # Submit the job to the main loop GLib.idle_add(closure) -except Exception: + +except Exception as _err: + _gtk_err_msg = str(_err) def gtk_run(closure): """Sink in case GTK cannot be used. Args: closure Ignored parameter """ - tools.warning("GTK 3.0 is unavailable: %s" % (err,)) + tools.warning(f"GTK 3.0 is unavailable: {_gtk_err_msg}") # ---------------------------------------------------------------------------- # # Data frame columns selection helper @@ -86,7 +86,6 @@ def select(data, *only_columns): Returns: (Sub-)dataframe, by reference """ - global Session # Unwrap data frame from session if isinstance(data, Session): data = data.data @@ -94,7 +93,7 @@ def select(data, *only_columns): if len(only_columns) == 0: return data # Intelligent selection - columns = list() + columns = [] for only_column in only_columns: only_column = only_column.lower() for column in data.columns: @@ -139,7 +138,7 @@ def to_string(x): Converted data to string """ if type(x) is float: - return "%e" % x + return f"{x:e}" return str(x).strip() def __init__(self, data, title="Display data"): @@ -152,7 +151,7 @@ def __init__(self, data, title="Display data"): # Make and fill list store store = Gtk.ListStore(*([str] * (len(data.columns) + 1))) for row in data.itertuples(): - store.append(list(self.to_string(x) for x in row)) + store.append([self.to_string(x) for x in row]) # Make the associated tree view view = Gtk.TreeView(store) columns = list(data.columns) @@ -198,19 +197,13 @@ def __init__(self, path_results): path_results = pathlib.Path(path_results) # Ensure directory exist if not path_results.exists(): - raise tools.UserException( - "Result directory %r cannot be accessed or does not exist" - % str(path_results) - ) + raise tools.UserException(f"Result directory {str(path_results)!r} cannot be accessed or does not exist") # Load configuration string path_config = path_results / "config" try: data_config = path_config.read_text().strip() except Exception as err: - tools.warning( - "Result directory %r: unable to read configuration (%s)" - % (str(path_results), err) - ) + tools.warning(f"Result directory {str(path_results)!r}: unable to read configuration ({err})") data_config = None # Load configuration json path_json = path_results / "config.json" @@ -218,34 +211,23 @@ def __init__(self, path_results): with path_json.open("r") as fd: data_json = json.load(fd) except Exception as err: - tools.warning( - "Result directory %r: unable to read JSON configuration (%s)" - % (str(path_results), err) - ) + tools.warning(f"Result directory {str(path_results)!r}: unable to read JSON configuration ({err})") data_json = None # Load training data path_study = path_results / "study" try: - data_study = pandas.read_csv( - path_study, sep="\t", index_col=0, na_values=" nan" - ) + data_study = pd.read_csv(path_study, sep="\t", index_col=0, na_values=" nan") data_study.index.name = "Step number" except Exception as err: - tools.warning( - "Result directory %r: unable to read training data (%s)" - % (str(path_results), err) - ) + tools.warning(f"Result directory {str(path_results)!r}: unable to read training data ({err})") data_study = None # Load evaluation data path_eval = path_results / "eval" try: - data_eval = pandas.read_csv(path_eval, sep="\t", index_col=0) + data_eval = pd.read_csv(path_eval, sep="\t", index_col=0) data_eval.index.name = "Step number" except Exception as err: - tools.warning( - "Result directory %r: unable to read evaluation data (%s)" - % (str(path_results), err) - ) + tools.warning(f"Result directory {str(path_results)!r}: unable to read evaluation data ({err})") data_eval = None # Merge data frames data = None @@ -286,10 +268,7 @@ def display(self, *only_columns, name=None): # Display the (selected sub)set display( self.get(*only_columns), - title=( - "Session data%s for %r" - % (" (subset)" if len(only_columns) > 0 else "", self.name) - ), + title=("Session data{} for {!r}".format(" (subset)" if len(only_columns) > 0 else "", self.name)), ) # Return self to enable chaining return self @@ -300,9 +279,7 @@ def has_known_ratio(self): Whether the session's GAR has a known ratio """ if self.json is None or "gar" not in self.json: - tools.warning( - "No valid JSON-formatted configuration, cannot tell whether the associated GAR has a ratio" - ) + tools.warning("No valid JSON-formatted configuration, cannot tell whether the associated GAR has a ratio") return False g = self.json["gar"] rule = aggregators.gars.get(g, None) @@ -333,9 +310,7 @@ def compute_epoch(self): return self # Compute epoch number if self.json is None or "dataset" not in self.json: - tools.warning( - "No valid JSON-formatted configuration, cannot compute the epoch number" - ) + tools.warning("No valid JSON-formatted configuration, cannot compute the epoch number") return self dataset_name = self.json["dataset"] training_size = { @@ -343,13 +318,11 @@ def compute_epoch(self): "fashionmnist": 60000, "cifar10": 50000, "cifar100": 50000, - }.get(dataset_name, None) + }.get(dataset_name) if training_size is None: - tools.warning( - "Unknown dataset %r, cannot compute the epoch number" % dataset_name - ) + tools.warning(f"Unknown dataset {dataset_name!r}, cannot compute the epoch number") return self - self.data[column_name] = self.data["Training point count"] / training_size + self.data[column_name] = self.data["Training point count"] / training_size # type: ignore[index] # Return self to enable chaining return self @@ -360,23 +333,19 @@ def compute_lr(self): """ column_name = "Learning rate" # Check if already there - if column_name in self.data.columns: + if column_name in self.data.columns: # type: ignore[union-attr] return self # Compute epoch number if self.json is None or "learning_rate" not in self.json: - tools.warning( - "No valid JSON-formatted configuration, cannot compute the learning rate" - ) + tools.warning("No valid JSON-formatted configuration, cannot compute the learning rate") return self lr = self.json["learning_rate"] lr_decay = self.json.get("learning_rate_decay", 0) lr_delta = self.json.get("learning_rate_decay_delta", 1) if lr_decay > 0: - self.data[column_name] = lr / ( - (self.data.index // lr_delta * lr_delta) / lr_decay + 1 - ) + self.data[column_name] = lr / ((self.data.index // lr_delta * lr_delta) / lr_decay + 1) # type: ignore[index] else: - self.data[column_name] = lr + self.data[column_name] = lr # type: ignore[index] # Return self to enable chaining return self @@ -460,26 +429,18 @@ def include(self, data, *cols, errs=None, lalp=1.0, ccnt=None): """ # Assert not already finalized if self._fin: - raise RuntimeError( - "Plot is already finalized and cannot include another line" - ) + raise RuntimeError("Plot is already finalized and cannot include another line") # Recover the dataframe if a session was given if isinstance(data, Session): data = data.data - elif not isinstance(data, pandas.DataFrame): - raise RuntimeError( - "Expected a Session or DataFrame for 'data', got a %r" - % tools.fullqual(type(data)) - ) + elif not isinstance(data, pd.DataFrame): + raise RuntimeError(f"Expected a Session or DataFrame for 'data', got a {tools.fullqual(type(data))!r}") # Get the x-axis values if self._idx is None: x = data.index.to_numpy() else: if self._idx not in data: - raise RuntimeError( - "No column named %r to use as index in the given session/dataframe" - % (self._idx,) - ) + raise RuntimeError(f"No column named {self._idx!r} to use as index in the given session/dataframe") x = data[self._idx].to_numpy() # Select semantic: empty list = select all if len(cols) == 0: @@ -501,20 +462,14 @@ def include(self, data, *cols, errs=None, lalp=1.0, ccnt=None): if axis is None: axis = self._get_ax(col) # Pick a new line style and color - linestyle, color = self._get_line_style( - self._cnt if ccnt is None else ccnt - ) + linestyle, color = self._get_line_style(self._cnt if ccnt is None else ccnt) # Plot the data (line or error line) davg = subd[scol].to_numpy() errn = None if errs is None else (scol + errs) if errn is not None and errn in data: derr = data[errn].to_numpy() - axis.fill_between( - x, davg - derr, davg + derr, facecolor=color, alpha=0.2 - ) - axis.plot( - x, davg, label=scol, linestyle=linestyle, color=color, alpha=lalp - ) + axis.fill_between(x, davg - derr, davg + derr, facecolor=color, alpha=0.2) + axis.plot(x, davg, label=scol, linestyle=linestyle, color=color, alpha=lalp) # Increase the counter only on success self._cnt += 1 # Reset axis for next iteration @@ -536,26 +491,18 @@ def include_single(self, data, key, col, err=None, lalp=1.0, ccnt=None): """ # Assert not already finalized if self._fin: - raise RuntimeError( - "Plot is already finalized and cannot include another line" - ) + raise RuntimeError("Plot is already finalized and cannot include another line") # Recover the dataframe if a session was given if isinstance(data, Session): data = data.data - elif not isinstance(data, pandas.DataFrame): - raise RuntimeError( - "Expected a Session or DataFrame for 'data', got a %r" - % tools.fullqual(type(data)) - ) + elif not isinstance(data, pd.DataFrame): + raise RuntimeError(f"Expected a Session or DataFrame for 'data', got a {tools.fullqual(type(data))!r}") # Get the x-axis values if self._idx is None: x = data.index.to_numpy() else: if self._idx not in data: - raise RuntimeError( - "No column named %r to use as index in the given session/dataframe" - % (self._idx,) - ) + raise RuntimeError(f"No column named {self._idx!r} to use as index in the given session/dataframe") x = data[self._idx].to_numpy() # Pick a new line style and color linestyle, color = self._get_line_style(self._cnt if ccnt is None else ccnt) @@ -626,14 +573,8 @@ def generator_sum(gen): return res (self._ax if self._tax is None else self._tax).legend( - generator_sum( - ax.get_legend_handles_labels()[0] for ax in self._axs.values() - ), - generator_sum( - ax.get_legend_handles_labels()[1] for ax in self._axs.values() - ) - if legend is None - else legend, + generator_sum(ax.get_legend_handles_labels()[0] for ax in self._axs.values()), + generator_sum(ax.get_legend_handles_labels()[1] for ax in self._axs.values()) if legend is None else legend, loc="best", ) # Plot the grid and labels @@ -643,17 +584,11 @@ def generator_sum(gen): self._ax.set_title(title) if zlabel is not None: if self._tax is None: - tools.warning( - "No secondary y-axis found, but its label %r was provided" - % (zlabel,) - ) + tools.warning(f"No secondary y-axis found, but its label {zlabel!r} was provided") else: self._tax.set_ylabel(zlabel) elif self._tax is not None: - tools.warning( - "No label provided for the secondary y-axis; using label %r from the primary" - % (ylabel,) - ) + tools.warning(f"No label provided for the secondary y-axis; using label {ylabel!r} from the primary") self._tax.set_ylabel(ylabel) self._ax.set_xlim(left=xmin, right=xmax) self._ax.set_ylim(bottom=ymin, top=ymax) @@ -734,16 +669,14 @@ def include(self, data): self """ # Convert 'pandas.Series' to numpy - if isinstance(data, pandas.Series): + if isinstance(data, pd.Series): data = data.to_numpy() # Make the histogram self._ax.hist(data, bins=self._bins) # Return self for chaining return self - def finalize( - self, title, xlabel, ylabel, xmin=None, xmax=None, ymin=None, ymax=None - ): + def finalize(self, title, xlabel, ylabel, xmin=None, xmax=None, ymin=None, ymax=None): """Finalize the plot, can be done only once and would prevent further inclusion. Args: title Plot title diff --git a/krum/__init__.py b/krum/__init__.py new file mode 100644 index 0000000..0b1e238 --- /dev/null +++ b/krum/__init__.py @@ -0,0 +1,3 @@ +"""Krum: Byzantine-resilient aggregation rules for distributed machine learning.""" + +__version__ = "0.1.0" diff --git a/aggregators/__init__.py b/krum/aggregators/__init__.py similarity index 82% rename from aggregators/__init__.py rename to krum/aggregators/__init__.py index 671bd7d..f1407c8 100644 --- a/aggregators/__init__.py +++ b/krum/aggregators/__init__.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file __init__.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Loading of the local modules. ### -""" -Gradient aggregation rules (GARs) for Byzantine-resilient distributed learning. +"""Gradient aggregation rules (GARs) for Byzantine-resilient distributed learning. Each rule combines a keyword-only aggregation function with a validation function and optional metadata used by the training and experiment scripts. @@ -47,11 +45,12 @@ """ import pathlib +from collections.abc import Callable +from typing import Any, cast import torch -import tools -from typing import Callable +from .. import tools # ---------------------------------------------------------------------------- # # Automated GAR loader @@ -63,8 +62,7 @@ def make_gar( upper_bound: Callable | None = None, influence: Callable | None = None, ) -> Callable: - """ - Wrap an unchecked GAR with validation and metadata. + """Wrap an unchecked GAR with validation and metadata. Parameters ---------- @@ -81,7 +79,7 @@ def make_gar( Function computing the accepted Byzantine-gradient ratio for a given set of honest and attack gradients. - Returns + Returns: ------- callable Checked or unchecked GAR selected according to ``__debug__``. The @@ -94,21 +92,18 @@ def checked(**kwargs): # Check parameter validity message = check(**kwargs) if message is not None: - raise tools.UserException( - "Aggregation rule %r cannot be used with the given parameters: %s" - % (name, message) - ) + raise tools.UserException(f"Aggregation rule {name!r} cannot be used with the given parameters: {message}") # Aggregation (hard to assert return value, duck-typing is allowed...) return unchecked(**kwargs) # Select which function to call by default - func = checked if __debug__ else unchecked + func = cast(Any, checked if __debug__ else unchecked) # Bind all the (sub) functions to the selected function - setattr(func, "check", check) - setattr(func, "checked", checked) - setattr(func, "unchecked", unchecked) - setattr(func, "upper_bound", upper_bound) - setattr(func, "influence", influence) + func.check = check + func.checked = checked + func.unchecked = unchecked + func.upper_bound = upper_bound + func.influence = influence # Return the selected function with the associated name return func @@ -120,8 +115,7 @@ def register( upper_bound: Callable | None = None, influence: Callable | None = None, ) -> None: - """ - Register a gradient aggregation rule. + """Register a gradient aggregation rule. Parameters ---------- @@ -139,16 +133,14 @@ def register( global gars # Check if name already in use if name in gars: - tools.warning("Unable to register %r GAR: name already in use" % name) + tools.warning(f"Unable to register {name!r} GAR: name already in use") return # Export the selected function with the associated name - gars[name] = make_gar( - unchecked, check, upper_bound=upper_bound, influence=influence - ) + gars[name] = make_gar(unchecked, check, upper_bound=upper_bound, influence=influence) # Registered rules (mapping name -> aggregation rule) -gars = dict() +gars = {} # Load all local modules with tools.Context("aggregators", None): diff --git a/aggregators/average.py b/krum/aggregators/average.py similarity index 83% rename from aggregators/average.py rename to krum/aggregators/average.py index 7e9946d..02d7deb 100644 --- a/aggregators/average.py +++ b/krum/aggregators/average.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file average.py # @author Sébastien Rouault @@ -13,7 +12,8 @@ # Simple arithmetic mean aggregation rule. ### -""" +"""Simplest aggregation rule, computing the arithmetic mean of all submitted gradients. + This is the simplest aggregation rule, computing the arithmetic mean of all submitted gradients. It serves as a baseline for comparison with Byzantine- resilient methods. @@ -32,9 +32,8 @@ - No parameters: No configuration required beyond the gradient list. - Output: Newly created tensor, does not alias any input. -Example +Example: ------- - >>> import torch >>> from aggregators import average >>> gradients = [torch.tensor([1., 2., 3.]), torch.tensor([4., 5., 6.])] @@ -42,17 +41,16 @@ tensor([2.5000, 3.5000, 4.5000]) """ -from . import register - import torch +from . import register + # ---------------------------------------------------------------------------- # # Average GAR def aggregate(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: - """ - Compute the arithmetic mean of all submitted gradients. + """Compute the arithmetic mean of all submitted gradients. Parameters ---------- @@ -62,21 +60,20 @@ def aggregate(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: **kwargs : object Additional keyword arguments, ignored by this rule. - Returns + Returns: ------- torch.Tensor The arithmetic mean of all input gradients. - Notes + Notes: ----- The output tensor is a new tensor, not aliasing any input tensor. """ - return sum(gradients) / len(gradients) + return torch.stack(gradients).mean(dim=0) def check(gradients: list[torch.Tensor], **kwargs) -> str | None: - """ - Check parameter validity for the averaging rule. + """Check parameter validity for the averaging rule. Parameters ---------- @@ -85,23 +82,19 @@ def check(gradients: list[torch.Tensor], **kwargs) -> str | None: **kwargs : object Additional keyword arguments, ignored by this rule. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(gradients, list) or len(gradients) < 1: - return ( - "Expected a list of at least one gradient to aggregate, got %r" % gradients - ) + return f"Expected a list of at least one gradient to aggregate, got {gradients!r}" + return None -def influence( - honests: list[torch.Tensor], attacks: list[torch.Tensor], **kwargs -) -> float: - """ - Compute the ratio of accepted Byzantine gradients. +def influence(honests: list[torch.Tensor], attacks: list[torch.Tensor], **kwargs) -> float: + """Compute the ratio of accepted Byzantine gradients. For arithmetic mean, all submitted gradients are used in the aggregation, so the influence ratio is simply the fraction of Byzantine gradients @@ -116,7 +109,7 @@ def influence( **kwargs : object Additional keyword arguments, ignored by this rule. - Returns + Returns: ------- float Ratio of Byzantine gradients in the aggregation (attackers / total). diff --git a/aggregators/brute.py b/krum/aggregators/brute.py similarity index 82% rename from aggregators/brute.py rename to krum/aggregators/brute.py index 5a8883c..01a7657 100644 --- a/aggregators/brute.py +++ b/krum/aggregators/brute.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file brute.py # @author Sébastien Rouault @@ -13,10 +12,11 @@ # Brute GAR. ### -""" +r"""The Brute aggregation rule exhaustively searches all subsets of gradients. + The Brute aggregation rule exhaustively searches all subsets of :math:`n - f` gradients and selects the subset with the smallest finite -diameter. The diameter of a subset is the maximum pairwise distance between + diameter. The diameter of a subset is the maximum pairwise distance between any two gradients in that subset. Use Case @@ -48,7 +48,7 @@ gradient dimension. - Space: :math:`O(n^2)` for storing pairwise distances. -Example +Example: ------- >>> import torch >>> from aggregators import brute @@ -68,25 +68,21 @@ import torch -import tools - +from .. import tools from . import register # Optional 'native' module try: - import native + from krum import native except ImportError: - native = None + native = None # type: ignore[assignment] # ---------------------------------------------------------------------------- # # Brute GAR -def _compute_selection( - gradients: list[torch.Tensor], f: int, **kwargs -) -> tuple[int, ...]: - """ - Select the gradient indices forming the smallest-diameter subset. +def _compute_selection(gradients: list[torch.Tensor], f: int, **kwargs) -> tuple[int, ...]: + """Select the gradient indices forming the smallest-diameter subset. Parameters ---------- @@ -97,12 +93,12 @@ def _compute_selection( **kwargs : object Additional keyword arguments, ignored by this helper. - Returns + Returns: ------- tuple of int Indices of the selected :math:`n - f` gradients. - Notes + Notes: ----- Candidate subsets containing non-finite pairwise distances are ignored. """ @@ -124,8 +120,7 @@ def _compute_selection( if not math.isfinite(cur_dist): break # Check if new maximum - if cur_dist > cur_diam: - cur_diam = cur_dist + cur_diam = max(cur_diam, cur_dist) else: # Check if new selected diameter if sel_iset is None or cur_diam < sel_diam: @@ -139,8 +134,7 @@ def _compute_selection( def aggregate(gradients: list[torch.Tensor], f: int, **kwargs) -> torch.Tensor | float: - """ - Compute the Brute aggregation (mean of smallest-diameter subset). + """Compute the Brute aggregation (mean of smallest-diameter subset). Parameters ---------- @@ -152,13 +146,13 @@ def aggregate(gradients: list[torch.Tensor], f: int, **kwargs) -> torch.Tensor | **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- torch.Tensor Mean of the selected :math:`n - f` gradients with smallest finite diameter. - Notes + Notes: ----- The returned tensor is newly computed and does not alias any input tensor. """ @@ -166,11 +160,8 @@ def aggregate(gradients: list[torch.Tensor], f: int, **kwargs) -> torch.Tensor | return sum(gradients[i] for i in sel_iset).div_(len(gradients) - f) -def aggregate_native( - gradients: list[torch.Tensor], f: int, **kwargs -) -> torch.Tensor | float: - """ - Compute the Brute aggregation using native C++/CUDA acceleration. +def aggregate_native(gradients: list[torch.Tensor], f: int, **kwargs) -> torch.Tensor | float: + """Compute the Brute aggregation using native C++/CUDA acceleration. Parameters ---------- @@ -181,17 +172,16 @@ def aggregate_native( **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- torch.Tensor | float Mean of the subset selected by the native Brute implementation. """ - return native.brute.aggregate(gradients, f) + return native.brute.aggregate(gradients, f) # type: ignore[attr-defined] def check(gradients: list[torch.Tensor], f: int, **kwargs) -> str | None: - """ - Check parameter validity for the Brute aggregation rule. + """Check parameter validity for the Brute aggregation rule. Parameters ---------- @@ -202,26 +192,24 @@ def check(gradients: list[torch.Tensor], f: int, **kwargs) -> str | None: **kwargs : object Additional keyword arguments, ignored by this check. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(gradients, list) or len(gradients) < 1: - return ( - "Expected a list of at least one gradient to aggregate, got %r" % gradients - ) + return f"Expected a list of at least one gradient to aggregate, got {gradients!r}" if not isinstance(f, int) or f < 1 or len(gradients) < 2 * f + 1: - return ( - "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" - % (f, (len(gradients) - 1) // 2) + return "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" % ( + f, + (len(gradients) - 1) // 2, ) + return None def upper_bound(n: int, f: int, d: int) -> float: - """ - Compute the theoretical Brute resilience bound. + """Compute the theoretical Brute resilience bound. Parameters ---------- @@ -232,7 +220,7 @@ def upper_bound(n: int, f: int, d: int) -> float: d : int Dimension of the gradient space. - Returns + Returns: ------- float Upper bound on the ratio between non-Byzantine standard deviation and @@ -241,11 +229,8 @@ def upper_bound(n: int, f: int, d: int) -> float: return (n - f) / (math.sqrt(8) * f) -def influence( - honests: list[torch.Tensor], attacks: list[torch.Tensor], f: int, **kwargs -) -> float: - """ - Compute the ratio of Byzantine gradients selected by Brute. +def influence(honests: list[torch.Tensor], attacks: list[torch.Tensor], f: int, **kwargs) -> float: + """Compute the ratio of Byzantine gradients selected by Brute. Parameters ---------- @@ -258,7 +243,7 @@ def influence( **kwargs : object Additional keyword arguments forwarded to the selection helper. - Returns + Returns: ------- float Fraction of selected gradients that come from ``attacks``. @@ -292,6 +277,5 @@ def influence( register(method_name, aggregate_native, check, upper_bound) else: tools.warning( - "GAR %r could not be registered since the associated native module %r is unavailable" - % (method_name, native_name) + f"GAR {method_name!r} could not be registered since the associated native module {native_name!r} is unavailable" ) diff --git a/aggregators/bulyan.py b/krum/aggregators/bulyan.py similarity index 78% rename from aggregators/bulyan.py rename to krum/aggregators/bulyan.py index 0e7bead..4727058 100644 --- a/aggregators/bulyan.py +++ b/krum/aggregators/bulyan.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file bulyan.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Bulyan over Multi-Krum GAR. ### -""" -Bulyan aggregation rule built on top of Multi-Krum. +r"""Bulyan aggregation rule built on top of Multi-Krum. Bulyan combines distance-based gradient selection with coordinate-wise robust averaging. It first selects a candidate set using a Multi-Krum-like @@ -57,9 +55,8 @@ Number of gradients to consider in each Multi-Krum selection step. Defaults to ``n - f - 2``. Must satisfy ``1 <= m <= n - f - 2``. -Example +Example: ------- - >>> import torch >>> from aggregators import bulyan >>> gradients = [ @@ -76,26 +73,25 @@ """ import math +from typing import Any import torch -import tools - +from .. import tools from . import register # Optional 'native' module try: - import native + from krum import native except ImportError: - native = None + native = None # type: ignore[assignment] # ---------------------------------------------------------------------------- # # Bulyan GAR class def aggregate(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch.Tensor: - """ - Compute the Bulyan aggregate. + """Compute the Bulyan aggregate. Parameters ---------- @@ -112,12 +108,12 @@ def aggregate(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch. Additional keyword arguments. They are accepted for compatibility with the GAR interface and ignored by this implementation. - Returns + Returns: ------- torch.Tensor Bulyan-aggregated gradient. - Notes + Notes: ----- The returned tensor is newly allocated and does not alias any input tensor. """ @@ -128,7 +124,7 @@ def aggregate(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch. if m is None: m = m_max # Compute all pairwise distances - distances = list([(math.inf, None)] * n for _ in range(n)) + distances: list[Any] = [[(math.inf, None)] * n for _ in range(n)] for gid_x, gid_y in tools.pairwise(tuple(range(n))): dist = gradients[gid_x].sub(gradients[gid_y]).norm().item() if not math.isfinite(dist): @@ -136,7 +132,7 @@ def aggregate(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch. distances[gid_x][gid_y] = (dist, gid_y) distances[gid_y][gid_x] = (dist, gid_x) # Compute the scores - scores = [None] * n + scores: list[Any] = [None] * n for gid in range(n): dists = distances[gid] dists.sort(key=lambda x: x[0]) @@ -144,44 +140,30 @@ def aggregate(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch. scores[gid] = (sum(dist for dist, _ in dists), gid) distances[gid] = dict(dists) # Selection loop - selected = torch.empty( - n - 2 * f - 2, d, dtype=gradients[0].dtype, device=gradients[0].device - ) + selected = torch.empty(n - 2 * f - 2, d, dtype=gradients[0].dtype, device=gradients[0].device) for i in range(selected.shape[0]): # Update 'm' m = min(m, m_max - i) # Compute the average of the selected gradients scores.sort(key=lambda x: x[0]) - selected[i] = sum(gradients[gid] for _, gid in scores[:m]).div_(m) + selected[i] = sum(gradients[gid] for _, gid in scores[:m]).div_(m) # type: ignore[arg-type] # Remove the gradient from the distances and scores - gid_prune = scores[0][1] + gid_prune = scores[0][1] # type: ignore[index] scores[0] = (math.inf, None) - for score, gid in scores[1:]: + for score, gid in scores[1:]: # type: ignore[assignment] if gid == gid_prune: - scores[gid] = (score - distance[gid][gid_prune], gid) + scores[gid] = (score - distances[gid][gid_prune], gid) # Coordinate-wise averaged median m = selected.shape[0] - 2 * f median = selected.median(dim=0).values - closests = ( - selected.clone() - .sub_(median) - .abs_() - .topk(m, dim=0, largest=False, sorted=False) - .indices - ) - closests.mul_(d).add_( - torch.arange(0, d, dtype=closests.dtype, device=closests.device) - ) - avgmed = selected.take(closests).mean(dim=0) + closests = selected.clone().sub_(median).abs_().topk(m, dim=0, largest=False, sorted=False).indices + closests.mul_(d).add_(torch.arange(0, d, dtype=closests.dtype, device=closests.device)) + return selected.take(closests).mean(dim=0) # Return resulting gradient - return avgmed -def aggregate_native( - gradients: list[torch.Tensor], f: int, m=None, **kwargs -) -> torch.Tensor: - """ - Compute the Bulyan aggregate using native C++/CUDA acceleration. +def aggregate_native(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> torch.Tensor: + """Compute the Bulyan aggregate using native C++/CUDA acceleration. Parameters ---------- @@ -196,7 +178,7 @@ def aggregate_native( Additional keyword arguments. They are accepted for compatibility with the GAR interface and ignored by this implementation. - Returns + Returns: ------- torch.Tensor Bulyan-aggregated gradient. @@ -205,12 +187,11 @@ def aggregate_native( if m is None: m = len(gradients) - f - 2 # Computation - return native.bulyan.aggregate(gradients, f, m) + return native.bulyan.aggregate(gradients, f, m) # type: ignore[attr-defined] def check(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> str | None: - """ - Check whether the Bulyan parameters satisfy the GAR contract. + """Check whether the Bulyan parameters satisfy the GAR contract. Parameters ---------- @@ -225,33 +206,26 @@ def check(gradients: list[torch.Tensor], f: int, m=None, **kwargs) -> str | None Additional keyword arguments. They are accepted for compatibility with the GAR interface and ignored by this check. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(gradients, list) or len(gradients) < 1: - return ( - "Expected a list of at least one gradient to aggregate, got %r" % gradients - ) + return f"Expected a list of at least one gradient to aggregate, got {gradients!r}" if not isinstance(f, int) or f < 1 or len(gradients) < 4 * f + 3: - return ( - "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" - % (f, (len(gradients) - 3) // 4) - ) - if m is not None and ( - not isinstance(m, int) or m < 1 or m > len(gradients) - f - 2 - ): - return ( - "Invalid number of selected gradients, got m = %r, expected 1 ≤ m ≤ %d" - % (f, len(gradients) - f - 2) + return "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" % ( + f, + (len(gradients) - 3) // 4, ) + if m is not None and (not isinstance(m, int) or m < 1 or m > len(gradients) - f - 2): + return "Invalid number of selected gradients, got m = %r, expected 1 ≤ m ≤ %d" % (f, len(gradients) - f - 2) + return None def upper_bound(n: int, f: int, d: int) -> float: - """ - Compute Bulyan's theoretical resilience upper bound. + """Compute Bulyan's theoretical resilience upper bound. Parameters ---------- @@ -263,7 +237,7 @@ def upper_bound(n: int, f: int, d: int) -> float: Gradient dimension. Accepted for compatibility with the GAR metadata interface; the current formula does not depend on it. - Returns + Returns: ------- float Upper bound on the ratio between non-Byzantine standard deviation and @@ -287,6 +261,5 @@ def upper_bound(n: int, f: int, d: int) -> float: register(method_name, aggregate_native, check, upper_bound=upper_bound) else: tools.warning( - "GAR %r could not be registered since the associated native module %r is unavailable" - % (method_name, native_name) + f"GAR {method_name!r} could not be registered since the associated native module {native_name!r} is unavailable" ) diff --git a/aggregators/krum.py b/krum/aggregators/krum.py similarity index 81% rename from aggregators/krum.py rename to krum/aggregators/krum.py index 5f05840..671d7bc 100644 --- a/aggregators/krum.py +++ b/krum/aggregators/krum.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file krum.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Multi-Krum GAR. ### -""" -Krum and Multi-Krum are distance-based Byzantine-resilient aggregation rules. +r"""Krum and Multi-Krum are distance-based Byzantine-resilient aggregation rules. For each candidate gradient, the rule computes a score by summing the distances to its :math:`n - f - 1` nearest neighbours. It then selects the :math:`m` @@ -65,9 +63,8 @@ Number of gradients to select for averaging. Defaults to ``n - f - 2``. Must satisfy ``1 <= m <= n - f - 2``. -Example +Example: ------- - >>> import torch >>> from aggregators import krum >>> gradients = [ @@ -86,25 +83,21 @@ import torch -import tools - +from .. import tools from . import register # Optional 'native' module try: - import native + from krum import native except ImportError: - native = None + native = None # type: ignore[assignment] # ---------------------------------------------------------------------------- # # Multi-Krum GAR -def _compute_scores( - gradients: list[torch.Tensor], f: int, m: int, **kwargs -) -> list[tuple[float, torch.Tensor]]: - """ - Compute Multi-Krum scores for all candidate gradients. +def _compute_scores(gradients: list[torch.Tensor], f: int, m: int, **kwargs) -> list[tuple[float, torch.Tensor]]: + """Compute Multi-Krum scores for all candidate gradients. Parameters ---------- @@ -117,7 +110,7 @@ def _compute_scores( **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- list of tuple[float, torch.Tensor] Candidate gradients paired with their scores, sorted by increasing @@ -132,10 +125,10 @@ def _compute_scores( dist = math.inf distances[i] = dist # Compute the scores - scores = list() + scores = [] for i in range(n): # Collect the distances - grad_dists = list() + grad_dists = [] for j in range(i): grad_dists.append(distances[(2 * n - j - 3) * j // 2 + i - 1]) for j in range(i + 1, n): @@ -148,11 +141,8 @@ def _compute_scores( return scores -def aggregate( - gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs -) -> torch.Tensor: - """ - Aggregate gradients with Multi-Krum. +def aggregate(gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs) -> torch.Tensor: + """Aggregate gradients with Multi-Krum. Parameters ---------- @@ -167,12 +157,12 @@ def aggregate( **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- torch.Tensor Average of the selected ``m`` gradients with the smallest Krum scores. - Notes + Notes: ----- The output tensor is newly created and does not alias any input tensor. """ @@ -184,11 +174,8 @@ def aggregate( return sum(grad for _, grad in scores[:m]).div_(m) -def aggregate_native( - gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs -) -> torch.Tensor: - """ - Aggregate gradients with the native Multi-Krum implementation. +def aggregate_native(gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs) -> torch.Tensor: + """Aggregate gradients with the native Multi-Krum implementation. Parameters ---------- @@ -201,7 +188,7 @@ def aggregate_native( **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- torch.Tensor Average of the selected gradients. @@ -210,14 +197,11 @@ def aggregate_native( if m is None: m = len(gradients) - f - 2 # Computation - return native.krum.aggregate(gradients, f, m) + return native.krum.aggregate(gradients, f, m) # type: ignore[attr-defined] -def check( - gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs -) -> str | None: - """ - Check whether Multi-Krum can be used with the given parameters. +def check(gradients: list[torch.Tensor], f: int, m: int | None = None, **kwargs) -> str | None: + """Check whether Multi-Krum can be used with the given parameters. Parameters ---------- @@ -230,33 +214,26 @@ def check( **kwargs : object Additional keyword arguments, ignored by this implementation. - Returns + Returns: ------- str or None ``None`` when the parameters are valid, otherwise a user-facing error message. """ if not isinstance(gradients, list) or len(gradients) < 1: - return ( - "Expected a list of at least one gradient to aggregate, got %r" % gradients - ) + return f"Expected a list of at least one gradient to aggregate, got {gradients!r}" if not isinstance(f, int) or f < 1 or len(gradients) < 2 * f + 3: - return ( - "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" - % (f, (len(gradients) - 3) // 2) - ) - if m is not None and ( - not isinstance(m, int) or m < 1 or m > len(gradients) - f - 2 - ): - return ( - "Invalid number of selected gradients, got m = %r, expected 1 ≤ m ≤ %d" - % (m, len(gradients) - f - 2) + return "Invalid number of Byzantine gradients to tolerate, got f = %r, expected 1 ≤ f ≤ %d" % ( + f, + (len(gradients) - 3) // 2, ) + if m is not None and (not isinstance(m, int) or m < 1 or m > len(gradients) - f - 2): + return "Invalid number of selected gradients, got m = %r, expected 1 ≤ m ≤ %d" % (m, len(gradients) - f - 2) + return None def upper_bound(n: int, f: int, d: int) -> float: - """ - Compute the theoretical Multi-Krum robustness bound. + """Compute the theoretical Multi-Krum robustness bound. Parameters ---------- @@ -268,7 +245,7 @@ def upper_bound(n: int, f: int, d: int) -> float: Dimension of the gradient space. This parameter is accepted for the standard GAR metadata contract and is not used by this formula. - Returns + Returns: ------- float Upper bound on the ratio between non-Byzantine standard deviation and @@ -284,8 +261,7 @@ def influence( m: int | None = None, **kwargs, ) -> float: - """ - Compute the ratio of Byzantine gradients selected by Multi-Krum. + """Compute the ratio of Byzantine gradients selected by Multi-Krum. Parameters ---------- @@ -300,7 +276,7 @@ def influence( **kwargs : object Additional keyword arguments forwarded to score computation. - Returns + Returns: ------- float Ratio of selected gradients that come from ``attacks``. @@ -336,6 +312,5 @@ def influence( register(method_name, aggregate_native, check, upper_bound) else: tools.warning( - "GAR %r could not be registered since the associated native module %r is unavailable" - % (method_name, native_name) + f"GAR {method_name!r} could not be registered since the associated native module {native_name!r} is unavailable" ) diff --git a/aggregators/median.py b/krum/aggregators/median.py similarity index 84% rename from aggregators/median.py rename to krum/aggregators/median.py index ab656b9..fc462c9 100644 --- a/aggregators/median.py +++ b/krum/aggregators/median.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file median.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Coordinate-wise median GAR. ### -""" -Coordinate-wise median aggregation rule. +r"""Coordinate-wise median aggregation rule. This rule computes the median of each coordinate independently across all submitted gradients. It delegates to ``torch.median`` and does not filter @@ -46,7 +44,7 @@ - :math:`n` is the total number of workers. - :math:`f` is the number of Byzantine workers. -Example +Example: ------- >>> import torch >>> from aggregators import median @@ -64,23 +62,21 @@ import torch -import tools - +from .. import tools from . import register # Optional 'native' module try: - import native + from krum import native except ImportError: - native = None + native = None # type: ignore[assignment] # ---------------------------------------------------------------------------- # # Coordinate-wise median GAR def aggregate(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: - """ - Compute the coordinate-wise median of all submitted gradients. + """Compute the coordinate-wise median of all submitted gradients. This method delegates to ``torch.median`` and does not filter non-finite values before aggregation. NaN values may propagate, while Inf values @@ -95,12 +91,12 @@ def aggregate(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: Additional keyword arguments, accepted for compatibility with the GAR interface and ignored by this implementation. - Returns + Returns: ------- torch.Tensor Coordinate-wise median of all input gradients. - Notes + Notes: ----- The returned tensor is newly computed and does not alias any input tensor. """ @@ -108,8 +104,7 @@ def aggregate(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: def aggregate_native(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: - """ - Compute the coordinate-wise median using native C++/CUDA acceleration. + """Compute the coordinate-wise median using native C++/CUDA acceleration. Parameters ---------- @@ -119,17 +114,16 @@ def aggregate_native(gradients: list[torch.Tensor], **kwargs) -> torch.Tensor: Additional keyword arguments, accepted for compatibility with the GAR interface and ignored by this implementation. - Returns + Returns: ------- torch.Tensor Coordinate-wise median of all input gradients. """ - return native.median.aggregate(gradients) + return native.median.aggregate(gradients) # type: ignore[attr-defined] def check(gradients: list[torch.Tensor], **kwargs) -> str | None: - """ - Check whether the median rule can be used with the given parameters. + """Check whether the median rule can be used with the given parameters. Parameters ---------- @@ -139,21 +133,19 @@ def check(gradients: list[torch.Tensor], **kwargs) -> str | None: Additional keyword arguments, accepted for compatibility with the GAR interface and ignored by this check. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(gradients, list) or len(gradients) < 1: - return ( - "Expected a list of at least one gradient to aggregate, got %r" % gradients - ) + return f"Expected a list of at least one gradient to aggregate, got {gradients!r}" + return None def upper_bound(n: int, f: int, d: int) -> float: - """ - Compute the theoretical coordinate-wise median robustness bound. + r"""Compute the theoretical coordinate-wise median robustness bound. Parameters ---------- @@ -165,13 +157,13 @@ def upper_bound(n: int, f: int, d: int) -> float: Gradient dimension. Accepted for compatibility with the GAR metadata interface; the current formula does not depend on it. - Returns + Returns: ------- float Upper bound on the ratio between non-Byzantine standard deviation and gradient norm. - Notes + Notes: ----- The bound formula is: @@ -197,6 +189,5 @@ def upper_bound(n: int, f: int, d: int) -> float: register(method_name, aggregate_native, check, upper_bound) else: tools.warning( - "GAR %r could not be registered since the associated native module %r is unavailable" - % (method_name, native_name) + f"GAR {method_name!r} could not be registered since the associated native module {native_name!r} is unavailable" ) diff --git a/attacks/__init__.py b/krum/attacks/__init__.py similarity index 87% rename from attacks/__init__.py rename to krum/attacks/__init__.py index 12c723e..6f9e281 100644 --- a/attacks/__init__.py +++ b/krum/attacks/__init__.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file __init__.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Loading of the local modules. ### -""" -Byzantine attack registry used to evaluate aggregation-rule robustness. +"""Byzantine attack registry used to evaluate aggregation-rule robustness. Each attack combines a keyword-only generation function with a validation function. Registered attacks are loaded dynamically and exposed as module-level @@ -46,20 +44,19 @@ """ import pathlib +from collections.abc import Callable +from typing import Any, cast import torch -import tools - -from typing import Callable +from .. import tools # ---------------------------------------------------------------------------- # # Automated attack loader def register(name: str, unchecked: Callable, check: Callable) -> None: - """ - Register a Byzantine attack. + """Register a Byzantine attack. Parameters ---------- @@ -72,7 +69,7 @@ def register(name: str, unchecked: Callable, check: Callable) -> None: Validation function associated with ``unchecked``. It must return ``None`` when parameters are valid, or an error message otherwise. - Returns + Returns: ------- None The attack is registered as a module-level callable. @@ -88,9 +85,7 @@ def checked(f_real, **kwargs): # Check parameter validity message = check(f_real=f_real, **kwargs) if message is not None: - raise tools.UserException( - f"Attack {name!r} cannot be used with the given parameters: {message}" - ) + raise tools.UserException(f"Attack {name!r} cannot be used with the given parameters: {message}") # Attack res = unchecked(f_real=f_real, **kwargs) # Forward asserted return value @@ -100,17 +95,17 @@ def checked(f_real, **kwargs): return res # Select which function to call by default - func = checked if __debug__ else unchecked + func = cast(Any, checked if __debug__ else unchecked) # Bind all the (sub) functions to the selected function - setattr(func, "check", check) - setattr(func, "checked", checked) - setattr(func, "unchecked", unchecked) + func.check = check + func.checked = checked + func.unchecked = unchecked # Export the selected function with the associated name attacks[name] = func # Registered attacks (mapping name -> attack) -attacks = dict() +attacks = {} # Load native and all local modules with tools.Context("attacks", None): diff --git a/attacks/identical.py b/krum/attacks/identical.py similarity index 85% rename from attacks/identical.py rename to krum/attacks/identical.py index 996dfb3..f9ae092 100644 --- a/attacks/identical.py +++ b/krum/attacks/identical.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file identical.py # @author Sébastien Rouault @@ -25,8 +24,7 @@ # 2019 Feb 16. ArXiv. URL: https://arxiv.org/pdf/1902.06156v1 ### -""" -Identical-gradient Byzantine attacks. +"""Identical-gradient Byzantine attacks. These attacks generate ``f_real`` references to the same newly created Byzantine gradient. The gradient is built from the average honest gradient plus @@ -61,9 +59,8 @@ negative : bool, optional Whether to negate the selected factor. Defaults to ``False``. -Example +Example: ------- - >>> import torch >>> from aggregators import average >>> from attacks import little @@ -81,22 +78,19 @@ """ import math +from collections.abc import Callable import torch -import tools - +from .. import tools from . import register -from typing import Callable - # ---------------------------------------------------------------------------- # # Generic attack implementation generator def make_attack(compute_direction: Callable) -> Callable: - """ - Create an identical-gradient attack from a direction function. + """Create an identical-gradient attack from a direction function. Parameters ---------- @@ -104,7 +98,7 @@ def make_attack(compute_direction: Callable) -> Callable: Function computing the attack direction from stacked honest gradients and their average. - Returns + Returns: ------- callable Attack function compatible with the attack registration contract. @@ -120,8 +114,7 @@ def attack( negative: bool = False, **kwargs, ) -> list[torch.Tensor]: - """ - Generate identical Byzantine gradients. + """Generate identical Byzantine gradients. Parameters ---------- @@ -143,7 +136,7 @@ def attack( **kwargs : object Additional keyword arguments forwarded to ``compute_direction``. - Returns + Returns: ------- list of torch.Tensor Generated Byzantine gradients. Each entry references the same newly @@ -152,7 +145,7 @@ def attack( """ # Fast path if f_real == 0: - return list() + return [] # Stack and compute the average honest gradient, and then the attack vector grad_stck = torch.stack(grad_honests) grad_avg = grad_stck.mean(dim=0) @@ -175,9 +168,8 @@ def eval_factor(factor): return aggregated.dot(aggregated).item() factor = tools.line_maximize(eval_factor, evals=math.ceil(-factor)) - else: - if negative: - factor = -factor + elif negative: + factor = -factor # Generate the Byzantine gradient from the given/computed factor byz_grad = grad_avg grad_att.mul_(factor) @@ -197,8 +189,7 @@ def check( negative: bool = False, **kwargs, ) -> str | None: - """ - Check parameter validity for identical-gradient attacks. + """Check parameter validity for identical-gradient attacks. Parameters ---------- @@ -215,33 +206,23 @@ def check( **kwargs : object Additional keyword arguments, ignored by this check. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(grad_honests, list) or len(grad_honests) == 0: - return "Expected a non-empty list of honest gradients, got %r" % (grad_honests,) + return f"Expected a non-empty list of honest gradients, got {grad_honests!r}" if not isinstance(f_real, int) or f_real < 0: - return ( - "Expected a non-negative number of Byzantine gradients to generate, got %r" - % (f_real,) - ) + return f"Expected a non-negative number of Byzantine gradients to generate, got {f_real!r}" if not callable(defense): - return "Expected a callable for the aggregation rule, got %r" % (defense,) - if not ( - (isinstance(factor, float) and factor > 0) - or (isinstance(factor, int) and factor != 0) - ): - return ( - "Expected a positive number or a negative integer for the attack factor, got %r" - % (factor,) - ) + return f"Expected a callable for the aggregation rule, got {defense!r}" + if not ((isinstance(factor, float) and factor > 0) or (isinstance(factor, int) and factor != 0)): + return f"Expected a positive number or a negative integer for the attack factor, got {factor!r}" if not isinstance(negative, bool): - return "Expected a boolean for optional parameter 'negative', got %r" % ( - negative, - ) + return f"Expected a boolean for optional parameter 'negative', got {negative!r}" + return None # ---------------------------------------------------------------------------- # @@ -254,8 +235,7 @@ def bulyan( target_idx: int | str = -1, **kwargs, ) -> torch.Tensor: - """ - Compute the Bulyan attack direction. + """Compute the Bulyan attack direction. This direction is adapted from "The Hidden Vulnerability of Distributed Learning in Byzantium". @@ -272,25 +252,21 @@ def bulyan( **kwargs : object Additional keyword arguments, ignored by this direction. - Returns + Returns: ------- torch.Tensor Attack direction. """ if target_idx == "all": return torch.ones_like(grad_avg) - else: - assert isinstance(target_idx, int), ( - "Expected an integer or \"all\" for 'target_idx', got %r" % (target_idx,) - ) - grad_att = torch.zeros_like(grad_avg) - grad_att[target_idx] = 1 - return grad_att + assert isinstance(target_idx, int), f"Expected an integer or \"all\" for 'target_idx', got {target_idx!r}" + grad_att = torch.zeros_like(grad_avg) + grad_att[target_idx] = 1 + return grad_att def empire(grad_stck: torch.Tensor, grad_avg: torch.Tensor, **kwargs) -> torch.Tensor: - """ - Compute the Empire attack direction. + """Compute the Empire attack direction. This direction is adapted from "Fall of Empires". @@ -303,7 +279,7 @@ def empire(grad_stck: torch.Tensor, grad_avg: torch.Tensor, **kwargs) -> torch.T **kwargs : object Additional keyword arguments, ignored by this direction. - Returns + Returns: ------- torch.Tensor Attack direction, equal to the negative average honest gradient. @@ -312,8 +288,7 @@ def empire(grad_stck: torch.Tensor, grad_avg: torch.Tensor, **kwargs) -> torch.T def little(grad_stck: torch.Tensor, grad_avg: torch.Tensor, **kwargs) -> torch.Tensor: - """ - Compute the Little attack direction. + """Compute the Little attack direction. This direction is adapted from "A Little Is Enough". @@ -326,7 +301,7 @@ def little(grad_stck: torch.Tensor, grad_avg: torch.Tensor, **kwargs) -> torch.T **kwargs : object Additional keyword arguments, ignored by this direction. - Returns + Returns: ------- torch.Tensor Attack direction, computed as the coordinate-wise standard deviation of diff --git a/attacks/nan.py b/krum/attacks/nan.py similarity index 83% rename from attacks/nan.py rename to krum/attacks/nan.py index 9da9b62..d6a9107 100644 --- a/attacks/nan.py +++ b/krum/attacks/nan.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file nan.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Attack that generates NaN gradient(s), hence the name. ### -""" -NaN-valued Byzantine gradient attack. +"""NaN-valued Byzantine gradient attack. This attack generates gradients filled with NaN (Not a Number) values in order to test whether an aggregation rule detects, rejects, or propagates non-finite @@ -34,9 +32,8 @@ - Does not alias any honest input gradient. - No parameters beyond gradient count. -Example +Example: ------- - >>> import torch >>> from attacks import nan >>> grad_honests = [torch.tensor([1., 2., 3.]), torch.tensor([4., 5., 6.])] @@ -55,11 +52,8 @@ # Non-finite gradient attack -def attack( - grad_honests: list[torch.Tensor], f_real: int, **kwargs -) -> list[torch.Tensor]: - """ - Generate NaN-valued Byzantine gradients. +def attack(grad_honests: list[torch.Tensor], f_real: int, **kwargs) -> list[torch.Tensor]: + """Generate NaN-valued Byzantine gradients. Parameters ---------- @@ -71,7 +65,7 @@ def attack( **kwargs : object Additional keyword arguments, ignored by this attack. - Returns + Returns: ------- list of torch.Tensor List containing ``f_real`` references to the same newly allocated @@ -80,7 +74,7 @@ def attack( """ # Fast path if f_real == 0: - return list() + return [] # Generate the non-finite Byzantine gradient byz_grad = torch.empty_like(grad_honests[0]) byz_grad.copy_(torch.tensor((math.nan,), dtype=byz_grad.dtype)) @@ -89,8 +83,7 @@ def attack( def check(grad_honests: list[torch.Tensor], f_real: int, **kwargs) -> str | None: - """ - Check parameter validity for the NaN attack. + """Check parameter validity for the NaN attack. Parameters ---------- @@ -101,19 +94,17 @@ def check(grad_honests: list[torch.Tensor], f_real: int, **kwargs) -> str | None **kwargs : object Additional keyword arguments, ignored by this check. - Returns + Returns: ------- str or None ``None`` when parameters are valid, otherwise a user-facing error message. """ if not isinstance(grad_honests, list) or len(grad_honests) == 0: - return "Expected a non-empty list of honest gradients, got %r" % (grad_honests,) + return f"Expected a non-empty list of honest gradients, got {grad_honests!r}" if not isinstance(f_real, int) or f_real < 0: - return ( - "Expected a non-negative number of Byzantine gradients to generate, got %r" - % (f_real,) - ) + return f"Expected a non-negative number of Byzantine gradients to generate, got {f_real!r}" + return None # ---------------------------------------------------------------------------- # diff --git a/experiments/__init__.py b/krum/experiments/__init__.py similarity index 68% rename from experiments/__init__.py rename to krum/experiments/__init__.py index 2848e7b..40eb7ba 100644 --- a/experiments/__init__.py +++ b/krum/experiments/__init__.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file __init__.py # @author Sébastien Rouault @@ -14,8 +13,7 @@ # Heavily relies on the module 'torchvision'. ### -""" -Experiment components for model training, dataset loading, and evaluation. +"""Experiment components for model training, dataset loading, and evaluation. This module groups the building blocks of a Krum training loop: @@ -29,7 +27,7 @@ Custom models and datasets can be added under ``experiments/models/`` and ``experiments/datasets/``; they are discovered automatically at import time. -Example +Example: ------- .. code-block:: python @@ -47,10 +45,27 @@ import pathlib -import tools +from krum import tools -# ---------------------------------------------------------------------------- # -# Load all local modules +from .checkpoint import Checkpoint, Storage +from .configuration import Configuration +from .dataset import Dataset, batch_dataset, get_default_transform, make_datasets, make_sampler +from .loss import Criterion, Loss +from .model import Model +from .optimizer import Optimizer -with tools.Context("experiments", None): - tools.import_directory(pathlib.Path(__file__).parent, globals()) +# Public API +__all__ = [ + "Checkpoint", + "Configuration", + "Criterion", + "Dataset", + "Loss", + "Model", + "Optimizer", + "Storage", + "batch_dataset", + "get_default_transform", + "make_datasets", + "make_sampler", +] diff --git a/experiments/checkpoint.py b/krum/experiments/checkpoint.py similarity index 84% rename from experiments/checkpoint.py rename to krum/experiments/checkpoint.py index 98c5c44..42db54b 100644 --- a/experiments/checkpoint.py +++ b/krum/experiments/checkpoint.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file checkpoint.py # @author Sébastien Rouault @@ -13,15 +12,13 @@ # Checkpoint helpers. ### -""" -Checkpoint management for model, optimizer, and arbitrary stateful objects. +"""Checkpoint management for model, optimizer, and arbitrary stateful objects. This module provides :class:`Checkpoint` for saving and restoring state dictionaries, and :class:`Storage` for plain-dictionary checkpointing. -Example +Example: ------- - >>> from experiments import Checkpoint, Model, Optimizer >>> ckpt = Checkpoint() >>> ckpt.snapshot(model).snapshot(optimizer) @@ -33,12 +30,12 @@ __all__ = ["Checkpoint", "Storage"] -import tools - import copy import pathlib + import torch +from .. import tools from .model import Model from .optimizer import Optimizer @@ -47,8 +44,7 @@ class Checkpoint: - """ - Collection of state dictionaries with saving/loading helpers. + """Collection of state dictionaries with saving/loading helpers. This class can snapshot any object implementing the ``state_dict`` / ``load_state_dict`` protocol (e.g. ``torch.nn.Module``, @@ -56,9 +52,8 @@ class Checkpoint: :class:`~experiments.model.Model` and :class:`~experiments.optimizer.Optimizer` wrappers automatically. - Example + Example: ------- - >>> ckpt = Checkpoint() >>> ckpt.snapshot(model, deepcopy=True) >>> ckpt.restore(model) @@ -72,8 +67,7 @@ class Checkpoint: @classmethod def _prepare(cls, instance): - """ - Prepare an instance for checkpointing. + """Prepare an instance for checkpointing. If the instance is a wrapped :class:`Model` or :class:`Optimizer`, the underlying PyTorch object is returned instead. @@ -83,12 +77,12 @@ def _prepare(cls, instance): instance : object Instance to snapshot or restore. - Returns + Returns: ------- tuple[object, str] Checkpoint-able instance and its fully-qualified storage key. - Raises + Raises: ------ tools.UserException If the instance lacks ``state_dict`` or ``load_state_dict``. @@ -104,23 +98,19 @@ def _prepare(cls, instance): for prop in ("state_dict", "load_state_dict"): if not callable(getattr(res, prop, None)): raise tools.UserException( - f"Given instance {instance!r} is not checkpoint-able " - f"(missing callable member {prop!r})" + f"Given instance {instance!r} is not checkpoint-able (missing callable member {prop!r})" ) # Return the instance and the associated storage key return res, tools.fullqual(inst_cls) def __init__(self): - """ - Create an empty checkpoint. - """ - self._store = dict() + """Create an empty checkpoint.""" + self._store = {} if __debug__: - self._copied = dict() + self._copied = {} def snapshot(self, instance, overwrite=False, deepcopy=False, nowarnref=False): - """ - Take (or overwrite) a snapshot of an instance's state dictionary. + """Take (or overwrite) a snapshot of an instance's state dictionary. Parameters ---------- @@ -135,12 +125,12 @@ def snapshot(self, instance, overwrite=False, deepcopy=False, nowarnref=False): Suppress the debug warning when restoring a reference is the intended behavior. - Returns + Returns: ------- Checkpoint Self, for chaining. - Raises + Raises: ------ tools.UserException If a snapshot already exists and ``overwrite`` is ``False``. @@ -148,9 +138,7 @@ def snapshot(self, instance, overwrite=False, deepcopy=False, nowarnref=False): instance, key = type(self)._prepare(instance) # Snapshot the state dictionary if not overwrite and key in self._store: - raise tools.UserException( - f"A snapshot for {key!r} is already stored in the checkpoint" - ) + raise tools.UserException(f"A snapshot for {key!r} is already stored in the checkpoint") if deepcopy: self._store[key] = copy.deepcopy(instance.state_dict()) else: @@ -162,8 +150,7 @@ def snapshot(self, instance, overwrite=False, deepcopy=False, nowarnref=False): return self def restore(self, instance, nothrow=False): - """ - Restore an instance from its stored snapshot. + """Restore an instance from its stored snapshot. Parameters ---------- @@ -172,12 +159,12 @@ def restore(self, instance, nothrow=False): nothrow : bool, optional If ``True``, silently skip when no snapshot is available. - Returns + Returns: ------- Checkpoint Self, for chaining. - Raises + Raises: ------ tools.UserException If no snapshot exists and ``nothrow`` is ``False``. @@ -194,15 +181,12 @@ def restore(self, instance, nothrow=False): f"may not be the one expected" ) elif not nothrow: - raise tools.UserException( - f"No snapshot for {key!r} is available in the checkpoint" - ) + raise tools.UserException(f"No snapshot for {key!r} is available in the checkpoint") # Enable chaining return self def load(self, filepath, overwrite=False): - """ - Load checkpoint data from a file. + """Load checkpoint data from a file. Parameters ---------- @@ -211,12 +195,12 @@ def load(self, filepath, overwrite=False): overwrite : bool, optional Whether to overwrite any existing snapshots. - Returns + Returns: ------- Checkpoint Self, for chaining. - Raises + Raises: ------ tools.UserException If the checkpoint is non-empty and ``overwrite`` is ``False``. @@ -235,8 +219,7 @@ def load(self, filepath, overwrite=False): return self def save(self, filepath, overwrite=False): - """ - Save the current checkpoint to a file. + """Save the current checkpoint to a file. Parameters ---------- @@ -245,12 +228,12 @@ def save(self, filepath, overwrite=False): overwrite : bool, optional Whether to overwrite an existing file. - Returns + Returns: ------- Checkpoint Self, for chaining. - Raises + Raises: ------ tools.UserException If the file exists and ``overwrite`` is ``False``. @@ -272,18 +255,16 @@ def save(self, filepath, overwrite=False): class Storage(dict): - """ - Plain dictionary that implements the ``state_dict`` protocol. + """Plain dictionary that implements the ``state_dict`` protocol. This allows arbitrary key/value data to be snapshotted and restored alongside models and optimizers using :class:`Checkpoint`. """ def state_dict(self): - """ - Return the dictionary itself as state. + """Return the dictionary itself as state. - Returns + Returns: ------- dict Self. @@ -291,8 +272,7 @@ def state_dict(self): return self def load_state_dict(self, state): - """ - Replace contents with the given state. + """Replace contents with the given state. Parameters ---------- diff --git a/experiments/configuration.py b/krum/experiments/configuration.py similarity index 86% rename from experiments/configuration.py rename to krum/experiments/configuration.py index 7110b9f..f3ec9e1 100644 --- a/experiments/configuration.py +++ b/krum/experiments/configuration.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file configuration.py # @author Sébastien Rouault @@ -13,15 +12,14 @@ # Configuration wrapper. ### -""" -Tensor configuration wrapper. +"""Tensor configuration wrapper. This module provides the :class:`Configuration` class, an immutable mapping that bundles ``device``, ``dtype``, and memory-transfer options. It is used throughout ``experiments`` to ensure every created or moved tensor uses the same configuration. -Example +Example: ------- .. code-block:: python @@ -34,18 +32,18 @@ __all__ = ["Configuration"] -import tools - from collections.abc import Mapping + import torch +from .. import tools + # ---------------------------------------------------------------------------- # # Trivial tensor configuration holder (dtype, device, ...) class class Configuration(Mapping): - """ - Immutable tensor configuration holder. + """Immutable tensor configuration holder. This class bundles ``device``, ``dtype``, and memory-transfer options into a single immutable mapping. It is used throughout ``experiments`` @@ -64,9 +62,8 @@ class Configuration(Mapping): relink : bool, optional Whether to relink instead of copying during parameter assignments. - Example + Example: ------- - >>> from experiments import Configuration >>> config = Configuration(device="cpu", dtype=torch.float32) >>> config["device"] @@ -77,8 +74,7 @@ class Configuration(Mapping): default_device = "cuda" if torch.cuda.is_available() else "cpu" def __init__(self, device=None, dtype=None, noblock=False, relink=False): - """ - Initialize the configuration. + """Initialize the configuration. Parameters ---------- @@ -113,10 +109,9 @@ def __init__(self, device=None, dtype=None, noblock=False, relink=False): self.relink = relink def __len__(self): - """ - Return the number of configuration entries. + """Return the number of configuration entries. - Returns + Returns: ------- int Number of entries in the configuration mapping. @@ -124,15 +119,14 @@ def __len__(self): return len(self._args) def __getitem__(self, name): - """ - Get a configuration value by name. + """Get a configuration value by name. Parameters ---------- name : str Configuration key (e.g. ``"device"``, ``"dtype"``). - Returns + Returns: ------- object Associated configuration value. @@ -140,10 +134,9 @@ def __getitem__(self, name): return self._args[name] def __iter__(self): - """ - Iterate over all configuration keys. + """Iterate over all configuration keys. - Returns + Returns: ------- iterator Iterator over configuration entry names. @@ -151,10 +144,9 @@ def __iter__(self): return self._args.__iter__() def __str__(self): - """ - Return a nicely printable representation. + """Return a nicely printable representation. - Returns + Returns: ------- str Human-readable configuration summary. @@ -164,16 +156,13 @@ def __str__(self): return str(temp) def __repr__(self): - """ - Return an evaluable string representation. + """Return an evaluable string representation. - Returns + Returns: ------- str Python-code string that evaluates to this configuration. """ display = {"non_blocking": "noblock"} - argrepr = (", ").join( - f"{display.get(key, key)}={val!r}" for key, val in self._args.items() - ) + argrepr = (", ").join(f"{display.get(key, key)}={val!r}" for key, val in self._args.items()) return f"Configuration({argrepr}, relink={self.relink})" diff --git a/experiments/dataset.py b/krum/experiments/dataset.py similarity index 88% rename from experiments/dataset.py rename to krum/experiments/dataset.py index 870fbb3..fcc9482 100644 --- a/experiments/dataset.py +++ b/krum/experiments/dataset.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file dataset.py # @author Sébastien Rouault @@ -13,37 +12,37 @@ # Dataset wrappers/helpers. ### -""" -Dataset loading, batching, and sampling utilities. +"""Dataset loading, batching, and sampling utilities. This module wraps ``torchvision.datasets`` and custom dataset modules into uniform infinite-batch generators. It also provides helpers for train/test splitting and raw-tensor batching. -Example +Example: ------- - >>> from experiments import Dataset, make_datasets >>> trainset, testset = make_datasets("cifar10", train_batch=128) >>> inputs, targets = trainset.sample(config) """ __all__ = [ - "get_default_transform", "Dataset", - "make_sampler", - "make_datasets", "batch_dataset", + "get_default_transform", + "make_datasets", + "make_sampler", ] -import tools - import pathlib import random import tempfile +import types +from typing import Any + import torch import torchvision -import types + +from .. import tools # ---------------------------------------------------------------------------- # # Default image transformations @@ -59,9 +58,7 @@ transforms_cifar = [ torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize( - (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010) - ), + torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ] # Per-dataset image transformations @@ -75,8 +72,7 @@ def get_default_transform(dataset, train): - """ - Return the default transform for a torchvision dataset. + """Return the default transform for a torchvision dataset. Parameters ---------- @@ -86,7 +82,7 @@ def get_default_transform(dataset, train): Whether to return the training transform. Ignored when ``dataset`` is ``None``. - Returns + Returns: ------- torchvision.transforms.Compose or None Composed transform, or ``None`` if the dataset is unknown. @@ -103,8 +99,7 @@ def get_default_transform(dataset, train): class Dataset: - """ - Unified dataset wrapper producing infinite batches. + """Unified dataset wrapper producing infinite batches. This class can wrap: @@ -125,7 +120,7 @@ class Dataset: **kwargs : object Forwarded to the dataset constructor when ``data`` is a string. - Raises + Raises: ------ tools.UnavailableException If ``data`` is an unknown dataset name. @@ -138,10 +133,9 @@ class Dataset: @classmethod def get_default_root(cls): - """ - Lazily initialize and return the default dataset cache directory. + """Lazily initialize and return the default dataset cache directory. - Returns + Returns: ------- pathlib.Path Path to the dataset cache. Falls back to the system temp @@ -164,17 +158,19 @@ def get_default_root(cls): return cls.__default_root # Map 'lower-case names' -> 'dataset class' available in PyTorch - __datasets = None + __datasets: dict[str, Any] | None = None + + # Optional PyTorch DataLoader (set externally for epoch-based iteration) + _loader: torch.utils.data.DataLoader | None = None @classmethod def _get_datasets(cls): - """ - Lazily build the name-to-builder mapping for datasets. + """Lazily build the name-to-builder mapping for datasets. This includes all ``torchvision.datasets`` plus custom datasets discovered under ``experiments/datasets/``. - Returns + Returns: ------- dict[str, callable] Lower-case dataset names mapped to builder functions. @@ -184,7 +180,7 @@ def _get_datasets(cls): if cls.__datasets is not None: return cls.__datasets # Initialize the dictionary - cls.__datasets = dict() + cls.__datasets = {} # Populate with TorchVision's datasets for name in dir(torchvision.datasets): if len(name) == 0 or name[0] == "_": @@ -224,23 +220,20 @@ def add_custom_datasets(name, module, _): exports = getattr(module, "__all__", None) if exports is None: tools.warning( - f"Dataset module {name!r} does not provide '__all__'; " - f"falling back to '__dict__' for name discovery" + f"Dataset module {name!r} does not provide '__all__'; falling back to '__dict__' for name discovery" ) exports = (n for n in dir(module) if len(n) > 0 and n[0] != "_") exported = False for dataset in exports: if not isinstance(dataset, str): - tools.warning( - f"Dataset module {name!r} exports non-string name " - f"{dataset!r}; ignored" - ) + tools.warning(f"Dataset module {name!r} exports non-string name {dataset!r}; ignored") continue constructor = getattr(module, dataset, None) if not callable(constructor): continue exported = True fullname = f"{name}-{dataset}" + assert cls.__datasets is not None if fullname in cls.__datasets: tools.warning( f"Unable to make available dataset {dataset!r} from module " @@ -249,10 +242,7 @@ def add_custom_datasets(name, module, _): continue cls.__datasets[fullname] = constructor if not exported: - tools.warning( - f"Dataset module {name!r} does not export any valid " - f"constructor name through '__all__'" - ) + tools.warning(f"Dataset module {name!r} does not export any valid constructor name through '__all__'") with tools.Context("datasets", None): tools.import_directory( @@ -263,8 +253,7 @@ def add_custom_datasets(name, module, _): return cls.__datasets def __init__(self, data, name=None, root=None, *args, **kwargs): - """ - Initialize the dataset wrapper. + """Initialize the dataset wrapper. Parameters ---------- @@ -288,7 +277,7 @@ def __init__(self, data, name=None, root=None, *args, **kwargs): if build is None: raise tools.UnavailableException(datasets, name, what="dataset name") root = root or type(self).get_default_root() - self._iter = build(root=root, *args, **kwargs) + self._iter = build(*args, root=root, **kwargs) elif isinstance(data, types.GeneratorType): if name is None: name = "" @@ -306,10 +295,9 @@ def single_batch(): self.name = name def __str__(self): - """ - Return a printable representation. + """Return a printable representation. - Returns + Returns: ------- str Human-readable dataset name. @@ -317,30 +305,25 @@ def __str__(self): return f"dataset {self.name}" def sample(self, config=None): - """ - Sample the next batch. + """Sample the next batch. Parameters ---------- config : experiments.Configuration or None, optional Target configuration for tensor placement. - Returns + Returns: ------- tuple Next batch, optionally moved to the target device. """ tns = next(self._iter) if config is not None: - tns = type(tns)( - tn.to(device=config["device"], non_blocking=config["non_blocking"]) - for tn in tns - ) + tns = type(tns)(tn.to(device=config["device"], non_blocking=config["non_blocking"]) for tn in tns) return tns def epoch(self, config=None): - """ - Return a finite epoch iterator. + """Return a finite epoch iterator. .. note:: @@ -351,7 +334,7 @@ def epoch(self, config=None): config : experiments.Configuration or None, optional Target configuration for tensor placement. - Returns + Returns: ------- generator Finite iterator over one epoch. @@ -386,15 +369,14 @@ def generator(): def make_sampler(loader): - """ - Create an infinite sampler from a DataLoader. + """Create an infinite sampler from a DataLoader. Parameters ---------- loader : torch.utils.data.DataLoader Finite data loader. - Yields + Yields: ------ tuple Batches, transparently restarting the loader when exhausted. @@ -422,8 +404,7 @@ def make_datasets( num_workers=1, **custom_args, ): - """ - Build training and testing dataset wrappers. + """Build training and testing dataset wrappers. Parameters ---------- @@ -443,24 +424,19 @@ def make_datasets( **custom_args : object Additional keyword arguments forwarded to the dataset constructor. - Returns + Returns: ------- tuple[Dataset, Dataset] Training and testing dataset wrappers. """ train_transforms = train_transforms or get_default_transform(dataset, True) test_transforms = test_transforms or get_default_transform(dataset, False) - num_workers_errmsg = ( - "Expected either a positive int or a tuple of 2 positive ints " - "for parameter 'num_workers'" - ) + num_workers_errmsg = "Expected either a positive int or a tuple of 2 positive ints for parameter 'num_workers'" if isinstance(num_workers, int): assert num_workers > 0, num_workers_errmsg train_workers = test_workers = num_workers else: - assert isinstance(num_workers, tuple) and len(num_workers) == 2, ( - num_workers_errmsg - ) + assert isinstance(num_workers, tuple) and len(num_workers) == 2, num_workers_errmsg train_workers, test_workers = num_workers assert isinstance(train_workers, int) and train_workers > 0, num_workers_errmsg assert isinstance(test_workers, int) and test_workers > 0, num_workers_errmsg @@ -488,8 +464,7 @@ def make_datasets( def batch_dataset(inputs, labels, train=False, batch_size=None, split=0.75): - """ - Batch a raw tensor dataset into infinite sampler generators. + """Batch a raw tensor dataset into infinite sampler generators. Parameters ---------- @@ -505,7 +480,7 @@ def batch_dataset(inputs, labels, train=False, batch_size=None, split=0.75): Fraction of samples for training when ``< 1``, or absolute count when ``>= 1``. - Returns + Returns: ------- generator Infinite sampler generator. @@ -553,7 +528,6 @@ def test_gen(inputs, labels, batch): train_len = split_pos batch_size = min(batch_size or train_len, train_len) return train_gen(inputs[:split_pos], labels[:split_pos], batch_size) - else: - test_len = dataset_len - split_pos - batch_size = min(batch_size or test_len, test_len) - return test_gen(inputs[split_pos:], labels[split_pos:], batch_size) + test_len = dataset_len - split_pos + batch_size = min(batch_size or test_len, test_len) + return test_gen(inputs[split_pos:], labels[split_pos:], batch_size) diff --git a/experiments/datasets/svm.py b/krum/experiments/datasets/svm.py similarity index 84% rename from experiments/datasets/svm.py rename to krum/experiments/datasets/svm.py index fe7e94c..22d8a24 100644 --- a/experiments/datasets/svm.py +++ b/krum/experiments/datasets/svm.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file svm.py # @author Sébastien Rouault @@ -21,13 +20,13 @@ Each builder downloads the raw LIBSVM file on first use, caches a pre-processed PyTorch tensor version, and returns an infinite-batch generator. -Example +Example: ------- >>> from experiments import Dataset >>> dataset = Dataset("svm-phishing", train=True, download=True) >>> inputs, labels = dataset.sample() -See Also +See Also: -------- experiments.batch_dataset : helper used internally to create the infinite sampler from raw tensors. @@ -38,19 +37,16 @@ import requests import torch -import experiments -import tools +from .. import dataset, tools # ---------------------------------------------------------------------------- # # Configuration #: Default URL for the raw phishing dataset (LIBSVM format). -default_url_phishing = ( - "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/phishing" -) +default_url_phishing = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/phishing" #: Default directory where pre-processed datasets are cached. -default_root = experiments.dataset.Dataset.get_default_root() +default_root = dataset.Dataset.get_default_root() # ---------------------------------------------------------------------------- # # Dataset lazy-loaders @@ -74,13 +70,13 @@ def get_phishing(root, url): URL to fetch the raw dataset from. If ``None`` and the cache is missing, a :class:`RuntimeError` is raised. - Returns + Returns: ------- tuple[torch.Tensor, torch.Tensor] ``(inputs, labels)`` where *inputs* has shape ``(11055, 68)`` and *labels* has shape ``(11055, 1)``. - Raises + Raises: ------ RuntimeError If the cache is missing and *url* is ``None``, or if the download @@ -110,14 +106,19 @@ def get_phishing(root, url): tools.info("Downloading dataset...", end="", flush=True) try: response = requests.get(url) + except requests.exceptions.SSLError: + tools.warning(" SSL verification failed, retrying without verification...", end="", flush=True) + try: + response = requests.get(url, verify=False) + except Exception as err: + tools.warning(" fail.") + raise RuntimeError(f"Unable to get dataset (at {url}): {err}") from err except Exception as err: tools.warning(" fail.") - raise RuntimeError(f"Unable to get dataset (at {url}): {err}") + raise RuntimeError(f"Unable to get dataset (at {url}): {err}") from err tools.info(" done.") if response.status_code != 200: - raise RuntimeError( - f"Unable to fetch raw dataset (at {url}): GET status code {response.status_code}" - ) + raise RuntimeError(f"Unable to fetch raw dataset (at {url}): GET status code {response.status_code}") # Pre-process dataset tools.info("Pre-processing dataset...", end="", flush=True) @@ -136,9 +137,7 @@ def get_phishing(root, url): line[int(offset) - 1] = float(value) except Exception as err: tools.warning(" fail.") - raise RuntimeError( - f"Unable to parse dataset (line {index + 1}, position {pos + 1}): {err}" - ) + raise RuntimeError(f"Unable to parse dataset (line {index + 1}, position {pos + 1}): {err}") from err labels.unsqueeze_(1) tools.info(" done.") @@ -159,7 +158,7 @@ def get_phishing(root, url): def phishing(train=True, batch_size=None, root=None, download=False, *args, **kwargs): - """Phishing dataset builder returning an infinite-batch generator. + r"""Phishing dataset builder returning an infinite-batch generator. Parameters ---------- @@ -179,12 +178,12 @@ def phishing(train=True, batch_size=None, root=None, download=False, *args, **kw **kwargs : object Ignored (kept for API compatibility). - Returns + Returns: ------- generator Infinite sampler yielding ``(inputs, labels)`` tuples. - Notes + Notes: ----- The dataset is split at position ``8400`` (≈ 76 % train / 24 % test). The split point was chosen for good divisibility @@ -195,6 +194,4 @@ def phishing(train=True, batch_size=None, root=None, download=False, *args, **kw root or default_root, None if download is None else default_url_phishing, ) - return experiments.batch_dataset( - inputs, labels, train, batch_size, split=8400 - ) + return dataset.batch_dataset(inputs, labels, train, batch_size, split=8400) diff --git a/experiments/loss.py b/krum/experiments/loss.py similarity index 81% rename from experiments/loss.py rename to krum/experiments/loss.py index c2ef9a7..e70d534 100644 --- a/experiments/loss.py +++ b/krum/experiments/loss.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file loss.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Loss/criterion wrappers/helpers. ### -""" -Loss and criterion wrappers for training and evaluation. +"""Loss and criterion wrappers for training and evaluation. This module provides: @@ -23,27 +21,25 @@ - :class:`Criterion` — non-derivable evaluation metrics (top-k accuracy, sigmoid accuracy). -Example +Example: ------- - >>> from experiments import Loss, Criterion >>> loss = Loss("crossentropy") + 0.01 * Loss("l2") >>> crit = Criterion("top-k", k=5) """ -__all__ = ["Loss", "Criterion"] - -import tools +__all__ = ["Criterion", "Loss"] import torch +from .. import tools + # ---------------------------------------------------------------------------- # # Loss wrapper class class Loss: - """ - Derivable loss function wrapper with composition support. + """Derivable loss function wrapper with composition support. Losses can be added (``loss1 + loss2``) and scaled (``0.5 * loss``). All standard PyTorch losses are available by lower-case name. @@ -60,7 +56,7 @@ class Loss: **kwargs : object Forwarded to the loss constructor when ``name_build`` is a string. - Raises + Raises: ------ tools.UnavailableException If ``name_build`` is an unknown string. @@ -70,8 +66,7 @@ class Loss: @staticmethod def _l1loss(output, target, params): - """ - L1 regularization on parameters. + """L1 regularization on parameters. Parameters ---------- @@ -82,7 +77,7 @@ def _l1loss(output, target, params): params : torch.Tensor Flat parameter tensor. - Returns + Returns: ------- torch.Tensor L1 norm of ``params``. @@ -91,8 +86,7 @@ def _l1loss(output, target, params): @staticmethod def _l2loss(output, target, params): - """ - L2 regularization on parameters. + """L2 regularization on parameters. Parameters ---------- @@ -103,7 +97,7 @@ def _l2loss(output, target, params): params : torch.Tensor Flat parameter tensor. - Returns + Returns: ------- torch.Tensor L2 norm of ``params``. @@ -112,10 +106,9 @@ def _l2loss(output, target, params): @classmethod def _l1loss_builder(cls): - """ - Build an L1 regularization loss instance. + """Build an L1 regularization loss instance. - Returns + Returns: ------- Loss L1 loss wrapper. @@ -124,10 +117,9 @@ def _l1loss_builder(cls): @classmethod def _l2loss_builder(cls): - """ - Build an L2 regularization loss instance. + """Build an L2 regularization loss instance. - Returns + Returns: ------- Loss L2 loss wrapper. @@ -139,15 +131,14 @@ def _l2loss_builder(cls): @staticmethod def _make_drop_params(builder): - """ - Wrap a PyTorch loss builder to drop the ``params`` argument. + """Wrap a PyTorch loss builder to drop the ``params`` argument. Parameters ---------- builder : callable Original loss constructor. - Returns + Returns: ------- callable Wrapped builder returning a loss that ignores ``params``. @@ -165,10 +156,9 @@ def drop_loss(output, target, params): @classmethod def _get_losses(cls): - """ - Lazily build the name-to-constructor mapping for losses. + """Lazily build the name-to-constructor mapping for losses. - Returns + Returns: ------- dict[str, callable] Lower-case loss names mapped to builders. @@ -177,7 +167,7 @@ def _get_losses(cls): if cls.__losses is not None: return cls.__losses # Initialize the dictionary - cls.__losses = dict() + cls.__losses = {} # Populate with PyTorch losses for name in dir(torch.nn.modules.loss): if len(name) < 5 or name[0] == "_" or name[-4:] != "Loss": @@ -191,8 +181,7 @@ def _get_losses(cls): return cls.__losses def __init__(self, name_build, *args, **kwargs): - """ - Initialize the loss wrapper. + """Initialize the loss wrapper. Parameters ---------- @@ -227,21 +216,19 @@ def __init__(self, name_build, *args, **kwargs): self._name = name def _str_make(self): - """ - Build the formatted loss string. + """Build the formatted loss string. - Returns + Returns: ------- str Human-readable loss description. """ - return self._name if self._fact is None else f"{self._fact} × {self._name}" + return self._name if self._fact is None else f"{self._fact} x {self._name}" def __str__(self): - """ - Return a printable representation. + """Return a printable representation. - Returns + Returns: ------- str Human-readable loss name. @@ -249,8 +236,7 @@ def __str__(self): return f"loss {self._str_make()}" def __call__(self, output, target, params): - """ - Compute the loss. + """Compute the loss. Parameters ---------- @@ -261,7 +247,7 @@ def __call__(self, output, target, params): params : torch.Tensor Flat parameter tensor (for regularization losses). - Returns + Returns: ------- torch.Tensor Scalar loss value. @@ -272,15 +258,14 @@ def __call__(self, output, target, params): return res def __add__(self, loss): - """ - Sum two losses. + """Sum two losses. Parameters ---------- loss : Loss Loss to add. - Returns + Returns: ------- Loss Composite loss. @@ -297,15 +282,14 @@ def add(output, target, params): ) def __mul__(self, factor): - """ - Scale the loss by a constant factor. + """Scale the loss by a constant factor. Parameters ---------- factor : float Scaling factor. - Returns + Returns: ------- Loss Scaled loss. @@ -326,15 +310,14 @@ def __rmul__(self, *args, **kwargs): return self.__mul__(*args, **kwargs) def __imul__(self, factor): - """ - Scale the loss in place. + """Scale the loss in place. Parameters ---------- factor : float Scaling factor. - Returns + Returns: ------- Loss Self. @@ -348,8 +331,7 @@ def __imul__(self, factor): class Criterion: - """ - Non-derivable evaluation metric wrapper. + """Non-derivable evaluation metric wrapper. Available criteria: @@ -367,20 +349,17 @@ class Criterion: **kwargs : object Forwarded to the criterion constructor. - Raises + Raises: ------ tools.UnavailableException If ``name_build`` is an unknown string. """ class _TopkCriterion: - """ - Top-k classification accuracy. - """ + """Top-k classification accuracy.""" def __init__(self, k=1): - """ - Initialize top-k criterion. + """Initialize top-k criterion. Parameters ---------- @@ -390,52 +369,40 @@ def __init__(self, k=1): self.k = k def __call__(self, output, target): - """ - Compute top-k accuracy. + """Compute top-k accuracy. Parameters ---------- output : torch.Tensor - Batch × model logits. + Batch x model logits. target : torch.Tensor - Batch × target index. + Batch x target index. - Returns + Returns: ------- torch.Tensor 1-D tensor ``[num_correct, batch_size]``. """ - res = ( - (output.topk(self.k, dim=1)[1] == target.view(-1).unsqueeze(1)) - .any(dim=1) - .sum() - ) - return torch.cat( - ( - res.unsqueeze(0), - torch.tensor( - target.shape[0], dtype=res.dtype, device=res.device - ).unsqueeze(0), - ) - ) + res = (output.topk(self.k, dim=1)[1] == target.view(-1).unsqueeze(1)).any(dim=1).sum() + return torch.cat(( + res.unsqueeze(0), + torch.tensor(target.shape[0], dtype=res.dtype, device=res.device).unsqueeze(0), + )) class _SigmoidCriterion: - """ - Binary accuracy with 0.5 threshold. - """ + """Binary accuracy with 0.5 threshold.""" def __call__(self, output, target): - """ - Compute sigmoid accuracy. + """Compute sigmoid accuracy. Parameters ---------- output : torch.Tensor - Batch × model logits (expected in ``[0, 1]``). + Batch x model logits (expected in ``[0, 1]``). target : torch.Tensor - Batch × target index (expected in ``{0, 1}``). + Batch x target index (expected in ``{0, 1}``). - Returns + Returns: ------- torch.Tensor 1-D tensor ``[num_correct, batch_size]``. @@ -451,10 +418,9 @@ def __call__(self, output, target): @classmethod def _get_criterions(cls): - """ - Lazily build the name-to-constructor mapping. + """Lazily build the name-to-constructor mapping. - Returns + Returns: ------- dict[str, type] Lower-case criterion names mapped to classes. @@ -470,8 +436,7 @@ def _get_criterions(cls): return cls.__criterions def __init__(self, name_build, *args, **kwargs): - """ - Initialize the criterion wrapper. + """Initialize the criterion wrapper. Parameters ---------- @@ -499,10 +464,9 @@ def __init__(self, name_build, *args, **kwargs): self._name = name def __str__(self): - """ - Return a printable representation. + """Return a printable representation. - Returns + Returns: ------- str Human-readable criterion name. @@ -510,8 +474,7 @@ def __str__(self): return f"criterion {self._name}" def __call__(self, output, target): - """ - Compute the criterion. + """Compute the criterion. Parameters ---------- @@ -520,7 +483,7 @@ def __call__(self, output, target): target : torch.Tensor Expected output. - Returns + Returns: ------- torch.Tensor 1-D tensor ``[num_correct, batch_size]``. diff --git a/experiments/model.py b/krum/experiments/model.py similarity index 86% rename from experiments/model.py rename to krum/experiments/model.py index 6b3b083..7b57b02 100644 --- a/experiments/model.py +++ b/krum/experiments/model.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file model.py # @author Sébastien Rouault @@ -13,17 +12,15 @@ # Model wrappers/helpers. ### -""" -Model wrapper with name resolution, initialization, and gradient handling. +"""Model wrapper with name resolution, initialization, and gradient handling. This module provides :class:`Model`, a unified interface that can instantiate ``torchvision`` models (by name), custom models from ``experiments/models/``, or arbitrary callables. It also manages parameter flattening, gradient extraction, and data-parallelism automatically. -Example +Example: ------- - >>> from experiments import Model, Configuration >>> config = Configuration(device="cpu") >>> model = Model("resnet18", config, num_classes=10) @@ -34,13 +31,14 @@ __all__ = ["Model"] -import tools - import pathlib +import types +from typing import Any + import torch import torchvision -import types +from .. import tools from .configuration import Configuration # ---------------------------------------------------------------------------- # @@ -48,8 +46,7 @@ class Model: - """ - Unified model wrapper with parameter and gradient management. + """Unified model wrapper with parameter and gradient management. Models are resolved by lower-case name from ``torchvision.models`` and from custom modules under ``experiments/models/``. Parameters are @@ -79,7 +76,7 @@ class Model: **kwargs : object Forwarded to the model constructor. - Raises + Raises: ------ tools.UnavailableException If ``name_build`` is an unknown string. @@ -88,17 +85,16 @@ class Model: """ # Map 'lower-case names' -> 'model constructor' - __models = None + __models: dict[str, Any] | None = None # Map 'lower-case names' -> 'tensor initializer' - __inits = None + __inits: dict[str, Any] | None = None @classmethod def _get_models(cls): - """ - Lazily build the name-to-constructor mapping for models. + """Lazily build the name-to-constructor mapping for models. - Returns + Returns: ------- dict[str, callable] Lower-case model names mapped to constructors. @@ -107,7 +103,7 @@ def _get_models(cls): if cls.__models is not None: return cls.__models # Initialize the dictionary - cls.__models = dict() + cls.__models = {} # Populate with TorchVision's models for name in dir(torchvision.models): if len(name) == 0 or name[0] == "_": @@ -122,23 +118,20 @@ def add_custom_models(name, module, _): exports = getattr(module, "__all__", None) if exports is None: tools.warning( - f"Model module {name!r} does not provide '__all__'; " - f"falling back to '__dict__' for name discovery" + f"Model module {name!r} does not provide '__all__'; falling back to '__dict__' for name discovery" ) exports = (n for n in dir(module) if len(n) > 0 and n[0] != "_") exported = False for model in exports: if not isinstance(model, str): - tools.warning( - f"Model module {name!r} exports non-string name " - f"{model!r}; ignored" - ) + tools.warning(f"Model module {name!r} exports non-string name {model!r}; ignored") continue constructor = getattr(module, model, None) if not callable(constructor): continue exported = True fullname = f"{name}-{model}" + assert cls.__models is not None if fullname in cls.__models: tools.warning( f"Unable to make available model {model!r} from module " @@ -147,10 +140,7 @@ def add_custom_models(name, module, _): continue cls.__models[fullname] = constructor if not exported: - tools.warning( - f"Model module {name!r} does not export any valid " - f"constructor name through '__all__'" - ) + tools.warning(f"Model module {name!r} does not export any valid constructor name through '__all__'") with tools.Context("models", None): tools.import_directory( @@ -162,10 +152,9 @@ def add_custom_models(name, module, _): @classmethod def _get_inits(cls): - """ - Lazily build the name-to-function mapping for initializers. + """Lazily build the name-to-function mapping for initializers. - Returns + Returns: ------- dict[str, callable] Lower-case initializer names mapped to functions. @@ -174,7 +163,7 @@ def _get_inits(cls): if cls.__inits is not None: return cls.__inits # Initialize the dictionary - cls.__inits = dict() + cls.__inits = {} # Populate with PyTorch's initialization functions for name in dir(torch.nn.init): if len(name) == 0 or name[0] == "_": @@ -189,7 +178,7 @@ def _get_inits(cls): def __init__( self, name_build, - config=Configuration(), + config=None, init_multi=None, init_multi_args=None, init_mono=None, @@ -197,8 +186,7 @@ def __init__( *args, **kwargs, ): - """ - Initialize the model wrapper. + """Initialize the model wrapper. Parameters ---------- @@ -219,13 +207,15 @@ def __init__( **kwargs : object Forwarded to the model constructor. """ + if config is None: + config = Configuration() def make_init(name, args): inits = type(self)._get_inits() func = inits.get(name, None) if func is None: raise tools.UnavailableException(inits, name, what="initializer name") - args = dict() if args is None else args + args = {} if args is None else args def init(params): return func(params, **args) @@ -261,9 +251,8 @@ def init(params): if len(param.shape) > 1: if init_multi is not None: init_multi(param) - else: - if init_mono is not None: - init_mono(param) + elif init_mono is not None: + init_mono(param) # Move parameters to target device model = model.to(**config) device = config["device"] @@ -285,10 +274,9 @@ def init(params): } def __str__(self): - """ - Return a printable representation. + """Return a printable representation. - Returns + Returns: ------- str Human-readable model name. @@ -297,10 +285,9 @@ def __str__(self): @property def config(self): - """ - Return the immutable configuration. + """Return the immutable configuration. - Returns + Returns: ------- experiments.Configuration Model configuration. @@ -308,8 +295,7 @@ def config(self): return self._config def default(self, name, new=None, erase=False): - """ - Get and/or set a named default. + """Get and/or set a named default. Parameters ---------- @@ -321,12 +307,12 @@ def default(self, name, new=None, erase=False): erase : bool, optional Force the value to ``None``. - Returns + Returns: ------- object Current (or old) value of the default. - Raises + Raises: ------ tools.UnavailableException If ``name`` is not a known default. @@ -339,25 +325,24 @@ def default(self, name, new=None, erase=False): return old def _resolve_defaults(self, **kwargs): - """ - Replace ``None`` values with registered defaults. + """Replace ``None`` values with registered defaults. Parameters ---------- **kwargs : object Keyword arguments where ``None`` means "use the default". - Returns + Returns: ------- list[object] Resolved values in argument order. - Raises + Raises: ------ RuntimeError If a required default is missing. """ - res = list() + res = [] for name, value in kwargs.items(): if value is None: value = self.default(name) @@ -367,8 +352,7 @@ def _resolve_defaults(self, **kwargs): return res def run(self, data, training=False): - """ - Forward pass through the model. + """Forward pass through the model. Parameters ---------- @@ -378,7 +362,7 @@ def run(self, data, training=False): Whether to use training mode (enables dropout, batch-norm updates, etc.). Defaults to evaluation mode. - Returns + Returns: ------- torch.Tensor Model output. @@ -394,10 +378,9 @@ def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) def get(self): - """ - Get a reference to the flat parameter vector. + """Get a reference to the flat parameter vector. - Returns + Returns: ------- torch.Tensor Flat parameter tensor. Future calls to :meth:`set` will modify @@ -406,8 +389,7 @@ def get(self): return self._params def set(self, params, relink=None): - """ - Overwrite parameters with the given flat vector. + """Overwrite parameters with the given flat vector. Parameters ---------- @@ -428,10 +410,9 @@ def set(self, params, relink=None): self._params.copy_(params, non_blocking=self._config["non_blocking"]) def get_gradient(self): - """ - Get (or create) the flat gradient vector. + """Get (or create) the flat gradient vector. - Returns + Returns: ------- torch.Tensor Flat gradient tensor. Future calls to :meth:`set_gradient` will @@ -446,8 +427,7 @@ def get_gradient(self): return gradient def set_gradient(self, gradient, relink=None): - """ - Overwrite the gradient with the given flat vector. + """Overwrite the gradient with the given flat vector. Parameters ---------- @@ -465,13 +445,10 @@ def set_gradient(self, gradient, relink=None): tools.relink(tools.grads_of(self._model.parameters()), gradient) self._gradient = gradient else: - self.get_gradient().copy_( - gradient, non_blocking=self._config["non_blocking"] - ) + self.get_gradient().copy_(gradient, non_blocking=self._config["non_blocking"]) def loss(self, dataset=None, loss=None, training=None): - """ - Estimate loss on a batch from the given dataset. + """Estimate loss on a batch from the given dataset. Parameters ---------- @@ -483,7 +460,7 @@ def loss(self, dataset=None, loss=None, training=None): Whether this is a training run. ``None`` guesses from ``torch.is_grad_enabled()``. - Returns + Returns: ------- torch.Tensor Scalar loss value. @@ -496,8 +473,7 @@ def loss(self, dataset=None, loss=None, training=None): @torch.enable_grad() def backprop(self, dataset=None, loss=None, outloss=False, **kwargs): - """ - Compute gradient on a batch from the given dataset. + """Compute gradient on a batch from the given dataset. Parameters ---------- @@ -510,7 +486,7 @@ def backprop(self, dataset=None, loss=None, outloss=False, **kwargs): **kwargs : object Forwarded to ``loss.backward()``. - Returns + Returns: ------- torch.Tensor or tuple[torch.Tensor, torch.Tensor] Flat gradient, optionally paired with the loss value. @@ -533,8 +509,7 @@ def backprop(self, dataset=None, loss=None, outloss=False, **kwargs): return self.get_gradient() def update(self, gradient, optimizer=None, relink=None): - """ - Update parameters using the given gradient and optimizer. + """Update parameters using the given gradient and optimizer. Parameters ---------- @@ -555,8 +530,7 @@ def update(self, gradient, optimizer=None, relink=None): @torch.no_grad() def eval(self, dataset=None, criterion=None): - """ - Evaluate the model on a batch from the given dataset. + """Evaluate the model on a batch from the given dataset. Parameters ---------- @@ -565,13 +539,11 @@ def eval(self, dataset=None, criterion=None): criterion : experiments.Criterion or None, optional Criterion function. ``None`` uses the default criterion. - Returns + Returns: ------- torch.Tensor Mean criterion value over the sampled batch. """ - dataset, criterion = self._resolve_defaults( - testset=dataset, criterion=criterion - ) + dataset, criterion = self._resolve_defaults(testset=dataset, criterion=criterion) inputs, targets = dataset.sample(self._config) return criterion(self.run(inputs), targets) diff --git a/experiments/models/simples.py b/krum/experiments/models/simples.py similarity index 89% rename from experiments/models/simples.py rename to krum/experiments/models/simples.py index c8e7f52..90afe6d 100644 --- a/experiments/models/simples.py +++ b/krum/experiments/models/simples.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file simples.py # @author Sébastien Rouault @@ -20,7 +19,7 @@ :class:`experiments.Model` loader because they are listed in ``__all__``. Each constructor returns a ready-to-use ``torch.nn.Module``. -Example +Example: ------- >>> from experiments import Model, Configuration >>> config = Configuration(device="cpu") @@ -28,11 +27,10 @@ >>> output = model.run(torch.randn(4, 1, 28, 28)) """ -__all__ = ["full", "conv", "logit", "linear"] +__all__ = ["conv", "full", "linear", "logit"] import torch - # ---------------------------------------------------------------------------- # # Simple fully-connected model, for MNIST @@ -40,7 +38,7 @@ class _Full(torch.nn.Module): """Small fully-connected classifier for MNIST. - The network flattens a 28×28 input image, passes it through a single + The network flattens a 28x28 input image, passes it through a single hidden layer of 100 units with ReLU activation, and finally outputs log-probabilities over 10 classes. """ @@ -59,14 +57,13 @@ def forward(self, x): x : torch.Tensor Input tensor of shape ``(N, 1, 28, 28)`` or ``(N, 28, 28)``. - Returns + Returns: ------- torch.Tensor Log-probability distribution of shape ``(N, 10)``. """ x = torch.nn.functional.relu(self._f1(x.view(-1, 28 * 28))) - x = torch.nn.functional.log_softmax(self._f2(x), dim=1) - return x + return torch.nn.functional.log_softmax(self._f2(x), dim=1) def full(*args, **kwargs): @@ -79,7 +76,7 @@ def full(*args, **kwargs): **kwargs : object Forwarded to :class:`_Full`. - Returns + Returns: ------- _Full A fresh fully-connected model instance. @@ -96,7 +93,7 @@ class _Conv(torch.nn.Module): The architecture consists of two convolutional blocks (convolution → ReLU → max-pooling) followed by two fully-connected - layers. It expects single-channel 28×28 images and produces + layers. It expects single-channel 28x28 images and produces log-probabilities over 10 classes. """ @@ -116,7 +113,7 @@ def forward(self, x): x : torch.Tensor Input tensor of shape ``(N, 1, 28, 28)``. - Returns + Returns: ------- torch.Tensor Log-probability distribution of shape ``(N, 10)``. @@ -126,8 +123,7 @@ def forward(self, x): x = torch.nn.functional.relu(self._c2(x)) x = torch.nn.functional.max_pool2d(x, 2, 2) x = torch.nn.functional.relu(self._f1(x.view(-1, 800))) - x = torch.nn.functional.log_softmax(self._f2(x), dim=1) - return x + return torch.nn.functional.log_softmax(self._f2(x), dim=1) def conv(*args, **kwargs): @@ -140,7 +136,7 @@ def conv(*args, **kwargs): **kwargs : object Forwarded to :class:`_Conv`. - Returns + Returns: ------- _Conv A fresh convolutional model instance. @@ -159,7 +155,7 @@ class _Logit(torch.nn.Module): classification or as a simple baseline. """ - def __init__(self, din, dout=1): + def __init__(self, din: int, dout: int = 1): """Initialise the linear layer. Parameters @@ -170,8 +166,8 @@ def __init__(self, din, dout=1): Number of output features. Defaults to ``1``. """ super().__init__() - self._din = din - self._dout = dout + self._din = din # type: ignore[attr-defined] + self._dout = dout # type: ignore[attr-defined] self._linear = torch.nn.Linear(din, dout) def forward(self, x): @@ -183,7 +179,7 @@ def forward(self, x): Input tensor of arbitrary shape; the last dimensions are flattened to ``din`` features. - Returns + Returns: ------- torch.Tensor Sigmoid-activated output of shape ``(..., dout)``. @@ -201,7 +197,7 @@ def logit(*args, **kwargs): **kwargs : object Forwarded to :class:`_Logit`. - Returns + Returns: ------- _Logit A fresh logistic-regression model instance. @@ -219,7 +215,7 @@ class _Linear(torch.nn.Module): Equivalent to a fully-connected layer with identity activation. """ - def __init__(self, din, dout=1): + def __init__(self, din: int, dout: int = 1): """Initialise the linear layer. Parameters @@ -230,8 +226,8 @@ def __init__(self, din, dout=1): Number of output features. Defaults to ``1``. """ super().__init__() - self._din = din - self._dout = dout + self._din = din # type: ignore[attr-defined] + self._dout = dout # type: ignore[attr-defined] self._linear = torch.nn.Linear(din, dout) def forward(self, x): @@ -243,7 +239,7 @@ def forward(self, x): Input tensor of arbitrary shape; the last dimensions are flattened to ``din`` features. - Returns + Returns: ------- torch.Tensor Linear output of shape ``(..., dout)``. @@ -261,7 +257,7 @@ def linear(*args, **kwargs): **kwargs : object Forwarded to :class:`_Linear`. - Returns + Returns: ------- _Linear A fresh linear model instance. diff --git a/experiments/optimizer.py b/krum/experiments/optimizer.py similarity index 85% rename from experiments/optimizer.py rename to krum/experiments/optimizer.py index 12e732c..09393bc 100644 --- a/experiments/optimizer.py +++ b/krum/experiments/optimizer.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file optimizer.py # @author Sébastien Rouault @@ -13,16 +12,14 @@ # Optimizer wrapper. ### -""" -Optimizer wrapper that resolves PyTorch optimizers by name. +"""Optimizer wrapper that resolves PyTorch optimizers by name. This module provides a thin wrapper around ``torch.optim`` that allows optimizers to be instantiated from CLI strings while exposing a uniform interface for learning-rate adjustments. -Example +Example: ------- - >>> from experiments import Optimizer, Model >>> model = Model("lenet", num_classes=10) >>> optim = Optimizer("adam", model, lr=0.001) @@ -31,17 +28,16 @@ __all__ = ["Optimizer"] -import tools - import torch +from .. import tools + # ---------------------------------------------------------------------------- # # Optimizer wrapper class class Optimizer: - """ - Optimizer wrapper with name resolution and LR control. + """Optimizer wrapper with name resolution and LR control. Parameters ---------- @@ -57,7 +53,7 @@ class Optimizer: Additional keyword arguments forwarded to the optimizer constructor. - Raises + Raises: ------ tools.UnavailableException If ``name_build`` is a string that does not match any known @@ -69,10 +65,9 @@ class Optimizer: @classmethod def _get_optimizers(cls): - """ - Lazily build the name-to-class mapping for PyTorch optimizers. + """Lazily build the name-to-class mapping for PyTorch optimizers. - Returns + Returns: ------- dict[str, type] Mapping from lower-case names to optimizer classes. @@ -81,7 +76,7 @@ def _get_optimizers(cls): if cls.__optimizers is not None: return cls.__optimizers # Initialize the dictionary - cls.__optimizers = dict() + cls.__optimizers = {} # Populate with TorchVision optimizers for name in dir(torch.optim): if len(name) == 0 or name[0] == "_": @@ -96,8 +91,7 @@ def _get_optimizers(cls): return cls.__optimizers def __init__(self, name_build, model, *args, **kwargs): - """ - Initialize the optimizer wrapper. + """Initialize the optimizer wrapper. Parameters ---------- @@ -127,20 +121,19 @@ def __init__(self, name_build, model, *args, **kwargs): self._name = name def __getattr__(self, *args): - """ - Forward attribute access to the wrapped optimizer. + """Forward attribute access to the wrapped optimizer. Parameters ---------- *args : object Either ``(name,)`` or ``(name, default)``. - Returns + Returns: ------- object Attribute from the wrapped optimizer instance. - Raises + Raises: ------ RuntimeError If called with more than two positional arguments. @@ -149,15 +142,12 @@ def __getattr__(self, *args): return getattr(self._optim, args[0]) if len(args) == 2: return getattr(self._optim, args[0], args[1]) - raise RuntimeError( - "'Optimizer.__getattr__' called with the wrong number of parameters" - ) + raise RuntimeError("'Optimizer.__getattr__' called with the wrong number of parameters") def __str__(self): - """ - Return a printable representation. + """Return a printable representation. - Returns + Returns: ------- str Human-readable optimizer name. @@ -165,8 +155,7 @@ def __str__(self): return f"optimizer {self._name}" def set_lr(self, lr): - """ - Set the learning rate for all parameter groups. + """Set the learning rate for all parameter groups. Parameters ---------- diff --git a/native/.gitignore b/krum/native/.gitignore similarity index 100% rename from native/.gitignore rename to krum/native/.gitignore diff --git a/native/README.md b/krum/native/README.md similarity index 98% rename from native/README.md rename to krum/native/README.md index 15cea3f..558bd98 100644 --- a/native/README.md +++ b/krum/native/README.md @@ -30,4 +30,4 @@ In a dependent SO directory, create a ``.deps`` file with a newline-separated li 3. Move the expensive part to ``native/`` if needed 4. Keep exactly the same functional contract -If native compilation fails, Python fallbacks continue to work with a warning. +If native compilation fails, Python fallbacks continue to work with a warning. \ No newline at end of file diff --git a/native/__init__.py b/krum/native/__init__.py similarity index 50% rename from native/__init__.py rename to krum/native/__init__.py index 7571e99..9a02fc2 100644 --- a/native/__init__.py +++ b/krum/native/__init__.py @@ -1,56 +1,11 @@ -# coding: utf-8 -### -# @file __init__.py -# @author Sébastien Rouault -# -# @section LICENSE -# -# Copyright © 2018-2019 École Polytechnique Fédérale de Lausanne (EPFL). -# See LICENSE file. -# -# @section DESCRIPTION -# -# Native (i.e. C++/CUDA) implementations automated building and loading. -### - -""" -Automated building and loading of C++/CUDA native PyTorch extensions. - -Subdirectories prefixed with ``so_`` produce shared libraries; those prefixed -with ``py_`` produce Python-loadable extensions that are injected into this -namespace (prefix stripped). Dependencies are declared in a ``.deps`` file -(one module name per line) and built recursively before the parent module. - -Environment variables ``NATIVE_OPT``, ``NATIVE_STD``, and ``NATIVE_QUIET`` -control debug/release mode, the C++ standard, and trace verbosity -respectively. -""" +"""Native (i.e. C++/CUDA) implementations automated building and loading.""" # ---------------------------------------------------------------------------- # # Initialization procedure -from pathlib import Path - -def _build_and_load(): - """Scan, compile, and load all native modules into the global namespace. - - Each subdirectory whose name starts with ``so_`` or ``py_`` is treated as a - native module. Sources are compiled via - :func:`torch.utils.cpp_extension.load`; ``py_`` modules are injected into - globals with the prefix stripped. Dependencies listed in ``.deps`` files - are built recursively before the parent module. - Raises - ------ - SystemExit - If ``NATIVE_OPT`` is set to an unrecognized value. - - Warns - ----- - UserWarning - If the ``include`` directory does not exist, or when a module fails - to build or load. - """ +def _build_and_load() -> None: + """Incrementally rebuild all libraries and bind all local modules in the global.""" glob = globals() # Standard imports import os @@ -63,7 +18,7 @@ def _build_and_load(): import torch.utils.cpp_extension # Internal imports - import tools + from krum import tools # Constants base_directory = pathlib.Path(__file__).parent.resolve() @@ -79,44 +34,39 @@ def _build_and_load(): debug_mode = False else: tools.fatal( - "%r defined in the environment, but with unexpected soft-boolean %r" - % (debug_mode_envname, "%s=%s" % (debug_mode_envname, raw)) + "{!r} defined in the environment, but with unexpected soft-boolean {!r}".format( + debug_mode_envname, f"{debug_mode_envname}={raw}" + ) ) else: debug_mode = __debug__ cpp_std_envname = "NATIVE_STD" - cpp_std = os.environ.get(cpp_std_envname, "c++14") + cpp_std = os.environ.get(cpp_std_envname, "c++17") ident_to_is_python = {"so_": False, "py_": True} source_suffixes = {".cpp", ".cc", ".C", ".cxx", ".c++"} - extra_cflags = ["-Wall", "-Wextra", "-Wfatal-errors", "-std=%s" % cpp_std] + extra_cflags = ["-Wall", "-Wextra", "-Wfatal-errors", f"-std={cpp_std}"] if torch.cuda.is_available(): - source_suffixes.update(set((".cu" + suffix) for suffix in source_suffixes)) + source_suffixes.update({(".cu" + suffix) for suffix in source_suffixes}) source_suffixes.add(".cu") extra_cflags.append("-DTORCH_CUDA_AVAILABLE") - extra_cuda_cflags = [ - "-DTORCH_CUDA_AVAILABLE", - "--expt-relaxed-constexpr", - "-std=%s" % cpp_std, - ] + extra_cuda_cflags = ["-DTORCH_CUDA_AVAILABLE", "--expt-relaxed-constexpr", f"-std={cpp_std}"] extra_ldflags = ["-Wl,-L" + base_directory.root] extra_include_path = base_directory / "include" try: extra_include_paths = [str(extra_include_path.resolve())] except Exception: extra_include_paths = None - warnings.warn("Not found include directory: " + repr(str(extra_include_path))) + warnings.warn("Not found include directory: " + repr(str(extra_include_path)), stacklevel=2) # Print configuration information - cpp_std_message = ( - "Native modules compiled with %s standard; (re)define %r in the environment to compile with another standard" - % (cpp_std, "%s=" % cpp_std_envname) + cpp_std_message = "Native modules compiled with {} standard; (re)define {!r} in the environment to compile with another standard".format( + cpp_std, f"{cpp_std_envname}=" ) if debug_mode: tools.warning(cpp_std_message) tools.warning( - "Native modules compiled in debug mode; %sdefine %r in the environment or%s run python with -O/-OO options to compile in release mode" - % ( + "Native modules compiled in debug mode; {}define {!r} in the environment or{} run python with -O/-OO options to compile in release mode".format( "re" if debug_mode_in_env else "", - "%s=1" % debug_mode_envname, + f"{debug_mode_envname}=1", " undefine it and" if debug_mode_in_env else "", ) ) @@ -126,65 +76,49 @@ def _build_and_load(): if quiet_envname not in os.environ: tools.trace(cpp_std_message) tools.trace( - "Native modules compiled in release mode; %sdefine %r in the environment or%s run python without -O/-OO options to compile in debug mode" - % ( + "Native modules compiled in release mode; {}define {!r} in the environment or{} run python without -O/-OO options to compile in debug mode".format( "re" if debug_mode_in_env else "", - "%s=0" % debug_mode_envname, + f"{debug_mode_envname}=0", " undefine it and" if debug_mode_in_env else "", ) ) - tools.trace( - "Define %r in the environment to hide these messages in release mode" - % quiet_envname - ) + tools.trace(f"Define {quiet_envname!r} in the environment to hide these messages in release mode") extra_cflags += ["-O3", "-DNDEBUG"] # Variables done_modules = [] fail_modules = [] # Local procedures - def build_and_load_one(path: Path, deps: list[Path] = []): - """Build and load a single native module, recursively handling its dependencies first. + def build_and_load_one(path, deps=None): + """Check if the given directory is a module to build and load. + + If it is a module, recursively build and load its dependencies first. Parameters ---------- path : pathlib.Path - Path to the module directory to build. + Given directory path. deps : list of pathlib.Path, optional - Stack of ancestor modules currently being processed, used for - cycle detection. + Dependent module paths. - Returns + Returns: ------- bool or None - ``True`` if the module was built and loaded successfully (or was - already built), ``False`` if building or loading failed, ``None`` - if the directory does not represent a valid module. - - Warns - ----- - UserWarning - Emitted when a module is skipped due to an invalid name, a missing - directory, a dependency cycle, a failed dependency, or a build/load - error. + ``True`` on success, ``False`` on failure, ``None`` if not a module. """ + if deps is None: + deps = [] nonlocal done_modules nonlocal fail_modules with tools.Context(path.name, "info"): ident = path.name[:3] - if ident in ident_to_is_python.keys(): + if ident in ident_to_is_python: # Is a module directory if len(path.name) <= 3 or path.name[3] == "_": - tools.warning( - "Skipped invalid module directory name " + repr(path.name) - ) + tools.warning("Skipped invalid module directory name " + repr(path.name)) return None if not path.exists(): - tools.warning( - "Unable to build and load " - + repr(str(path.name)) - + ": module does not exist" - ) + tools.warning("Unable to build and load " + repr(str(path.name)) + ": module does not exist") fail_modules.append(path) # Mark as failed return False is_python_module = ident_to_is_python[ident] @@ -195,17 +129,11 @@ def build_and_load_one(path: Path, deps: list[Path] = []): return True if path in fail_modules: if len(deps) == 0: - tools.warning( - "Was unable to build and load " + repr(str(path.name)) - ) + tools.warning("Was unable to build and load " + repr(str(path.name))) return False # Check for dependency cycle (disallowed as they may mess with the linker) if path in deps: - tools.warning( - "Unable to build and load " - + repr(str(path.name)) - + ": dependency cycle found" - ) + tools.warning("Unable to build and load " + repr(str(path.name)) + ": dependency cycle found") fail_modules.append(path) # Mark as failed return False # Build and load dependencies @@ -213,10 +141,8 @@ def build_and_load_one(path: Path, deps: list[Path] = []): depsfile = path / dependencies_file if depsfile.exists(): for modname in depsfile.read_text().splitlines(): - res = build_and_load_one( - base_directory / modname, deps + [path] - ) - if res == False: # Unable to build a dependency + res = build_and_load_one(base_directory / modname, deps + [path]) + if not res: # Unable to build a dependency if len(deps) == 0: tools.warning( "Unable to build and load " @@ -227,24 +153,12 @@ def build_and_load_one(path: Path, deps: list[Path] = []): ) fail_modules.append(path) # Mark as failed return False - elif ( - res == True - ): # Module and its sub-dependencies was/were built and loaded successfully - this_ldflags.append( - "-Wl,--library=:" - + str( - ( - base_directory / modname / (modname + ".so") - ).resolve() - ) - ) + if res: # Module and its sub-dependencies was/were built and loaded successfully + this_ldflags.append(str((base_directory / modname / (modname + ".so")).resolve())) # List sources sources = [] for subpath in path.iterdir(): - if ( - subpath.is_file() - and ("").join(subpath.suffixes) in source_suffixes - ): + if subpath.is_file() and ("").join(subpath.suffixes) in source_suffixes: sources.append(str(subpath)) # Build and load this module try: @@ -262,16 +176,12 @@ def build_and_load_one(path: Path, deps: list[Path] = []): if is_python_module: glob[path.name[3:]] = res except Exception as err: - tools.warning( - "Unable to build and load " - + repr(str(path.name)) - + ": " - + str(err) - ) + tools.warning("Unable to build and load " + repr(str(path.name)) + ": " + str(err)) fail_modules.append(path) # Mark as failed return False done_modules.append(path) # Mark as built and loaded return True + return None # Main loop for path in base_directory.iterdir(): @@ -279,9 +189,7 @@ def build_and_load_one(path: Path, deps: list[Path] = []): try: build_and_load_one(path) except Exception as err: - tools.warning( - "Exception while processing " + repr(str(path)) + ": " + str(err) - ) + tools.warning("Exception while processing " + repr(str(path)) + ": " + str(err)) with tools.Context("traceback", "trace"): traceback.print_exc() @@ -289,7 +197,7 @@ def build_and_load_one(path: Path, deps: list[Path] = []): # ---------------------------------------------------------------------------- # # Initialization -import tools as _tools +from krum import tools as _tools with _tools.Context("native", None): _build_and_load() diff --git a/native/include/aggregator.hpp b/krum/native/include/aggregator.hpp similarity index 100% rename from native/include/aggregator.hpp rename to krum/native/include/aggregator.hpp diff --git a/native/include/array.hpp b/krum/native/include/array.hpp similarity index 100% rename from native/include/array.hpp rename to krum/native/include/array.hpp diff --git a/native/include/combinations.hpp b/krum/native/include/combinations.hpp similarity index 100% rename from native/include/combinations.hpp rename to krum/native/include/combinations.hpp diff --git a/native/include/common.hpp b/krum/native/include/common.hpp similarity index 100% rename from native/include/common.hpp rename to krum/native/include/common.hpp diff --git a/native/include/constexpr.hpp b/krum/native/include/constexpr.hpp similarity index 100% rename from native/include/constexpr.hpp rename to krum/native/include/constexpr.hpp diff --git a/native/include/cub/.placeholder b/krum/native/include/cub/.placeholder similarity index 100% rename from native/include/cub/.placeholder rename to krum/native/include/cub/.placeholder diff --git a/native/include/cudarray.cu.hpp b/krum/native/include/cudarray.cu.hpp similarity index 100% rename from native/include/cudarray.cu.hpp rename to krum/native/include/cudarray.cu.hpp diff --git a/native/include/exception.hpp b/krum/native/include/exception.hpp similarity index 100% rename from native/include/exception.hpp rename to krum/native/include/exception.hpp diff --git a/native/include/operations.cu.hpp b/krum/native/include/operations.cu.hpp similarity index 100% rename from native/include/operations.cu.hpp rename to krum/native/include/operations.cu.hpp diff --git a/native/include/operations.hpp b/krum/native/include/operations.hpp similarity index 100% rename from native/include/operations.hpp rename to krum/native/include/operations.hpp diff --git a/native/include/optional.hpp b/krum/native/include/optional.hpp similarity index 100% rename from native/include/optional.hpp rename to krum/native/include/optional.hpp diff --git a/native/include/pytorch.hpp b/krum/native/include/pytorch.hpp similarity index 100% rename from native/include/pytorch.hpp rename to krum/native/include/pytorch.hpp diff --git a/native/include/string_view.hpp b/krum/native/include/string_view.hpp similarity index 100% rename from native/include/string_view.hpp rename to krum/native/include/string_view.hpp diff --git a/native/include/threadpool.hpp b/krum/native/include/threadpool.hpp similarity index 100% rename from native/include/threadpool.hpp rename to krum/native/include/threadpool.hpp diff --git a/native/py_brute/.deps b/krum/native/py_brute/.deps similarity index 100% rename from native/py_brute/.deps rename to krum/native/py_brute/.deps diff --git a/native/py_brute/brute.cpp b/krum/native/py_brute/brute.cpp similarity index 100% rename from native/py_brute/brute.cpp rename to krum/native/py_brute/brute.cpp diff --git a/native/py_brute/brute.cu b/krum/native/py_brute/brute.cu similarity index 100% rename from native/py_brute/brute.cu rename to krum/native/py_brute/brute.cu diff --git a/native/py_brute/rule.cpp b/krum/native/py_brute/rule.cpp similarity index 100% rename from native/py_brute/rule.cpp rename to krum/native/py_brute/rule.cpp diff --git a/native/py_brute/rule.hpp b/krum/native/py_brute/rule.hpp similarity index 100% rename from native/py_brute/rule.hpp rename to krum/native/py_brute/rule.hpp diff --git a/native/py_bulyan/.deps b/krum/native/py_bulyan/.deps similarity index 100% rename from native/py_bulyan/.deps rename to krum/native/py_bulyan/.deps diff --git a/native/py_bulyan/bulyan.cpp b/krum/native/py_bulyan/bulyan.cpp similarity index 100% rename from native/py_bulyan/bulyan.cpp rename to krum/native/py_bulyan/bulyan.cpp diff --git a/native/py_bulyan/bulyan.cu b/krum/native/py_bulyan/bulyan.cu similarity index 99% rename from native/py_bulyan/bulyan.cu rename to krum/native/py_bulyan/bulyan.cu index 4be456d..0a5fbcc 100644 --- a/native/py_bulyan/bulyan.cu +++ b/krum/native/py_bulyan/bulyan.cu @@ -87,7 +87,7 @@ template __global__ void distances_finalize(T* flat_dist, T* distances, /** Special single-block merge-sort for indirected array of limited length. * @param value Input constant value array * @param rank (Input/)output only "rank" array - * @param length Length of the arrays (at most 2 × #threads/block) + * @param length Length of the arrays (at most 2 x #threads/block) **/ template __global__ void merge_sort_limited(T const* value, size_t* rank, size_t length) { // Shared variable declaration @@ -191,7 +191,7 @@ template __global__ void compute_scores_prune_distances(T const* flat_d * @param scores Input/output score array * @param distances Input constant pruned distance matrix * @param ranks Output only "rank" array - * @param n Length of the arrays (at most 2 × #threads/block) + * @param n Length of the arrays (at most 2 x #threads/block) **/ template __global__ void remove_smallest_scoring_gradient(T* scores, T const* distances, size_t const* ranks, size_t n) { // Compute thread position to maximize spreading over the available warps diff --git a/native/py_bulyan/rule.cpp b/krum/native/py_bulyan/rule.cpp similarity index 100% rename from native/py_bulyan/rule.cpp rename to krum/native/py_bulyan/rule.cpp diff --git a/native/py_bulyan/rule.hpp b/krum/native/py_bulyan/rule.hpp similarity index 100% rename from native/py_bulyan/rule.hpp rename to krum/native/py_bulyan/rule.hpp diff --git a/native/py_krum/.deps b/krum/native/py_krum/.deps similarity index 100% rename from native/py_krum/.deps rename to krum/native/py_krum/.deps diff --git a/native/py_krum/krum.cpp b/krum/native/py_krum/krum.cpp similarity index 100% rename from native/py_krum/krum.cpp rename to krum/native/py_krum/krum.cpp diff --git a/native/py_krum/krum.cu b/krum/native/py_krum/krum.cu similarity index 100% rename from native/py_krum/krum.cu rename to krum/native/py_krum/krum.cu diff --git a/native/py_krum/rule.cpp b/krum/native/py_krum/rule.cpp similarity index 100% rename from native/py_krum/rule.cpp rename to krum/native/py_krum/rule.cpp diff --git a/native/py_krum/rule.hpp b/krum/native/py_krum/rule.hpp similarity index 100% rename from native/py_krum/rule.hpp rename to krum/native/py_krum/rule.hpp diff --git a/native/py_median/.deps b/krum/native/py_median/.deps similarity index 100% rename from native/py_median/.deps rename to krum/native/py_median/.deps diff --git a/native/py_median/median.cpp b/krum/native/py_median/median.cpp similarity index 100% rename from native/py_median/median.cpp rename to krum/native/py_median/median.cpp diff --git a/native/py_median/median.cu b/krum/native/py_median/median.cu similarity index 100% rename from native/py_median/median.cu rename to krum/native/py_median/median.cu diff --git a/native/py_median/rule.cpp b/krum/native/py_median/rule.cpp similarity index 100% rename from native/py_median/rule.cpp rename to krum/native/py_median/rule.cpp diff --git a/native/py_median/rule.hpp b/krum/native/py_median/rule.hpp similarity index 100% rename from native/py_median/rule.hpp rename to krum/native/py_median/rule.hpp diff --git a/native/so_threadpool/.deps b/krum/native/so_threadpool/.deps similarity index 100% rename from native/so_threadpool/.deps rename to krum/native/so_threadpool/.deps diff --git a/native/so_threadpool/threadpool.cpp b/krum/native/so_threadpool/threadpool.cpp similarity index 100% rename from native/so_threadpool/threadpool.cpp rename to krum/native/so_threadpool/threadpool.cpp diff --git a/tools/__init__.py b/krum/tools/__init__.py similarity index 74% rename from tools/__init__.py rename to krum/tools/__init__.py index 592e681..33698f5 100644 --- a/tools/__init__.py +++ b/krum/tools/__init__.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file __init__.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Bunch of useful tools, but each too small to have its own package. ### -""" -Core Utility Module for Krum. +"""Core Utility Module for Krum. This module provides the fundamental infrastructure utilities used throughout Krum, including logging, error handling, and common operations. @@ -53,15 +51,14 @@ import threading import traceback from pathlib import Path +from typing import Any, Callable, TextIO # ---------------------------------------------------------------------------- # # User exception base class, print string representation and exit(1) on uncaught class UserException(Exception): - """ - Base exception for user-facing errors. - """ + """Base exception for user-facing errors.""" pass @@ -71,9 +68,7 @@ class UserException(Exception): class Context: - """ - Per-thread logging context and color manager. - """ + """Per-thread logging context and color manager.""" # Constants __colors = { @@ -96,9 +91,7 @@ class Context: @classmethod def __local_init(self): - """ - Initialize thread-local context state if necessary. - """ + """Initialize thread-local context state if necessary.""" if not hasattr(self.__local, "stack"): self.__local.stack = [] # List of pairs (context name, color code) self.__local.header = "" # Current header string @@ -106,18 +99,15 @@ def __local_init(self): @classmethod def __rebuild(self): - """ - Rebuild the current log header and color from the context stack. - """ + """Rebuild the current log header and color from the context stack.""" # Collect current header and color header = "" color = None for ctx, clr in reversed(self.__local.stack): if ctx is not None: header = "[" + ctx + "] " + header - if clr is not None: - if color is None: - color = clr + if clr is not None and color is None: + color = clr if color is None: color = self.__clrend # Prepend thread name if not main thread @@ -130,10 +120,9 @@ def __rebuild(self): @classmethod def _get(self): - """ - Return the current thread-local header and color escape sequences. + """Return the current thread-local header and color escape sequences. - Returns + Returns: ------- tuple[str, str, str, str] Current header, header color prefix, message color prefix, and color @@ -148,8 +137,7 @@ def _get(self): ) def __init__(self, cntxtname: str | None, colorname: str | None) -> None: - """ - Create a context stack entry. + """Create a context stack entry. Parameters ---------- @@ -164,18 +152,15 @@ def __init__(self, cntxtname: str | None, colorname: str | None) -> None: if colorname is None: colorcode = None else: - assert colorname in type(self).__colors, "Unknown color name " + repr( - colorname - ) + assert colorname in type(self).__colors, "Unknown color name " + repr(colorname) colorcode = type(self).__colors[colorname] # Finalization self.__pair = (cntxtname, colorcode) def __enter__(self): - """ - Enter the logging context. + """Enter the logging context. - Returns + Returns: ------- Context This context manager instance. @@ -186,8 +171,7 @@ def __enter__(self): return self def __exit__(self, *args, **kwargs) -> None: - """ - Leave the logging context. + """Leave the logging context. Parameters ---------- @@ -201,13 +185,10 @@ def __exit__(self, *args, **kwargs) -> None: class ContextIOWrapper: - """ - Context-aware text I/O wrapper. - """ + """Context-aware text I/O wrapper.""" - def __init__(self, output: object, nocolor: bool | None = None) -> None: - """ - Wrap a text output stream. + def __init__(self, output: TextIO, nocolor: bool | None = None) -> None: + """Wrap a text output stream. Parameters ---------- @@ -227,15 +208,14 @@ def __init__(self, output: object, nocolor: bool | None = None) -> None: self.__nocolor = nocolor def __getattr__(self, name: str) -> object: - """ - Forward non-overridden attribute access to the wrapped stream. + """Forward non-overridden attribute access to the wrapped stream. Parameters ---------- name : str Attribute name. - Returns + Returns: ------- object Attribute value from the wrapped stream. @@ -243,15 +223,14 @@ def __getattr__(self, name: str) -> object: return getattr(self.__output, name) def write(self, text: str) -> int: - """ - Write text with the active context prefix and color. + """Write text with the active context prefix and color. Parameters ---------- text : str Text to write. - Returns + Returns: ------- int Return value forwarded from the wrapped stream's ``write`` method. @@ -277,24 +256,22 @@ def write(self, text: str) -> int: return self.__output.write(text + clrend) -def _make_color_print(color: str) -> object: - """ - Build a ``print`` wrapper that runs inside a colored context. +def _make_color_print(color: str) -> Callable[..., object]: + """Build a ``print`` wrapper that runs inside a colored context. Parameters ---------- color : str Target color name. - Returns + Returns: ------- object Print wrapper closure. """ def color_print(*args, context: str | None = None, **kwargs) -> object: - """ - Print inside the configured colored context. + """Print inside the configured colored context. Parameters ---------- @@ -305,7 +282,7 @@ def color_print(*args, context: str | None = None, **kwargs) -> object: **kwargs : object Keyword arguments forwarded to :func:`print`. - Returns + Returns: ------- object Return value forwarded from :func:`print`. @@ -316,14 +293,16 @@ def color_print(*args, context: str | None = None, **kwargs) -> object: return color_print -# Shortcut for colored print -for color in ["trace", "info", "success", "warning", "error"]: - globals()[color] = _make_color_print(color) +# Explicit colored print shortcuts (required for static type checkers) +trace = _make_color_print("trace") +info = _make_color_print("info") +success = _make_color_print("success") +warning = _make_color_print("warning") +error = _make_color_print("error") def fatal(*args, with_traceback: bool = False, **kwargs) -> None: - """ - Print an error message and terminate the process with exit code 1. + """Print an error message and terminate the process with exit code 1. Parameters ---------- @@ -350,24 +329,22 @@ def fatal(*args, with_traceback: bool = False, **kwargs) -> None: # Uncaught exception context wrapping -def uncaught_wrap(hook: object) -> object: - """ - Wrap an uncaught exception hook with contextual logging. +def uncaught_wrap(hook: Callable[..., Any]) -> Callable[..., Any]: + """Wrap an uncaught exception hook with contextual logging. Parameters ---------- hook : object Uncaught exception hook to wrap. - Returns + Returns: ------- object Wrapped uncaught exception hook. """ def uncaught_call(etype: type, evalue: object, traceback: object) -> object: - """ - Handle uncaught exceptions with user-facing context. + """Handle uncaught exceptions with user-facing context. Parameters ---------- @@ -378,7 +355,7 @@ def uncaught_call(etype: type, evalue: object, traceback: object) -> object: traceback : object Traceback associated with the exception. - Returns + Returns: ------- object Return value forwarded from the wrapped hook for non-user exceptions. @@ -389,6 +366,7 @@ def uncaught_call(etype: type, evalue: object, traceback: object) -> object: else: with Context("uncaught", "error"): return hook(etype, evalue, traceback) + return None return uncaught_call @@ -399,12 +377,11 @@ def uncaught_call(etype: type, evalue: object, traceback: object) -> object: # ---------------------------------------------------------------------------- # # Local module loading and post-processing -_imported = dict() # Map symbol name -> module source name +_imported = {} # Map symbol name -> module source name def import_exported_symbols(name: str, module, scope: dict) -> None: - """ - Import a module's exported symbols into a target scope. + """Import a module's exported symbols into a target scope. Parameters ---------- @@ -417,7 +394,7 @@ def import_exported_symbols(name: str, module, scope: dict) -> None: """ global _imported if hasattr(module, "__all__"): - for symname in getattr(module, "__all__"): + for symname in module.__all__: # Check name if not hasattr(module, symname): with Context(None, "warning"): @@ -425,18 +402,11 @@ def import_exported_symbols(name: str, module, scope: dict) -> None: continue if symname in _imported: with Context(None, "warning"): - print( - "Symbol " - + repr(symname) - + " already exported by " - + repr(_imported[symname]) - ) + print("Symbol " + repr(symname) + " already exported by " + repr(_imported[symname])) continue if symname in scope: with Context(None, "warning"): - print( - "Symbol " + repr(symname) + " already exported by '__init__.py'" - ) + print("Symbol " + repr(symname) + " already exported by '__init__.py'") continue # Import in module scope scope[symname] = getattr(module, symname) @@ -446,11 +416,10 @@ def import_exported_symbols(name: str, module, scope: dict) -> None: def import_directory( dirpath: Path, scope: dict, - post: object = import_exported_symbols, - ignore: list[str] = ["__init__"], + post: Callable[..., Any] | None = import_exported_symbols, + ignore: list[str] | None = None, ) -> None: - """ - Import every Python module from a directory into a target scope. + """Import every Python module from a directory into a target scope. Parameters ---------- @@ -464,6 +433,8 @@ def import_directory( Module names to ignore. """ # Import in the scope of the caller + if ignore is None: + ignore = ["__init__"] for path in dirpath.iterdir(): if path.is_file() and path.suffix == ".py": name = path.stem @@ -478,15 +449,86 @@ def import_directory( post(name, getattr(base, name), scope) except Exception as err: with Context(None, "warning"): - print( - "Loading failed for module " - + repr(path.name) - + ": " - + str(err) - ) + print("Loading failed for module " + repr(path.name) + ": " + str(err)) with Context("traceback", "trace"): traceback.print_exc() -with Context("tools", None): - import_directory(Path(__file__).parent, globals()) +from .jobs import Command, Jobs, dict_to_cmdlist +from .misc import ( + ClassRegister, + MethodCallReplicator, + TimedContext, + UnavailableException, + deltatime_format, + deltatime_point, + fatal_unavailable, + fullqual, + get_loaded_dependencies, + interactive, + line_maximize, + localtime, + onetime, + pairwise, + parse_keyval, +) +from .pytorch import ( + AccumulatedTimedContext, + WeightedMSELoss, + compute_avg_dev_max, + flatten, + grad_of, + grads_of, + pnm, + regression, + relink, + weighted_mse_loss, +) + +__all__ = [ + # Logging & context + "Context", + "ContextIOWrapper", + "UserException", + "trace", + "info", + "success", + "warning", + "error", + "fatal", + "uncaught_wrap", + # Module loading + "import_exported_symbols", + "import_directory", + # misc + "ClassRegister", + "MethodCallReplicator", + "TimedContext", + "UnavailableException", + "deltatime_format", + "deltatime_point", + "fatal_unavailable", + "fullqual", + "get_loaded_dependencies", + "interactive", + "line_maximize", + "localtime", + "onetime", + "pairwise", + "parse_keyval", + # pytorch + "AccumulatedTimedContext", + "WeightedMSELoss", + "compute_avg_dev_max", + "flatten", + "grad_of", + "grads_of", + "pnm", + "regression", + "relink", + "weighted_mse_loss", + # jobs + "Command", + "Jobs", + "dict_to_cmdlist", +] diff --git a/krum/tools/jobs.py b/krum/tools/jobs.py new file mode 100644 index 0000000..f19fe5b --- /dev/null +++ b/krum/tools/jobs.py @@ -0,0 +1,444 @@ +### +# @file jobs.py +# @author Sébastien Rouault +# +# @section LICENSE +# +# Copyright © 2020-2021 École Polytechnique Fédérale de Lausanne (EPFL). +# See LICENSE file. +# +# @section DESCRIPTION +# +# Simple job management for reproduction scripts. +### + +"""Experiment job management helpers. + +This module provides utilities for running and managing experiment jobs in a +reproducible manner. + +Classes and Functions +--------------------- + +Job orchestration + ``Command`` encapsulates a command with seed, device, and result-directory + arguments. + ``Jobs`` manages parallel execution of experiments on multiple devices. + +Helpers + ``dict_to_cmdlist`` converts dictionaries into command-line argument lists. + ``move_directory`` moves an existing directory aside with versioning. + +Example: +------- + +.. code-block:: python + + from tools import Command, Jobs, dict_to_cmdlist + + cmd = Command(["python", "train.py", "--lr", "0.01"]) + jobs = Jobs("./results", devices=["cuda:0", "cuda:1"]) + jobs.submit("exp1", cmd) + jobs.wait() + jobs.close() +""" + +__all__ = ["Command", "Jobs", "dict_to_cmdlist"] + +import shlex +import subprocess +import threading +from collections.abc import Callable, Iterable, Sequence +from pathlib import Path +from typing import Any + +from krum import tools + +# ---------------------------------------------------------------------------- # +# Helpers + + +def move_directory(path: Path) -> Path: + """Move an existing directory aside with versioning. + + If a directory already exists at the given path, it is renamed with an + incremental suffix (for example, ``results.0``, ``results.1``) before a + new directory is created. + + Parameters + ---------- + path : pathlib.Path + Directory path to move aside if it already exists. + + Returns: + ------- + pathlib.Path + The input path, returned unchanged for chaining. + + Raises: + ------ + RuntimeError + If ``path`` exists but is not a directory (or a symlink to one). + + Example: + ------- + >>> from pathlib import Path + >>> move_directory(Path("results")) + # Moves existing "results" to "results.0" if it exists + """ + # Move directory if it exists + if path.exists(): + if not path.is_dir(): + raise RuntimeError(f"Expected to find nothing or (a symlink to) a directory at {str(path)!r}") + i = 0 + while True: + mvpath = path.parent / f"{path.name}.{i}" + if not mvpath.exists(): + path.rename(mvpath) + break + i += 1 + # Enable chaining + return path + + +def dict_to_cmdlist(dp: dict[str, Any]) -> list[str]: + """Convert a dictionary into command-line arguments. + + This helper is useful for turning experiment configurations into CLI + arguments. + + Parameters + ---------- + dp : dict of str to Any + Dictionary mapping parameter names to values. + + Returns: + ------- + list of str + Command-line arguments such as ``["--lr", "0.01", "--batch", "32"]``. + + Notes: + ----- + - Boolean values are included only when they are ``True``. + - Lists and tuples expand to repeated ``--name value`` pairs. + + Example: + ------- + >>> dict_to_cmdlist({"lr": 0.01, "batch": 32, "debug": True}) + ['--lr', '0.01', '--batch', '32', '--debug'] + >>> dict_to_cmdlist({"layers": [64, 128]}) + ['--layers', '64', '--layers', '128'] + """ + cmd: list[str] = [] + for name, value in dp.items(): + if isinstance(value, bool): + if value: + cmd.append(f"--{name}") + elif isinstance(value, (list, tuple)): + cmd.append(f"--{name}") + for subval in value: + cmd.append(str(subval)) + elif value is not None: + cmd.append(f"--{name}") + cmd.append(str(value)) + return cmd + + +# ---------------------------------------------------------------------------- # +# Job command class + + +class Command: + """Command wrapper that adds standard runtime arguments. + + This class wraps a base command and automatically appends seed, device, and + result-directory arguments when building the final command line. + + Parameters + ---------- + command : iterable of str + Base command as an iterable of strings (e.g. ``["python", "train.py"]``). + The iterable is copied on instantiation. + + Attributes: + ---------- + _basecmd : list of str + Internal copy of the base command. + """ + + def __init__(self, command: Iterable[str]) -> None: + """Initialize the command builder. + + Parameters + ---------- + command : iterable of str + Base command as an iterable of strings. + """ + self._basecmd: list[str] = list(command) + + def build(self, seed: int | str, device: str, resdir: Path | str) -> list[str]: + """Build the final command line. + + Parameters + ---------- + seed : int or str + Seed to use for the experiment. + device : str + Device on which to run the experiment (e.g. ``"cuda:0"``). + resdir : pathlib.Path or str + Target directory path for results. + + Returns: + ------- + list of str + Final command list ready to be passed to ``subprocess.run``. + """ + cmd = self._basecmd.copy() + for name, value in ( + ("seed", seed), + ("device", device), + ("result-directory", resdir), + ): + cmd.append(f"--{name}") + cmd.append(shlex.quote(value if isinstance(value, str) else str(value))) + return cmd + + +# ---------------------------------------------------------------------------- # +# Job class + + +class Jobs: + """Job execution manager for parallel experiments. + + Manages parallel execution of experiments across multiple devices, + with support for result tracking and error handling. + + Parameters + ---------- + res_dir : pathlib.Path or str + Directory to store results. + devices : list of str, optional + List of device names (e.g. ``["cuda:0", "cuda:1"]``). + Defaults to ``["cpu"]`` if none specified. + devmult : int, optional + Number of parallel jobs per device. Default is ``1``. + seeds : sequence of int, optional + Seeds to use for repeating experiments. Default is ``range(1, 6)``. + + Attributes: + ---------- + _res_dir : pathlib.Path + Resolved result directory. + _jobs : list of tuple or None + Pending job queue as ``(name, seed, command)`` tuples, or ``None`` + when the manager has been closed. + _workers : list of threading.Thread + Worker thread pool, one entry per active slot. + _devices : list of str + Devices used for execution. + _seeds : tuple of int + Seeds used for repeating experiments. + _lock : threading.Lock + Main lock protecting shared state. + _cvready : threading.Condition + Condition variable to signal that new jobs are available or that + workers must shut down. + _cvdone : threading.Condition + Condition variable to signal that all submitted jobs have been + processed. + """ + + @staticmethod + def _run( + topdir: Path, + name: str, + seed: int, + device: str, + command: Command, + ) -> None: + """Run a single experiment with the given parameters. + + Parameters + ---------- + topdir : pathlib.Path + Parent result directory. + name : str + Experiment unique name. + seed : int + Experiment seed. + device : str + Device on which to run the experiment. + command : Command + Command builder to use. + """ + # Add seed to name + name = f"{name}-{seed}" + # Process experiment + with tools.Context(name, "info"): + finaldir = topdir / name + # Check whether the experiment was already successful + if finaldir.exists(): + tools.info("Experiment already processed.") + return + # Move-make the pending result directory + resdir = move_directory(topdir / f"{name}.pending") + resdir.mkdir(mode=0o755, parents=True) + # Build the command + args = command.build(seed, device, resdir) + # Launch the experiment and capture the standard output/error + tools.trace(" ".join(shlex.quote(arg) for arg in args)) + cmd_res = subprocess.run(args, check=False, capture_output=True) + if cmd_res.returncode == 0: + tools.info("Experiment successful") + else: + tools.warning("Experiment failed") + finaldir = topdir / f"{name}.failed" + move_directory(finaldir) + resdir.rename(finaldir) + (finaldir / "stdout.log").write_bytes(cmd_res.stdout) + (finaldir / "stderr.log").write_bytes(cmd_res.stderr) + + def _worker_entrypoint(self, device: str) -> None: + """Worker thread entry point. + + Continuously picks pending jobs from the queue and executes them on + the assigned device until the manager is closed. + + Parameters + ---------- + device : str + Device assigned to this worker. + """ + while True: + # Take a pending experiment, or exit if requested + with self._lock: + while True: + # Check if must exit + if self._jobs is None: + return + # Check and pick the first pending experiment, if available + if len(self._jobs) > 0: + name, seed, command = self._jobs.pop() + break + # Wait for new job notification + self._cvready.wait() + # Run the picked experiment + self._run(self._res_dir, name, seed, device, command) + + def __init__( + self, + res_dir: Path | str, + devices: list[str] | None = None, + devmult: int = 1, + seeds: Sequence[int] | None = None, + ) -> None: + """Initialize the experiment launcher. + + Parameters + ---------- + res_dir : pathlib.Path or str + Target directory path for results. + devices : list of str, optional + Devices on which to run experiments (e.g. ``["cuda:0"]``). + Defaults to ``["cpu"]``. + devmult : int, optional + Device multiplier. Defaults to 1. + seeds : sequence of int, optional + Seeds to use for the experiments. Defaults to ``range(1, 6)``. + """ + # Initialize instance + if devices is None: + devices = ["cpu"] + if seeds is None: + seeds = tuple(range(1, 6)) + self._res_dir: Path = Path(res_dir) + self._jobs: list[tuple[str, int, Command]] | None = [] + self._workers: list[threading.Thread] = [] + self._devices: list[str] = devices + self._seeds: tuple[int, ...] = tuple(seeds) + self._lock = threading.Lock() + self._cvready = threading.Condition( + lock=self._lock + ) # Signal jobs have been added and must be processed, or the worker must quit + self._cvdone = threading.Condition(lock=self._lock) # Signal jobs have all been processed + # Launch the worker pool + for _ in range(devmult): + for device in devices: + thread = threading.Thread(target=self._worker_entrypoint, name=device, args=(device,)) + thread.start() + self._workers.append(thread) + + def get_seeds(self) -> tuple[int, ...]: + """Get the list of seeds used for repeating the experiments. + + Returns: + ------- + tuple of int + Seeds used by this manager. + """ + return self._seeds + + def close(self) -> None: + """Close and wait for the worker pool, discarding not-yet-started submissions.""" + # Close the manager + with self._lock: + # Check if already closed + if self._jobs is None: + return + # Reset submission list + self._jobs = None + # Notify all the workers + self._cvready.notify_all() + # Wait for all the workers + for worker in self._workers: + worker.join() + + def submit(self, name: str, command: Command) -> None: + """Submit a job for execution. + + The job is repeated for every seed configured in the manager. + + Parameters + ---------- + name : str + Job identifier. + command : Command + Command builder to execute. + + Raises: + ------ + RuntimeError + If the manager has already been closed. + """ + with self._lock: + # Check if not closed + if self._jobs is None: + raise RuntimeError("Experiment manager cannot take new jobs as it has been closed") + # Submit the experiment with each seed + for seed in self._seeds: + self._jobs.insert(0, (name, seed, command)) + self._cvready.notify(n=len(self._seeds)) + + def wait(self, predicate: Callable[[], bool] | None = None) -> None: + """Wait for all the submitted jobs to be processed. + + Parameters + ---------- + predicate : callable returning bool, optional + Optional custom predicate. If provided, waiting stops when the + predicate returns ``True``. + """ + while True: + with self._lock: + # Wait for condition or timeout + self._cvdone.wait(timeout=1.0) + # Check status + if self._jobs is None: + break + if len(self._jobs) == 0: + break + if not any(worker.is_alive() for worker in self._workers): + break + if predicate is not None and predicate(): + break diff --git a/tools/misc.py b/krum/tools/misc.py similarity index 81% rename from tools/misc.py rename to krum/tools/misc.py index f938c96..3d7b135 100644 --- a/tools/misc.py +++ b/krum/tools/misc.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file misc.py # @author Sébastien Rouault @@ -13,8 +12,7 @@ # Miscellaneous Python helpers. ### -""" -Utilities shared across the repository. +"""Utilities shared across the repository. This module groups small helpers used for exception handling, parsing, timing, interactive exploration, and light registry patterns. @@ -42,7 +40,7 @@ ``pairwise``, ``line_maximize``, ``interactive``, and ``get_loaded_dependencies`` cover assorted convenience tasks. -Example +Example: ------- .. code-block:: python @@ -60,21 +58,21 @@ """ __all__ = [ + "ClassRegister", + "MethodCallReplicator", + "TimedContext", "UnavailableException", + "deltatime_format", + "deltatime_point", "fatal_unavailable", - "MethodCallReplicator", - "ClassRegister", - "parse_keyval", "fullqual", - "onetime", - "TimedContext", - "interactive", "get_loaded_dependencies", + "interactive", "line_maximize", - "pairwise", "localtime", - "deltatime_point", - "deltatime_format", + "onetime", + "pairwise", + "parse_keyval", ] import os @@ -84,18 +82,17 @@ import threading import time import traceback +from collections.abc import Callable +from typing import Any -import tools +from . import Context, UserException, fatal, trace, warning # ---------------------------------------------------------------------------- # # Unavailable user exception class -def make_unavailable_exception_text( - data: list[str], name: str, what: str = "entry" -) -> str: - """ - Build the message used by :class:`UnavailableException`. +def make_unavailable_exception_text(data: list[str], name: str, what: str = "entry") -> str: + """Build the message used by :class:`UnavailableException`. Parameters ---------- @@ -106,7 +103,7 @@ def make_unavailable_exception_text( what : str, optional Human-readable description of the named objects. - Returns + Returns: ------- str User-facing message that lists the available names, or states that no @@ -114,17 +111,16 @@ def make_unavailable_exception_text( """ # Preparation if len(data) == 0: - end = "no %s available" % what + end = f"no {what} available" else: - sep = "%s· " % os.linesep - end = "expected one of:%s%s" % (sep, sep.join(data)) + sep = f"{os.linesep}· " + end = f"expected one of:{sep}{sep.join(data)}" # Final string cat - return "Unknown %s %r, %s" % (what, name, end) + return f"Unknown {what} {name!r}, {end}" def fatal_unavailable(*args, **kwargs) -> None: - """ - Report an unavailable entry as a fatal user-facing error. + """Report an unavailable entry as a fatal user-facing error. Parameters ---------- @@ -135,15 +131,14 @@ def fatal_unavailable(*args, **kwargs) -> None: Keyword arguments forwarded to :func:`make_unavailable_exception_text`. """ - tools.fatal(make_unavailable_exception_text(*args, **kwargs)) + fatal(make_unavailable_exception_text(*args, **kwargs)) -class UnavailableException(tools.UserException): +class UnavailableException(UserException): """User-facing exception raised when a selected registry entry is missing.""" def __init__(self, *args, **kwargs) -> None: - """ - Initialize the exception message. + """Initialize the exception message. Parameters ---------- @@ -174,8 +169,7 @@ class MethodCallReplicator: """ def __init__(self, *args: object) -> None: - """ - Bind the instances that should receive replicated method calls. + """Bind the instances that should receive replicated method calls. Parameters ---------- @@ -183,22 +177,19 @@ def __init__(self, *args: object) -> None: Instances on which to replicate method calls, in call order. """ # Assertions - assert len(args) > 0, ( - "Expected at least one instance on which to forward method calls" - ) + assert len(args) > 0, "Expected at least one instance on which to forward method calls" # Finalization self.__instances = args def __getattr__(self, name: str) -> object: - """ - Return a closure that replicates the named method call. + """Return a closure that replicates the named method call. Parameters ---------- name : str Name of the method or callable attribute to replicate. - Returns + Returns: ------- object Callable that forwards its arguments to every target callable and @@ -209,8 +200,7 @@ def __getattr__(self, name: str) -> object: # Replication closure def calls(*args, **kwargs) -> list[object]: - """ - Call each target callable with the provided arguments. + """Call each target callable with the provided arguments. Parameters ---------- @@ -219,7 +209,7 @@ def calls(*args, **kwargs) -> list[object]: **kwargs : object Keyword arguments forwarded to every target callable. - Returns + Returns: ------- list[object] Results returned by the target callables, in instance order. @@ -238,8 +228,7 @@ class ClassRegister: """Minimal registry mapping user-visible names to classes.""" def __init__(self, singular: str, optplural: str | None = None) -> None: - """ - Create an empty class registry. + """Create an empty class registry. Parameters ---------- @@ -258,11 +247,10 @@ def __init__(self, singular: str, optplural: str | None = None) -> None: def itemize(self) -> list[str]: """Return the registered class names.""" - return self.__register.keys() + return list(self.__register.keys()) def register(self, name: str, cls: type) -> None: - """ - Register a class under a user-visible name. + """Register a class under a user-visible name. Parameters ---------- @@ -276,18 +264,13 @@ def register(self, name: str, cls: type) -> None: "Name " + repr(name) + " already in use while registering " - + repr( - getattr( - cls, "__name__", "" - ) - ) + + repr(getattr(cls, "__name__", "")) ) # Registering self.__register[name] = cls def instantiate(self, name: str, *args, **kwargs) -> object: - """ - Instantiate the class registered under ``name``. + """Instantiate the class registered under ``name``. Parameters ---------- @@ -298,14 +281,14 @@ def instantiate(self, name: str, *args, **kwargs) -> object: **kwargs : object Keyword arguments forwarded to the class constructor. - Returns + Returns: ------- object Instance of the registered class. - Raises + Raises: ------ - tools.UserException + UserException If ``name`` is not registered. """ # Assertions @@ -314,14 +297,8 @@ def instantiate(self, name: str, *args, **kwargs) -> object: if len(self.__register) == 0: cause += "no registered " + self.__denoms[0] else: - cause += ( - "available " - + self.__denoms[1] - + ": '" - + ("', '").join(self.__register.keys()) - + "'" - ) - raise tools.UserException(cause) + cause += "available " + self.__denoms[1] + ": '" + ("', '").join(self.__register.keys()) + "'" + raise UserException(cause) # Instantiation return self.__register[name](*args, **kwargs) @@ -331,8 +308,7 @@ def instantiate(self, name: str, *args, **kwargs) -> object: def parse_keyval_auto_convert(val: str) -> object: - """ - Infer and convert the type represented by a string. + """Infer and convert the type represented by a string. Conversion is attempted in this order: boolean literals, integer, float, then string. @@ -342,7 +318,7 @@ def parse_keyval_auto_convert(val: str) -> object: val : str String value to convert. - Returns + Returns: ------- object Converted value, or ``val`` unchanged if no non-string type matches. @@ -351,7 +327,7 @@ def parse_keyval_auto_convert(val: str) -> object: low = val.lower() if low == "false": return False - elif low == "true": + if low == "true": return True # Try guess number for cls in (int, float): @@ -363,11 +339,8 @@ def parse_keyval_auto_convert(val: str) -> object: return val -def parse_keyval( - list_keyval: list[str], defaults: dict[str, object] | None = None -) -> dict[str, object]: - """ - Parse ``:`` strings into a typed dictionary. +def parse_keyval(list_keyval: list[str], defaults: dict[str, object] | None = None) -> dict[str, object]: + """Parse ``:`` strings into a typed dictionary. This helper is used for command-line options such as ``--gar-args lr:0.01``. Keys present in ``defaults`` are converted to the @@ -383,20 +356,19 @@ def parse_keyval( inference and are copied into the returned dictionary when the corresponding key is not explicitly provided. - Returns + Returns: ------- dict[str, object] Parsed key/value pairs with converted values. - Raises + Raises: ------ - tools.UserException + UserException If an entry is malformed, a key is provided more than once, or conversion to a default value's type fails. - Example + Example: ------- - >>> parse_keyval(["lr:0.01", "batch:32"], defaults={"lr": 0.1}) {'lr': 0.01, 'batch': 32} >>> parse_keyval(["debug:true", "workers:4"], defaults={}) @@ -410,21 +382,10 @@ def parse_keyval( for entry in list_keyval: pos = entry.find(sep) if pos < 0: - raise tools.UserException( - "Expected list of " - + repr(":") - + ", got " - + repr(entry) - + " as one entry" - ) + raise UserException("Expected list of " + repr(":") + ", got " + repr(entry) + " as one entry") key = entry[:pos] if key in parsed: - raise tools.UserException( - "Key " - + repr(key) - + " had already been specified with value " - + repr(parsed[key]) - ) + raise UserException("Key " + repr(key) + " had already been specified with value " + repr(parsed[key])) val = entry[pos + len(sep) :] # Guess/assert type constructibility if key in defaults: @@ -435,12 +396,12 @@ def parse_keyval( else: val = cls(val) except Exception: - raise tools.UserException( + raise UserException( "Required key " + repr(key) + " expected a value of type " + repr(getattr(type(defaults[key]), "__name__", "")) - ) + ) from None else: val = parse_keyval_auto_convert(val) # Bind (converted) value to associated key @@ -458,23 +419,21 @@ def parse_keyval( def fullqual(obj: object) -> str: - """ - Return a class or instance's fully qualified name. + """Return a class or instance's fully qualified name. Parameters ---------- obj : object Class or instance to describe. - Returns + Returns: ------- str Fully qualified class name. Instances are prefixed with ``"instance of "``. - Example + Example: ------- - >>> fullqual(str) 'builtins.str' >>> fullqual(pathlib.Path(".")) @@ -487,7 +446,7 @@ def fullqual(obj: object) -> str: prelude = "instance of " obj = type(obj) # Rebuilding - return "%s%s.%s" % ( + return "{}{}.{}".format( prelude, getattr(obj, "__module__", ""), getattr(obj, "__qualname__", ""), @@ -498,9 +457,8 @@ def fullqual(obj: object) -> str: # Basic "full-qualification" string builder for a given instance/class -def onetime(name: str | None = None) -> tuple[callable, callable]: - """ - Create or retrieve a thread-safe one-shot flag. +def onetime(name: str | None = None) -> tuple[Callable[..., Any], Callable[..., Any]]: + """Create or retrieve a thread-safe one-shot flag. Parameters ---------- @@ -508,7 +466,7 @@ def onetime(name: str | None = None) -> tuple[callable, callable]: Optional global flag name. Reusing the same name returns the same getter/setter pair. - Returns + Returns: ------- tuple[callable, callable] ``(getter, setter)`` pair. ``getter`` returns whether the flag has been @@ -524,8 +482,7 @@ def onetime(name: str | None = None) -> tuple[callable, callable]: # Management closures def getter(*args, **kwargs): - """ - Return whether the one-shot flag has been set. + """Return whether the one-shot flag has been set. Parameters ---------- @@ -534,7 +491,7 @@ def getter(*args, **kwargs): **kwargs : object Ignored keyword arguments. - Returns + Returns: ------- bool ``True`` once the associated setter has been called, otherwise @@ -546,8 +503,7 @@ def getter(*args, **kwargs): return value def setter(*args, **kwargs): - """ - Set the one-shot flag to ``True``. + """Set the one-shot flag to ``True``. Parameters ---------- @@ -569,62 +525,59 @@ def setter(*args, **kwargs): # Register for the onetime variables -onetime_register = dict() +onetime_register = {} # ---------------------------------------------------------------------------- # # Plain context augmented with simple execution time measurement -class TimedContext(tools.Context): +class TimedContext(Context): """Context manager that logs the elapsed runtime of a block.""" def __init__(self, *args, **kwargs) -> None: - """ - Initialize the timed context. + """Initialize the timed context. Parameters ---------- *args : object - Positional arguments forwarded to ``tools.Context``. + Positional arguments forwarded to ``Context``. **kwargs : object - Keyword arguments forwarded to ``tools.Context``. + Keyword arguments forwarded to ``Context``. """ super().__init__(*args, **kwargs) def __enter__(self): - """ - Start timing and enter the parent context. + """Start timing and enter the parent context. - Returns + Returns: ------- object - Value returned by ``tools.Context.__enter__``. + Value returned by ``Context.__enter__``. """ self._chrono = time.time() return super().__enter__() def __exit__(self, *args, **kwargs) -> None: - """ - Stop timing, log elapsed time, and exit the parent context. + """Stop timing, log elapsed time, and exit the parent context. Parameters ---------- *args : object - Positional arguments forwarded to ``tools.Context.__exit__``. + Positional arguments forwarded to ``Context.__exit__``. **kwargs : object - Keyword arguments forwarded to ``tools.Context.__exit__``. + Keyword arguments forwarded to ``Context.__exit__``. """ # Measure elapsed runtime (in ns) runtime = (time.time() - self._chrono) * 1000000000.0 # Recover ideal unit - for unit in ("ns", "µs", "ms"): + for _unit in ("ns", "µs", "ms"): if runtime < 1000.0: break runtime /= 1000.0 else: unit = "s" # Format and print string - tools.trace(f"Execution time: {runtime:.3g} {unit}") + trace(f"Execution time: {runtime:.3g} {unit}") # Forward call super().__exit__(*args, **kwargs) @@ -639,8 +592,7 @@ def interactive( prompt: str = ">>> ", cprmpt: str = "... ", ) -> None: - """ - Run a small interactive Python prompt. + """Run a small interactive Python prompt. Press ``Ctrl+D`` or send an equivalent EOF signal to leave the prompt. @@ -663,7 +615,7 @@ def interactive( except Exception: caller = None if glbs is None: - tools.warning( + warning( "Unable to recover caller's frame, locals and globals", context="interactive", ) @@ -671,7 +623,7 @@ def interactive( if caller is not None and hasattr(caller, "f_globals"): glbs = caller.f_globals else: - glbs = dict() + glbs = {} if lcls is None: if caller is not None and hasattr(caller, "f_locals"): lcls = caller.f_locals @@ -686,9 +638,7 @@ def interactive( # Input new line try: line = input() - print( - "\033[A" - ) # Trick to "advertise" new line on stdout after new line on stdin + print("\033[A") # Trick to "advertise" new line on stdout after new line on stdin except BaseException as err: if any(isinstance(err, cls) for cls in (EOFError, KeyboardInterrupt)): print() # Since no new line was printed by pressing ENTER @@ -707,9 +657,7 @@ def interactive( command = line try: exec(command, glbs, lcls) - except ( - SyntaxError - ): # Heuristic that we are dealing with a multi-line statement + except SyntaxError: # Heuristic that we are dealing with a multi-line statement continue elif len(line) > 0: command += os.linesep + line @@ -717,7 +665,7 @@ def interactive( else: # Multi-line statement is complete exec(command, glbs, lcls) except Exception: - with tools.Context("uncaught", "error"): + with Context("uncaught", "error"): traceback.print_exc() command = "" statement = False @@ -727,47 +675,49 @@ def interactive( # List non-standard, currently loaded module names and metadata. +# Module flavor constants +IS_STANDARD = 0 +IS_SITE = 1 +IS_LOCAL = 2 + + def get_loaded_dependencies() -> list[tuple[str, str | None, int]]: - """ - List currently loaded non-built-in root modules. + """List currently loaded non-built-in root modules. - Returns + Returns: ------- list[tuple[str, str | None, int]] Tuples of ``(root_module_name, version, flavor)``. ``version`` is the module's ``__version__`` attribute when present, otherwise ``None``. ``flavor`` is one of ``IS_STANDARD``, ``IS_SITE``, or ``IS_LOCAL``. - Raises + Raises: ------ RuntimeError If Python's site-packages locations cannot be discovered on the current platform. """ # Get the site-packages directories, and make "flavor"-detection closure - path_sites = tuple( - pathlib.Path(path) - for path in site.getsitepackages() + [site.getusersitepackages()] - ) + path_sites = tuple(pathlib.Path(path) for path in site.getsitepackages() + [site.getusersitepackages()]) def flavor_of(path): path = pathlib.Path(path) for path_site in path_sites: try: path.relative_to(path_site) - return get_loaded_dependencies.IS_SITE + return IS_SITE except ValueError: pass for path_site in path_sites: try: path.relative_to(path_site.parent) - return get_loaded_dependencies.IS_STANDARD + return IS_STANDARD except ValueError: pass - return get_loaded_dependencies.IS_LOCAL + return IS_LOCAL # Iterate over the loaded modules - res = list() + res = [] for name, module in sys.modules.items(): # Skip non-root modules if "." in name: @@ -786,24 +736,18 @@ def flavor_of(path): return res -# Register constants -get_loaded_dependencies.IS_STANDARD = 0 -get_loaded_dependencies.IS_SITE = 1 -get_loaded_dependencies.IS_LOCAL = 2 - # ---------------------------------------------------------------------------- # -# Find the x maximizing a function y = f(x), with (x, y) ∊ ℝ⁺× ℝ +# Find the x maximizing a function y = f(x), with (x, y) ∊ ℝ⁺x ℝ def line_maximize( - scape: callable, + scape: Callable[..., Any], evals: int = 16, start: float = 0.0, delta: float = 1.0, ratio: float = 0.8, ) -> float: - """ - Best-effort argmax search for a scalar function on non-negative inputs. + """Best-effort argmax search for a scalar function on non-negative inputs. The search first expands while values improve, then contracts the step size to refine the best point found within the evaluation budget. @@ -823,7 +767,7 @@ def line_maximize( Step contraction ratio, expected to be between ``0.5`` and ``1.0`` excluded. - Returns + Returns: ------- float Best point found under the evaluation budget. @@ -871,22 +815,20 @@ def line_maximize( def pairwise(data: list | tuple): - """ - Yield unordered pairs from an indexable collection. + """Yield unordered pairs from an indexable collection. Parameters ---------- data : list | tuple Indexable collection such as a ``list`` or ``tuple``. - Yields + Yields: ------ tuple Tuples ``(data[i], data[j])`` for every ``i < j``. - Example + Example: ------- - >>> list(pairwise([1, 2, 3])) [(1, 2), (1, 3), (2, 3)] >>> list(pairwise("ab")) @@ -903,10 +845,9 @@ def pairwise(data: list | tuple): def localtime() -> str: - """ - Return the current local time formatted for logs. + """Return the current local time formatted for logs. - Returns + Returns: ------- str Local time as ``YYYY/MM/DD HH:MM:SS``. @@ -916,10 +857,9 @@ def localtime() -> str: def deltatime_point() -> int: - """ - Capture an opaque point in monotonic time. + """Capture an opaque point in monotonic time. - Returns + Returns: ------- int Monotonic timestamp rounded to seconds. The value is intended for use @@ -930,8 +870,7 @@ def deltatime_point() -> int: def deltatime_format(a: int, b: int) -> tuple[int, str]: - """ - Compute and format elapsed time between two captured points. + """Compute and format elapsed time between two captured points. Parameters ---------- @@ -940,7 +879,7 @@ def deltatime_format(a: int, b: int) -> tuple[int, str]: b : int Later point returned by :func:`deltatime_point`. - Returns + Returns: ------- tuple[int, str] Tuple ``(seconds, text)`` containing elapsed seconds and a diff --git a/tools/pytorch.py b/krum/tools/pytorch.py similarity index 80% rename from tools/pytorch.py rename to krum/tools/pytorch.py index 7c20669..1145936 100644 --- a/tools/pytorch.py +++ b/krum/tools/pytorch.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file pytorch.py # @author Sébastien Rouault @@ -13,7 +12,8 @@ # Helpers relative to PyTorch. ### -""" +"""Helper functions for PyTorch tensor manipulation and gradient handling. + This module provides helper functions for PyTorch tensor manipulation, gradient handling, and common operations used throughout Krum. @@ -44,7 +44,7 @@ - ``regression``: Generic optimization for free variables - ``pnm``: Export tensor to PGM/PBM format -Example +Example: ------- .. code-block:: python @@ -62,55 +62,52 @@ """ __all__ = [ - "relink", + "AccumulatedTimedContext", + "WeightedMSELoss", + "compute_avg_dev_max", "flatten", "grad_of", "grads_of", - "compute_avg_dev_max", - "AccumulatedTimedContext", - "weighted_mse_loss", - "WeightedMSELoss", - "regression", "pnm", + "regression", + "relink", + "weighted_mse_loss", ] import io import time -import torch import types +from collections.abc import Callable -from typing import Callable - +import torch # ---------------------------------------------------------------------------- # # "Flatten" and "relink" operations def relink(tensors: list[torch.Tensor], common: torch.Tensor) -> torch.Tensor: - """ - Relink tensors to share a common contiguous memory storage. + """Relink tensors to share a common contiguous memory storage. Parameters ---------- - tensors : iterable of torch.Tensor + tensors : list of torch.Tensor Tensors to relink. All must have the same dtype. common : torch.Tensor Flat tensor of sufficient size to use as underlying storage. Must have the same dtype as the given tensors. - Returns + Returns: ------- torch.Tensor The common tensor, with ``linked_tensors`` attribute set. - Notes + Notes: ----- The returned tensor has a ``linked_tensors`` attribute pointing to the original tensors. This allows updating all tensors simultaneously. - Example + Example: ------- - >>> import torch >>> from tools import relink >>> t1 = torch.tensor([1., 2.]) @@ -129,33 +126,31 @@ def relink(tensors: list[torch.Tensor], common: torch.Tensor) -> torch.Tensor: tensor.data = common[pos:npos].view(*tensor.shape) pos = npos # Finalize and return - common.linked_tensors = tensors + common.linked_tensors = tensors # type: ignore[attr-defined] return common def flatten(tensors: list[torch.Tensor]) -> torch.Tensor: - """ - Flatten tensors into a single contiguous tensor. + """Flatten tensors into a single contiguous tensor. Parameters ---------- - tensors : iterable of torch.Tensor + tensors : list of torch.Tensor Tensors to flatten. All must have the same dtype. - Returns + Returns: ------- torch.Tensor Flat tensor containing all data from input tensors, stored in a contiguous memory segment. - Notes + Notes: ----- The returned tensor shares memory with the original tensors. Modifications to the flat tensor will reflect in the original tensors. - Example + Example: ------- - >>> import torch >>> from tools import flatten >>> t1 = torch.tensor([1., 2.]) @@ -177,23 +172,21 @@ def flatten(tensors: list[torch.Tensor]) -> torch.Tensor: def grad_of(tensor: torch.Tensor) -> torch.Tensor: - """ - Get the gradient of a given tensor, create zero gradient if missing. + """Get the gradient of a given tensor, create zero gradient if missing. Parameters ---------- tensor : torch.Tensor A tensor that may have a gradient attached. - Returns + Returns: ------- torch.Tensor The gradient tensor. If none existed, a zero gradient is created and attached to the tensor. - Example + Example: ------- - >>> import torch >>> from tools import grad_of >>> x = torch.randn(3, requires_grad=True) @@ -212,22 +205,20 @@ def grad_of(tensor: torch.Tensor) -> torch.Tensor: def grads_of(tensors: list[torch.Tensor]): - """ - Generator that gets or creates gradients for multiple tensors. + """Generator that gets or creates gradients for multiple tensors. Parameters ---------- - tensors : iterable of torch.Tensor + tensors : list of torch.Tensor Tensors that may have gradients attached. - Yields + Yields: ------ torch.Tensor Gradient for each tensor. - Example + Example: ------- - >>> import torch >>> from tools import grads_of >>> params = [torch.randn(3, requires_grad=True) for _ in range(2)] @@ -248,25 +239,27 @@ def grads_of(tensors: list[torch.Tensor]): def compute_avg_dev_max( samples: list[torch.Tensor], -) -> tuple[torch.Tensor, float, float, float]: - """ - Compute average, average norm, norm deviation, and max absolute value. +) -> tuple[torch.Tensor | None, float, float, float]: + """Compute average, average norm, norm deviation, and max absolute value. Parameters ---------- samples : list of torch.Tensor List of tensors to compute statistics on. - Returns + Returns: ------- tuple[torch.Tensor, float, float, float] Tuple containing: average tensor, average norm, norm deviation, and max absolute value. - Notes + Notes: ----- The returned tensor is newly created and does not alias any input tensor. """ + # Handle empty list gracefully + if len(samples) == 0: + return None, float("nan"), float("nan"), float("nan") # Stack all samples stacked = torch.stack(samples) # Compute average tensor @@ -286,8 +279,7 @@ def compute_avg_dev_max( class AccumulatedTimedContext: - """ - Accumulated timed context manager with optional CUDA synchronization. + """Accumulated timed context manager with optional CUDA synchronization. This context manager measures elapsed time across multiple entries, with optional CUDA synchronization to ensure accurate GPU timing. @@ -298,7 +290,7 @@ class AccumulatedTimedContext: Whether to synchronize CUDA before and after timing. Defaults to ``False``. - Example + Example: ------- >>> import torch >>> from tools import AccumulatedTimedContext @@ -309,25 +301,28 @@ class AccumulatedTimedContext: >>> print(atc.current_runtime()) """ - def __init__(self, sync: bool = False) -> None: - """ - Initialize the accumulated timed context. + def __init__(self, sync: bool | float = False) -> None: + """Initialize the accumulated timed context. Parameters ---------- - sync : bool, optional - Whether to synchronize CUDA before and after timing. Defaults to - ``False``. + sync : bool or float, optional + Whether to synchronize CUDA before and after timing, or a float + representing an already accumulated runtime. Defaults to ``False``. """ - self._sync = sync - self._start = None - self._elapsed = 0.0 + if isinstance(sync, float): + self._sync = False + self._start = None + self._elapsed = sync + else: + self._sync = sync + self._start = None + self._elapsed = 0.0 def __enter__(self): - """ - Enter the context and start timing. + """Enter the context and start timing. - Returns + Returns: ------- AccumulatedTimedContext Self reference for context management. @@ -338,8 +333,7 @@ def __enter__(self): return self def __exit__(self, *args) -> None: - """ - Exit the context, stop timing, and accumulate elapsed time. + """Exit the context, stop timing, and accumulate elapsed time. Parameters ---------- @@ -348,13 +342,13 @@ def __exit__(self, *args) -> None: """ if self._sync and torch.cuda.is_available(): torch.cuda.synchronize() + assert self._start is not None self._elapsed += time.perf_counter() - self._start def current_runtime(self) -> float: - """ - Return the accumulated runtime. + """Return the accumulated runtime. - Returns + Returns: ------- float Total accumulated time in seconds. @@ -366,11 +360,8 @@ def current_runtime(self) -> float: # Weighted MSE loss -def weighted_mse_loss( - input: torch.Tensor, target: torch.Tensor, weight: torch.Tensor -) -> torch.Tensor: - """ - Compute weighted mean squared error loss. +def weighted_mse_loss(input: torch.Tensor, target: torch.Tensor, weight: torch.Tensor) -> torch.Tensor: + """Compute weighted mean squared error loss. Parameters ---------- @@ -381,12 +372,12 @@ def weighted_mse_loss( weight : torch.Tensor Weight tensor for each element. - Returns + Returns: ------- torch.Tensor Weighted MSE loss value. - Notes + Notes: ----- The returned tensor is newly created and does not alias any input tensor. """ @@ -394,23 +385,17 @@ def weighted_mse_loss( class WeightedMSELoss(torch.nn.Module): - """ - Weighted MSE loss module. + """Weighted MSE loss module. This module wraps :func:`weighted_mse_loss` as a PyTorch module. """ def __init__(self) -> None: - """ - Initialize the weighted MSE loss module. - """ + """Initialize the weighted MSE loss module.""" super().__init__() - def forward( - self, input: torch.Tensor, target: torch.Tensor, weight: torch.Tensor - ) -> torch.Tensor: - """ - Compute weighted MSE loss. + def forward(self, input: torch.Tensor, target: torch.Tensor, weight: torch.Tensor) -> torch.Tensor: + """Compute weighted MSE loss. Parameters ---------- @@ -421,7 +406,7 @@ def forward( weight : torch.Tensor Weight tensor. - Returns + Returns: ------- torch.Tensor Weighted MSE loss value. @@ -441,8 +426,7 @@ def regression( opt=None, steps=1000, ) -> float: - """ - Generic optimization for free variables. + """Generic optimization for free variables. Parameters ---------- @@ -459,7 +443,7 @@ def regression( steps : int, optional Number of optimization steps. Defaults to 1000. - Returns + Returns: ------- float Final loss value after optimization. @@ -471,10 +455,10 @@ def regression( for _ in range(steps): opt.zero_grad() result = func(vars, data) - l = loss(result, data["target"]) - l.backward() + loss_func = loss(result, data["target"]) + loss_func.backward() opt.step() - return l.item() + return loss_func.item() # ---------------------------------------------------------------------------- # @@ -482,8 +466,7 @@ def regression( def pnm(fd: io.BufferedWriter, tn: torch.Tensor) -> None: - """ - Export tensor to PGM/PBM format. + """Export tensor to PGM/PBM format. Parameters ---------- @@ -493,7 +476,7 @@ def pnm(fd: io.BufferedWriter, tn: torch.Tensor) -> None: Tensor to export. Supports float32/float64 for grayscale (PGM) or boolean/integer for binary (PBM). - Notes + Notes: ----- - Grayscale format (PGM): For float32/float64 tensors, normalizes to 0-255. - Binary format (PBM): For other dtypes, converts to binary values. @@ -505,12 +488,12 @@ def pnm(fd: io.BufferedWriter, tn: torch.Tensor) -> None: if M - m < 1e-8: M = m + 1 t = ((tn - m) / (M - m) * 255).byte().cpu() - fd.write(f"P5\n{tn.shape[1]}\n{tn.shape[0]}\n255\n") + fd.write(f"P5\n{tn.shape[1]}\n{tn.shape[0]}\n255\n".encode()) fd.write(t.numpy().tobytes()) else: # Binary t = (tn > 0).byte().cpu() - fd.write(f"P4\n{tn.shape[1]}\n{tn.shape[0]}\n") + fd.write(f"P4\n{tn.shape[1]}\n{tn.shape[0]}\n".encode()) # Pad to byte boundary w = (tn.shape[1] + 7) // 8 pad = w * 8 - tn.shape[1] diff --git a/pyproject.toml b/pyproject.toml index 9a7299b..377e261 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,20 +1,45 @@ +[build-system] +requires = ["setuptools>=77.0", "wheel"] +build-backend = "setuptools.build_meta" + [project] name = "krum" version = "0.1.0" description = "Byzantine-resilient aggregation rules for distributed machine learning." readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.15" +license = "MIT" +license-files = ["LICENSE"] +authors = [ + {name = "Sébastien Rouault", email = "sebastien.rouault@alumni.epfl.ch"}, + {name = "Arthur Danjou", email = "arthur.danjou@dauphine.eu"}, +] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] dependencies = [ "matplotlib>=3.10.9", "ninja>=1.13.0", "numpy>=2.2.6", + "pandas>=2.0.0", "requests>=2.33.1", "torch>=2.11.0", "torchvision>=0.26.0", ] -[dependency-groups] -docs = [ +[project.optional-dependencies] +dev = [ + "pre-commit>=4.0.0", + "ruff>=0.9.0", + "ty>=0.0.34", "sphinx>=8.1.3", "sphinx-copybutton>=0.5.2", "sphinx-favicon>=1.0.1", @@ -22,3 +47,65 @@ docs = [ "sphinx-contributors>=0.3.0", "shibuya>=2026.1.9", ] + +[project.urls] +Homepage = "https://github.com/calicarpa/krum" +Repository = "https://github.com/calicarpa/krum" +Issues = "https://github.com/calicarpa/krum/issues" + +[tool.setuptools.packages.find] +where = ["."] +include = ["krum*"] + +[tool.setuptools.package-data] +"krum.native" = ["**/*.hpp", "**/*.cpp", "**/*.cu", "**/*.h", "**/*.deps", "**/.placeholder"] + +[tool.ruff] +target-version = "py310" +line-length = 120 +exclude = [ + "repositories", + ".venv", + ".git", + ".ruff_cache", + "build", + "dist", +] +fix = true +show-fixes = true +preview = true + +[tool.ruff.lint] +explicit-preview-rules = true +select = [ + "E", # pycodestyle errors + "F", # Pyflakes + "I", # isort + "W", # pycodestyle warnings + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "RET", # flake8-return + "D", # flake8-docstrings +] +ignore = [ + "E402", # module level import not at top of file (intentional in this codebase) + "E501", # line too long (handled by formatter) + "UP031", # percent format (legacy codebase, too noisy to fix now) +] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] # unused imports (re-exports) + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false +line-ending = "auto" + +[dependency-groups] +dev = [ + "sphinx-autobuild>=2024.10.3", +] diff --git a/reproduce.py b/reproduce.py index 417acb1..d1520af 100644 --- a/reproduce.py +++ b/reproduce.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file reproduce.py # @author Sébastien Rouault @@ -13,7 +12,7 @@ # Reproduce the (missing) experiments and plots. ### -import tools +from krum import tools tools.success("Module loading...") @@ -24,7 +23,7 @@ import torch -import experiments +from krum import experiments # ---------------------------------------------------------------------------- # # Miscellaneous initializations @@ -44,6 +43,7 @@ def process_commandline(): """Parse the command-line and perform checks. + Returns: Parsed configuration """ @@ -104,11 +104,11 @@ def check_make_dir(path): # Preprocess/resolve the devices to use if args.devices == "auto": if torch.cuda.is_available(): - args.devices = list(f"cuda:{i}" for i in range(torch.cuda.device_count())) + args.devices = [f"cuda:{i}" for i in range(torch.cuda.device_count())] else: args.devices = ["cpu"] else: - args.devices = list(name.strip() for name in args.devices.split(",")) + args.devices = [name.strip() for name in args.devices.split(",")] # ---------------------------------------------------------------------------- # # Serial preloading of the dataset @@ -160,13 +160,7 @@ def make_command(params): for md, mda in (("simples-logit", "din:68"),): for gar, attacks in ( ("average", (("nan", None),)), - ( - "brute", - ( - ("little", ("factor:1.5", "negative:True")), - ("empire", "factor:1.1"), - ), - ), + ("brute", (("little", ("factor:1.5", "negative:True")), ("empire", "factor:1.1"))), ): for attack, attargs in attacks: for epsilon in (None, 0.1, 0.2, 0.5): @@ -195,16 +189,17 @@ def make_command(params): # Check if exit requested before going to plotting the results if exit_is_requested(): - exit(0) + sys.exit(0) # ---------------------------------------------------------------------------- # # Produce graphs # Import additional modules try: + import numpy as np + import pandas as pd + import histogram - import numpy - import pandas except ImportError as err: tools.fatal(f"Unable to plot results: {err}") @@ -214,6 +209,7 @@ def make_command(params): def compute_avg_err(name, *cols, avgs="", errs="-err"): """Compute the average and standard deviation of the selected columns over the given experiment. + Args: name Given experiment name ... Selected column names (through 'histogram.select') @@ -223,25 +219,20 @@ def compute_avg_err(name, *cols, avgs="", errs="-err"): Data frames, each for the computed columns """ # Load all the runs for the given experiment name, and keep only a subset - datas = tuple( - histogram.select( - histogram.Session(args.data_directory / f"{name}-{seed}"), *cols - ) - for seed in seeds - ) + datas = tuple(histogram.select(histogram.Session(args.data_directory / f"{name}-{seed}"), *cols) for seed in seeds) # Make the aggregated data frames def make_df(col): nonlocal datas # For every selected columns subds = tuple(histogram.select(data, col).dropna() for data in datas) - res = pandas.DataFrame(index=subds[0].index) + res = pd.DataFrame(index=subds[0].index) for col in subds[0]: # Generate compound column names avgn = col + avgs errn = col + errs # Compute compound columns - numds = numpy.stack(tuple(subd[col].to_numpy() for subd in subds)) + numds = np.stack(tuple(subd[col].to_numpy() for subd in subds)) res[avgn] = numds.mean(axis=0) res[errn] = numds.std(axis=0) # Return the built data frame @@ -253,47 +244,31 @@ def make_df(col): with tools.Context("plot", "info"): # Plot all the experiments - for ds, dsa in (("svm-phishing", None),): - for md, mda in (("simples-logit", "din:68"),): + for ds, _dsa in (("svm-phishing", None),): + for md, _mda in (("simples-logit", "din:68"),): for epsilon in (None, 0.1, 0.2, 0.5): for batch_size in (10, 25, 50, 100, 250, 500): - legend = list() - results = list() + legend = [] + results = [] # Pre-process results for all available combinations of GAR and attack for gar, attacks in ( ("average", (("nan", None),)), - ( - "brute", - ( - ("little", ("factor:1.5", "negative:True")), - ("empire", "factor:1.1"), - ), - ), + ("brute", (("little", ("factor:1.5", "negative:True")), ("empire", "factor:1.1"))), ): for attack, _ in attacks: name = f"{ds}-{md}-{gar}-{attack}-e_{'inf' if epsilon is None else epsilon}-b_{batch_size}" key = f"{gar_to_legend.get(gar, gar.capitalize())} ({'no attack' if gar == 'average' else attack})" legend.append(key) - results.append( - compute_avg_err(name, "Accuracy", "Average loss") - ) + results.append(compute_avg_err(name, "Accuracy", "Average loss")) # Plot top-1 cross-accuracy plot = histogram.LinePlot() for crossacc, _ in results: plot.include(crossacc, "Accuracy", errs="-err", lalp=0.8) plot.finalize( - None, - "Step number", - "Cross-accuracy", - xmin=0, - xmax=1000, - ymin=0, - ymax=1, - legend=legend, + None, "Step number", "Cross-accuracy", xmin=0, xmax=1000, ymin=0, ymax=1, legend=legend ) plot.save( - args.plot_directory - / f"{ds}-{md}-e_{'inf' if epsilon is None else epsilon}-b_{batch_size}.png", + args.plot_directory / f"{ds}-{md}-e_{'inf' if epsilon is None else epsilon}-b_{batch_size}.png", xsize=3, ysize=1.5, ) @@ -302,14 +277,7 @@ def make_df(col): for _, avgloss in results: plot.include(avgloss, "Average loss", errs="-err", lalp=0.8) plot.finalize( - None, - "Step number", - "Average loss", - xmin=0, - xmax=1000, - ymin=0, - ymax=0.6, - legend=legend, + None, "Step number", "Average loss", xmin=0, xmax=1000, ymin=0, ymax=0.6, legend=legend ) plot.save( args.plot_directory diff --git a/tools/jobs.py b/tools/jobs.py deleted file mode 100644 index 6b47b5e..0000000 --- a/tools/jobs.py +++ /dev/null @@ -1,268 +0,0 @@ -# coding: utf-8 -### -# @file jobs.py -# @author Sébastien Rouault -# -# @section LICENSE -# -# Copyright © 2020-2021 École Polytechnique Fédérale de Lausanne (EPFL). -# See LICENSE file. -# -# @section DESCRIPTION -# -# Simple job management for reproduction scripts. -### - -""" -Experiment job management helpers. - -This module provides utilities for running and managing experiment jobs in a -reproducible manner. - -Classes and Functions ---------------------- - -Job orchestration - ``Command`` encapsulates a command with seed, device, and result-directory - arguments. - ``Jobs`` manages parallel execution of experiments on multiple devices. - -Helpers - ``dict_to_cmdlist`` converts dictionaries into command-line argument lists. - ``move_directory`` moves an existing directory aside with versioning. - -Example -------- - -.. code-block:: python - - from tools import Command, Jobs, dict_to_cmdlist - - cmd = Command(["python", "train.py", "--lr", "0.01"]) - jobs = Jobs("./results", devices=["cuda:0", "cuda:1"]) - jobs.submit("exp1", cmd) - jobs.wait() - jobs.close() -""" - -__all__ = ["dict_to_cmdlist", "Command", "Jobs"] - -import threading -from pathlib import Path - - -# ---------------------------------------------------------------------------- # -# Helpers - - -def move_directory(path: Path) -> Path: - """ - Move an existing directory aside with versioning. - - If a directory already exists at the given path, it is renamed with an - incremental suffix (for example, ``results.0``, ``results.1``) before a - new directory is created. - - Parameters - ---------- - path : pathlib.Path - Directory path to move aside if it already exists. - - Returns - ------- - pathlib.Path - The input path, returned unchanged for chaining. - - Example - ------- - >>> from pathlib import Path - >>> move_directory(Path("results")) - # Moves existing "results" to "results.0" if it exists - """ - # Move directory if it exists - if path.exists(): - if not path.is_dir(): - raise RuntimeError( - f"Expected to find nothing or (a symlink to) a directory at {str(path)!r}" - ) - i = 0 - while True: - mvpath = path.parent / f"{path.name}.{i}" - if not mvpath.exists(): - path.rename(mvpath) - break - i += 1 - # Enable chaining - return path - - -def dict_to_cmdlist(dp: dict) -> list[str]: - """ - Convert a dictionary into command-line arguments. - - This helper is useful for turning experiment configurations into CLI - arguments. - - Parameters - ---------- - dp : dict - Dictionary mapping parameter names to values. - - Returns - ------- - list of str - Command-line arguments such as ``["--lr", "0.01", "--batch", "32"]``. - - Notes - ----- - - Boolean values are included only when they are ``True``. - - Lists and tuples expand to repeated ``--name value`` pairs. - - Example - ------- - >>> dict_to_cmdlist({"lr": 0.01, "batch": 32, "debug": True}) - ['--lr', '0.01', '--batch', '32', '--debug'] - >>> dict_to_cmdlist({"layers": [64, 128]}) - ['--layers', '64', '--layers', '128'] - """ - cmd = list() - for name, value in dp.items(): - if isinstance(value, bool): - if value: - cmd.append(f"--{name}") - elif isinstance(value, (list, tuple)): - for v in value: - cmd.append(f"--{name}") - cmd.append(str(v)) - else: - cmd.append(f"--{name}") - cmd.append(str(value)) - return cmd - - -# ---------------------------------------------------------------------------- # -# Command wrapper - - -class Command: - """ - Command wrapper that adds standard runtime arguments. - - This class wraps a base command and automatically appends seed, device, and - result-directory arguments when executing it. - """ - - def __init__( - self, - base: list[str], - seed: int | None = None, - device: str | None = None, - result_directory: Path | None = None, - ) -> None: - """ - Initialize the command wrapper. - - Parameters - ---------- - base : list of str - Base command as a list of strings. - seed : int, optional - Random seed to add. - device : str, optional - Device to add, for example ``"cuda:0"``. - result_directory : pathlib.Path, optional - Result directory path to add. - """ - self._base = base - self._seed = seed - self._device = device - self._result_directory = result_directory - - def __call__(self) -> list[str]: - """ - Build the full command list with optional runtime arguments. - - Returns - ------- - list of str - Base command extended with ``--seed``, ``--device``, and - ``--result-directory`` when they were provided at initialization. - """ - cmd = list(self._base) - if self._seed is not None: - cmd.extend(["--seed", str(self._seed)]) - if self._device is not None: - cmd.extend(["--device", self._device]) - if self._result_directory is not None: - cmd.extend(["--result-directory", str(self._result_directory)]) - return cmd - - -# ---------------------------------------------------------------------------- # -# Jobs management - - -class Jobs: - """ - Job execution manager for parallel experiments. - - Manages parallel execution of experiments across multiple devices, - with support for result tracking and error handling. - """ - - def __init__( - self, result_directory: Path, devices: list[str] | None = None, devmult: int = 1 - ) -> None: - """ - Initialize jobs manager. - - Parameters - ---------- - result_directory : pathlib.Path - Directory to store results. - devices : list of str, optional - List of device names (e.g., ["cuda:0", "cuda:1"]). - Defaults to CPU if none specified. - devmult : int, optional - Number of parallel jobs per device. Default is 1. - """ - self._result_directory = result_directory - self._devices = devices or ["cpu"] - self._devmult = devmult - self._pending = [] - self._lock = threading.Lock() - - def submit(self, name: str, command: list[str]) -> None: - """ - Submit a job for execution. - - Parameters - ---------- - name : str - Job identifier. - command : list of str - Full command to execute (as returned by ``Command.__call__``). - """ - with self._lock: - self._pending.append((name, command)) - - def wait(self, exit_is_requested: bool | None = None) -> None: - """ - Wait for all pending jobs to complete. - - Parameters - ---------- - exit_is_requested : bool or None, optional - Optional external flag to request early termination. - """ - # Implementation depends on threading - pass - - def close(self) -> None: - """ - Close the jobs manager and release resources. - - Notes - ----- - No-op in the current stub implementation. - """ diff --git a/train.py b/train.py index f9204db..9797f92 100644 --- a/train.py +++ b/train.py @@ -1,4 +1,3 @@ -# coding: utf-8 ### # @file train.py # @author Sébastien Rouault @@ -13,7 +12,7 @@ # Simulate a training session under attack. ### -import tools +from krum import tools tools.success("Module loading...") @@ -24,12 +23,11 @@ import pathlib import signal import sys + import torch import torchvision -import aggregators -import attacks -import experiments +from krum import aggregators, attacks, experiments # ---------------------------------------------------------------------------- # # Miscellaneous initializations @@ -49,67 +47,32 @@ def process_commandline(): """Parse the command-line and perform checks. + Returns: Parsed configuration """ # Description parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter) parser.add_argument( - "--seed", - type=int, - default=-1, - help="Fixed seed to use for reproducibility purpose, negative for random seed", - ) - parser.add_argument( - "--device", - type=str, - default="auto", - help='Device on which to run the experiment, "auto" by default', - ) - parser.add_argument( - "--device-gar", - type=str, - default="same", - help='Device on which to run the GAR, "same" for no change of device', - ) - parser.add_argument( - "--nb-steps", - type=int, - default=300, - help="Number of training steps to do, non-positive for no limit", + "--seed", type=int, default=-1, help="Fixed seed to use for reproducibility purpose, negative for random seed" ) parser.add_argument( - "--nb-workers", type=int, default=11, help="Total number of worker machines" + "--device", type=str, default="auto", help='Device on which to run the experiment, "auto" by default' ) parser.add_argument( - "--nb-decl-byz", - type=int, - default=4, - help="Number of Byzantine worker(s) to support", + "--device-gar", type=str, default="same", help='Device on which to run the GAR, "same" for no change of device' ) parser.add_argument( - "--nb-real-byz", - type=int, - default=0, - help="Number of actual Byzantine worker(s)", + "--nb-steps", type=int, default=300, help="Number of training steps to do, non-positive for no limit" ) + parser.add_argument("--nb-workers", type=int, default=11, help="Total number of worker machines") + parser.add_argument("--nb-decl-byz", type=int, default=4, help="Number of Byzantine worker(s) to support") + parser.add_argument("--nb-real-byz", type=int, default=0, help="Number of actual Byzantine worker(s)") + parser.add_argument("--gar", type=str, default="average", help="(Byzantine-resilient) aggregation rule to use") parser.add_argument( - "--gar", - type=str, - default="average", - help="(Byzantine-resilient) aggregation rule to use", - ) - parser.add_argument( - "--gar-args", - nargs="*", - help="Additional GAR-dependent arguments to pass to the aggregation rule", - ) - parser.add_argument( - "--privacy", - action="store_true", - default=False, - help="Gaussian privacy noise ε constant", + "--gar-args", nargs="*", help="Additional GAR-dependent arguments to pass to the aggregation rule" ) + parser.add_argument("--privacy", action="store_true", default=False, help="Gaussian privacy noise ε constant") parser.add_argument( "--privacy-epsilon", type=float, @@ -129,51 +92,21 @@ def process_commandline(): help="Maximum L2-norm, above which clipping occurs, for the estimated gradients", ) parser.add_argument("--attack", type=str, default="nan", help="Attack to use") - parser.add_argument( - "--attack-args", - nargs="*", - help="Additional attack-dependent arguments to pass to the attack", - ) - parser.add_argument( - "--model", type=str, default="simples-conv", help="Model to train" - ) - parser.add_argument( - "--model-args", - nargs="*", - help="Additional model-dependent arguments to pass to the model", - ) + parser.add_argument("--attack-args", nargs="*", help="Additional attack-dependent arguments to pass to the attack") + parser.add_argument("--model", type=str, default="simples-conv", help="Model to train") + parser.add_argument("--model-args", nargs="*", help="Additional model-dependent arguments to pass to the model") parser.add_argument("--loss", type=str, default="nll", help="Loss to use") + parser.add_argument("--loss-args", nargs="*", help="Additional loss-dependent arguments to pass to the loss") + parser.add_argument("--criterion", type=str, default="top-k", help="Criterion to use") parser.add_argument( - "--loss-args", - nargs="*", - help="Additional loss-dependent arguments to pass to the loss", - ) - parser.add_argument( - "--criterion", type=str, default="top-k", help="Criterion to use" - ) - parser.add_argument( - "--criterion-args", - nargs="*", - help="Additional criterion-dependent arguments to pass to the criterion", + "--criterion-args", nargs="*", help="Additional criterion-dependent arguments to pass to the criterion" ) parser.add_argument("--dataset", type=str, default="mnist", help="Dataset to use") parser.add_argument( - "--dataset-args", - nargs="*", - help="Additional dataset-dependent arguments to pass to the dataset", - ) - parser.add_argument( - "--batch-size", - type=int, - default=25, - help="Batch-size to use for training, 0 for maximum", - ) - parser.add_argument( - "--batch-size-test", - type=int, - default=100, - help="Batch-size to use for testing, 0 for maximum", + "--dataset-args", nargs="*", help="Additional dataset-dependent arguments to pass to the dataset" ) + parser.add_argument("--batch-size", type=int, default=25, help="Batch-size to use for training, 0 for maximum") + parser.add_argument("--batch-size-test", type=int, default=100, help="Batch-size to use for testing, 0 for maximum") parser.add_argument( "--test-repeat", type=int, @@ -186,12 +119,7 @@ def process_commandline(): default=False, help="Whether to disable any dataset tranformation (e.g. random flips)", ) - parser.add_argument( - "--learning-rate", - type=float, - default=0.01, - help="Learning rate to use for training", - ) + parser.add_argument("--learning-rate", type=float, default=0.01, help="Learning rate to use for training") parser.add_argument( "--learning-rate-decay", type=int, @@ -204,26 +132,14 @@ def process_commandline(): default=5000, help="How many steps between two learning rate updates, must be a positive integer", ) + parser.add_argument("--momentum", type=float, default=0.9, help="Momentum to use for training") + parser.add_argument("--dampening", type=float, default=0.0, help="Dampening to use for training") + parser.add_argument("--weight-decay", type=float, default=0, help="Weight decay to use for training") parser.add_argument( - "--momentum", type=float, default=0.9, help="Momentum to use for training" - ) - parser.add_argument( - "--dampening", type=float, default=0.0, help="Dampening to use for training" + "--l1-regularize", type=float, default=None, help="Add L1 regularization of the given factor to the loss" ) parser.add_argument( - "--weight-decay", type=float, default=0, help="Weight decay to use for training" - ) - parser.add_argument( - "--l1-regularize", - type=float, - default=None, - help="Add L1 regularization of the given factor to the loss", - ) - parser.add_argument( - "--l2-regularize", - type=float, - default=None, - help="Add L2 regularization of the given factor to the loss", + "--l2-regularize", type=float, default=None, help="Add L2 regularization of the given factor to the loss" ) parser.add_argument( "--result-directory", @@ -253,7 +169,7 @@ def process_commandline(): for name in ("gar", "attack", "model", "dataset", "loss", "criterion"): name = f"{name}_args" keyval = getattr(args, name) - setattr(args, name, dict() if keyval is None else tools.parse_keyval(keyval)) + setattr(args, name, {} if keyval is None else tools.parse_keyval(keyval)) # Count the number of real honest workers args.nb_honests = args.nb_workers - args.nb_real_byz if args.nb_honests < 0: @@ -266,46 +182,27 @@ def process_commandline(): if args.dampening < 0.0: tools.fatal(f"Invalid arguments: negative dampening factor {args.dampening}") if args.weight_decay < 0.0: - tools.fatal( - f"Invalid arguments: negative weight decay factor {args.weight_decay}" - ) + tools.fatal(f"Invalid arguments: negative weight decay factor {args.weight_decay}") # Check the learning rate and associated options if args.learning_rate <= 0: - tools.fatal( - f"Invalid arguments: non-positive learning rate {args.learning_rate}" - ) + tools.fatal(f"Invalid arguments: non-positive learning rate {args.learning_rate}") if args.learning_rate_decay < 0: - tools.fatal( - f"Invalid arguments: negative learning rate decay {args.learning_rate_decay}" - ) + tools.fatal(f"Invalid arguments: negative learning rate decay {args.learning_rate_decay}") if args.learning_rate_decay_delta <= 0: - tools.fatal( - f"Invalid arguments: non-positive learning rate decay delta {args.learning_rate_decay_delta}" - ) + tools.fatal(f"Invalid arguments: non-positive learning rate decay delta {args.learning_rate_decay_delta}") # Check the privacy-related metrics if args.gradient_clip <= 0.0: - tools.fatal( - f"Invalid arguments: non-positive gradient clip constant {args.gradient_clip}" - ) + tools.fatal(f"Invalid arguments: non-positive gradient clip constant {args.gradient_clip}") if args.privacy: if args.privacy_epsilon <= 0.0 or args.privacy_epsilon >= 1.0: - tools.fatal( - f"Invalid arguments: off-bounds (]0, 1[) ε constant {args.privacy_epsilon}" - ) + tools.fatal(f"Invalid arguments: off-bounds (]0, 1[) ε constant {args.privacy_epsilon}") if args.privacy_delta <= 0.0 or args.privacy_delta >= 1.0: - tools.fatal( - f"Invalid arguments: off-bounds (]0, 1[) δ constant {args.privacy_delta}" - ) + tools.fatal(f"Invalid arguments: off-bounds (]0, 1[) δ constant {args.privacy_delta}") args.privacy_sensitivity = 2 * args.gradient_clip / args.batch_size # Print configuration def cmd_make_tree(subtree, level=0): - if ( - isinstance(subtree, tuple) - and len(subtree) > 0 - and isinstance(subtree[0], tuple) - and len(subtree[0]) == 2 - ): + if isinstance(subtree, tuple) and len(subtree) > 0 and isinstance(subtree[0], tuple) and len(subtree[0]) == 2: label_len = max(len(label) for label, _ in subtree) iterator = subtree elif isinstance(subtree, dict): @@ -321,105 +218,71 @@ def cmd_make_tree(subtree, level=0): res += f"{os.linesep}{level_spc}· {label}{' ' * (label_len - len(label))}{cmd_make_tree(node, level + 1)}" return res - cmdline_config = "Configuration" + cmd_make_tree( + cmdline_config = "Configuration" + cmd_make_tree(( + ("Reproducibility", "not enforced" if args.seed < 0 else f"enforced (seed {args.seed})"), + ("#workers", args.nb_workers), + ("#declared Byz.", args.nb_decl_byz), + ("#actually Byz.", args.nb_real_byz), + ("Model", (("Name", args.model), ("Arguments", args.model_args))), ( + "Dataset", ( - "Reproducibility", - "not enforced" if args.seed < 0 else f"enforced (seed {args.seed})", - ), - ("#workers", args.nb_workers), - ("#declared Byz.", args.nb_decl_byz), - ("#actually Byz.", args.nb_real_byz), - ("Model", (("Name", args.model), ("Arguments", args.model_args))), - ( - "Dataset", + ("Name", args.dataset), + ("Arguments", args.dataset_args), ( - ("Name", args.dataset), - ("Arguments", args.dataset_args), + "Batch size", ( - "Batch size", - ( - ("Training", args.batch_size or "max"), - ( - "Testing", - f"{args.batch_size_test or 'max'} × {args.test_repeat}", - ), - ), + ("Training", args.batch_size or "max"), + ("Testing", f"{args.batch_size_test or 'max'} x {args.test_repeat}"), ), - ("Transforms", "none" if args.no_transform else "default"), ), + ("Transforms", "none" if args.no_transform else "default"), ), + ), + ( + "Loss", ( - "Loss", + ("Name", args.loss), + ("Arguments", args.loss_args), ( - ("Name", args.loss), - ("Arguments", args.loss_args), + "Regularization", ( - "Regularization", - ( - ( - "l1", - "none" - if args.l1_regularize is None - else args.l1_regularize, - ), - ( - "l2", - "none" - if args.l2_regularize is None - else args.l2_regularize, - ), - ), + ("l1", "none" if args.l1_regularize is None else args.l1_regularize), + ("l2", "none" if args.l2_regularize is None else args.l2_regularize), ), ), ), + ), + ("Criterion", (("Name", args.criterion), ("Arguments", args.criterion_args))), + ( + "Optimizer", ( - "Criterion", - (("Name", args.criterion), ("Arguments", args.criterion_args)), - ), - ( - "Optimizer", + ("Name", "sgd"), ( - ("Name", "sgd"), + "Learning rate", ( - "Learning rate", - ( - ("Initial", args.learning_rate), - ( - "Half-decay", - args.learning_rate_decay - if args.learning_rate_decay > 0 - else "none", - ), - ( - "Update delta", - args.learning_rate_decay_delta - if args.learning_rate_decay > 0 - else "n/a", - ), - ), + ("Initial", args.learning_rate), + ("Half-decay", args.learning_rate_decay if args.learning_rate_decay > 0 else "none"), + ("Update delta", args.learning_rate_decay_delta if args.learning_rate_decay > 0 else "n/a"), ), - ("Momentum", args.momentum), - ("Dampening", args.dampening), - ("Weight decay", args.weight_decay), ), + ("Momentum", args.momentum), + ("Dampening", args.dampening), + ("Weight decay", args.weight_decay), ), - ("Attack", (("Name", args.attack), ("Arguments", args.attack_args))), - ("Aggregation", (("Name", args.gar), ("Arguments", args.gar_args))), + ), + ("Attack", (("Name", args.attack), ("Arguments", args.attack_args))), + ("Aggregation", (("Name", args.gar), ("Arguments", args.gar_args))), + ( + "Differential privacy", ( - "Differential privacy", - ( - ("Enabled?", "yes" if args.privacy else "no"), - ("ε constant", args.privacy_epsilon if args.privacy else "n/a"), - ("δ constant", args.privacy_delta if args.privacy else "n/a"), - ( - "l2-sensitivity", - args.privacy_sensitivity if args.privacy else "n/a", - ), - ), + ("Enabled?", "yes" if args.privacy else "no"), + ("ε constant", args.privacy_epsilon if args.privacy else "n/a"), + ("δ constant", args.privacy_delta if args.privacy else "n/a"), + ("l2-sensitivity", args.privacy_sensitivity if args.privacy else "n/a"), ), - ) - ) + ), + )) print(cmdline_config) # ---------------------------------------------------------------------------- # @@ -429,6 +292,7 @@ def cmd_make_tree(subtree, level=0): def result_make(name, *fields): """Make and bind a new result file with a name, initialize with a header line. + Args: name Name of the result file fields... Name of each field, in order @@ -454,6 +318,7 @@ def result_make(name, *fields): def result_get(name): """Get a valid descriptor to the bound result file, or 'None' if the given name is not bound. + Args: name Given name Returns: @@ -465,11 +330,12 @@ def result_get(name): return None # Return the bound descriptor, if any global result_fds - return result_fds.get(name, None) + return result_fds.get(name) def result_store(fd, *entries): """Store a line in a valid result file. + Args: fd Descriptor of the valid result file entries... Object(s) to convert to string and write in order in a new line @@ -483,16 +349,14 @@ def result_store(fd, *entries): reproducible = args.seed >= 0 if reproducible: torch.manual_seed(args.seed) - import numpy + import numpy as np - numpy.random.seed(args.seed) + np.random.seed(args.seed) torch.backends.cudnn.deterministic = reproducible torch.backends.cudnn.benchmark = not reproducible # Configurations config = experiments.Configuration( - dtype=torch.float32, - device=(None if args.device.lower() == "auto" else args.device), - noblock=True, + dtype=torch.float32, device=(None if args.device.lower() == "auto" else args.device), noblock=True ) if args.device_gar.lower() == "same": config_gar = config @@ -550,9 +414,7 @@ def result_store(fd, *entries): if args.privacy: param = model.get() privacy_factor = ( - args.privacy_sensitivity - * math.sqrt(2 * math.log(1.25 / args.privacy_delta)) - / args.privacy_epsilon + args.privacy_sensitivity * math.sqrt(2 * math.log(1.25 / args.privacy_delta)) / args.privacy_epsilon ) grad_noise = torch.distributions.normal.Normal( torch.zeros_like(param), torch.ones_like(param).mul_(privacy_factor) @@ -564,11 +426,9 @@ def result_store(fd, *entries): resdir.mkdir(mode=0o755, parents=True, exist_ok=True) args.result_directory = resdir except Exception as err: - tools.warning( - f"Unable to create the result directory {str(resdir)!r} ({err}); no result will be stored" - ) + tools.warning(f"Unable to create the result directory {str(resdir)!r} ({err}); no result will be stored") else: - result_fds = dict() + result_fds = {} try: # Make evaluation file if args.evaluation_delta > 0: @@ -593,24 +453,21 @@ def result_store(fd, *entries): "Attack-defense cosine", ) # Store the configuration info and JSON representation - (args.result_directory / "config").write_text( - cmdline_config + os.linesep - ) + (args.result_directory / "config").write_text(cmdline_config + os.linesep) with (args.result_directory / "config.json").open("w") as fd: def convert_to_supported_json_type(x): if type(x) in {str, int, float, bool, type(None), dict, list}: return x - elif type(x) is set: + if type(x) is set: return list(x) - else: - return str(x) + return str(x) - datargs = dict( - (name, convert_to_supported_json_type(getattr(args, name))) + datargs = { + name: convert_to_supported_json_type(getattr(args, name)) for name in dir(args) if len(name) > 0 and name[0] != "_" - ) + } del convert_to_supported_json_type json.dump(datargs, fd, ensure_ascii=False, indent="\t") except Exception as err: @@ -625,6 +482,7 @@ def convert_to_supported_json_type(x): def compute_avg_dev(values): """Compute the arithmetic mean and standard deviation of a list of values. + Args: values Iterable of values Returns: @@ -661,12 +519,8 @@ class StopTrainingLoop(Exception): while not exit_is_requested(): # ------------------------------------------------------------------------ # # Evaluate if any milestone is reached - milestone_evaluation = ( - args.evaluation_delta > 0 and steps % args.evaluation_delta == 0 - ) - milestone_user_input = ( - args.user_input_delta > 0 and steps % args.user_input_delta == 0 - ) + milestone_evaluation = args.evaluation_delta > 0 and steps % args.evaluation_delta == 0 + milestone_user_input = args.user_input_delta > 0 and steps % args.user_input_delta == 0 milestone_any = milestone_evaluation or milestone_user_input # Training notification (end) if milestone_any and was_training: @@ -674,7 +528,7 @@ class StopTrainingLoop(Exception): was_training = False # Evaluation milestone reached if milestone_evaluation: - print("Accuracy (step %d)..." % steps, end="", flush=True) + print(f"Accuracy (step {steps})...", end="", flush=True) with atc_evaluate: res = model.eval() for _ in range(args.test_repeat - 1): @@ -701,11 +555,11 @@ class StopTrainingLoop(Exception): was_training = True # ------------------------------------------------------------------------ # # Compute (honest) losses (if it makes sense), gradients and voting data - grad_honests = list() - loss_honests = list() + grad_honests = [] + loss_honests = [] # For each honest worker with atc_gradient: - for i in range(args.nb_honests): + for _i in range(args.nb_honests): grad, loss = model.backprop(outloss=True) grad = grad.clone().detach_() # Loss append @@ -722,18 +576,15 @@ class StopTrainingLoop(Exception): # Compute average loss ('len(loss_honests) > 0' is guaranteed) loss_avg = sum(loss_honests) / len(loss_honests) # Compute the sampled and honest gradients norm average, norm deviation and max absolute coordinate - honest_grad_avg, honest_norm_avg, honest_norm_dev, honest_norm_max = ( - tools.compute_avg_dev_max(grad_honests) + honest_grad_avg, honest_norm_avg, honest_norm_dev, honest_norm_max = tools.compute_avg_dev_max( + grad_honests ) # Move the honest gradients to the GAR device if config_gar is not config: - grad_honests_gar = list( - grad.to( - device=config_gar["device"], - non_blocking=config_gar["non_blocking"], - ) + grad_honests_gar = [ + grad.to(device=config_gar["device"], non_blocking=config_gar["non_blocking"]) for grad in grad_honests - ) + ] else: grad_honests_gar = grad_honests # ------------------------------------------------------------------------ # @@ -756,28 +607,18 @@ class StopTrainingLoop(Exception): # Aggregate and update the model with atc_aggregate: grad_defense = defense.checked( - gradients=(grad_honests_gar + grad_attacks), - f=args.nb_decl_byz, - model=model, - **args.gar_args, + gradients=(grad_honests_gar + grad_attacks), f=args.nb_decl_byz, model=model, **args.gar_args ) # Move the defense gradient back to the main device if config_gar is not config: for grad in grad_attacks: - grad.data = grad.to( - device=config["device"], non_blocking=config["non_blocking"] - ) - grad_defense = grad_defense.to( - device=config["device"], non_blocking=config["non_blocking"] - ) + grad.data = grad.to(device=config["device"], non_blocking=config["non_blocking"]) + grad_defense = grad_defense.to(device=config["device"], non_blocking=config["non_blocking"]) # Compute l2-distance from origin (if needed for study) if fd_study is not None: l2_origin = model.get().sub(params_origin).norm().item() # Model update (possibly updating the learning rate) - if ( - args.learning_rate_decay > 0 - and steps % args.learning_rate_decay_delta == 0 - ): + if args.learning_rate_decay > 0 and steps % args.learning_rate_decay_delta == 0: current_lr = args.learning_rate / (steps / args.learning_rate_decay + 1) optimizer.set_lr(current_lr) model.update(grad_defense) @@ -785,8 +626,8 @@ class StopTrainingLoop(Exception): # Store study (if requested) if fd_study is not None: # Compute the sampled and honest gradients norm average, norm deviation and max absolute coordinate - attack_grad_avg, attack_norm_avg, attack_norm_dev, attack_norm_max = ( - tools.compute_avg_dev_max(grad_attacks) + attack_grad_avg, attack_norm_avg, attack_norm_dev, attack_norm_max = tools.compute_avg_dev_max( + grad_attacks ) # Compute the defense norm average and max absolute coordinate defense_grad = grad_defense # (Mere renaming for consistency) @@ -796,31 +637,20 @@ class StopTrainingLoop(Exception): cosin_honatt = ( math.nan if attack_grad_avg is None - else torch.dot(honest_grad_avg, attack_grad_avg) - .div_(honest_norm_avg) - .div_(attack_norm_avg) - .item() + else torch.dot(honest_grad_avg, attack_grad_avg).div_(honest_norm_avg).div_(attack_norm_avg).item() # type: ignore[arg-type] ) cosin_hondef = ( - torch.dot(honest_grad_avg, defense_grad) - .div_(honest_norm_avg) - .div_(defense_norm_avg) - .item() + torch.dot(honest_grad_avg, defense_grad).div_(honest_norm_avg).div_(defense_norm_avg).item() # type: ignore[arg-type] ) cosin_attdef = ( math.nan if attack_grad_avg is None - else torch.dot(attack_grad_avg, defense_grad) - .div_(attack_norm_avg) - .div_(defense_norm_avg) - .item() + else torch.dot(attack_grad_avg, defense_grad).div_(attack_norm_avg).div_(defense_norm_avg).item() # type: ignore[arg-type] ) # Store the result (float-to-string format chosen so not to lose precision) - float_format = { - torch.float16: "%.4e", - torch.float32: "%.8e", - torch.float64: "%.16e", - }.get(config["dtype"], "%s") + float_format = {torch.float16: "%.4e", torch.float32: "%.8e", torch.float64: "%.16e"}.get( + config["dtype"], "%s" + ) result_store( fd_study, steps, @@ -851,7 +681,7 @@ class StopTrainingLoop(Exception): # Print and store timing counters with tools.Context("perf", "info"): - perfs = dict() + perfs = {} perf_params = ( (atc_gradient, "grad", "Gradient computation (per worker)", args.nb_honests), (atc_noise, "noise", "Noise addition (per worker)", args.nb_honests), diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..31ca74a --- /dev/null +++ b/uv.lock @@ -0,0 +1,2428 @@ +version = 1 +revision = 3 +requires-python = ">=3.10, <3.15" +resolution-markers = [ + "python_full_version >= 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