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style, unit test
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‎_doc/conf.py‎

Lines changed: 37 additions & 47 deletions
Original file line numberDiff line numberDiff line change
@@ -1,51 +1,41 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import sys
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sys.path.insert(
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0,
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os.path.abspath(
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os.path.join(
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os.path.dirname(__file__),
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'..')))
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from td3a_cpp_deep import __version__ # noqa
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4+
from onnx_array_api import __version__
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.intersphinx',
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'sphinx.ext.todo',
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'sphinx.ext.coverage',
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'sphinx.ext.mathjax',
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'sphinx.ext.ifconfig',
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'sphinx.ext.viewcode',
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'sphinx.ext.githubpages',
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'sphinx_gallery.gen_gallery',
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'alabaster',
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'matplotlib.sphinxext.plot_directive',
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'pyquickhelper.sphinxext.sphinx_runpython_extension',
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'pyquickhelper.sphinxext.sphinx_epkg_extension',
7+
"sphinx.ext.autodoc",
8+
"sphinx.ext.intersphinx",
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"sphinx.ext.todo",
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"sphinx.ext.coverage",
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"sphinx.ext.mathjax",
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"sphinx.ext.ifconfig",
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"sphinx.ext.viewcode",
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"sphinx.ext.githubpages",
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"sphinx_gallery.gen_gallery",
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"matplotlib.sphinxext.plot_directive",
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"pyquickhelper.sphinxext.sphinx_runpython_extension",
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"pyquickhelper.sphinxext.sphinx_epkg_extension",
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]
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30-
templates_path = ['_templates']
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html_logo = '_static/logo.png'
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source_suffix = '.rst'
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master_doc = 'index'
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project = 'onnx-array-api'
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copyright = '2023, Xavier Dupré'
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author = 'Xavier Dupré'
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templates_path = ["_templates"]
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html_logo = "_static/logo.png"
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source_suffix = ".rst"
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master_doc = "index"
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project = "onnx-array-api"
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copyright = "2023, Xavier Dupré"
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author = "Xavier Dupré"
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version = __version__
3829
release = __version__
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language = 'en'
30+
language = "en"
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exclude_patterns = []
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pygments_style = 'sphinx'
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pygments_style = "sphinx"
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todo_include_todos = True
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4435
html_theme = "furo"
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html_theme_path = [alabaster.get_path()]
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36+
html_theme_path = ["_static"]
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html_theme_options = {}
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html_static_path = ['_static']
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html_static_path = ["_static"]
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intersphinx_mapping = {
@@ -60,20 +50,20 @@
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sphinx_gallery_conf = {
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# path to your examples scripts
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'examples_dirs': os.path.join(os.path.dirname(__file__), 'examples'),
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"examples_dirs": os.path.join(os.path.dirname(__file__), "examples"),
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# path where to save gallery generated examples
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'gallery_dirs': 'auto_examples'
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"gallery_dirs": "auto_examples",
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}
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epkg_dictionary = {
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'JIT': 'https://en.wikipedia.org/wiki/Just-in-time_compilation',
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'onnx': 'https://onnx.ai/onnx/',
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'ONNX': 'https://onnx.ai/',
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'onnxruntime': 'https://onnxruntime.ai/',
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'numpy': 'https://numpy.org/',
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'numba': 'https://numba.pydata.org/',
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'python': 'https://www.python.org/',
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'scikit-learn': 'https://scikit-learn.org/stable/',
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'sphinx-gallery': 'https://github.com/sphinx-gallery/sphinx-gallery',
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'torch': 'https://pytorch.org/docs/stable/torch.html',
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"JIT": "https://en.wikipedia.org/wiki/Just-in-time_compilation",
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"onnx": "https://onnx.ai/onnx/",
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"ONNX": "https://onnx.ai/",
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"onnxruntime": "https://onnxruntime.ai/",
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"numpy": "https://numpy.org/",
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"numba": "https://numba.pydata.org/",
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"python": "https://www.python.org/",
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"scikit-learn": "https://scikit-learn.org/stable/",
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"sphinx-gallery": "https://github.com/sphinx-gallery/sphinx-gallery",
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"torch": "https://pytorch.org/docs/stable/torch.html",
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}

‎_doc/examples/plot_piecewise_linear.py‎

Lines changed: 4 additions & 107 deletions
Original file line numberDiff line numberDiff line change
@@ -1,118 +1,15 @@
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"""
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3-
.. _l-example-dot-profile:
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.. _l-onnx-array-api-example:
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55
Compares implementations for a Piecewise Linear
66
===============================================
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A pieceise linear function is implemented and trained
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following the tutorial :epkg:`Custom C++ and Cuda Extensions`.
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First example.
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.. contents::
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:local:
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Piecewise linear regression
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+++++++++++++++++++++++++++
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One function
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++++++++++++
1615
"""
17-
import time
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import pandas
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import matplotlib.pyplot as plt
20-
import torch
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from td3a_cpp_deep.fcts.piecewise_linear import (
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PiecewiseLinearFunction,
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PiecewiseLinearFunctionC,
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PiecewiseLinearFunctionCBetter)
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def train_piecewise_linear(x, y, device, cls,
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max_iter=400, learning_rate=1e-4):
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30-
alpha_pos = torch.tensor([1], dtype=torch.float32).to(device)
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alpha_neg = torch.tensor([0.5], dtype=torch.float32).to(device)
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alpha_pos.requires_grad_()
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alpha_neg.requires_grad_()
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losses = []
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fct = cls.apply
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for t in range(max_iter):
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y_pred = fct(x, alpha_neg, alpha_pos)
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loss = (y_pred - y).pow(2).sum()
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loss.backward()
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losses.append(loss)
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45-
with torch.no_grad():
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alpha_pos -= learning_rate * alpha_pos.grad
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alpha_neg -= learning_rate * alpha_neg.grad
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# Manually zero the gradients after updating weights
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alpha_pos.grad.zero_()
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alpha_neg.grad.zero_()
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return losses, alpha_neg, alpha_pos
54-
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################################
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# Python implementation
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# +++++++++++++++++++++
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60-
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
61-
print("device:", device)
62-
x = torch.randn(100, 1, dtype=torch.float32)
63-
y = x * 0.2 + (x > 0).to(torch.float32) * x * 1.5 + torch.randn(100, 1) / 5
64-
x = x.to(device).requires_grad_()
65-
y = y.to(device).requires_grad_()
66-
67-
begin = time.perf_counter()
68-
losses, alpha_neg, alpha_pos = train_piecewise_linear(
69-
x, y, device, PiecewiseLinearFunction)
70-
end = time.perf_counter()
71-
print(f"duration={end - begin}, alpha_neg={alpha_neg} "
72-
f"alpha_pos={alpha_pos}")
73-
74-
################################
75-
# C++ implementation
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# ++++++++++++++++++
77-
78-
begin = time.perf_counter()
79-
losses, alpha_neg, alpha_pos = train_piecewise_linear(
80-
x, y, device, PiecewiseLinearFunctionC)
81-
end = time.perf_counter()
82-
print(f"duration={end - begin}, alpha_neg={alpha_neg} "
83-
f"alpha_pos={alpha_pos}")
84-
85-
################################
86-
# C++ implementation, second try
87-
# ++++++++++++++++++++++++++++++
88-
89-
begin = time.perf_counter()
90-
losses, alpha_neg, alpha_pos = train_piecewise_linear(
91-
x, y, device, PiecewiseLinearFunctionCBetter)
92-
end = time.perf_counter()
93-
print(f"duration={end - begin}, alpha_neg={alpha_neg} "
94-
f"alpha_pos={alpha_pos}")
95-
96-
#################################
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# The C++ implementation is very close to the python code.
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# The second implementation in C++ is faster because
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# it reuses created tensors.
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##################################
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# Graphs
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# ++++++
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105-
df = pandas.DataFrame()
106-
df['x'] = x.cpu().detach().numpy().ravel()
107-
df['y'] = y.cpu().detach().numpy().ravel()
108-
df['yp'] = PiecewiseLinearFunction.apply(
109-
x, alpha_neg, alpha_pos).cpu().detach().numpy()
110-
111-
fig, ax = plt.subplots(1, 2, figsize=(10, 4))
112-
df.plot.scatter(x="x", y='y', label="y", color="blue", ax=ax[0])
113-
df.plot.scatter(x="x", y='yp', ax=ax[0], label="yp", color="orange")
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ax[1].plot([float(lo.detach()) for lo in losses], label="loss")
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ax[1].legend()
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# plt.show()

‎_doc/tutorial/overview.rst‎

Lines changed: 1 addition & 1 deletion
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@@ -106,7 +106,7 @@ is available with class `OrtTensor
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from onnxruntime.capi._pybind_state import OrtDevice as C_OrtDevice
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from onnxruntime.capi._pybind_state import OrtMemType
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from onnxruntime.capi._pybind_state import (
109-
OrtValue as C_OrtValue, # pylint: disable=E0611
109+
OrtValue as C_OrtValue,
110110
)
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
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