Note
Click here to download the full example code
Benchmark, comparison scikit-learn - forward-backward#
The benchmark compares the processing time between scikit-learn and onnxruntime-training on a linear regression and a neural network. It replicates the benchmark implemented in Benchmark, comparison scikit-learn - onnxruntime-training but uses the forward backward approach developped in Train a linear regression with forward backward.
First comparison: neural network#
import warnings
import time
import numpy
import matplotlib.pyplot as plt
from pandas import DataFrame
from onnxruntime import get_device
from pyquickhelper.pycode.profiling import profile, profile2graph
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from mlprodict.onnx_conv import to_onnx
from onnxcustom.utils.onnx_helper import onnx_rename_weights
from onnxcustom.training.optimizers_partial import (
OrtGradientForwardBackwardOptimizer)
X, y = make_regression(1000, n_features=100, bias=2)
X = X.astype(numpy.float32)
y = y.astype(numpy.float32)
X_train, X_test, y_train, y_test = train_test_split(X, y)
Benchmark function.
def benchmark(X, y, skl_model, train_session, name, verbose=True):
"""
:param skl_model: model from scikit-learn
:param train_session: instance of OrtGradientForwardBackwardOptimizer
:param name: experiment name
:param verbose: to debug
"""
print(f"[benchmark] {name}")
begin = time.perf_counter()
skl_model.fit(X, y)
duration_skl = time.perf_counter() - begin
length_skl = len(skl_model.loss_curve_)
print(f"[benchmark] skl={length_skl!r} iterations - {duration_skl!r} seconds")
begin = time.perf_counter()
train_session.fit(X, y)
duration_ort = time.perf_counter() - begin
length_ort = len(train_session.train_losses_)
print(f"[benchmark] ort={length_ort!r} iteration - {duration_ort!r} seconds")
return dict(skl=duration_skl, ort=duration_ort, name=name,
iter_skl=length_skl, iter_ort=length_ort,
losses_skl=skl_model.loss_curve_,
losses_ort=train_session.train_losses_)
Common parameters and model
batch_size = 15
max_iter = 100
nn = MLPRegressor(hidden_layer_sizes=(50, 10), max_iter=max_iter,
solver='sgd', learning_rate_init=5e-4, alpha=0,
n_iter_no_change=max_iter * 3, batch_size=batch_size,
nesterovs_momentum=False, momentum=0,
learning_rate="invscaling")
with warnings.catch_warnings():
warnings.simplefilter('ignore')
nn.fit(X_train, y_train)
Conversion to ONNX and trainer initialization
onx = to_onnx(nn, X_train[:1].astype(numpy.float32), target_opset=15)
onx = onnx_rename_weights(onx)
train_session = OrtGradientForwardBackwardOptimizer(
onx, device='cpu', learning_rate=1e-5,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches = [benchmark(X_train, y_train, nn, train_session, name='NN-CPU')]
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 7.790165482991142 seconds
[benchmark] ort=100 iteration - 10.420564076019218 seconds
Profiling#
def clean_name(text):
pos = text.find('onnxruntime')
if pos >= 0:
return text[pos:]
pos = text.find('sklearn')
if pos >= 0:
return text[pos:]
pos = text.find('onnxcustom')
if pos >= 0:
return text[pos:]
pos = text.find('site-packages')
if pos >= 0:
return text[pos:]
return text
ps = profile(lambda: benchmark(X_train, y_train,
nn, train_session, name='NN-CPU'))[0]
root, nodes = profile2graph(ps, clean_text=clean_name)
text = root.to_text()
print(text)
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 8.747815678012557 seconds
[benchmark] ort=100 iteration - 12.0197639449907 seconds
filter -- 18 18 -- 0.00007 0.00023 -- /usr/local/lib/python3.9/logging/__init__.py:787:filter (filter)
filter -- 12 12 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:351:filter (filter)
filter -- 6 6 -- 0.00010 0.00012 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:484:filter (filter)
<built-in method builtins.isinstance> -- 18 18 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<built-in method builtins.hasattr> -- 18 18 -- 0.00002 0.00002 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
acquire -- 30 30 -- 0.00007 0.00011 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire)
<method 'acquire' of '_thread.RLock' objects> -- 30 30 -- 0.00004 0.00004 -- ~:0:<method 'acquire' of '_thread.RLock' objects> (<method 'acquire' of '_thread.RLock' objects>)
release -- 30 30 -- 0.00006 0.00007 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release)
<method 'release' of '_thread.RLock' objects> -- 30 30 -- 0.00001 0.00001 -- ~:0:<method 'release' of '_thread.RLock' objects> (<method 'release' of '_thread.RLock' objects>)
emit -- 12 12 -- 0.00009 0.00105 -- /usr/local/lib/python3.9/logging/__init__.py:1067:emit (emit)
format -- 12 12 -- 0.00004 0.00055 -- /usr/local/lib/python3.9/logging/__init__.py:912:format (format)
format -- 12 12 -- 0.00009 0.00051 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:531:format (format)
format -- 12 12 -- 0.00008 0.00038 -- /usr/local/lib/python3.9/logging/__init__.py:646:format (format)
usesTime -- 12 12 -- 0.00002 0.00008 -- /usr/local/lib/python3.9/logging/__init__.py:624:usesTime (usesTime)
usesTime -- 12 12 -- 0.00004 0.00006 -- /usr/local/lib/python3.9/logging/__init__.py:417:usesTime (usesTime)
<method 'find' of 'str' objects> -- 12 12 -- 0.00002 0.00002 -- ~:0:<method 'find' of 'str' objects> (<method 'find' of 'str' objects>)
formatMessage -- 12 12 -- 0.00002 0.00009 -- /usr/local/lib/python3.9/logging/__init__.py:630:formatMessage (formatMessage)
format -- 12 12 -- 0.00002 0.00007 -- /usr/local/lib/python3.9/logging/__init__.py:428:format (format)
_format -- 12 12 -- 0.00005 0.00005 -- /usr/local/lib/python3.9/logging/__init__.py:425:_format (_format)
getMessage -- 12 12 -- 0.00006 0.00013 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:89:getMessage (getMessage)
getMessage -- 12 12 -- 0.00006 0.00006 -- /usr/local/lib/python3.9/logging/__init__.py:354:getMessage (getMessage)
<built-in method builtins.getattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
colorize -- 2 2 -- 0.00001 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/console.py:72:colorize (colorize)
escseq -- 4 4 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/console.py:73:escseq (escseq)
<built-in method builtins.getattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
flush -- 12 12 -- 0.00006 0.00035 -- /usr/local/lib/python3.9/logging/__init__.py:1056:flush (flush)
acquire -- 12 12 -- 0.00002 0.00003 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire) +++
release -- 12 12 -- 0.00003 0.00003 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release) +++
flush -- 6 6 -- 0.00002 0.00021 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:557:flush (flush)
<method 'flush' of '_io.TextIOWrapper' objects> -- 6 6 -- 0.00018 0.00018 -- ~:0:<method 'flush' of '_io.TextIOWrapper' objects> (<method 'flush' of '_io.TextIOWrapper' objects>)
<built-in method builtins.hasattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
write -- 6 6 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:549:write (write)
write -- 6 6 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:567:write (write)
isEnabledFor -- 12 12 -- 0.00002 0.00002 -- /usr/local/lib/python3.9/logging/__init__.py:1677:isEnabledFor (isEnabledFor)
inner -- 6 6 -- 0.00002 0.00002 -- /usr/local/lib/python3.9/typing.py:256:inner (inner)
cast -- 6 6 -- 0.00000 0.00000 -- /usr/local/lib/python3.9/typing.py:1326:cast (cast)
__init__ -- 2 2 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/warnings.py:403:__init__ (__init__)
<lambda> -- 1 1 -- 0.00001 20.76979 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark_fwbw.py:122:<lambda> (<lambda>)
benchmark -- 1 1 -- 0.00011 20.76978 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark_fwbw.py:46:benchmark (benchmark)
fit -- 1 1 -- 0.00542 12.01973 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:288:fit (fit)
__init__ -- 1 1 -- 0.00004 0.00008 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:31:__init__ (__init__)
get_ort_device -- 1 1 -- 0.00000 0.00000 -- onnxruntime_helper.py:63:get_ort_device (get_ort_device)
numpy_to_ort_value -- 2 2 -- 0.00000 0.00003 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value) +++
needs_grad -- 3 3 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:104:needs_grad (needs_grad)
needs_grad -- 3 3 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:194:needs_grad (needs_grad)
get_full_state -- 101 101 -- 0.00074 0.00212 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:155:get_full_state (get_full_state) +++
set_state -- 2 2 -- 0.00012 0.00036 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:216:set_state (set_state)
_get_att_state -- 2 2 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:147:_get_att_state (_get_att_state) +++
numpy_to_ort_value -- 12 12 -- 0.00004 0.00019 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value) +++
<method 'append' of 'list' objects> -- 28 28 -- 0.00001 0.00001 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.isinstance> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<listcomp> -- 1 1 -- 0.00005 0.00253 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:339:<listcomp> (<listcomp>)
get_initializer -- 7 7 -- 0.00018 0.00248 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:278:get_initializer (get_initializer)
to_array -- 6 6 -- 0.00016 0.00231 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/onnx/numpy_helper.py:22:to_array (to_array)
uses_external_data -- 6 6 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/onnx/external_data_helper.py:243:uses_external_data (uses_external_data)
<method 'HasField...essage' objects> -- 12 12 -- 0.00003 0.00003 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>) +++
<built-in method numpy.asarray> -- 6 6 -- 0.00193 0.00193 -- ~:0:<built-in method numpy.asarray> (<built-in method numpy.asarray>) +++
_iteration -- 100 100 -- 1.37064 11.96793 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:424:_iteration (_iteration)
iter_ortvalue -- 5100 5100 -- 0.10846 0.60348 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:147:iter_ortvalue (iter_ortvalue)
_next_iter -- 5000 5000 -- 0.03258 0.32072 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:98:_next_iter (_next_iter)
<method 'randint'...mState' objects> -- 5000 5000 -- 0.27692 0.27692 -- ~:0:<method 'randint' of 'numpy.random.mtrand.RandomState' objects> (<method 'randint' of 'numpy.random.mtrand.RandomState' objects>)
<built-in method builtins.len> -- 5000 5000 -- 0.00701 0.01122 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
numpy_to_ort_value -- 10000 10000 -- 0.02661 0.14375 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value) +++
<built-in method builtins.len> -- 5200 5200 -- 0.01564 0.03055 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
forward -- 5000 5000 -- 1.05582 1.38071 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:662:forward (forward)
input_to_ort -- 5000 5000 -- 0.19456 0.27563 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:585:input_to_ort (input_to_ort) +++
save_for_backward -- 5000 5000 -- 0.04008 0.04008 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:637:save_for_backward (save_for_backward)
<method 'append' of 'list' objects> -- 5000 5000 -- 0.00917 0.00917 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
backward -- 5000 5000 -- 1.46262 1.63052 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:740:backward (backward)
input_to_ort -- 5000 5000 -- 0.11651 0.15352 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:585:input_to_ort (input_to_ort) +++
saved_tensors -- 5000 5000 -- 0.00559 0.00559 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:649:saved_tensors (saved_tensors)
<method 'pop' of 'list' objects> -- 5000 5000 -- 0.00879 0.00879 -- ~:0:<method 'pop' of 'list' objects> (<method 'pop' of 'list' objects>)
loss_gradient -- 5000 5000 -- 1.59570 2.27523 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_loss.py:61:loss_gradient (loss_gradient)
clear_binding_inputs -- 5000 5000 -- 0.01725 0.04092 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:145:clear_binding_inputs (clear_binding_inputs)
_cache_in_clear -- 5000 5000 -- 0.01702 0.02366 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:134:_cache_in_clear (_cache_in_clear)
<built-in method builtins.id> -- 5000 5000 -- 0.00664 0.00664 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>) +++
_bind_input_ortvalue -- 10000 10000 -- 0.06674 0.28929 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:177:_bind_input_ortvalue (_bind_input_ortvalue) +++
_call_iobinding -- 5000 5000 -- 0.34012 0.34012 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_loss.py:58:_call_iobinding (_call_iobinding)
<built-in method builtins.hasattr> -- 10000 10000 -- 0.00921 0.00921 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
penalty_loss -- 5000 5000 -- 0.00331 0.00331 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_penalty.py:101:penalty_loss (penalty_loss)
update_weights -- 30000 30000 -- 0.01706 0.01706 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_penalty.py:115:update_weights (update_weights)
update_weights -- 30000 30000 -- 1.27114 4.61854 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:258:update_weights (update_weights)
_bind_input_ortvalue -- 90000 90000 -- 0.39182 1.21091 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:177:_bind_input_ortvalue (_bind_input_ortvalue) +++
_bind_output_ortvalue -- 30000 30000 -- 0.16603 0.43053 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:221:_bind_output_ortvalue (_bind_output_ortvalue)
_bio_cache -- 30000 30000 -- 0.08816 0.10822 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:156:_bio_cache (_bio_cache) +++
_bio_ptr -- 30000 30000 -- 0.14210 0.14210 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:173:_bio_ptr (_bio_ptr) +++
<built-in method ...tins.isinstance> -- 30000 30000 -- 0.01419 0.01419 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_call_iobinding -- 30000 30000 -- 1.24907 1.24907 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:33:_call_iobinding (_call_iobinding)
value -- 30000 30000 -- 0.03108 0.03108 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:186:value (value) +++
<built-in method on...tvalue_from_numpy> -- 30000 30000 -- 0.38493 0.38493 -- ~:0:<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy> (<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy>) +++
<built-in method builtins.hasattr> -- 60000 60000 -- 0.04086 0.04086 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
<method 'mean' of 'numpy.ndarray' objects> -- 100 100 -- 0.00064 0.01249 -- ~:0:<method 'mean' of 'numpy.ndarray' objects> (<method 'mean' of 'numpy.ndarray' objects>) +++
<built-in method numpy.array> -- 100 100 -- 0.00743 0.00743 -- ~:0:<built-in method numpy.array> (<built-in method numpy.array>) +++
<method 'append' of 'list' objects> -- 5000 5000 -- 0.00541 0.00541 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.len> -- 30100 30100 -- 0.04313 0.04313 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_create_training_session -- 1 1 -- 0.00003 0.03695 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:659:_create_training_session (_create_training_session)
__init__ -- 1 1 -- 0.00017 0.03687 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:59:__init__ (__init__)
<listcomp> -- 1 1 -- 0.00003 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:96:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:99:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:117:<listcomp> (<listcomp>)
_init_next -- 1 1 -- 0.00018 0.03664 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:169:_init_next (_init_next)
<listcomp> -- 1 1 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:179:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:181:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:184:<listcomp> (<listcomp>)
_create_onnx_graphs -- 1 1 -- 0.00786 0.03642 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:299:_create_onnx_graphs (_create_onnx_graphs)
<listcomp> -- 1 1 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:411:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:412:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00003 0.00004 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:414:<listcomp> (<listcomp>)
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<listcomp> -- 1 1 -- 0.00008 0.00010 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:419:<listcomp> (<listcomp>)
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copyto -- 6 6 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/multiarray.py:1071:copyto (copyto)
unique -- 101 101 -- 0.00093 0.00872 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:138:unique (unique)
_unpack_tuple -- 101 101 -- 0.00022 0.00029 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:125:_unpack_tuple (_unpack_tuple)
<built-in method builtins.len> -- 101 101 -- 0.00006 0.00006 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_unique1d -- 101 101 -- 0.00511 0.00659 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:323:_unique1d (_unique1d)
<method 'flatten' of 'numpy.ndarray' objects> -- 101 101 -- 0.00074 0.00074 -- ~:0:<method 'flatten' of 'numpy.ndarray' objects> (<method 'flatten' of 'numpy.ndarray' objects>)
<method 'sort' of 'numpy.ndarray' objects> -- 101 101 -- 0.00015 0.00015 -- ~:0:<method 'sort' of 'numpy.ndarray' objects> (<method 'sort' of 'numpy.ndarray' objects>)
<built-in method numpy.asanyarray> -- 101 101 -- 0.00004 0.00004 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<built-in method numpy.empty> -- 101 101 -- 0.00056 0.00056 -- ~:0:<built-in method numpy.empty> (<built-in method numpy.empty>) +++
<built-in method numpy.asanyarray> -- 101 101 -- 0.00091 0.00091 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<built-in method onnxruntime....1_state.ortvalue_from_numpy> -- 40014 40014 -- 0.50225 0.50225 -- ~:0:<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy> (<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy>)
<built-in method builtins.id> -- 135001 135001 -- 0.10006 0.10006 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>)
<method 'get' of 'dict' objects> -- 33 33 -- 0.00002 0.00002 -- ~:0:<method 'get' of 'dict' objects> (<method 'get' of 'dict' objects>)
<method 'HasField' of 'google...._message.CMessage' objects> -- 18 18 -- 0.00005 0.00005 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>)
<built-in method numpy.asarray> -- 11 11 -- 0.00195 0.00195 -- ~:0:<built-in method numpy.asarray> (<built-in method numpy.asarray>)
<built-in method numpy.asanyarray> -- 20304 20304 -- 0.01419 0.01419 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>)
<method 'reduce' of 'numpy.ufunc' objects> -- 20109 20109 -- 0.44313 0.44313 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>)
<built-in method builtins.issubclass> -- 40206 40206 -- 0.02915 0.02915 -- ~:0:<built-in method builtins.issubclass> (<built-in method builtins.issubclass>)
<built-in method posix.fspath> -- 24 24 -- 0.00001 0.00001 -- ~:0:<built-in method posix.fspath> (<built-in method posix.fspath>)
<built-in method _thread.get_ident> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method _thread.get_ident> (<built-in method _thread.get_ident>)
<method 'rfind' of 'str' objects> -- 18 18 -- 0.00003 0.00003 -- ~:0:<method 'rfind' of 'str' objects> (<method 'rfind' of 'str' objects>)
if GPU is available#
if get_device().upper() == 'GPU':
train_session = OrtGradientForwardBackwardOptimizer(
onx, device='cuda', learning_rate=1e-5,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(X_train, y_train, nn,
train_session, name='NN-GPU'))
Linear Regression#
lr = MLPRegressor(hidden_layer_sizes=tuple(), max_iter=max_iter,
solver='sgd', learning_rate_init=5e-2, alpha=0,
n_iter_no_change=max_iter * 3, batch_size=batch_size,
nesterovs_momentum=False, momentum=0,
learning_rate="invscaling")
with warnings.catch_warnings():
warnings.simplefilter('ignore')
lr.fit(X, y)
onx = to_onnx(lr, X_train[:1].astype(numpy.float32), target_opset=15)
train_session = OrtGradientForwardBackwardOptimizer(
onx, device='cpu', learning_rate=5e-4,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(X_train, y_train, lr, train_session, name='LR-CPU'))
if get_device().upper() == 'GPU':
train_session = OrtGradientForwardBackwardOptimizer(
onx, device='cuda', learning_rate=5e-4,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(X_train, y_train, nn,
train_session, name='LR-GPU'))
[benchmark] LR-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 2.7954707790049724 seconds
[benchmark] ort=100 iteration - 5.851402219006559 seconds
GPU profiling#
if get_device().upper() == 'GPU':
ps = profile(lambda: benchmark(X_train, y_train,
lr, train_session, name='LR-GPU'))[0]
root, nodes = profile2graph(ps, clean_text=clean_name)
text = root.to_text()
print(text)
Graphs#
Dataframe first.
df = DataFrame(benches).set_index('name')
df
text output
print(df)
skl ... losses_ort
name ...
NN-CPU 7.790165 ... [20040.064, 14734.923, 14281.07, 13588.74, 128...
LR-CPU 2.795471 ... [13949.843, 13848.307, 13304.381, 13053.381, 1...
[2 rows x 6 columns]
Graphs.
fig, ax = plt.subplots(1, 2, figsize=(10, 4))
df[['skl', 'ort']].plot.bar(title="Processing time", ax=ax[0])
ax[0].tick_params(axis='x', rotation=30)
for bench in benches:
ax[1].plot(bench['losses_skl'][1:], label='skl-' + bench['name'])
ax[1].plot(bench['losses_ort'][1:], label='ort-' + bench['name'])
ax[1].set_yscale('log')
ax[1].set_title("Losses")
ax[1].legend()
<matplotlib.legend.Legend object at 0x7f669c2dd250>
The gradient update are not exactly the same. It should be improved for a fair comprison.
fig.savefig("plot_orttraining_benchmark_fwbw.png")
# plt.show()
Total running time of the script: ( 1 minutes 1.204 seconds)