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("[benchmark] %s" % name)
begin = time.perf_counter()
skl_model.fit(X, y)
duration_skl = time.perf_counter() - begin
length_skl = len(skl_model.loss_curve_)
print("[benchmark] skl=%r iterations - %r seconds" % (
length_skl, duration_skl))
begin = time.perf_counter()
train_session.fit(X, y)
duration_ort = time.perf_counter() - begin
length_ort = len(train_session.train_losses_)
print("[benchmark] ort=%r iteration - %r seconds" % (
length_ort, duration_ort))
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')]
Out:
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 10.036501206457615 seconds
[benchmark] ort=100 iteration - 10.246101723983884 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)
Out:
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 11.541221112012863 seconds
[benchmark] ort=100 iteration - 12.097604973241687 seconds
filter -- 18 18 -- 0.00008 0.00020 -- /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:350:filter (filter)
filter -- 6 6 -- 0.00006 0.00008 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:483: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 0.00012 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire)
<method 'acquire' of '_thread.RLock' objects> -- 30 30 -- 0.00005 0.00005 -- ~:0:<method 'acquire' of '_thread.RLock' objects> (<method 'acquire' of '_thread.RLock' objects>)
release -- 30 30 -- 0.00006 0.00008 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release)
<method 'release' of '_thread.RLock' objects> -- 30 30 -- 0.00002 0.00002 -- ~:0:<method 'release' of '_thread.RLock' objects> (<method 'release' of '_thread.RLock' objects>)
emit -- 12 12 -- 0.00009 0.00115 -- /usr/local/lib/python3.9/logging/__init__.py:1067:emit (emit)
format -- 12 12 -- 0.00004 0.00060 -- /usr/local/lib/python3.9/logging/__init__.py:912:format (format)
format -- 12 12 -- 0.00010 0.00056 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:528:format (format)
format -- 12 12 -- 0.00008 0.00041 -- /usr/local/lib/python3.9/logging/__init__.py:646:format (format)
usesTime -- 12 12 -- 0.00002 0.00009 -- /usr/local/lib/python3.9/logging/__init__.py:624:usesTime (usesTime)
usesTime -- 12 12 -- 0.00004 0.00007 -- /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 -- 0.00007 0.00014 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:88: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:81:colorize (colorize)
escseq -- 4 4 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/console.py:82:escseq (escseq)
<built-in method builtins.getattr> -- 12 12 -- 0.00002 0.00002 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
flush -- 12 12 -- 0.00007 0.00039 -- /usr/local/lib/python3.9/logging/__init__.py:1056:flush (flush)
acquire -- 12 12 -- 0.00002 0.00004 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire) +++
release -- 12 12 -- 0.00003 0.00004 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release) +++
flush -- 6 6 -- 0.00003 0.00022 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:554:flush (flush)
<method 'flush' of '_io.TextIOWrapper' objects> -- 6 6 -- 0.00019 0.00019 -- ~: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 -- 0.00003 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:546:write (write)
write -- 6 6 -- 0.00003 0.00004 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:564: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 23.66170 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark_fwbw.py:124:<lambda> (<lambda>)
benchmark -- 1 1 -- 0.02055 23.66169 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark_fwbw.py:46:benchmark (benchmark)
fit -- 1 1 -- 0.00571 12.09756 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:289:fit (fit)
__init__ -- 1 1 -- 0.00005 0.00011 -- 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 -- 0.00001 0.00005 -- onnxruntime_helper.py:134: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 -- 0.00070 0.00222 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:155:get_full_state (get_full_state) +++
set_state -- 2 2 -- 0.00013 0.00041 -- 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 0.00022 -- onnxruntime_helper.py:134: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 -- 0.00006 0.00303 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:340:<listcomp> (<listcomp>)
get_initializer -- 7 7 -- 0.00020 0.00297 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:280:get_initializer (get_initializer)
to_array -- 6 6 -- 0.00019 0.00278 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/onnx/numpy_helper.py:21:to_array (to_array)
uses_external_data -- 6 6 -- 0.00002 0.00004 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/onnx/external_data_helper.py:224:uses_external_data (uses_external_data)
<method 'HasField...essage' objects> -- 12 12 -- 0.00004 0.00004 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>) +++
<method 'astype' ...darray' objects> -- 6 6 -- 0.00010 0.00010 -- ~:0:<method 'astype' of 'numpy.ndarray' objects> (<method 'astype' of 'numpy.ndarray' objects>) +++
<built-in method numpy.asarray> -- 6 6 -- 0.00234 0.00234 -- ~:0:<built-in method numpy.asarray> (<built-in method numpy.asarray>) +++
_iteration -- 100 100 -- 1.60233 12.03529 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:426:_iteration (_iteration)
iter_ortvalue -- 5100 5100 -- 0.12527 0.65378 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:147:iter_ortvalue (iter_ortvalue)
_next_iter -- 5000 5000 -- 0.03467 0.33117 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:98:_next_iter (_next_iter)
<method 'randint'...mState' objects> -- 5000 5000 -- 0.28371 0.28371 -- ~:0:<method 'randint' of 'numpy.random.mtrand.RandomState' objects> (<method 'randint' of 'numpy.random.mtrand.RandomState' objects>)
<built-in method builtins.len> -- 5000 5000 -- 0.00758 0.01280 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
numpy_to_ort_value -- 10000 10000 -- 0.02982 0.16306 -- onnxruntime_helper.py:134:numpy_to_ort_value (numpy_to_ort_value) +++
<built-in method builtins.len> -- 5200 5200 -- 0.01680 0.03428 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
forward -- 5000 5000 -- 1.21651 1.60318 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:654:forward (forward)
input_to_ort -- 5000 5000 -- 0.24313 0.33225 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:577:input_to_ort (input_to_ort) +++
save_for_backward -- 5000 5000 -- 0.04523 0.04523 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:629:save_for_backward (save_for_backward)
<method 'append' of 'list' objects> -- 5000 5000 -- 0.00919 0.00919 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
backward -- 5000 5000 -- 1.58726 1.77357 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:732:backward (backward)
input_to_ort -- 5000 5000 -- 0.13343 0.17137 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:577:input_to_ort (input_to_ort) +++
saved_tensors -- 5000 5000 -- 0.00697 0.00697 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:641:saved_tensors (saved_tensors)
<method 'pop' of 'list' objects> -- 5000 5000 -- 0.00796 0.00796 -- ~:0:<method 'pop' of 'list' objects> (<method 'pop' of 'list' objects>)
loss_gradient -- 5000 5000 -- 0.26729 1.08337 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_loss.py:61:loss_gradient (loss_gradient)
clear_binding_inputs -- 5000 5000 -- 0.01950 0.04610 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:146:clear_binding_inputs (clear_binding_inputs)
_cache_in_clear -- 5000 5000 -- 0.02011 0.02660 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:135:_cache_in_clear (_cache_in_clear)
<built-in method builtins.id> -- 5000 5000 -- 0.00649 0.00649 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>) +++
_bind_input_ortvalue -- 10000 10000 -- 0.07427 0.34762 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:178:_bind_input_ortvalue (_bind_input_ortvalue) +++
_call_iobinding -- 5000 5000 -- 0.41261 0.41261 -- 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 -- 0.00975 0.00975 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
penalty_loss -- 5000 5000 -- 0.00396 0.00396 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_penalty.py:101:penalty_loss (penalty_loss)
update_weights -- 30000 30000 -- 0.01911 0.01911 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_penalty.py:115:update_weights (update_weights)
update_weights -- 30000 30000 -- 1.37146 5.21990 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:258:update_weights (update_weights)
_bind_input_ortvalue -- 90000 90000 -- 0.44386 1.41558 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:178:_bind_input_ortvalue (_bind_input_ortvalue) +++
_bind_output_ortvalue -- 30000 30000 -- 0.14137 0.45005 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:224:_bind_output_ortvalue (_bind_output_ortvalue)
_bio_cache -- 30000 30000 -- 0.09486 0.11775 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:157:_bio_cache (_bio_cache) +++
_bio_ptr -- 30000 30000 -- 0.17543 0.17543 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/_base_onnx_function.py:174:_bio_ptr (_bio_ptr) +++
<built-in method ...tins.isinstance> -- 30000 30000 -- 0.01550 0.01550 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_call_iobinding -- 30000 30000 -- 1.46079 1.46079 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/sgd_learning_rate.py:33:_call_iobinding (_call_iobinding)
value -- 30000 30000 -- 0.03511 0.03511 -- 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 -- 0.43931 0.43931 -- ~: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 -- 0.04760 0.04760 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
<method 'mean' of 'numpy.ndarray' objects> -- 100 100 -- 0.00070 0.01538 -- ~:0:<method 'mean' of 'numpy.ndarray' objects> (<method 'mean' of 'numpy.ndarray' objects>) +++
<built-in method numpy.array> -- 100 100 -- 0.00832 0.00832 -- ~:0:<built-in method numpy.array> (<built-in method numpy.array>) +++
<method 'append' of 'list' objects> -- 5000 5000 -- 0.00582 0.00582 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.len> -- 30100 30100 -- 0.04656 0.04656 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_create_training_session -- 1 1 -- 0.00003 0.04624 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers_partial.py:661:_create_training_session (_create_training_session)
__init__ -- 1 1 -- 0.00019 0.04617 -- 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:118:<listcomp> (<listcomp>)
_init_next -- 1 1 -- 0.00020 0.04591 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:171:_init_next (_init_next)
<listcomp> -- 1 1 -- 0.00003 0.00003 -- 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:183:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:186:<listcomp> (<listcomp>)
_create_onnx_graphs -- 1 1 -- 0.00980 0.04565 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:301:_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:413:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:414:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00003 0.00004 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:416:<listcomp> (<listcomp>)
_provider_nam..._device_type -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:271:_provider_name_to_device_type (_provider_name_to_device_type) +++
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ndim -- 2 2 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3164:ndim (ndim)
mean -- 15000 15000 -- 0.15235 1.55255 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3356:mean (mean)
_mean -- 15000 15000 -- 0.70261 1.40020 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:162:_mean (_mean) +++
zeros_like -- 6 6 -- 0.00007 0.00032 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/numeric.py:76:zeros_like (zeros_like)
copyto -- 6 6 -- 0.00003 0.00010 -- <__array_function__ internals>:177:copyto (copyto)
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)
empty_like -- 6 6 -- 0.00004 0.00010 -- <__array_function__ internals>:177:empty_like (empty_like)
empty_like -- 6 6 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/multiarray.py:80:empty_like (empty_like)
unique -- 101 101 -- 0.00103 0.01009 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:138:unique (unique)
_unpack_tuple -- 101 101 -- 0.00026 0.00032 -- 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 -- 0.00007 0.00007 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_unique1d -- 101 101 -- 0.00600 0.00768 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:320:_unique1d (_unique1d)
<method 'flatten' of 'numpy.ndarray' objects> -- 101 101 -- 0.00082 0.00082 -- ~:0:<method 'flatten' of 'numpy.ndarray' objects> (<method 'flatten' of 'numpy.ndarray' objects>)
<method 'sort' of 'numpy.ndarray' objects> -- 101 101 -- 0.00022 0.00022 -- ~:0:<method 'sort' of 'numpy.ndarray' objects> (<method 'sort' of 'numpy.ndarray' objects>)
<built-in method numpy.asanyarray> -- 101 101 -- 0.00005 0.00005 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<built-in method numpy.empty> -- 101 101 -- 0.00059 0.00059 -- ~:0:<built-in method numpy.empty> (<built-in method numpy.empty>) +++
<built-in method numpy.asanyarray> -- 101 101 -- 0.00106 0.00106 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<method 'HasField' of 'google...._message.CMessage' objects> -- 18 18 -- 0.00006 0.00006 -- ~: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 -- 0.00236 0.00236 -- ~:0:<built-in method numpy.asarray> (<built-in method numpy.asarray>)
<built-in method builtins.all> -- 10004 10004 -- 0.04346 0.08832 -- ~:0:<built-in method builtins.all> (<built-in method builtins.all>)
<lambda> -- 40000 40000 -- 0.03280 0.04486 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/ortgradient.py:597:<lambda> (<lambda>)
<built-in method builtins.isinstance> -- 40000 40000 -- 0.01206 0.01206 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<built-in method numpy.asanyarray> -- 20304 20304 -- 0.01707 0.01707 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>)
<method 'reduce' of 'numpy.ufunc' objects> -- 20103 20103 -- 0.49995 0.49995 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>)
<built-in method builtins.issubclass> -- 40206 40206 -- 0.03522 0.03522 -- ~:0:<built-in method builtins.issubclass> (<built-in method builtins.issubclass>)
<method 'get' of 'dict' objects> -- 33 33 -- 0.00003 0.00003 -- ~:0:<method 'get' of 'dict' objects> (<method 'get' of 'dict' objects>)
<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'))
Out:
[benchmark] LR-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 3.828441008925438 seconds
[benchmark] ort=100 iteration - 4.971634171903133 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)
Out:
skl ... losses_ort
name ...
NN-CPU 10.036501 ... [9429.534, 7956.5093, 8667.631, 8639.109, 7085...
LR-CPU 3.828441 ... [7251.0786, 7841.8813, 7501.6963, 7084.753, 64...
[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()
Out:
<matplotlib.legend.Legend object at 0x7f9360cfb370>
The gradient update are not exactly the same. It should be improved for a fair comprison.
fig.savefig(__file__ + ".png")
# plt.show()
Traceback (most recent call last):
File "somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark_fwbw.py", line 218, in <module>
fig.savefig(__file__ + ".png")
NameError: name '__file__' is not defined
Total running time of the script: ( 1 minutes 9.506 seconds)