In [25]:
import pandas
pandas.DataFrame({'loss': train_session.train_losses_}).plot();
onnxruntime-training is an extension onnxruntime or more precisely the same library compiled with different settings. It provides a way to compute a gradient of a function defined by an ONNX graph.
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%load_ext mlprodict
Let's choose a simple regression problem defined by $z = -1 - 2x + 3y + \frac{1}{2}x^2 -\frac{1}{3} y^2 +\epsilon$ and we try to approximate by a function $f(x,y) = a + bx + cy + dx^2 + ey^2$. Every coefficient is determined from an optimization problem solved with a stochastic gradient descent.
from typing import Any
import numpy
import mlprodict.npy.numpy_onnx_impl as npnx
from mlprodict.npy import onnxnumpy_default, NDArray
@onnxnumpy_default
def fct(x: NDArray[(None, 2), numpy.float32]) -> NDArray[(None, 1), numpy.float32]:
coef_x = numpy.array([[-2, 3]], dtype=numpy.float32)
coef_x2 = numpy.array([[0.5, -0.33333]], dtype=numpy.float32)
bias = numpy.array([-1], dtype=numpy.float32)
poly = x * coef_x + x * x * coef_x2
y = poly[:, 0] + poly[:, 1] + bias
return y.reshape((-1, 1))
x = numpy.array([[0, 0], [1, 0], [0, 1], [1, 1], [2, 2]], dtype=numpy.float32)
fct(x)
array([[-1. ],
[-2.5 ],
[ 1.6666701 ],
[ 0.16667008],
[ 1.6666799 ]], dtype=float32)
%onnxview fct.to_onnx()
from mlprodict.plotting.text_plot import onnx_simple_text_plot
print(onnx_simple_text_plot(fct.to_onnx()))
opset: domain='' version=14
input: name='x' type=dtype('float32') shape=(0, 2)
init: name='init' type=dtype('float32') shape=(0,) -- array([ 0.5 , -0.33333], dtype=float32)
init: name='init_1' type=dtype('float32') shape=(0,) -- array([-2., 3.], dtype=float32)
init: name='init_2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='init_3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
init: name='init_5' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='init_b10' type=dtype('float32') shape=(0,) -- array([-1.], dtype=float32)
init: name='init_b11' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
Mul(x, x) -> out_mul_0
Mul(out_mul_0, init) -> out_mul_0_1
Mul(x, init_1) -> out_mul_0_2
Add(out_mul_0_2, out_mul_0_1) -> out_add_0
Slice(out_add_0, init_2, init_3, init_2) -> out_sli_0
Squeeze(out_sli_0, init_2) -> out_squ_0
Slice(out_add_0, init_5, init_2, init_2) -> out_sli_0_1
Squeeze(out_sli_0_1, init_2) -> out_squ_0_1
Add(out_squ_0_1, out_squ_0) -> out_add_0_1
Add(out_add_0_1, init_b10) -> out_add_0_2
Reshape(out_add_0_2, init_b11) -> y
output: name='y' type=dtype('float32') shape=(0, 1)
Let's look into the gradient.
from onnxcustom.training.grad_helper import onnx_derivative, DerivativeOptions
onx = fct.to_onnx()
grad = onnx_derivative(onx)
%onnxview grad
from mlprodict.plotting.text_plot import onnx_text_plot_io, onnx_simple_text_plot
print(onnx_text_plot_io(grad))
opset: domain='' version=14
opset: domain='com.microsoft.nchwc' version=1
opset: domain='ai.onnx.ml' version=2
opset: domain='com.ms.internal.nhwc' version=1
opset: domain='ai.onnx.training' version=1
opset: domain='ai.onnx.preview.training' version=1
opset: domain='com.microsoft' version=1
opset: domain='com.microsoft.experimental' version=1
input: name='x' type=dtype('float32') shape=(0, 2)
input: name='init' type=dtype('float32') shape=(1, 2)
input: name='init_1' type=dtype('float32') shape=(1, 2)
input: name='init_b10' type=dtype('float32') shape=(1,)
input: name='y_grad' type=dtype('float32') shape=(0, 1)
init: name='init_5' type=dtype('int64') shape=(0,)
init: name='init_2' type=dtype('int64') shape=(0,)
init: name='init_3' type=dtype('int64') shape=(0,)
output: name='x_grad' type=dtype('float32') shape=(0, 2)
output: name='init_grad' type=dtype('float32') shape=(1, 2)
output: name='init_1_grad' type=dtype('float32') shape=(1, 2)
output: name='init_b10_grad' type=dtype('float32') shape=(1,)
from mlprodict.onnx_tools.onnx_manipulations import onnx_rename_names
renamed = onnx_rename_names(grad)
print(onnx_simple_text_plot(renamed))
opset: domain='' version=14
opset: domain='com.microsoft.nchwc' version=1
opset: domain='ai.onnx.ml' version=2
opset: domain='com.ms.internal.nhwc' version=1
opset: domain='ai.onnx.training' version=1
opset: domain='ai.onnx.preview.training' version=1
opset: domain='com.microsoft' version=1
opset: domain='com.microsoft.experimental' version=1
input: name='x' type=dtype('float32') shape=(0, 2)
input: name='init' type=dtype('float32') shape=(1, 2)
input: name='init_1' type=dtype('float32') shape=(1, 2)
input: name='init_b10' type=dtype('float32') shape=(1,)
input: name='y_grad' type=dtype('float32') shape=(0, 1)
init: name='i0' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='i1' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='i2' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
Mul(x, x) -> r0
Mul(r0, init) -> r1
Shape(r1) -> r32
Mul(x, init_1) -> r2
Add(r2, r1) -> r3
Slice(r3, i1, i2, i1) -> r4
Squeeze(r4, i1) -> r5
Shape(r5) -> r18
Slice(r3, i0, i1, i1) -> r6
Squeeze(r6, i1) -> r7
Add(r7, r5) -> r8
Add(r8, init_b10) -> r9
Shape(r9) -> r10
Reshape(y_grad, r10, allowzero=0) -> r11
Shape(init_b10) -> r12
Shape(r8) -> r13
BroadcastGradientArgs(r13, r12) -> r14, r15
ReduceSum(r11, r14, keepdims=1, noop_with_empty_axes=1) -> r16
Reshape(r16, r13, allowzero=0) -> r17
Shape(r7) -> r19
BroadcastGradientArgs(r19, r18) -> r20, r21
ReduceSum(r17, r21, keepdims=1, noop_with_empty_axes=1) -> r22
Reshape(r22, r18, allowzero=0) -> r23
Unsqueeze(r23, i1) -> r24
Shape(r3) -> r25
SliceGrad(r24, r25, i1, i2, i1) -> r26
ReduceSum(r17, r20, keepdims=1, noop_with_empty_axes=1) -> r27
Reshape(r27, r19, allowzero=0) -> r28
Unsqueeze(r28, i1) -> r29
SliceGrad(r29, r25, i0, i1, i1) -> r30
Sum(r30, r26) -> r31
Shape(r2) -> r33
BroadcastGradientArgs(r33, r32) -> r34, r35
ReduceSum(r31, r35, keepdims=1, noop_with_empty_axes=1) -> r36
Reshape(r36, r32, allowzero=0) -> r37
Mul(r37, init) -> r38
Shape(init) -> r39
Shape(r0) -> r40
BroadcastGradientArgs(r40, r39) -> r41, r42
ReduceSum(r38, r41, keepdims=1, noop_with_empty_axes=1) -> r43
Reshape(r43, r40, allowzero=0) -> r44
Mul(r44, x) -> r45
ReduceSum(r31, r34, keepdims=1, noop_with_empty_axes=1) -> r46
Reshape(r46, r33, allowzero=0) -> r47
Mul(r47, init_1) -> r48
Shape(init_1) -> r49
Shape(x) -> r50
BroadcastGradientArgs(r50, r49) -> r51, r52
ReduceSum(r48, r51, keepdims=1, noop_with_empty_axes=1) -> r53
Reshape(r53, r50, allowzero=0) -> r54
Sum(r54, r45, r45) -> x_grad
ReduceSum(r11, r15, keepdims=1, noop_with_empty_axes=1) -> r55
Reshape(r55, r12, allowzero=0) -> init_b10_grad
Mul(r37, r0) -> r56
ReduceSum(r56, r42, keepdims=1, noop_with_empty_axes=1) -> r57
Reshape(r57, r39, allowzero=0) -> init_grad
Mul(r47, x) -> r58
ReduceSum(r58, r52, keepdims=1, noop_with_empty_axes=1) -> r59
Reshape(r59, r49, allowzero=0) -> init_1_grad
output: name='x_grad' type=dtype('float32') shape=(0, 2)
output: name='init_grad' type=dtype('float32') shape=(1, 2)
output: name='init_1_grad' type=dtype('float32') shape=(1, 2)
output: name='init_b10_grad' type=dtype('float32') shape=(1,)
set(n.op_type for n in grad.graph.node)
{'Add',
'BroadcastGradientArgs',
'Mul',
'ReduceSum',
'Reshape',
'Shape',
'Slice',
'SliceGrad',
'Squeeze',
'Sum',
'Unsqueeze'}
The resulting graph assumes the gradient for y_grad is known. That's the case for a layer in a neural network. In our case, this gradient should come from the loss. Let's add it to the graph.
from onnxcustom.utils.orttraining_helper import add_loss_output
onx_loss = add_loss_output(onx)
%onnxview onx_loss
print(onnx_simple_text_plot(onx_loss))
opset: domain='' version=14
input: name='x' type=dtype('float32') shape=(0, 2)
input: name='label' type=dtype('float32') shape=(0, 1)
init: name='init' type=dtype('float32') shape=(0,) -- array([ 0.5 , -0.33333], dtype=float32)
init: name='init_1' type=dtype('float32') shape=(0,) -- array([-2., 3.], dtype=float32)
init: name='init_2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='init_3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
init: name='init_5' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='init_b10' type=dtype('float32') shape=(0,) -- array([-1.], dtype=float32)
init: name='init_b11' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
Mul(x, x) -> out_mul_0
Mul(out_mul_0, init) -> out_mul_0_1
Mul(x, init_1) -> out_mul_0_2
Add(out_mul_0_2, out_mul_0_1) -> out_add_0
Slice(out_add_0, init_2, init_3, init_2) -> out_sli_0
Squeeze(out_sli_0, init_2) -> out_squ_0
Slice(out_add_0, init_5, init_2, init_2) -> out_sli_0_1
Squeeze(out_sli_0_1, init_2) -> out_squ_0_1
Add(out_squ_0_1, out_squ_0) -> out_add_0_1
Add(out_add_0_1, init_b10) -> out_add_0_2
Reshape(out_add_0_2, init_b11) -> y
Sub(y, label) -> loss_diff
Mul(loss_diff, loss_diff) -> loss_diff_2
ReduceSum(loss_diff_2) -> loss
output: name='loss' type=dtype('float32') shape=(1, 1)
output: name='y' type=dtype('float32') shape=(0, 1)
The graph has 5 inputs: x, label or the expected target, and the weights and two outputs, the function output and the loss. We don't need the first one so we remove it.
from mlprodict.onnx_tools.onnx_manipulations import select_model_inputs_outputs
onx_loss_only = select_model_inputs_outputs(onx_loss, outputs=['loss'])
print(onnx_simple_text_plot(onx_loss_only))
opset: domain='' version=14
input: name='x' type=dtype('float32') shape=(0, 2)
input: name='label' type=dtype('float32') shape=(0, 1)
init: name='init' type=dtype('float32') shape=(0,) -- array([ 0.5 , -0.33333], dtype=float32)
init: name='init_1' type=dtype('float32') shape=(0,) -- array([-2., 3.], dtype=float32)
init: name='init_2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='init_3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
init: name='init_5' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='init_b10' type=dtype('float32') shape=(0,) -- array([-1.], dtype=float32)
init: name='init_b11' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
Mul(x, x) -> out_mul_0
Mul(out_mul_0, init) -> out_mul_0_1
Mul(x, init_1) -> out_mul_0_2
Add(out_mul_0_2, out_mul_0_1) -> out_add_0
Slice(out_add_0, init_5, init_2, init_2) -> out_sli_0_1
Squeeze(out_sli_0_1, init_2) -> out_squ_0_1
Slice(out_add_0, init_2, init_3, init_2) -> out_sli_0
Squeeze(out_sli_0, init_2) -> out_squ_0
Add(out_squ_0_1, out_squ_0) -> out_add_0_1
Add(out_add_0_1, init_b10) -> out_add_0_2
Reshape(out_add_0_2, init_b11) -> y
Sub(y, label) -> loss_diff
Mul(loss_diff, loss_diff) -> loss_diff_2
ReduceSum(loss_diff_2) -> loss
output: name='loss' type=dtype('float32') shape=(1, 1)
grad_loss = onnx_rename_names(onnx_derivative(
onx_loss_only, options=DerivativeOptions.FillGrad | DerivativeOptions.KeepOutputs))
%onnxview grad_loss
print(onnx_simple_text_plot(grad_loss))
opset: domain='' version=14
opset: domain='com.microsoft.nchwc' version=1
opset: domain='ai.onnx.ml' version=2
opset: domain='com.ms.internal.nhwc' version=1
opset: domain='ai.onnx.training' version=1
opset: domain='ai.onnx.preview.training' version=1
opset: domain='com.microsoft' version=1
opset: domain='com.microsoft.experimental' version=1
input: name='x' type=dtype('float32') shape=(0, 2)
input: name='label' type=dtype('float32') shape=(0, 1)
input: name='init' type=dtype('float32') shape=(1, 2)
input: name='init_1' type=dtype('float32') shape=(1, 2)
input: name='init_b10' type=dtype('float32') shape=(1,)
init: name='i0' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
init: name='i1' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='i2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='i3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
Mul(x, init_1) -> r0
Shape(r0) -> r47
Mul(x, x) -> r1
Mul(r1, init) -> r2
Add(r0, r2) -> r3
Slice(r3, i1, i2, i2) -> r4
Squeeze(r4, i2) -> r5
Shape(r5) -> r33
Slice(r3, i2, i3, i2) -> r6
Squeeze(r6, i2) -> r7
Add(r5, r7) -> r8
Add(r8, init_b10) -> r9
Reshape(r9, i0, allowzero=0) -> r10
Sub(r10, label) -> r11
Mul(r11, r11) -> r12
ReduceSum(r12, keepdims=1, noop_with_empty_axes=0) -> loss
Shape(loss) -> r76
ConstantOfShape(r76) -> r14
Shape(r12) -> r13
Expand(r14, r13) -> r15
Mul(r15, r11) -> r16
Sum(r16, r16) -> r17
Shape(label) -> r18
Shape(r10) -> r19
BroadcastGradientArgs(r19, r18) -> r20, r21
ReduceSum(r17, r20, keepdims=1, noop_with_empty_axes=1) -> r22
Reshape(r22, r19, allowzero=0) -> r23
Shape(r9) -> r24
Reshape(r23, r24, allowzero=0) -> r25
Shape(init_b10) -> r26
Shape(r8) -> r27
BroadcastGradientArgs(r27, r26) -> r28, r29
ReduceSum(r25, r28, keepdims=1, noop_with_empty_axes=1) -> r30
Reshape(r30, r27, allowzero=0) -> r31
Shape(r7) -> r32
BroadcastGradientArgs(r33, r32) -> r34, r35
ReduceSum(r31, r34, keepdims=1, noop_with_empty_axes=1) -> r36
Reshape(r36, r33, allowzero=0) -> r37
Unsqueeze(r37, i2) -> r38
Shape(r3) -> r39
SliceGrad(r38, r39, i1, i2, i2) -> r40
ReduceSum(r31, r35, keepdims=1, noop_with_empty_axes=1) -> r41
Reshape(r41, r32, allowzero=0) -> r42
Unsqueeze(r42, i2) -> r43
SliceGrad(r43, r39, i2, i3, i2) -> r44
Sum(r44, r40) -> r45
Shape(r2) -> r46
BroadcastGradientArgs(r47, r46) -> r48, r49
ReduceSum(r45, r48, keepdims=1, noop_with_empty_axes=1) -> r50
Reshape(r50, r47, allowzero=0) -> r51
Mul(r51, init_1) -> r52
Shape(init_1) -> r53
Shape(x) -> r54
BroadcastGradientArgs(r54, r53) -> r55, r56
ReduceSum(r52, r55, keepdims=1, noop_with_empty_axes=1) -> r57
Reshape(r57, r54, allowzero=0) -> r58
ReduceSum(r45, r49, keepdims=1, noop_with_empty_axes=1) -> r59
Reshape(r59, r46, allowzero=0) -> r60
Mul(r60, init) -> r61
Shape(init) -> r62
Shape(r1) -> r63
BroadcastGradientArgs(r63, r62) -> r64, r65
ReduceSum(r61, r64, keepdims=1, noop_with_empty_axes=1) -> r66
Reshape(r66, r63, allowzero=0) -> r67
Mul(r67, x) -> r68
Sum(r68, r68, r58) -> x_grad
ReduceSum(r17, r21, keepdims=1, noop_with_empty_axes=1) -> r69
Reshape(r69, r18, allowzero=0) -> r70
Neg(r70) -> label_grad
ReduceSum(r25, r29, keepdims=1, noop_with_empty_axes=1) -> r71
Reshape(r71, r26, allowzero=0) -> init_b10_grad
Mul(r51, x) -> r72
ReduceSum(r72, r56, keepdims=1, noop_with_empty_axes=1) -> r73
Reshape(r73, r53, allowzero=0) -> init_1_grad
Mul(r60, r1) -> r74
ReduceSum(r74, r65, keepdims=1, noop_with_empty_axes=1) -> r75
Reshape(r75, r62, allowzero=0) -> init_grad
output: name='x_grad' type=dtype('float32') shape=(0, 2)
output: name='label_grad' type=dtype('float32') shape=(0, 1)
output: name='init_grad' type=dtype('float32') shape=(1, 2)
output: name='init_1_grad' type=dtype('float32') shape=(1, 2)
output: name='init_b10_grad' type=dtype('float32') shape=(1,)
output: name='loss' type=dtype('float32') shape=(1, 1)
Let's compute the gradient.
x
array([[0., 0.],
[1., 0.],
[0., 1.],
[1., 1.],
[2., 2.]], dtype=float32)
y = fct(x)
y
array([[-1. ],
[-2.5 ],
[ 1.6666701 ],
[ 0.16667008],
[ 1.6666799 ]], dtype=float32)
from mlprodict.onnxrt import OnnxInference
oinf = OnnxInference(grad_loss, runtime='onnxruntime1')
import pprint
init = numpy.array([[2, 3]], dtype=numpy.float32)
init_1 = numpy.array([[0.5, 0.33333]], dtype=numpy.float32)
init_b10 = numpy.array([1], dtype=numpy.float32)
result = oinf.run({'x': x, 'label': y,
'init': init, 'init_1': init_1, 'init_b10': init_b10})
pprint.pprint(result)
{'init_1_grad': array([[109.333244, 102.666565]], dtype=float32),
'init_b10_grad': array([76.6666], dtype=float32),
'init_grad': array([[193.33316, 186.66649]], dtype=float32),
'label_grad': array([[ -4. ],
[-12. ],
[ -5.33332 ],
[-13.333321],
[-41.99996 ]], dtype=float32),
'loss': array([[532.5546]], dtype=float32),
'x_grad': array([[ 2. , 1.33332 ],
[ 54. , 3.99996 ],
[ 2.66666 , 33.777676],
[ 59.999943, 84.44432 ],
[356.99966 , 517.9994 ]], dtype=float32)}
We could use this gradient to implement a stochastic gradient descent in python. Two comments:
We need to implemented the second option. A simple addition between two OrtValue must be done with an ONNX graph.
X = numpy.random.randn(100, 2).astype(numpy.float32) / 10
y = fct(X) + (numpy.random.randn(100, 1) / 1000).astype(numpy.float32)
X.shape, y.shape
((100, 2), (100, 1))
print(onnx_simple_text_plot(onx))
opset: domain='' version=14
input: name='x' type=dtype('float32') shape=(0, 2)
init: name='init' type=dtype('float32') shape=(0,) -- array([ 0.5 , -0.33333], dtype=float32)
init: name='init_1' type=dtype('float32') shape=(0,) -- array([-2., 3.], dtype=float32)
init: name='init_2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='init_3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
init: name='init_5' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='init_b10' type=dtype('float32') shape=(0,) -- array([-1.], dtype=float32)
init: name='init_b11' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
Mul(x, x) -> out_mul_0
Mul(out_mul_0, init) -> out_mul_0_1
Mul(x, init_1) -> out_mul_0_2
Add(out_mul_0_2, out_mul_0_1) -> out_add_0
Slice(out_add_0, init_2, init_3, init_2) -> out_sli_0
Squeeze(out_sli_0, init_2) -> out_squ_0
Slice(out_add_0, init_5, init_2, init_2) -> out_sli_0_1
Squeeze(out_sli_0_1, init_2) -> out_squ_0_1
Add(out_squ_0_1, out_squ_0) -> out_add_0_1
Add(out_add_0_1, init_b10) -> out_add_0_2
Reshape(out_add_0_2, init_b11) -> y
output: name='y' type=dtype('float32') shape=(0, 1)
from onnxcustom.training.optimizers import OrtGradientOptimizer
train_session = OrtGradientOptimizer(
onx_loss, ['init', 'init_1', 'init_b10'], learning_rate=1e-1,
batch_size=5, max_iter=100)
train_session.fit(X, y)
OrtGradientOptimizer(model_onnx='ir_version...', weights_to_train=['init', 'init_1', 'init_b10'], loss_output_name='loss', max_iter=100, training_optimizer_name='SGDOptimizer', batch_size=5, learning_rate=LearningRateSGD(eta0=0.1, alpha=0.0001, power_t=0.25, learning_rate='invscaling'), value=0.03162277660168379, device='cpu', warm_start=False, verbose=0, validation_every=10, saved_gradient=None, sample_weight_name='weight')
train_session.trained_coef_
{'init': array([[-0.34785354, 1.1399053 ]], dtype=float32),
'init_1': array([[-1.9156165, 2.4292002]], dtype=float32),
'init_b10': array([-1.0016667], dtype=float32)}
train_session.train_losses_[-5:]
[0.0036812867, 0.0038135047, 0.0037041684, 0.0037206002, 0.0032002896]
import pandas
pandas.DataFrame({'loss': train_session.train_losses_}).plot();
This second implementation uses TrainingAgent.
from onnxcustom.training.optimizers_partial import OrtGradientForwardBackwardOptimizer
train_session = OrtGradientForwardBackwardOptimizer(
onx, ['init', 'init_1', 'init_b10'], learning_rate=1e-1,
batch_size=2, max_iter=100)
train_session.fit(X, y)
OrtGradientForwardBackwardOptimizer(model_onnx='ir_version...', weights_to_train=['init', 'init_1', 'init_b10'], loss_output_name='loss', max_iter=100, training_optimizer_name='SGDOptimizer', batch_size=2, learning_rate=LearningRateSGD(eta0=0.1, alpha=0.0001, power_t=0.25, learning_rate='invscaling'), value=0.03162277660168379, device='cpu', warm_start=False, verbose=0, validation_every=10, learning_loss=SquareLearningLoss(), enable_logging=False, weight_name=None, learning_penalty=NoLearningPenalty(), exc=True)
train_session.train_losses_[-5:]
[0.00040441833, 0.00037421435, 0.00049950054, 0.00042527347, 0.00031072882]
pandas.DataFrame({'loss': train_session.train_losses_}).plot();
train_session.trained_coef_
{'init': <onnxruntime.capi.onnxruntime_pybind11_state.OrtValue at 0x162a9199fb0>,
'init_1': <onnxruntime.capi.onnxruntime_pybind11_state.OrtValue at 0x162a91a20f0>,
'init_b10': <onnxruntime.capi.onnxruntime_pybind11_state.OrtValue at 0x162a91a2030>}
{k: v.numpy() for k, v in train_session.trained_coef_.items()}
{'init': array([[-0.35357383, 0.6850407 ]], dtype=float32),
'init_1': array([[-1.916494 , 2.8799832]], dtype=float32),
'init_b10': array([-1.0036615], dtype=float32)}
Not the same weights? What about the prediction?
trained_onx = train_session.get_trained_onnx()
print(onnx_simple_text_plot(trained_onx))
opset: domain='' version=14
input: name='x' type=dtype('float32') shape=(0, 2)
init: name='init' type=dtype('float32') shape=(0,) -- array([-0.35357383, 0.6850407 ], dtype=float32)
init: name='init_1' type=dtype('float32') shape=(0,) -- array([-1.916494 , 2.8799832], dtype=float32)
init: name='init_2' type=dtype('int64') shape=(0,) -- array([1], dtype=int64)
init: name='init_3' type=dtype('int64') shape=(0,) -- array([2], dtype=int64)
init: name='init_5' type=dtype('int64') shape=(0,) -- array([0], dtype=int64)
init: name='init_b10' type=dtype('float32') shape=(0,) -- array([-1.0036615], dtype=float32)
init: name='init_b11' type=dtype('int64') shape=(0,) -- array([-1, 1], dtype=int64)
Mul(x, x) -> out_mul_0
Mul(out_mul_0, init) -> out_mul_0_1
Mul(x, init_1) -> out_mul_0_2
Add(out_mul_0_2, out_mul_0_1) -> out_add_0
Slice(out_add_0, init_2, init_3, init_2) -> out_sli_0
Squeeze(out_sli_0, init_2) -> out_squ_0
Slice(out_add_0, init_5, init_2, init_2) -> out_sli_0_1
Squeeze(out_sli_0_1, init_2) -> out_squ_0_1
Add(out_squ_0_1, out_squ_0) -> out_add_0_1
Add(out_add_0_1, init_b10) -> out_add_0_2
Reshape(out_add_0_2, init_b11) -> y
output: name='y' type=dtype('float32') shape=(0, 1)
oinf = OnnxInference(trained_onx)
oinf.run({'x': X})['y'][:5]
array([[-0.6123954],
[-1.303561 ],
[-2.0257921],
[-1.2778704],
[-0.9708453]], dtype=float32)
y[:5]
array([[-0.58675164],
[-1.3148587 ],
[-2.0666485 ],
[-1.272753 ],
[-0.95404863]], dtype=float32)
It works.
import warnings
import time
import numpy
import matplotlib.pyplot as plt
from pandas import DataFrame
from onnxruntime import get_device
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from skl2onnx import to_onnx
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)
batch_size = 15
max_iter = 100
nn = MLPRegressor(hidden_layer_sizes=(50, 10), max_iter=max_iter,
solver='sgd', learning_rate_init=5e-5,
n_iter_no_change=max_iter * 3, batch_size=batch_size,
learning_rate="invscaling",
# default values
momentum=0.9, nesterovs_momentum=True, power_t=0.5)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
nn.fit(X_train, y_train)
Conversion to ONNX
from onnxcustom.utils.onnx_helper import onnx_rename_weights
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=5e-5,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
train_session.fit(X_train, y_train)
OrtGradientForwardBackwardOptimizer(model_onnx='ir_version...', weights_to_train="['I0_coeff...", loss_output_name='loss', max_iter=100, training_optimizer_name='SGDOptimizer', batch_size=15, learning_rate=LearningRateSGD(eta0=5e-05, alpha=0.0001, power_t=0.25, learning_rate='invscaling'), value=1.5811388300841898e-05, device='cpu', warm_start=False, verbose=0, validation_every=10, learning_loss=SquareLearningLoss(), enable_logging=False, weight_name=None, learning_penalty=NoLearningPenalty(), exc=True)
pandas.DataFrame(dict(skl_loss=nn.loss_curve_, ort_loss=train_session.train_losses_)).plot();
%timeit -n 1 -r 1 nn.fit(X_train, y_train)
C:\Python395_x64\lib\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(
1.98 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
%timeit -n 1 -r 1 train_session.fit(X_train, y_train)
1.88 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
from onnxcustom.training.sgd_learning_rate import LearningRateSGDNesterov
train_session2 = OrtGradientForwardBackwardOptimizer(
onx, device='cpu', warm_start=False, max_iter=max_iter, batch_size=batch_size,
learning_rate=LearningRateSGDNesterov(1e-5, nesterov=True, momentum=0.9))
train_session2.fit(X_train, y_train)
OrtGradientForwardBackwardOptimizer(model_onnx='ir_version...', weights_to_train="['I0_coeff...", loss_output_name='loss', max_iter=100, training_optimizer_name='SGDOptimizer', batch_size=15, learning_rate=LearningRateSGDNesterov(eta0=1e-05, alpha=0.0001, power_t=0.25, learning_rate='invscaling', momentum=0.9, nesterov=True), value=3.162277660168379e-06, device='cpu', warm_start=False, verbose=0, validation_every=10, learning_loss=SquareLearningLoss(), enable_logging=False, weight_name=None, learning_penalty=NoLearningPenalty(), exc=True)
pandas.DataFrame(dict(skl_loss=nn.loss_curve_,
ort_loss=train_session.train_losses_,
ort_loss2=train_session2.train_losses_)).plot();
%timeit -n 1 -r 1 train_session2.fit(X_train, y_train)
2.26 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
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
from pyquickhelper.pycode.profiling import profile, profile2graph
ps = profile(lambda:train_session2.fit(X, y))[0]
root, nodes = profile2graph(ps, clean_text=clean_name)
text = root.to_text()
print(text)
<lambda> -- 1 1 -- 0.00001 3.78074 -- <ipython-input-81-1255a3a5f723>:18:<lambda> (<lambda>)
fit -- 1 1 -- 0.00181 3.78073 -- onnxcustom/onnxcustom/training/optimizers_partial.py:263:fit (fit)
__init__ -- 1 1 -- 0.00002 0.00003 -- onnxcustom/onnxcustom/training/data_loader.py:26:__init__ (__init__)
get_ort_device -- 1 1 -- 0.00000 0.00000 -- onnxruntime_helper.py:55:get_ort_device (get_ort_device)
numpy_to_ort_value -- 2 2 -- 0.00000 0.00001 -- onnxruntime_helper.py:120:numpy_to_ort_value (numpy_to_ort_value) +++
needs_grad -- 3 3 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/optimizers_partial.py:99:needs_grad (needs_grad)
needs_grad -- 3 3 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:299:needs_grad (needs_grad)
get_full_state -- 101 101 -- 0.00020 0.00093 -- onnxcustom/onnxcustom/training/optimizers_partial.py:147:get_full_state (get_full_state) +++
set_state -- 4 4 -- 0.00008 0.00026 -- onnxcustom/onnxcustom/training/optimizers_partial.py:196:set_state (set_state)
_get_att_state -- 4 4 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/optimizers_partial.py:139:_get_att_state (_get_att_state) +++
numpy_to_ort_value -- 24 24 -- 0.00002 0.00011 -- onnxruntime_helper.py:120:numpy_to_ort_value (numpy_to_ort_value) +++
<built-in method numpy.zeros> -- 12 12 -- 0.00002 0.00002 -- ~:0:<built-in method numpy.zeros> (<built-in method numpy.zeros>)
<method 'append' of 'list' objects> -- 56 56 -- 0.00001 0.00001 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.isinstance> -- 24 24 -- 0.00000 0.00000 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<listcomp> -- 1 1 -- 0.00001 0.00095 -- onnxcustom/onnxcustom/training/optimizers_partial.py:311:<listcomp> (<listcomp>)
get_initializer -- 7 7 -- 0.00004 0.00094 -- onnxcustom/onnxcustom/training/ortgradient.py:269:get_initializer (get_initializer) +++
<listcomp> -- 1 1 -- 0.00001 0.00083 -- onnxcustom/onnxcustom/training/optimizers_partial.py:315:<listcomp> (<listcomp>)
get_initializer -- 7 7 -- 0.00004 0.00082 -- onnxcustom/onnxcustom/training/ortgradient.py:269:get_initializer (get_initializer) +++
_iteration -- 100 100 -- 0.41903 3.74610 -- onnxcustom/onnxcustom/training/optimizers_partial.py:397:_iteration (_iteration)
iter_ortvalue -- 6800 6800 -- 0.02838 0.14761 -- onnxcustom/onnxcustom/training/data_loader.py:139:iter_ortvalue (iter_ortvalue)
_next_iter -- 6700 6700 -- 0.00946 0.07207 -- onnxcustom/onnxcustom/training/data_loader.py:93:_next_iter (_next_iter)
<built-in method builtins.len> -- 6700 6700 -- 0.00245 0.00423 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
<method 'randint' o...domState' objects> -- 6700 6700 -- 0.05838 0.05838 -- ~:0:<method 'randint' of 'numpy.random.mtrand.RandomState' objects> (<method 'randint' of 'numpy.random.mtrand.RandomState' objects>)
numpy_to_ort_value -- 13400 13400 -- 0.00658 0.03860 -- onnxruntime_helper.py:120:numpy_to_ort_value (numpy_to_ort_value) +++
<built-in method builtins.len> -- 6900 6900 -- 0.00467 0.00855 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
forward -- 6700 6700 -- 0.31685 0.44643 -- onnxcustom/onnxcustom/training/ortgradient.py:623:forward (forward)
input_to_ort -- 6700 6700 -- 0.08002 0.11492 -- onnxcustom/onnxcustom/training/ortgradient.py:552:input_to_ort (input_to_ort) +++
save_for_backward -- 6700 6700 -- 0.01032 0.01032 -- onnxcustom/onnxcustom/training/ortgradient.py:604:save_for_backward (save_for_backward)
<method 'append' of 'list' objects> -- 6700 6700 -- 0.00434 0.00434 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
backward -- 6700 6700 -- 0.43012 0.48957 -- onnxcustom/onnxcustom/training/ortgradient.py:702:backward (backward)
input_to_ort -- 6700 6700 -- 0.04148 0.05262 -- onnxcustom/onnxcustom/training/ortgradient.py:552:input_to_ort (input_to_ort) +++
saved_tensors -- 6700 6700 -- 0.00207 0.00207 -- onnxcustom/onnxcustom/training/ortgradient.py:613:saved_tensors (saved_tensors)
<method 'pop' of 'list' objects> -- 6700 6700 -- 0.00476 0.00476 -- ~:0:<method 'pop' of 'list' objects> (<method 'pop' of 'list' objects>)
loss_gradient -- 6700 6700 -- 0.05841 0.26967 -- onnxcustom/onnxcustom/training/sgd_learning_loss.py:53:loss_gradient (loss_gradient)
clear_binding_inputs -- 6700 6700 -- 0.00545 0.01270 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:130:clear_binding_inputs (clear_binding_inputs)
_cache_in_clear -- 6700 6700 -- 0.00568 0.00725 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:119:_cache_in_clear (_cache_in_clear)
<built-in method builtins.id> -- 6700 6700 -- 0.00157 0.00157 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>) +++
_bind_input_ortvalue -- 13400 13400 -- 0.02070 0.07545 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:159:_bind_input_ortvalue (_bind_input_ortvalue) +++
_call_iobinding -- 6700 6700 -- 0.11997 0.11997 -- onnxcustom/onnxcustom/training/sgd_learning_loss.py:50:_call_iobinding (_call_iobinding)
<built-in method builtins.hasattr> -- 13400 13400 -- 0.00315 0.00315 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
penalty_loss -- 6700 6700 -- 0.00112 0.00112 -- onnxcustom/onnxcustom/training/sgd_learning_penalty.py:84:penalty_loss (penalty_loss)
update_weights -- 40200 40200 -- 0.00651 0.00651 -- onnxcustom/onnxcustom/training/sgd_learning_penalty.py:95:update_weights (update_weights)
update_weights -- 40200 40200 -- 0.40487 1.94238 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:345:update_weights (update_weights)
_bind_input_ortvalue -- 201000 201000 -- 0.19630 0.51693 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:159:_bind_input_ortvalue (_bind_input_ortvalue) +++
_bind_output_ortvalue -- 80400 80400 -- 0.07458 0.18952 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:202:_bind_output_ortvalue (_bind_output_ortvalue)
_bio_cache -- 80400 80400 -- 0.04417 0.05406 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:138:_bio_cache (_bio_cache) +++
_bio_ptr -- 80400 80400 -- 0.05222 0.05222 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:155:_bio_ptr (_bio_ptr) +++
_bio_do_bind_out -- 12 12 -- 0.00003 0.00003 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:198:_bio_do_bind_out (_bio_do_bind_out)
<built-in method builtins.isinstance> -- 80400 80400 -- 0.00863 0.00863 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_call_iobinding -- 40200 40200 -- 0.63987 0.63987 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:28:_call_iobinding (_call_iobinding)
value -- 40200 40200 -- 0.00953 0.00953 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:160:value (value) +++
<built-in method onnx...ortvalue_from_numpy> -- 80400 80400 -- 0.16512 0.16512 -- ~: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> -- 80400 80400 -- 0.01655 0.01655 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
<method 'mean' of 'numpy.ndarray' objects> -- 100 100 -- 0.00026 0.00426 -- ~:0:<method 'mean' of 'numpy.ndarray' objects> (<method 'mean' of 'numpy.ndarray' objects>)
_mean -- 100 100 -- 0.00163 0.00400 -- site-packages/numpy/core/_methods.py:162:_mean (_mean)
_count_reduce_items -- 100 100 -- 0.00097 0.00107 -- site-packages/numpy/core/_methods.py:66:_count_reduce_items (_count_reduce_items)
<built-in method ...lize_axis_index> -- 200 200 -- 0.00010 0.00010 -- ~:0:<built-in method numpy.core._multiarray_umath.normalize_axis_index> (<built-in method numpy.core._multiarray_umath.normalize_axis_index>)
<built-in method numpy.asanyarray> -- 100 100 -- 0.00004 0.00004 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>)
<method 'reduce' of...py.ufunc' objects> -- 100 100 -- 0.00109 0.00109 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>)
<built-in method builtins.hasattr> -- 100 100 -- 0.00006 0.00006 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
<built-in method builtins.isinstance> -- 100 100 -- 0.00004 0.00004 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<built-in method builtins.issubclass> -- 200 200 -- 0.00007 0.00007 -- ~:0:<built-in method builtins.issubclass> (<built-in method builtins.issubclass>)
<built-in method numpy.array> -- 100 100 -- 0.00358 0.00358 -- ~:0:<built-in method numpy.array> (<built-in method numpy.array>)
<method 'append' of 'list' objects> -- 6700 6700 -- 0.00169 0.00169 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.len> -- 40300 40300 -- 0.01424 0.01424 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_create_training_session -- 1 1 -- 0.00001 0.02824 -- onnxcustom/onnxcustom/training/optimizers_partial.py:626:_create_training_session (_create_training_session)
__init__ -- 1 1 -- 0.00008 0.02820 -- onnxcustom/onnxcustom/training/ortgradient.py:54:__init__ (__init__)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:91:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:94:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:113:<listcomp> (<listcomp>)
_init_next -- 1 1 -- 0.00010 0.02809 -- onnxcustom/onnxcustom/training/ortgradient.py:163:_init_next (_init_next)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:173:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:175:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:178:<listcomp> (<listcomp>)
_create_onnx_graphs -- 1 1 -- 0.00662 0.02797 -- onnxcustom/onnxcustom/training/ortgradient.py:287:_create_onnx_graphs (_create_onnx_graphs)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:396:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:397:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00002 -- onnxcustom/onnxcustom/training/ortgradient.py:399:<listcomp> (<listcomp>)
_provider_name_to_device_type -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:260:_provider_name_to_device_type (_provider_name_to_device_type) +++
<listcomp> -- 1 1 -- 0.00002 0.00002 -- onnxcustom/onnxcustom/training/ortgradient.py:404:<listcomp> (<listcomp>)
_provider_name_to_device_type -- 7 7 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:260:_provider_name_to_device_type (_provider_name_to_device_type) +++
<listcomp> -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:410:<listcomp> (<listcomp>)
_provider_name_to_device_type -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:260:_provider_name_to_device_type (_provider_name_to_device_type) +++
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:479:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:480:<listcomp> (<listcomp>)
get_inputs -- 1 1 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:111:get_inputs (get_inputs)
get_outputs -- 1 1 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:115:get_outputs (get_outputs)
__init__ -- 2 2 -- 0.00004 0.02063 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:283:__init__ (__init__)
get -- 2 2 -- 0.00001 0.00004 -- C:/Python395_x64/lib/_collections_abc.py:759:get (get)
__getitem__ -- 2 2 -- 0.00001 0.00003 -- C:/Python395_x64/lib/os.py:674:__getitem__ (__getitem__)
encodekey -- 2 2 -- 0.00001 0.00002 -- C:/Python395_x64/lib/os.py:746:encodekey (encodekey)
check_str -- 2 2 -- 0.00000 0.00000 -- C:/Python395_x64/lib/os.py:740:check_str (check_str)
__init__ -- 2 2 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:101:__init__ (__init__)
_create_inference_session -- 2 2 -- 0.02045 0.02055 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:346:_create_inference_session (_create_inference_session)
check_and_nor...rovider_args -- 2 2 -- 0.00004 0.00008 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:25:check_and_normalize_provider_args (check_and_normalize_provider_args)
set_provider_options -- 2 2 -- 0.00001 0.00001 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:53:set_provider_options (set_provider_options)
<dictcomp> -- 2 2 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:62:<dictcomp> (<dictcomp>)
<listcomp> -- 2 2 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:75:<listcomp> (<listcomp>)
<listcomp> -- 2 2 -- 0.00000 0.00000 -- onnxruntime/build/Windows/Release/Release/onnxruntime/capi/onnxruntime_inference_collection.py:78:<listcomp> (<listcomp>)
load_model -- 2 2 -- 0.00001 0.00049 -- site-packages/onnx/__init__.py:107:load_model (load_model)
_load_bytes -- 2 2 -- 0.00002 0.00003 -- site-packages/onnx/__init__.py:30:_load_bytes (_load_bytes)
inner -- 4 4 -- 0.00000 0.00000 -- C:/Python395_x64/lib/typing.py:262:inner (inner) +++
cast -- 4 4 -- 0.00000 0.00000 -- C:/Python395_x64/lib/typing.py:1333:cast (cast) +++
_get_file_path -- 2 2 -- 0.00000 0.00000 -- site-packages/onnx/__init__.py:50:_get_file_path (_get_file_path)
load_model_from_string -- 2 2 -- 0.00001 0.00045 -- site-packages/onnx/__init__.py:147:load_model_from_string (load_model_from_string)
_deserialize -- 2 2 -- 0.00001 0.00044 -- site-packages/onnx/__init__.py:81:_deserialize (_deserialize)
inner -- 2 2 -- 0.00000 0.00000 -- C:/Python395_x64/lib/typing.py:262:inner (inner) +++
cast -- 2 2 -- 0.00000 0.00000 -- C:/Python395_x64/lib/typing.py:1333:cast (cast) +++
<method 'Pa...' objects> -- 2 2 -- 0.00042 0.00042 -- ~:0:<method 'ParseFromString' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'ParseFromString' of 'google.protobuf.pyext._message.CMessage' objects>)
<built-in method builtins.len> -- 16 16 -- 0.00000 0.00000 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
<method 'Serializ...essage' objects> -- 1 1 -- 0.00014 0.00014 -- ~:0:<method 'SerializeToString' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'SerializeToString' of 'google.protobuf.pyext._message.CMessage' objects>)
new_instance -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:211:new_instance (new_instance)
__init__ -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/ortgradient.py:501:__init__ (__init__)
device_to_providers -- 1 1 -- 0.00003 0.00003 -- onnxruntime_helper.py:133:device_to_providers (device_to_providers)
value -- 100 100 -- 0.00003 0.00003 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:160:value (value) +++
init_learning_rate -- 1 1 -- 0.00000 0.00001 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:307:init_learning_rate (init_learning_rate)
init_learning_rate -- 1 1 -- 0.00000 0.00000 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:176:init_learning_rate (init_learning_rate)
update_learning_rate -- 100 100 -- 0.00015 0.00098 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:314:update_learning_rate (update_learning_rate)
update_learning_rate -- 100 100 -- 0.00084 0.00084 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:194:update_learning_rate (update_learning_rate)
proto_type_to_dtype -- 6 6 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/utils/onnx_helper.py:53:proto_type_to_dtype (proto_type_to_dtype)
<method 'append' of 'list' objects> -- 107 107 -- 0.00003 0.00003 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<built-in method builtins.len> -- 108 108 -- 0.00002 0.00002 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
<method 'randn' of 'numpy...nd.RandomState' objects> -- 6 6 -- 0.00040 0.00040 -- ~:0:<method 'randn' of 'numpy.random.mtrand.RandomState' objects> (<method 'randn' of 'numpy.random.mtrand.RandomState' objects>)
inner -- 6 6 -- 0.00001 0.00001 -- C:/Python395_x64/lib/typing.py:262:inner (inner)
cast -- 6 6 -- 0.00000 0.00000 -- C:/Python395_x64/lib/typing.py:1333:cast (cast)
_bio_cache -- 294800 294800 -- 0.18126 0.22052 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:138:_bio_cache (_bio_cache)
<built-in method builtins.id> -- 294800 294800 -- 0.03926 0.03926 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>) +++
_bio_ptr -- 294800 294800 -- 0.20762 0.20762 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:155:_bio_ptr (_bio_ptr)
_bind_input_ortvalue -- 214400 214400 -- 0.21699 0.59239 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:159:_bind_input_ortvalue (_bind_input_ortvalue)
_bio_cache -- 214400 214400 -- 0.13709 0.16646 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:138:_bio_cache (_bio_cache) +++
_bio_do_bind_in -- 14000 14000 -- 0.03012 0.03012 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:151:_bio_do_bind_in (_bio_do_bind_in)
_bio_ptr -- 214400 214400 -- 0.15540 0.15540 -- onnxcustom/onnxcustom/training/_base_onnx_function.py:155:_bio_ptr (_bio_ptr) +++
<built-in method builtins.isinstance> -- 214400 214400 -- 0.02341 0.02341 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_get_att_state -- 205 205 -- 0.00007 0.00007 -- onnxcustom/onnxcustom/training/optimizers_partial.py:139:_get_att_state (_get_att_state)
get_full_state -- 101 301 -- 0.00049 0.00093 -- onnxcustom/onnxcustom/training/optimizers_partial.py:147:get_full_state (get_full_state)
_get_att_state -- 201 201 -- 0.00007 0.00007 -- onnxcustom/onnxcustom/training/optimizers_partial.py:139:_get_att_state (_get_att_state) +++
<listcomp> -- 100 100 -- 0.00021 0.00072 -- onnxcustom/onnxcustom/training/optimizers_partial.py:152:<listcomp> (<listcomp>)
get_full_state -- 200 200 -- 0.00030 0.00050 -- onnxcustom/onnxcustom/training/optimizers_partial.py:147:get_full_state (get_full_state) +++
<built-in method builtins.getattr> -- 201 201 -- 0.00004 0.00004 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
<built-in method builtins.hasattr> -- 201 201 -- 0.00005 0.00005 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
<built-in method builtins.isinstance> -- 301 301 -- 0.00007 0.00007 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_provider_name_to_device_type -- 9 9 -- 0.00001 0.00001 -- onnxcustom/onnxcustom/training/ortgradient.py:260:_provider_name_to_device_type (_provider_name_to_device_type)
get_initializer -- 14 14 -- 0.00008 0.00175 -- onnxcustom/onnxcustom/training/ortgradient.py:269:get_initializer (get_initializer)
to_array -- 12 12 -- 0.00009 0.00168 -- site-packages/onnx/numpy_helper.py:21:to_array (to_array)
uses_external_data -- 12 12 -- 0.00001 0.00001 -- site-packages/onnx/external_data_helper.py:224:uses_external_data (uses_external_data)
<method 'HasField' of '...age.CMessage' objects> -- 12 12 -- 0.00000 0.00000 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>) +++
<method 'astype' of 'numpy.ndarray' objects> -- 12 12 -- 0.00006 0.00006 -- ~:0:<method 'astype' of 'numpy.ndarray' objects> (<method 'astype' of 'numpy.ndarray' objects>) +++
<method 'reshape' of 'numpy.ndarray' objects> -- 12 12 -- 0.00002 0.00002 -- ~:0:<method 'reshape' of 'numpy.ndarray' objects> (<method 'reshape' of 'numpy.ndarray' objects>) +++
<built-in method numpy.asarray> -- 12 12 -- 0.00148 0.00148 -- ~:0:<built-in method numpy.asarray> (<built-in method numpy.asarray>)
<built-in method builtins.getattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
<method 'HasField' of 'go...ssage.CMessage' objects> -- 24 24 -- 0.00001 0.00001 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>) +++
input_to_ort -- 13400 13400 -- 0.12150 0.16754 -- onnxcustom/onnxcustom/training/ortgradient.py:552:input_to_ort (input_to_ort)
<built-in method builtins.all> -- 13400 13400 -- 0.01681 0.03690 -- ~:0:<built-in method builtins.all> (<built-in method builtins.all>) +++
<built-in method builtins.isinstance> -- 13400 13400 -- 0.00712 0.00712 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<built-in method builtins.len> -- 13400 13400 -- 0.00202 0.00202 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
value -- 40300 40300 -- 0.00955 0.00955 -- onnxcustom/onnxcustom/training/sgd_learning_rate.py:160:value (value)
numpy_to_ort_value -- 13426 13426 -- 0.00661 0.03872 -- onnxruntime_helper.py:120:numpy_to_ort_value (numpy_to_ort_value)
<built-in method onnxruntim...state.ortvalue_from_numpy> -- 13426 13426 -- 0.03211 0.03211 -- ~:0:<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy> (<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy>) +++
<method 'astype' of 'numpy.ndarray' objects> -- 18 18 -- 0.00014 0.00014 -- ~:0:<method 'astype' of 'numpy.ndarray' objects> (<method 'astype' of 'numpy.ndarray' objects>)
<method 'append' of 'list' objects> -- 13575 13575 -- 0.00608 0.00608 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>)
<built-in method builtins.hasattr> -- 94120 94120 -- 0.01981 0.01981 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>)
<built-in method builtins.isinstance> -- 362251 362251 -- 0.04476 0.04477 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>)
__instancecheck__ -- 4 4 -- 0.00001 0.00001 -- C:/Python395_x64/lib/abc.py:96:__instancecheck__ (__instancecheck__)
<built-in method builtins.len> -- 67437 67437 -- 0.02341 0.02908 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>)
__len__ -- 13600 13600 -- 0.00567 0.00567 -- onnxcustom/onnxcustom/training/data_loader.py:89:__len__ (__len__)
<method 'reshape' of 'numpy.ndarray' objects> -- 14 14 -- 0.00002 0.00002 -- ~:0:<method 'reshape' of 'numpy.ndarray' objects> (<method 'reshape' of 'numpy.ndarray' objects>)
<built-in method builtins.getattr> -- 213 213 -- 0.00005 0.00005 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>)
<built-in method onnxruntime....1_state.ortvalue_from_numpy> -- 93826 93826 -- 0.19723 0.19723 -- ~: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> -- 301501 301501 -- 0.04083 0.04083 -- ~:0:<built-in method builtins.id> (<built-in method builtins.id>)
<method 'HasField' of 'google...._message.CMessage' objects> -- 36 36 -- 0.00001 0.00001 -- ~:0:<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects> (<method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects>)
<built-in method builtins.all> -- 13404 13404 -- 0.01681 0.03690 -- ~:0:<built-in method builtins.all> (<built-in method builtins.all>)
<lambda> -- 53600 53600 -- 0.01461 0.02009 -- onnxcustom/onnxcustom/training/ortgradient.py:572:<lambda> (<lambda>)
<built-in method builtins.isinstance> -- 53600 53600 -- 0.00548 0.00548 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
_iteration -- 100 100 -- 0.41903 3.74610 --
iter_ortvalue -- 6800 6800 -- 0.02838 0.14761 --
_next_iter -- 6700 6700 -- 0.00946 0.07207 --
<built-in method builtins.len> -- 6700 6700 -- 0.00245 0.00423 --
<method 'randint' o...domState' objects> -- 6700 6700 -- 0.05838 0.05838 --
numpy_to_ort_value -- 13400 13400 -- 0.00658 0.03860 --
<built-in method builtins.len> -- 6900 6900 -- 0.00467 0.00855 --
forward -- 6700 6700 -- 0.31685 0.44643 --
input_to_ort -- 6700 6700 -- 0.08002 0.11492 --
save_for_backward -- 6700 6700 -- 0.01032 0.01032 --
<method 'append' of 'list' objects> -- 6700 6700 -- 0.00434 0.00434 --
backward -- 6700 6700 -- 0.43012 0.48957 --
input_to_ort -- 6700 6700 -- 0.04148 0.05262 --
saved_tensors -- 6700 6700 -- 0.00207 0.00207 --
<method 'pop' of 'list' objects> -- 6700 6700 -- 0.00476 0.00476 --
loss_gradient -- 6700 6700 -- 0.05841 0.26967 --
clear_binding_inputs -- 6700 6700 -- 0.00545 0.01270 --
_cache_in_clear -- 6700 6700 -- 0.00568 0.00725 --
<built-in method builtins.id> -- 6700 6700 -- 0.00157 0.00157 --
_bind_input_ortvalue -- 13400 13400 -- 0.02070 0.07545 --
_call_iobinding -- 6700 6700 -- 0.11997 0.11997 --
<built-in method builtins.hasattr> -- 13400 13400 -- 0.00315 0.00315 --
penalty_loss -- 6700 6700 -- 0.00112 0.00112 --
update_weights -- 40200 40200 -- 0.00651 0.00651 --
update_weights -- 40200 40200 -- 0.40487 1.94238 --
_bind_input_ortvalue -- 201000 201000 -- 0.19630 0.51693 --
_bind_output_ortvalue -- 80400 80400 -- 0.07458 0.18952 --
_bio_cache -- 80400 80400 -- 0.04417 0.05406 --
_bio_ptr -- 80400 80400 -- 0.05222 0.05222 --
_bio_do_bind_out -- 12 12 -- 0.00003 0.00003 --
<built-in method builtins.isinstance> -- 80400 80400 -- 0.00863 0.00863 --
_call_iobinding -- 40200 40200 -- 0.63987 0.63987 --
value -- 40200 40200 -- 0.00953 0.00953 --
<built-in method onnx...ortvalue_from_numpy> -- 80400 80400 -- 0.16512 0.16512 --
<built-in method builtins.hasattr> -- 80400 80400 -- 0.01655 0.01655 --
<method 'mean' of 'numpy.ndarray' objects> -- 100 100 -- 0.00026 0.00426 --
_mean -- 100 100 -- 0.00163 0.00400 --
_count_reduce_items -- 100 100 -- 0.00097 0.00107 --
<built-in method ...lize_axis_index> -- 200 200 -- 0.00010 0.00010 --
<built-in method numpy.asanyarray> -- 100 100 -- 0.00004 0.00004 --
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<built-in method builtins.issubclass> -- 200 200 -- 0.00007 0.00007 --
<built-in method numpy.array> -- 100 100 -- 0.00358 0.00358 --
<method 'append' of 'list' objects> -- 6700 6700 -- 0.00169 0.00169 --
<built-in method builtins.len> -- 40300 40300 -- 0.01424 0.01424 --
_create_training_session -- 1 1 -- 0.00001 0.02824 --
__init__ -- 1 1 -- 0.00008 0.02820 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
<listcomp> -- 1 1 -- 0.00000 0.00000 --
<listcomp> -- 1 1 -- 0.00000 0.00000 --
_init_next -- 1 1 -- 0.00010 0.02809 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
<listcomp> -- 1 1 -- 0.00000 0.00000 --
<listcomp> -- 1 1 -- 0.00000 0.00000 --
_create_onnx_graphs -- 1 1 -- 0.00662 0.02797 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
<listcomp> -- 1 1 -- 0.00001 0.00002 --
_provider_name_to_device_type -- 1 1 -- 0.00000 0.00000 --
<listcomp> -- 1 1 -- 0.00002 0.00002 --
_provider_name_to_device_type -- 7 7 -- 0.00000 0.00000 --
<listcomp> -- 1 1 -- 0.00000 0.00000 --
_provider_name_to_device_type -- 1 1 -- 0.00000 0.00000 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
<listcomp> -- 1 1 -- 0.00001 0.00001 --
get_inputs -- 1 1 -- 0.00000 0.00000 --
get_outputs -- 1 1 -- 0.00000 0.00000 --
__init__ -- 2 2 -- 0.00004 0.02063 --
get -- 2 2 -- 0.00001 0.00004 --
__getitem__ -- 2 2 -- 0.00001 0.00003 --
encodekey -- 2 2 -- 0.00001 0.00002 --
check_str -- 2 2 -- 0.00000 0.00000 --
__init__ -- 2 2 -- 0.00000 0.00000 --
_create_inference_session -- 2 2 -- 0.02045 0.02055 --
check_and_nor...rovider_args -- 2 2 -- 0.00004 0.00008 --
set_provider_options -- 2 2 -- 0.00001 0.00001 --
<dictcomp> -- 2 2 -- 0.00000 0.00000 --
<listcomp> -- 2 2 -- 0.00000 0.00000 --
<listcomp> -- 2 2 -- 0.00000 0.00000 --
load_model -- 2 2 -- 0.00001 0.00049 --
_load_bytes -- 2 2 -- 0.00002 0.00003 --
inner -- 4 4 -- 0.00000 0.00000 --
cast -- 4 4 -- 0.00000 0.00000 --
_get_file_path -- 2 2 -- 0.00000 0.00000 --
load_model_from_string -- 2 2 -- 0.00001 0.00045 --
_deserialize -- 2 2 -- 0.00001 0.00044 --
inner -- 2 2 -- 0.00000 0.00000 --
cast -- 2 2 -- 0.00000 0.00000 --
<method 'Pa...' objects> -- 2 2 -- 0.00042 0.00042 --
<built-in method builtins.len> -- 16 16 -- 0.00000 0.00000 --
<method 'Serializ...essage' objects> -- 1 1 -- 0.00014 0.00014 --
new_instance -- 1 1 -- 0.00000 0.00000 --
__init__ -- 1 1 -- 0.00000 0.00000 --
device_to_providers -- 1 1 -- 0.00003 0.00003 --
value -- 100 100 -- 0.00003 0.00003 --import os
if not os.path.exists("mlp_onnx_ort"):
os.mkdir("mlp_onnx_ort")
train_session2.save_onnx_graph("mlp_onnx_ort")
{'model_onnx': 'mlp_onnx_ort\\GradFBOptimizer.model_onnx.onnx',
'learning_rate': {'axpyw_onnx_': 'mlp_onnx_ort\\LRateSGDNesterov.learning_rate.axpyw_onnx_.onnx'},
'learning_loss': {'loss_grad_onnx_': 'mlp_onnx_ort\\SquareLLoss.learning_loss.loss_grad_onnx_.onnx',
'loss_score_onnx_': 'mlp_onnx_ort\\SquareLLoss.learning_loss.loss_score_onnx_.onnx'},
'learning_penalty': {},
'zero_onnx_': 'mlp_onnx_ort\\GradFBOptimizer.zero_onnx_.onnx',
'train_function_': {'_trained_onnx': 'mlp_onnx_ort\\OrtGradientForwardBackwardFunction_1523278698000.train_function_._trained_onnx.onnx',
'_optimized_pre_grad_model': 'mlp_onnx_ort\\OrtGradientForwardBackwardFunction_1523278698000.train_function_._optimized_pre_grad_model.onnx'}}
Weights are updated with the following ONNX graph:
%onnxview train_session2.learning_rate.axpyw_onnx_