torchcde Consider opening a GH discussions

Consider opening a GH discussions

Open darleybarreto opened this issue 2 years ago • 13 comments

Hi Patrick,

First I would like to thank you and everyone involved in this package and related research 🎉 ! I've been trying to use it, but I'm seeing some nans and I would really appreciate your (and/or others) insights on this. Therefore I would like to know your thoughts about opening a GH discussions tab for non-issues related conversations, which is basically a forum inside GH.

Best regards,

Oct 22 '21 12:10 darleybarreto

Hi there! Glad you like it. So the traffic here is low-volume enough that I'm happy to field these kinds of queries just via issues, rather than creating a whole separate tab. What kind of issue are you having?

Oct 22 '21 20:10 patrick-kidger

The instances of my dataset are multi-dimensional, each feature is a time series sampled at the same rate (at the same time). Given the rate of the sampling, I know when piece of data is missing for any instance, and since all features are sampled together, they are all missing for a particular time. Therefore I found your example of irregular data very useful to me, because I have missing data and the instances may have different sizes.

Unfortunately I can't show the code, but it's very similar to irregular_data.py, before calling

coeffs = torchcde.hermite_cubic_coefficients_with_backward_differences(x)

x has the shape N x Time_idx x (N_features + N_masks), where

N is the number of instances
Time_idx is the index indicating order of the series (from 0-1)
N_features is the number of features of the instances (collected at the same time)
N_masks is the mask given by the cunsum of not nans in the features, in the example you used maska and maskb, so it's one for each feature.

Calling cdeint gives:

Traceback (most recent call last):
  File "XXXXXX", line YYYY, in forward
    zt = torchcde.cdeint(X=X, func=self.func, z0=z0, t=X.interval)
  File lib/site-packages/torchcde/solver.py", line 227, in cdeint
    out = odeint(func=vector_field, y0=z0, t=t, **kwargs)
  File lib/site-packages/torchdiffeq/_impl/adjoint.py", line 198, in odeint_adjoint
    ans = OdeintAdjointMethod.apply(shapes, func, y0, t, rtol, atol, method, options, event_fn, adjoint_rtol, adjoint_atol,
  File lib/site-packages/torchdiffeq/_impl/adjoint.py", line 25, in forward
    ans = odeint(func, y0, t, rtol=rtol, atol=atol, method=method, options=options, event_fn=event_fn)
  File lib/site-packages/torchdiffeq/_impl/odeint.py", line 77, in odeint
    solution = solver.integrate(t)
  File lib/site-packages/torchdiffeq/_impl/solvers.py", line 30, in integrate
    solution[i] = self._advance(t[i])
  File lib/site-packages/torchdiffeq/_impl/rk_common.py", line 194, in _advance
    self.rk_state = self._adaptive_step(self.rk_state)
  File lib/site-packages/torchdiffeq/_impl/rk_common.py", line 228, in _adaptive_step
    assert t0 + dt > t0, 'underflow in dt {}'.format(dt.item())
AssertionError: underflow in dt nan

That's definitely not enough detail, so please let me know anything else you need to know.

Thanks in advance!

EDIT: Fix errors.

Oct 25 '21 11:10 darleybarreto

It looks like your tensor x has the wrong shape. As per here, the tensor must have shape (..., length, channels) -- in particular it is the penultimate dimension that corresponds to time.

Oct 25 '21 14:10 patrick-kidger

Sorry, there was two errors, it should be N x Time_idx x (N_features + N_masks), where Time_idx is the time index of size Max_series_size.

Oct 25 '21 14:10 darleybarreto

There's two main possibilities here. The first is that your dynamics are really stiff, or otherwise maladapted to your choice of numerical solver. Trying adjusting tolerances, changing the integration method, etc.

The second possibility is that you're passing nan data in somewhere. I suggest running your code in a debugger (python -m pdb -c continue your_script.py) and stepping through the stack trace to see if that's the case.

Oct 26 '21 10:10 patrick-kidger

Using autograd.detect_anomaly() around loss.backward() I see

RuntimeError: Function 'OdeintAdjointMethodBackward' returned nan values in its 2th output.
Segmentation fault (core dumped)

So it seems to be during the backward pass instead of forward, then when the weights are updated, it propagates nan and affects the dt there. torchdiffeq has these solvers:

SOLVERS = {
    'dopri8': Dopri8Solver,
    'dopri5': Dopri5Solver,
    'bosh3': Bosh3Solver,
    'fehlberg2': Fehlberg2,
    'adaptive_heun': AdaptiveHeunSolver,
    'euler': Euler,
    'midpoint': Midpoint,
    'rk4': RK4,
    'explicit_adams': AdamsBashforth,
    'implicit_adams': AdamsBashforthMoulton,
    # Backward compatibility: use the same name as before
    'fixed_adams': AdamsBashforthMoulton,
    # ~Backwards compatibility
    'scipy_solver': ScipyWrapperODESolver,
}

Would you recommend any? Or should I try all of them? I think the default is dopri5.

Oct 26 '21 12:10 darleybarreto

Right. So via a debugger or otherwise, try and track down where that nan is coming from. Is it a nan arising from e.g. a division by zero, or is it a nan arising from some nan data accidentally getting passed in, etc?

In terms of solvers: dopri8 is the highest-order/most-accurate solver torchdiffeq supports. You can try that and see if it helps resolve nans due to stiffness issues. You can also try any of the fixed-step solvers (euler, midpoint, rk4) -- these won't do any adaptive stepping so the actual solution accuracy might be slightly questionable, but in doing so they just entirely ignore stiffness issues, which can at least help diagnose whether that's the root cause.

Oct 26 '21 12:10 patrick-kidger

OK, thanks to clarify that! If I remember correctly, I have to add some hooks in backward of OdeintAdjointMethod, because the debugger can't reach inside. I will add infos here as I progress so it might help others with similar issues in the future.

Oct 26 '21 13:10 darleybarreto

After a couple days debugging, I found where the nans come from.

Testing your example and adding this simple print in here:

def _advance(self, next_t):
    """Interpolate through the next time point, integrating as necessary."""
    n_steps = 0
    while next_t > self.rk_state.t1:
        assert n_steps < self.max_num_steps, 'max_num_steps exceeded ({}>={})'.format(n_steps, self.max_num_steps)
        
        print(f"_advance rk_state.t1: {self.rk_state.t1.item()}, rk_state[1].max(): {self.rk_state[1].max()}") # <------
        
        self.rk_state = self._adaptive_step(self.rk_state)
        n_steps += 1
    return _interp_evaluate(self.rk_state.interp_coeff, self.rk_state.t0, self.rk_state.t1, next_t)

Gives this log

_advance rk_state.t1: 0.0, rk_state[1].max(): 0.6735
_advance rk_state.t1: 0.04928882047533989, rk_state[1].max(): 0.6516
_advance rk_state.t1: 0.5262108913124267, rk_state[1].max(): 0.4341
_advance rk_state.t1: 0.5262108913124267, rk_state[1].max(): 0.4341
_advance rk_state.t1: 0.5262108913124267, rk_state[1].max(): 0.4341
_advance rk_state.t1: 0.8388604449826595, rk_state[1].max(): 0.3720
_advance rk_state.t1: 0.8388604449826595, rk_state[1].max(): 0.3720
_advance rk_state.t1: 0.8388604449826595, rk_state[1].max(): 0.3720
_advance rk_state.t1: 1.2413736745448192, rk_state[1].max(): 0.2678
_advance rk_state.t1: 1.6452975697103063, rk_state[1].max(): 0.2058
_advance rk_state.t1: 1.6452975697103063, rk_state[1].max(): 0.2058
_advance rk_state.t1: 1.6452975697103063, rk_state[1].max(): 0.2058
_advance rk_state.t1: 1.9259946636448355, rk_state[1].max(): 0.1567
_advance rk_state.t1: 1.9259946636448355, rk_state[1].max(): 0.1567
_advance rk_state.t1: 1.9259946636448355, rk_state[1].max(): 0.1567
_advance rk_state.t1: 1.9259946636448355, rk_state[1].max(): 0.1567
_advance rk_state.t1: 2.161198239424196, rk_state[1].max(): 0.2510
_advance rk_state.t1: 2.3964018152035567, rk_state[1].max(): 0.5493
_advance rk_state.t1: 2.9041546902150066, rk_state[1].max(): 0.6106
_advance rk_state.t1: 2.9041546902150066, rk_state[1].max(): 0.6106
_advance rk_state.t1: 2.9041546902150066, rk_state[1].max(): 0.6106
_advance rk_state.t1: 3.246865634346614, rk_state[1].max(): 0.1907
_advance rk_state.t1: 3.590699609675312, rk_state[1].max(): 0.5668
_advance rk_state.t1: 3.590699609675312, rk_state[1].max(): 0.5668
_advance rk_state.t1: 3.957096870011467, rk_state[1].max(): 0.5368
_advance rk_state.t1: 3.957096870011467, rk_state[1].max(): 0.5368
_advance rk_state.t1: 3.957096870011467, rk_state[1].max(): 0.5368
_advance rk_state.t1: 3.957096870011467, rk_state[1].max(): 0.5368
_advance rk_state.t1: 4.299201574020484, rk_state[1].max(): 0.5672
_advance rk_state.t1: 4.641306278029501, rk_state[1].max(): 1.0275
_advance rk_state.t1: 4.641306278029501, rk_state[1].max(): 1.0275
_advance rk_state.t1: 4.641306278029501, rk_state[1].max(): 1.0275
_advance rk_state.t1: 4.972289255010118, rk_state[1].max(): 0.7999
_advance rk_state.t1: 5.792152661845133, rk_state[1].max(): 0.5974
_advance rk_state.t1: 5.792152661845133, rk_state[1].max(): 0.5974
_advance rk_state.t1: 5.792152661845133, rk_state[1].max(): 0.5974
_advance rk_state.t1: 6.257605844512485, rk_state[1].max(): 0.2813
_advance rk_state.t1: 6.723059027179836, rk_state[1].max(): 1.0523
_advance rk_state.t1: 6.723059027179836, rk_state[1].max(): 1.0523
_advance rk_state.t1: 7.259864301980258, rk_state[1].max(): 0.6561
_advance rk_state.t1: 7.811695970089863, rk_state[1].max(): 1.1571
_advance rk_state.t1: 7.811695970089863, rk_state[1].max(): 1.1571
_advance rk_state.t1: 8.379805863283046, rk_state[1].max(): 0.8760
_advance rk_state.t1: 8.947915756476228, rk_state[1].max(): 0.8692
_advance rk_state.t1: 9.812561832178414, rk_state[1].max(): 1.0917
_advance rk_state.t1: 9.812561832178414, rk_state[1].max(): 1.0917
_advance rk_state.t1: 9.812561832178414, rk_state[1].max(): 1.0917
_advance rk_state.t1: 9.812561832178414, rk_state[1].max(): 1.0917
_advance rk_state.t1: 10.309899357470842, rk_state[1].max(): 0.7788
_advance rk_state.t1: 10.808682372875015, rk_state[1].max(): 1.1575
_advance rk_state.t1: 10.808682372875015, rk_state[1].max(): 1.1575
_advance rk_state.t1: 11.609765960209105, rk_state[1].max(): 0.1880
_advance rk_state.t1: 12.410849547543194, rk_state[1].max(): 0.4723
_advance rk_state.t1: 12.410849547543194, rk_state[1].max(): 0.4723
_advance rk_state.t1: 12.879195750839349, rk_state[1].max(): 0.4028
_advance rk_state.t1: 12.879195750839349, rk_state[1].max(): 0.4028
_advance rk_state.t1: 13.547844510305358, rk_state[1].max(): 0.9315
_advance rk_state.t1: 13.547844510305358, rk_state[1].max(): 0.9315
_advance rk_state.t1: 13.547844510305358, rk_state[1].max(): 0.9315
_advance rk_state.t1: 13.920466029877456, rk_state[1].max(): 0.7602
_advance rk_state.t1: 13.920466029877456, rk_state[1].max(): 0.7602
_advance rk_state.t1: 14.570199075566366, rk_state[1].max(): 1.1983

But when I tried run my code, there's a divergence in this loop.

See the log

_advance rk_state.t1: 0.0, rk_state[1].max(): 6645.447265625
_advance rk_state.t1: 0.0009780435357242823, rk_state[1].max(): 6701.6884765625
_advance rk_state.t1: 0.003318463720914399, rk_state[1].max(): 6828.640625
_advance rk_state.t1: 0.006506793139308999, rk_state[1].max(): 6902.06201171875
_advance rk_state.t1: 0.011090239357828765, rk_state[1].max(): 7955.19775390625
_advance rk_state.t1: 0.016817937438102053, rk_state[1].max(): 10094.625
_advance rk_state.t1: 0.023523618884005375, rk_state[1].max(): 22943.123046875
_advance rk_state.t1: 0.030229300329908698, rk_state[1].max(): 43281.59375
_advance rk_state.t1: 0.037605061221655994, rk_state[1].max(): 97077.1015625
_advance rk_state.t1: 0.04498082211340329, rk_state[1].max(): 214645.34375
_advance rk_state.t1: 0.05365379166921811, rk_state[1].max(): 530304.8125
_advance rk_state.t1: 0.05365379166921811, rk_state[1].max(): 530304.8125
_advance rk_state.t1: 0.062332706194036955, rk_state[1].max(): 1363184.875
_advance rk_state.t1: 0.07230306541729502, rk_state[1].max(): 4118484.25
_advance rk_state.t1: 0.08279613113146184, rk_state[1].max(): 13171374.0
_advance rk_state.t1: 0.09328919684562866, rk_state[1].max(): 41926996.0
_advance rk_state.t1: 0.10378226255979547, rk_state[1].max(): 133298904.0
_advance rk_state.t1: 0.1149933309860242, rk_state[1].max(): 458799392.0
_advance rk_state.t1: 0.12662884398182422, rk_state[1].max(): 1655075840.0
_advance rk_state.t1: 0.13826435697762426, rk_state[1].max(): 5970431488.0
_advance rk_state.t1: 0.15009750858126236, rk_state[1].max(): 22010773504.0
_advance rk_state.t1: 0.16196899343651058, rk_state[1].max(): 81487888384.0
_advance rk_state.t1: 0.173860167751514, rk_state[1].max(): 302337327104.0
_advance rk_state.t1: 0.18575134206651742, rk_state[1].max(): 1121733574656.0
_advance rk_state.t1: 0.19764251638152083, rk_state[1].max(): 4161855291392.0
_advance rk_state.t1: 0.20953369069652425, rk_state[1].max(): 15441306058752.0
_advance rk_state.t1: 0.22146533771912597, rk_state[1].max(): 57546345807872.0
_advance rk_state.t1: 0.23339698474172768, rk_state[1].max(): 214462514593792.0
_advance rk_state.t1: 0.2453286317643294, rk_state[1].max(): 799254423535616.0
_advance rk_state.t1: 0.25726027878693114, rk_state[1].max(): 2978643571638272.0
_advance rk_state.t1: 0.2691919258095329, rk_state[1].max(): 1.1100743482212352e+16
_advance rk_state.t1: 0.2811235728321346, rk_state[1].max(): 4.137002264323686e+16
_advance rk_state.t1: 0.29305521985473637, rk_state[1].max(): 1.541768877011108e+17
_advance rk_state.t1: 0.3049868668773381, rk_state[1].max(): 5.7458313004056576e+17
_advance rk_state.t1: 0.31691851389993986, rk_state[1].max(): 2.1413435627536384e+18
_advance rk_state.t1: 0.3288501609225416, rk_state[1].max(): 7.980314768026108e+18
_advance rk_state.t1: 0.34078180794514334, rk_state[1].max(): 2.9740863742550213e+19
_advance rk_state.t1: 0.3527134549677451, rk_state[1].max(): 1.1083759784800603e+20
_advance rk_state.t1: 0.36464510199034683, rk_state[1].max(): 4.1306716715079434e+20
_advance rk_state.t1: 0.3765767490129486, rk_state[1].max(): 1.539409728578668e+21
_advance rk_state.t1: 0.3885284548558497, rk_state[1].max(): 5.749734948214797e+21
_advance rk_state.t1: 0.4004828538600257, rk_state[1].max(): 2.14817806611895e+22
_advance rk_state.t1: 0.4124372528642017, rk_state[1].max(): 8.02588291033547e+22
_advance rk_state.t1: 0.42439663768944963, rk_state[1].max(): 3.000226914880112e+23
_advance rk_state.t1: 0.43636225514789034, rk_state[1].max(): 1.1223114386595352e+24
_advance rk_state.t1: 0.4483293670660971, rk_state[1].max(): 4.198985948407945e+24
_advance rk_state.t1: 0.46029647898430387, rk_state[1].max(): 1.5709975899561966e+25
_advance rk_state.t1: 0.47226359090251063, rk_state[1].max(): 5.877688370049084e+25
_advance rk_state.t1: 0.4842307028207174, rk_state[1].max(): 2.199061448598856e+26
_advance rk_state.t1: 0.49619781473892416, rk_state[1].max(): 8.227506849161255e+26
_advance rk_state.t1: 0.5081649266571309, rk_state[1].max(): 3.078217038017068e+27
_advance rk_state.t1: 0.5201320385753376, rk_state[1].max(): 1.151675626269504e+28
_advance rk_state.t1: 0.5320991504935444, rk_state[1].max(): 4.308847739430682e+28
_advance rk_state.t1: 0.5440662624117512, rk_state[1].max(): 1.6120999359308776e+29
_advance rk_state.t1: 0.5560333743299579, rk_state[1].max(): 6.031468429369366e+29
_advance rk_state.t1: 0.5680004862481647, rk_state[1].max(): 2.2565964998125944e+30
_advance rk_state.t1: 0.5799675981663714, rk_state[1].max(): 8.442769201813588e+30
_advance rk_state.t1: 0.5919347100845782, rk_state[1].max(): 3.1587551259640996e+31
_advance rk_state.t1: 0.6039043947757843, rk_state[1].max(): 1.1821426967224933e+32
_advance rk_state.t1: 0.6158740794669904, rk_state[1].max(): 4.4240905365337014e+32
_advance rk_state.t1: 0.6278437641581964, rk_state[1].max(): 1.65568630987734e+33
_advance rk_state.t1: 0.6398134488494025, rk_state[1].max(): 6.196296559926234e+33
_advance rk_state.t1: 0.6517831335406086, rk_state[1].max(): 2.3189228989298065e+34
_advance rk_state.t1: 0.6637528182318146, rk_state[1].max(): 8.67841325662091e+34
_advance rk_state.t1: 0.6757225029230207, rk_state[1].max(): 3.2478386896764637e+35
_advance rk_state.t1: 0.6876921876142268, rk_state[1].max(): 1.2154817866545994e+36
_advance rk_state.t1: 0.6996618723054329, rk_state[1].max(): 4.5488602315697825e+36
_advance rk_state.t1: 0.7116315569966389, rk_state[1].max(): 1.7023807253114588e+37
_advance rk_state.t1: 0.723601241687845, rk_state[1].max(): 6.371048643349772e+37
_advance rk_state.t1: 0.7355709263790511, rk_state[1].max(): 2.3843223913765084e+38

Would you happen to know what hyperparameters might help here?

Oct 29 '21 16:10 darleybarreto

Probably you're doing something like using a neural network vector field without a tanh at the end.

Generally speaking you want to constrain the rate of change of hidden state, because of issues like the one you're describing. See Section 6.2 of the original nCDE paper.

Oct 29 '21 17:10 patrick-kidger

You were right, I replaced all activations with tanh. I also added here:

def integrate(self, t):
    solution = torch.empty(len(t), *self.y0.shape, dtype=self.y0.dtype, device=self.y0.device)
    solution[0] = self.y0
    t = t.to(self.dtype)
    self._before_integrate(t)
    for i in range(1, len(t)):
    	print(LOOP INTEGRATE", self.rk_state[1].max(), "\n") # <-----
        solution[i] = self._advance(t[i])
    return solution

At the 9th epoch, I see this:

See log

LOOP INTEGRATE tensor(430.7609)

_advance rk_state.t1: 0.0, rk_state[1].max(): 430.76092529296875 _advance rk_state.t1: 0.0212772898375988, rk_state[1].max(): 393.1483154296875 _advance rk_state.t1: 0.0212772898375988, rk_state[1].max(): 393.1483154296875 _advance rk_state.t1: 0.03861570544150989, rk_state[1].max(): 320.4320373535156 _advance rk_state.t1: 0.05725040769919968, rk_state[1].max(): 321.9886474609375 _advance rk_state.t1: 0.08448551800698134, rk_state[1].max(): 403.7205505371094 .... omitting because GH chars max number _advance rk_state.t1: 6.888946152505082, rk_state[1].max(): 739.8065185546875 _advance rk_state.t1: 6.918775537832372, rk_state[1].max(): 842.4842529296875 _advance rk_state.t1: 6.948604923159661, rk_state[1].max(): 922.146484375 _advance rk_state.t1: 6.981748016688462, rk_state[1].max(): 944.7467651367188 _advance rk_state.t1: 7.014891110217262, rk_state[1].max(): 666.915283203125 _advance rk_state.t1: 7.056683442164856, rk_state[1].max(): 307.0375671386719 _advance rk_state.t1: 7.115485195533801, rk_state[1].max(): 167.74630737304688 _advance rk_state.t1: 7.189098106092694, rk_state[1].max(): 609.44189453125 _advance rk_state.t1: 7.189098106092694, rk_state[1].max(): 609.44189453125 _advance rk_state.t1: 7.189098106092694, rk_state[1].max(): 609.44189453125 _advance rk_state.t1: 7.241469455202709, rk_state[1].max(): 1059.0848388671875 _advance rk_state.t1: 7.241469455202709, rk_state[1].max(): 1059.0848388671875 _advance rk_state.t1: 7.241469455202709, rk_state[1].max(): 1059.0848388671875 _advance rk_state.t1: 7.275095050268044, rk_state[1].max(): 956.2805786132812 _advance rk_state.t1: 7.30872064533338, rk_state[1].max(): 1144.899169921875 _advance rk_state.t1: 7.30872064533338, rk_state[1].max(): 1144.899169921875 _advance rk_state.t1: 7.337339096637567, rk_state[1].max(): 1499.8428955078125 _advance rk_state.t1: 7.365957547941755, rk_state[1].max(): 1844.5777587890625 _advance rk_state.t1: 7.395221928355749, rk_state[1].max(): 1945.3175048828125 _advance rk_state.t1: 7.424486308769743, rk_state[1].max(): 2045.252197265625 _advance rk_state.t1: 7.424486308769743, rk_state[1].max(): 2045.252197265625 _advance rk_state.t1: 7.44967542806329, rk_state[1].max(): 1992.78369140625 _advance rk_state.t1: 7.44967542806329, rk_state[1].max(): 1992.78369140625 _advance rk_state.t1: 7.471143034916027, rk_state[1].max(): 2519.159423828125 _advance rk_state.t1: 7.492610641768764, rk_state[1].max(): 2507.117431640625 _advance rk_state.t1: 7.519083592956316, rk_state[1].max(): 2141.774169921875 _advance rk_state.t1: 7.519083592956316, rk_state[1].max(): 2141.774169921875 _advance rk_state.t1: 7.537242548528309, rk_state[1].max(): 2124.72265625 _advance rk_state.t1: 7.537242548528309, rk_state[1].max(): 2124.72265625 _advance rk_state.t1: 7.537242548528309, rk_state[1].max(): 2124.72265625 _advance rk_state.t1: 7.550684370366488, rk_state[1].max(): 1842.5989990234375 _advance rk_state.t1: 7.550684370366488, rk_state[1].max(): 1842.5989990234375 _advance rk_state.t1: 7.550684370366488, rk_state[1].max(): 1842.5989990234375 _advance rk_state.t1: 7.560578683889778, rk_state[1].max(): 1696.1090087890625 _advance rk_state.t1: 7.570472997413068, rk_state[1].max(): 1681.18603515625 _advance rk_state.t1: 7.570472997413068, rk_state[1].max(): 1681.18603515625 .... omitting because GH chars max number _advance rk_state.t1: 27.93551823840477, rk_state[1].max(): 31.13615608215332 _advance rk_state.t1: 28.1836800016003, rk_state[1].max(): 0.0 _advance rk_state.t1: 28.449252480435476, rk_state[1].max(): 0.0

LOOP INTEGRATE tensor(-0.)

_advance rk_state.t1: -29.0, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.999999000000003, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.999989000000028, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.99988900000028, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.998889000002805, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.988889000028053, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.88888900028053, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.88888900028053, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.673414653475536, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.673414653475536, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.199960448953274, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.199960448953274, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.199960448953274, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.199960448953274, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.014870007245825, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.006486216889275, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00457716654293, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00457716654293, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00457716654293, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00457716654293, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.003161073302877, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.003161073302877, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.003161073302877, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.003161073302877, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.002169023205525, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.002169023205525, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.002169023205525, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.002169023205525, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00142183380241, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00142183380241, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00142183380241, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00142183380241, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.00038869701414, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000069426273594, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -28.000027954280625, rk_state[1].max(): -0.0 _advance rk_state.t1: -27.999863344755607, rk_state[1].max(): 8.130182266235352 _advance rk_state.t1: -27.999698735230588, rk_state[1].max(): 8.126178741455078 _advance rk_state.t1: -27.998853240732718, rk_state[1].max(): 8.105562210083008 _advance rk_state.t1: -27.99330927552803, rk_state[1].max(): 7.97026252746582 _advance rk_state.t1: -27.973692108132404, rk_state[1].max(): 20.209978103637695 _advance rk_state.t1: -27.88675900891094, rk_state[1].max(): 72.9245834350586 _advance rk_state.t1: -27.88675900891094, rk_state[1].max(): 72.9245834350586 _advance rk_state.t1: -27.83241472656605, rk_state[1].max(): 95.3282470703125 _advance rk_state.t1: -27.83241472656605, rk_state[1].max(): 95.3282470703125 _advance rk_state.t1: -27.83241472656605, rk_state[1].max(): 95.3282470703125 _advance rk_state.t1: -27.78470798334973, rk_state[1].max(): 102.0097885131836 _advance rk_state.t1: -27.73700124013341, rk_state[1].max(): 110.0472640991211 _advance rk_state.t1: -27.65867071702403, rk_state[1].max(): 126.55400848388672 _advance rk_state.t1: -27.65867071702403, rk_state[1].max(): 126.55400848388672 _advance rk_state.t1: -27.58553726900098, rk_state[1].max(): 119.69310760498047 _advance rk_state.t1: -27.51240382097793, rk_state[1].max(): 106.32828521728516 _advance rk_state.t1: -27.51240382097793, rk_state[1].max(): 106.32828521728516 _advance rk_state.t1: -27.457530744487247, rk_state[1].max(): 82.05793762207031 _advance rk_state.t1: -27.402657667996564, rk_state[1].max(): 50.437400817871094 _advance rk_state.t1: -27.323574375813966, rk_state[1].max(): 5.633184432983398 _advance rk_state.t1: -27.323574375813966, rk_state[1].max(): 5.633184432983398 _advance rk_state.t1: -27.25953968270269, rk_state[1].max(): 46.58814239501953 _advance rk_state.t1: -27.25953968270269, rk_state[1].max(): 46.58814239501953 _advance rk_state.t1: -27.20740090497253, rk_state[1].max(): 80.74925231933594 _advance rk_state.t1: -27.20740090497253, rk_state[1].max(): 80.74925231933594 _advance rk_state.t1: -27.164347890121352, rk_state[1].max(): 97.62422180175781 _advance rk_state.t1: -27.121294875270173, rk_state[1].max(): 125.00833892822266 _advance rk_state.t1: -27.121294875270173, rk_state[1].max(): 125.00833892822266 _advance rk_state.t1: -27.089321042463677, rk_state[1].max(): 264.89385986328125 _advance rk_state.t1: -27.089321042463677, rk_state[1].max(): 264.89385986328125 _advance rk_state.t1: -27.062139275019756, rk_state[1].max(): 162.02098083496094 _advance rk_state.t1: -27.062139275019756, rk_state[1].max(): 162.02098083496094 _advance rk_state.t1: -27.041249763718962, rk_state[1].max(): 197.13710021972656 _advance rk_state.t1: -27.041249763718962, rk_state[1].max(): 197.13710021972656 _advance rk_state.t1: -27.041249763718962, rk_state[1].max(): 197.13710021972656 _advance rk_state.t1: -27.026278769347254, rk_state[1].max(): 308.5970153808594 _advance rk_state.t1: -27.01109829792243, rk_state[1].max(): 258.78204345703125 _advance rk_state.t1: -27.01109829792243, rk_state[1].max(): 258.78204345703125 _advance rk_state.t1: -27.002956186087637, rk_state[1].max(): 264.0741271972656 _advance rk_state.t1: -27.002956186087637, rk_state[1].max(): 264.0741271972656 _advance rk_state.t1: -27.002956186087637, rk_state[1].max(): 264.0741271972656 _advance rk_state.t1: -27.002956186087637, rk_state[1].max(): 264.0741271972656 _advance rk_state.t1: -27.002956186087637, rk_state[1].max(): 264.0741271972656 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000472151885955, rk_state[1].max(): 266.6177673339844 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -27.000197127957655, rk_state[1].max(): 266.91436767578125 _advance rk_state.t1: -26.99938458729487, rk_state[1].max(): 267.5133972167969 _advance rk_state.t1: -26.998572046632084, rk_state[1].max(): 268.03179931640625 _advance rk_state.t1: -26.994063790043842, rk_state[1].max(): 270.9820861816406 _advance rk_state.t1: -26.97232900733037, rk_state[1].max(): 269.957275390625 _advance rk_state.t1: -26.937399894188648, rk_state[1].max(): 365.9190673828125 _advance rk_state.t1: -26.902470781046926, rk_state[1].max(): 531.6653442382812 _advance rk_state.t1: -26.859328195147203, rk_state[1].max(): 501.95343017578125 _advance rk_state.t1: -26.814440167191314, rk_state[1].max(): 531.3722534179688 _advance rk_state.t1: -26.759906600025694, rk_state[1].max(): 563.519775390625 _advance rk_state.t1: -26.759906600025694, rk_state[1].max(): 563.519775390625 _advance rk_state.t1: -26.759906600025694, rk_state[1].max(): 563.519775390625 _advance rk_state.t1: -26.7336451299766, rk_state[1].max(): 567.5189819335938 _advance rk_state.t1: -26.7336451299766, rk_state[1].max(): 567.5189819335938 _advance rk_state.t1: -26.7336451299766, rk_state[1].max(): 567.5189819335938 _advance rk_state.t1: -26.710736327454388, rk_state[1].max(): 547.001220703125 _advance rk_state.t1: -26.685541654013118, rk_state[1].max(): 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rk_state[1].max(): 9.325635273593478e+22 _advance rk_state.t1: -7.794848864385236, rk_state[1].max(): 9.325635273593478e+22 _advance rk_state.t1: -7.792881715711583, rk_state[1].max(): 1.093716463329494e+23 _advance rk_state.t1: -7.792881715711583, rk_state[1].max(): 1.093716463329494e+23 _advance rk_state.t1: -7.791081895759164, rk_state[1].max(): 1.2563152349240114e+23 _advance rk_state.t1: -7.789282075806745, rk_state[1].max(): 1.4662354895311316e+23 .... omitting because GH chars max number _advance rk_state.t1: -0.8130662484625999, rk_state[1].max(): 1.5149891344471805e+37 _advance rk_state.t1: -0.8130662484625999, rk_state[1].max(): 1.5149891344471805e+37 _advance rk_state.t1: -0.7947013762332956, rk_state[1].max(): 1.5942044936903826e+37 _advance rk_state.t1: -0.7758370382398517, rk_state[1].max(): 1.628910105058371e+37 _advance rk_state.t1: -0.7758370382398517, rk_state[1].max(): 1.628910105058371e+37 _advance rk_state.t1: -0.7584581332157282, rk_state[1].max(): 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rk_state[1].max(): 1.3608246940558446e+37 _advance rk_state.t1: -0.6267486424914464, rk_state[1].max(): 1.3402331044707972e+37 _advance rk_state.t1: -0.6267486424914464, rk_state[1].max(): 1.3402331044707972e+37 _advance rk_state.t1: -0.6161266905474544, rk_state[1].max(): 1.3164129355720286e+37 _advance rk_state.t1: -0.6161266905474544, rk_state[1].max(): 1.3164129355720286e+37 _advance rk_state.t1: -0.6077087292949697, rk_state[1].max(): 1.2968386355936645e+37 _advance rk_state.t1: -0.5992907680424849, rk_state[1].max(): 1.276735725315005e+37 _advance rk_state.t1: -0.5872281284375022, rk_state[1].max(): 1.2471206184623532e+37 _advance rk_state.t1: -0.5751654888325194, rk_state[1].max(): 1.216799049930194e+37 _advance rk_state.t1: -0.5571772804549316, rk_state[1].max(): 1.170698400551694e+37 Traceback (most recent call last):

The second time it enters integrate, t1 starts really small and the method breaks even before t1 becomes non-negative. Do you think this happens due to the data dynamics?

Nov 04 '21 18:11 darleybarreto

To be clear, I don't suggest making all activations tanh. Rather, parameterising your vector field as something like tanh(mlp(...)), where mlp is some MLP using e.g. a softplus activation.

It's certainly possible for this kind of thing to happen due to the data dynamics. Are you normalising your input data? (You should be.)

I'd also suggest trying a fixed solver with a small time step, and just making sure that the output values you get then seem sane. If not, then that's an indication of a possible problem independent of anything to do with the numerical integration.

Nov 06 '21 12:11 patrick-kidger

Are you normalising your input data? (You should be.)

I wasn't 😞 . That did solve my problem! Thank you so much for you attention and help with this!

Nov 10 '21 01:11 darleybarreto

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Consider opening a GH discussions

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