# ERROR (theano.gof.opt) & slowness in execution in sampling

**URL:** <https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963>\
**Category:** Questions\
**Created:** [March 20, 2019, 3:21pm UTC](https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963 "2019-03-20T15:21:45Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![Mahak](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/mahak/32/1566_2.png) [@Mahak](https://discourse.pymc.io/u/Mahak)\
**Post date:** [March 20, 2019, 3:21pm UTC](https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963/1 "2019-03-20T15:21:45Z")

</div>

Dear All,  
I finished writing my forward model for pymc model using theano. This is my preliminary project and once it’s get done accurately, I can move forward to my main project. I got no error in the section of pymc\_model but further sampling takes **too much time** and return with **error**. Considering my current knowledge about PyMc3, I have no clue how can I improve my model. Here I am posting the error message and also attaching my code.  
Description: Main\_code along with the code for forward model and pymc model. Please run Main\_Code.  
I am looking for your kind suggestions. Please help me to improve the code _pymc\_forward\_model_.  
Thanking you

Sincerely,  
mahak

**Attached File**

[Main\_Code.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/3/370ce85c1d494985aae7108e48f4442b49ba1e92.py) (1.6 KB)  
[Forward\_Model.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/1/1c0f1666af0692e1bb58e2fe22e2a0db89fc43f1.py) (3.9 KB)  
[pymc\_forward\_model.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/f/fa8160ac56e5dde92eeff9d6ab73b05a288dde85.py) (4.2 KB)

**Output Error**  
ERROR (theano.gof.opt): Optimization failure due to: local\_mul\_zero  
ERROR (theano.gof.opt): node: Elemwise{mul,no\_inplace}(TensorConstant{(1, 1) of -1.0}, Elemwise{add,no\_inplace}.0)  
ERROR (theano.gof.opt): TRACEBACK:  
ERROR (theano.gof.opt): Traceback (most recent call last):  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\gof\opt.py”, line 2034, in process\_node  
replacements = lopt.transform(node)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\opt.py”, line 5811, in local\_mul\_zero  
value = get\_scalar\_constant\_value(i)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py”, line 511, in get\_scalar\_constant\_value  
v.owner.op.perform(v.owner, const, ret)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\elemwise.py”, line 718, in perform  
super(Elemwise, self).perform(node, inputs, output\_storage)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\gof\op.py”, line 778, in perform  
“Did you used Theano flags mode=FAST\_COMPILE?”  
theano.gof.utils.MethodNotDefined: (‘perform’, \<class ‘theano.tensor.elemwise.Elemwise’\>, ‘Elemwise’, ‘Did you used Theano flags mode=FAST\_COMPILE? You can use optimizer=fast\_compile instead.’)

Sequential sampling (2 chains in 1 job)  
HamiltonianMC: [sigma, Tens, Dip\_s, Stk\_s, Wid, DIP, Azim, Depth, N, E, Len]  
0%| | 103/52000 [00:30\<1:19:03, 10.94it/s]  
Traceback (most recent call last):  
File “invert\_Okada.py”, line 92, in   
trace = pm.sample(50000, tune=2000, init=‘none’, step=step, cores=1)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\sampling.py”, line 459, in sample  
trace = \_sample\_many(\*\*sample\_args)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\sampling.py”, line 505, in \_sample\_many  
step=step, random\_seed=random\_seed[i], \*\*kwargs)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\sampling.py”, line 549, in \_sample  
for it, strace in enumerate(sampling):  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\tqdm\_tqdm.py”, line 1022, in **iter**  
for obj in iterable:  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\sampling.py”, line 645, in \_iter\_sample  
point, states = step.step(point)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\step\_methods\arraystep.py”, line 247, in step  
apoint, stats = self.astep(array)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\step\_methods\hmc\base\_hmc.py”, line 135, in astep  
self.potential.raise\_ok(self.\_logp\_dlogp\_func.\_ordering.vmap)  
File “C:\Users\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\step\_methods\hmc\quadpotential.py”, line 231, in raise\_ok  
raise ValueError(’\n’.join(errmsg))  
ValueError: Mass matrix contains zeros on the diagonal.  
The derivative of RV `sigma_log__`.ravel()[0] is zero.  
The derivative of RV `Tens_interval__`.ravel()[0] is zero.  
The derivative of RV `Dip_s_interval__`.ravel()[0] is zero.  
The derivative of RV `Stk_s_interval__`.ravel()[0] is zero.  
The derivative of RV `Wid_interval__`.ravel()[0] is zero.  
The derivative of RV `DIP_interval__`.ravel()[0] is zero.  
The derivative of RV `Azim_interval__`.ravel()[0] is zero.  
The derivative of RV `Depth_interval__`.ravel()[0] is zero.  
The derivative of RV `N_interval__`.ravel()[0] is zero.  
The derivative of RV `E_interval__`.ravel()[0] is zero.  
The derivative of RV `Len_interval__`.ravel()[0] is zero.

---

<div class="post-metadata">

**Author:** ![junpenglao](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/junpenglao/32/8_2.png) [@junpenglao](https://discourse.pymc.io/u/junpenglao)\
**Post date:** [March 20, 2019, 3:42pm UTC](https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963/2 "2019-03-20T15:42:28Z")

</div>

Your model could be optimized by vectorizing some operation (the nested function is hard to read and whenever you are calling the same function on different inputs, there is a high chance you can vectorized those and make it more compact), but there is nothing inherently wrong of the way you write down the model. The big red flag is more the way you call the sampling:

```python
with basic_model:
    step=pm.HamiltonianMC()
    trace = pm.sample(50000, tune=2000, init='none', step=step, cores=1)

```

Which should be:

```python
with basic_model:
    trace = pm.sample(1000, tune=2000)

```

Always start with the default sampling option!

And if this still gives you error, you will need to optimized the model building part.

---

<div class="post-metadata">

**Author:** ![Mahak](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/mahak/32/1566_2.png) [@Mahak](https://discourse.pymc.io/u/Mahak)\
**Post date:** [March 21, 2019, 12:01pm UTC](https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963/3 "2019-03-21T12:01:53Z")

</div>

> [@junpenglao](#):
>
> And if this still gives you error, you will need to optimized the model building part.

Can you please explain little bit this line. I have made many test, also using NUTS (default) sampler but I am always getting almost same error as following:

```python
ERROR (theano.gof.opt): Optimization failure due to: local_mul_zero
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(TensorConstant{(1, 1) of -1.0}, Elemwise{add,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "C:\Users\singh.CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\gof\opt.py", line 2034, in process_node
    replacements = lopt.transform(node)
  File "C:\Users\singh.CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\opt.py", line 5811, in local_mul_zero
    value = get_scalar_constant_value(i)
  File "C:\Users\singh.CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py", line 511, in get_scalar_constant_value
    v.owner.op.perform(v.owner, const, ret)
  File "C:\Users\singh.CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\elemwise.py", line 718, in perform
    super(Elemwise, self).perform(node, inputs, output_storage)
  File "C:\Users\singh.CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\gof\op.py", line 778, in perform
    "Did you used Theano flags mode=FAST_COMPILE?"
theano.gof.utils.MethodNotDefined: ('perform', <class 'theano.tensor.elemwise.Elemwise'>, 'Elemwise', 'Did you used Theano flags mode=FAST_COMPILE? You can use optimizer=fast_compile instead.')

```

---

<div class="post-metadata">

**Author:** ![Mahak](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/mahak/32/1566_2.png) [@Mahak](https://discourse.pymc.io/u/Mahak)\
**Post date:** [March 27, 2019, 4:09pm UTC](https://discourse.pymc.io/t/error-theano-gof-opt-slowness-in-execution-in-sampling/2963/4 "2019-03-27T16:09:28Z")

</div>

Dear All,  
I have improved my forward model and now completely removed the loop. The code is simpler and smaller now. _But still I am having hard time to understand the use of theano_. I am attaching my codes again below where **Forward\_Model** is my numerical code to calculate the observed data, **pymc\_forward\_model** is the one I am trying to adapt for pymc3 model and both codes are called in the **Main\_Code**.

Your help to improve **pymc\_forward\_model** will be very beneficial for me. Please note that I am _newbie to pymc3_

Thanking You very much  
Sincerely  
Mahak

[Forward\_Model.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/0/0c87ef3ddbc054b54587e59e8ec13db7af8bf00e.py) (4.2 KB)  
[Main\_Code.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/a/a96517c003ca4d4d419c56404ab32ea95976564c.py) (1.5 KB)  
[pymc\_forward\_model.py](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/4/43358bc762e947c19aff1e9ac1b9eef0878d6d2a.py) (3.8 KB)

import pymc3 as pm  
import pymc\_forward\_model as PFM  
My\_model = pm.Model()  
with My\_model:  
E=pm.Normal(‘E’, mu=0, sd=500)  
N=pm.Normal(‘N’, mu=0, sd=500)  
Depth=pm.Uniform(‘Depth’,lower=-6000,upper=-1000)  
Azim=pm.Uniform(‘Azim’,lower=0,upper=360)  
Bounded\_DIP = pm.Bound(pm.Normal, lower=10, upper=89.9)  
DIP = Bounded\_DIP(‘DIP’, mu=50, sd=3.0)  
Len=pm.Uniform(‘Len’,lower=100,upper=10000)  
Wid=pm.Uniform(‘Wid’,lower=100,upper=10000)  
Bounded\_Tens = pm.Bound(pm.Normal, lower=0, upper=10)  
Tens = Bounded\_Tens(‘Tens’, mu=0, sd=0.5)  
sigma=1  
unknowns = [E,N,Depth,Azim,DIP,Len,Wid,Stk\_s1,Dip\_s1,Tens]  
mu1 = PFM.fun1(unknowns, x,y,z, Poisson)  
Y\_obs = pm.Normal(‘Y\_obs’, mu=mu1, sd=sigma, observed=U)

**Error:**

C:\CT\Desktop\Discourse\>python Main\_Code.py  
Traceback (most recent call last):  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\type.py”, line 269, in dtype\_specs  
}[self.dtype]  
KeyError: ‘object’

During handling of the above exception, another exception occurred:

Traceback (most recent call last):  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py”, line 246, in constant  
ttype = TensorType(dtype=x\_.dtype, broadcastable=bcastable)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\type.py”, line 51, in **init**  
self.dtype\_specs() # error checking is done there  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\type.py”, line 272, in dtype\_specs  
% (self. **class**. **name** , self.dtype))  
TypeError: Unsupported dtype for TensorType: object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py”, line 194, in as\_tensor\_variable  
return constant(x, name=name, ndim=ndim)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py”, line 266, in constant  
raise TypeError(“Could not convert %s to TensorType” % x, type(x))  
TypeError: (‘Could not convert [Elemwise{add,no\_inplace}.0 Elemwise{add,no\_inplace}.0\n Elemwise{add,no\_inplace}.0] to TensorType’, \<class ‘numpy.ndarray’\>)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):  
File “Main\_Code.py”, line 41, in   
Y\_obs = pm.Normal(‘Y\_obs’, mu=mu1, sd=sigma, observed=U)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\distributions\distribution.py”, line 41, in **new**  
dist = cls.dist(\*args, \*\*kwargs)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\distributions\distribution.py”, line 52, in dist  
dist. **init** (\*args, \*\*kwargs)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\pymc3\distributions\continuous.py”, line 431, in **init**  
self.mean = self.median = self.mode = self.mu = mu = tt.as\_tensor\_variable(mu)  
File “C:\CT\AppData\Local\Continuum\anaconda3\lib\site-packages\theano\tensor\basic.py”, line 200, in as\_tensor\_variable  
raise AsTensorError(“Cannot convert %s to TensorType” % str\_x, type(x))  
theano.tensor.var.AsTensorError: (‘Cannot convert [Elemwise{add,no\_inplace}.0 Elemwise{add,no\_inplace}.0\n Elemwise{add,no\_inplace}.0] to TensorType’, \<class ‘numpy.ndarray’\>)