Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

As a follow-up to #2023, this PR adds caffe's backward pass wrt. inputs to GpuCorrMM to implement border_mode="full". It passes all the tests and it's about 2-4x faster than simulating a full convolution with a padded valid convolution. On the way, it cleans up the code and adds some more elaborate documentation.

There are some caveats, though, all stemming from the fact that the implementation in caffe is meant as a backward pass for a valid correlation:

• With border_mode="valid", GpuCorrMM doesn't flip the kernels, but with border_mode="full", it does.
• With border_mode="valid", subsampling is for subsampling the output, but with border_mode="full", it is for upsampling the input.
• With border_mode="valid", pad is for padding the input, but with border_mode="full", it is for cropping the output.
• With border_mode="full", it needs a different memory layout for the kernels.

Currently, GpuCorrMM directly wraps the underlying algorithm, and local_conv_gemm() copes with the different peculiarities, because I wasn't sure whether the GpuCorrMM Op should be inserting dimshuffles, kernel flips and gpu_contiguouses on its own.

Looking at the end result, although it's quite fast, local_conv_gemm() now basically undoes everything that ConvOp.grad() does. It might be possible to write an optimizer that replaces the gradient of a valid convolution with a properly parameterized GpuCorrMM Op, instead of just replacing the full convolution Op that is part of the gradient (introducing redundant dimshuffles etc. on the way). This way we would leverage the caffe implementation better. The alternative would be to modify the CUDA code to perform a subsampled, padded, full correlation, as would be expected from a Gpu Correlation Op. This would be cleaner, but it would also mean a lot more work and we wouldn't profit as much from caffe's implementation for the case of subsampling != (1,1) or padding != (0,0) (granted, this may be an uninteresting case in practice anyway). /edit: Another alternative would be splitting this into two Ops: GpuCorrMM for valid correlation with padding and subsampling (the caffe forward pass), and GpuConvMM for full convolution with upsampling and cropping (the caffe backward pass). This way it would be obvious how the operations differ, but the memory layout required for the second Op would still be unintuitive. Yet another alternative would be splitting it into GpuCorrMM and GpuCorrMM_gradInput. This way it would be obvious how to use GpuCorrMM_gradInput for computing the gradient of GpuCorrMM. We could even add GpuCorrMM_gradKernel for the gradient wrt. weights; it seems caffe does things slightly different there as well. In both cases, GpuCorrMM should get a grad() method to define its gradient (so it can be used directly in a CNN to avoid kernel flipping and whatnot) and local_conv_gemm() should still be able to replace any GpuConv instances with a gemm-based Op (so it can be used with existing CNN implementations).

The caffe convolution works and passes test, however, code needs some cleaning, which are marked with TODO in comments. I created a new file, theano/sandbox/cuda/tests/test_conv_gemm.py that calls GpuConvMM.

TODO:

• [x] Add support for the full mode

Other possible follow up in gh-2015

NEWS.txt

• Add faster convolution (Arjun Jain, Frederic B.)

Fix and adapt an existing wrapper for Baidu CTC, then integrate it into tensor.nnet.

If the warp-ctc library is not on the compiler's default library path, one should set the ctc.root variable to point to a local copy of the aforementioned library.

Before executing the CTC wrapepr, it is necessary to have the library libwarpctc.so on the LD path, so one also has to change the LD_LIBRARY_PATH environment variable to include the directory that contains the library.

For example:

export LD_LIBRARY_PATH=$CTC_LIB/build:$LD_LIBRARY_PATH
THEANO_FLAGS=`floatX=float32,ctc.root=/home/user/warp-ctc' python theano/tensor/nnet/tests/test_ctc.py

Tasks:

• [x] Port CPU implementation (done using a COp)
• [x] Add tests and verification of results for CPU implementation (test done, more test cases required)
• [x] CPU to GPU optimization
• [x] Fix and port GPUOp to new backend
• [x] Add tests and verification of results for GPU implementation
• [x] Use OpenMP in CPU wrapper
• [x] Use CUDA stream in GPU wrapper

fix #3871

Summary edit to reflect changes from code review on August 30 at 12PM EST.

-Updates theano.gradient.grad to correctly discriminate between inputs that are not connected to the cost and inputs that have zero gradient -Updates theano.gradient.grad to compute only those gradients requested by the user -Removes the UncomputableOp machinery that was used to hack NaNs into the old grad method -Introduces NullType, used to represent undefined or unimplemented gradients -Adds an optional connection_pattern method to the definition of the op contract. Currently this is only used by the shape op, and will probably only be needed extremely rarely in the future. -Adds more unit tests to make sure gradient.grad works correctly -Changes one tests to KnownFailureTest. The test asserted that a disconnected input error was raised when one shouldn't be. Later this test can be changed to assert that the grad method does the right thing, but we are still arguing on theano-dev about what the right thing is (0 or NaN) in this case. -Introduces the connection_pattern method and DisconnectedType

I recommend when reading this pull request you look at the final diff rather than each commit individually. I made several changes in the process of finishing this pull request so several lines in the early commits were later removed.

I am getting

>>> import theano
WARNING (theano.gof.compilelock): Overriding existing lock by dead process '83772' (I am process '83854')
Fatal Python error: PyThreadState_Get: no current thread
Abort trap: 6

which then kills Python. The Mac OS X bug report is

Process:         python2.7 [91560]
Path:            /sw/*/python2.7
Identifier:      python2.7
Version:         ???
Code Type:       X86-64 (Native)
Parent Process:  bash [41983]
User ID:         503

Date/Time:       2013-04-03 13:18:48.483 -0600
OS Version:      Mac OS X 10.8.3 (12D78)
Report Version:  10

Interval Since Last Report:          276342 sec
Crashes Since Last Report:           1
Per-App Crashes Since Last Report:   1
Anonymous UUID:                      563FC21E-12C7-8C2A-E396-225C427EB6F4

Crashed Thread:  0  Dispatch queue: com.apple.main-thread

Exception Type:  EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000

Application Specific Information:
abort() called

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0   libsystem_kernel.dylib          0x00007fff82c4fd46 __kill + 10
1   libsystem_c.dylib               0x00007fff8a54adf0 abort + 177
2   org.python.python               0x00000001078abeaa Py_FatalError + 49
3   org.python.python               0x00000001078aa370 PyThreadState_Get + 28
4   org.python.python               0x00000001078a5f16 Py_InitModule4_64 + 58
5   lazylinker_ext.so               0x0000000107852f6c initlazylinker_ext + 124 (mod.cpp:1004)
6   libpython2.7.dylib              0x0000000105208331 _PyImport_LoadDynamicModule + 177
7   libpython2.7.dylib              0x0000000105208051 import_submodule + 337
8   libpython2.7.dylib              0x0000000105207bdc load_next + 268
9   libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
10  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
11  libpython2.7.dylib              0x00000001051ed4d7 PyEval_EvalFrameEx + 9911
12  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
13  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
14  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
15  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
16  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
17  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
18  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
19  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
20  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
21  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
22  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
23  libpython2.7.dylib              0x0000000105207bdc load_next + 268
24  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
25  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
26  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
27  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
28  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
29  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
30  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
31  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
32  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
33  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
34  libpython2.7.dylib              0x0000000105207bdc load_next + 268
35  libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
36  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
37  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
38  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
39  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
40  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
41  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
42  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
43  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
44  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
45  libpython2.7.dylib              0x0000000105207bdc load_next + 268
46  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
47  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
48  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
49  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
50  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
51  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
52  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
53  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
54  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
55  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
56  libpython2.7.dylib              0x0000000105207bdc load_next + 268
57  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
58  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
59  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
60  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
61  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
62  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
63  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
64  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
65  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
66  libpython2.7.dylib              0x0000000105207823 load_package + 371
67  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
68  libpython2.7.dylib              0x0000000105207bdc load_next + 268
69  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
70  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
71  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
72  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
73  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
74  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
75  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
76  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
77  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
78  libpython2.7.dylib              0x0000000105207823 load_package + 371
79  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
80  libpython2.7.dylib              0x0000000105207bdc load_next + 268
81  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
82  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
83  libpython2.7.dylib              0x00000001051ef91f PyEval_EvalFrameEx + 19199
84  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
85  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
86  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
87  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
88  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
89  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
90  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
91  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
92  libpython2.7.dylib              0x0000000105207bdc load_next + 268
93  libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
94  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
95  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
96  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
97  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
98  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
99  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
100 libpython2.7.dylib              0x000000010521184e PyRun_FileExFlags + 174
101 libpython2.7.dylib              0x00000001052113b9 PyRun_SimpleFileExFlags + 777
102 libpython2.7.dylib              0x0000000105223458 Py_Main + 2952
103 libdyld.dylib                   0x00007fff8b7c67e1 start + 1

Thread 0 crashed with X86 Thread State (64-bit):
  rax: 0x0000000000000000  rbx: 0x00007fff5aaaf1f0  rcx: 0x00007fff5aaaf1d8  rdx: 0x0000000000000000
  rdi: 0x00000000000165a8  rsi: 0x0000000000000006  rbp: 0x00007fff5aaaf200  rsp: 0x00007fff5aaaf1d8
   r8: 0x00000000000003f5   r9: 0x0000000000000012  r10: 0x00007fff82c51342  r11: 0x0000000000000202
  r12: 0x00000001052b6628  r13: 0x0000000107856696  r14: 0x00000000000003f5  r15: 0x0000000000000000
  rip: 0x00007fff82c4fd46  rfl: 0x0000000000000202  cr2: 0x00007fff71683ff0
Logical CPU: 0

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This is with Fink Python, as you can see. I have

>>> import numpy
>>> import scipy
>>> numpy.__version__
'1.7.0'
>>> scipy.__version__
'0.10.1'

I'm experiencing what appears to be a serious bug. According to git bisect, the recent commit 14af439 is the culprit, merged in gh-1202.

The following code now gives the wrong result on my machine. It should be 30.0, but after this commit results in 0.0.

import numpy as np

import theano
import theano.tensor as tt
v = tt.vector()
f = theano.function([v], tt.dot(v, v))
testval = np.arange(5).astype(theano.config.floatX)
print f(testval)

news:

• Compile less C modules.
• Add Theano flags check_inputs=True. If False, won't check node inputs in c code. It can be used to speed up compilation, reduce overhead (particularly for scalars) and reduce the number of generated C files.

Internal:

• Add optional Op.check_inputs attribute, if False, as flag check_inputs, but for this op only and it is always the case.
• Change Type.c_extract() and Type.c_declare() signature to have new check_inputs parameter.

I've tested the current master and the libgpuarray with my code on 2 different GPUs and for both backends.

• On a Quadro K5200 (Kepler, SM=3.5), both backends reproduced equivalent results.
• On a GTX980 (Maxwell, SM=5.2), gpuarray backend produces completely nonsense batch losses and the training diverges while old backend works without problem.

How can we debug this?

Related to #5620 .

Task list :

• [x] C code for grad inputs and tests

• [x] All tests need to pass.

• [x] Change weight tensor dimension ordering instead of dimshuffle

• [x] Integrate unshared tests with AbstractConv

• [x] GPU code

Issue #5976. Supervisor class is kept but we added a has_destroyers attribute to fgraph, but some tests regarding dumping the pickle failed because a new attribute was added to the fgraph. I changed it like this :

fgraph.destroyers = [get_destroyers_of, has_destroyers]

@nouiz what do you suggest?

fix https://github.com/Theano/Theano/issues/6230

I just tried running this example http://deeplearning.net/software/theano/tutorial/printing_drawing.html for printing theano graphs from ipython, and I got this error. Anyone possibly know why? If I try accessing theano.printing.pydotprint, it has no trouble finding the attribute.

RuntimeError Traceback (most recent call last) in () 70 theano.printing.pydotprint(predict, 71 outfile="pics/logreg_pydotprint_predic.png", ---> 72 var_with_name_simple=True) 73 # before compilation 74 theano.printing.pydotprint_variables(prediction, /usr/local/lib/python2.7/dist-packages/theano/printing.pyc in pydotprint(fct, outfile, compact, format, with_ids, high_contrast, cond_highlight, colorCodes, max_label_size, scan_graphs, var_with_name_simple, print_output_file, assert_nb_all_strings) 566 567 if not pydot_imported: --> 568 raise RuntimeError("Failed to import pydot. You must install pydot" 569 " for pydotprint to work.") 570 return

Anyone know what this error means and how I can fix it. Any other tips on the code would be great too, I'm a first year mechanical engineer at uni with very little coding experience.

My installed packages are: CUDA 11.7, cudnn 8.5, theano 1.0.5 When I run the convolution network in the test file of Michael Nielsen's book on Deep Learning:

    import network3
    from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer
    training_data, validation_data, test_data = network3.load_data_shared()
    mini_batch_size = 10
    net = Network([
            ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),filter_shape=(20, 1, 5, 5), poolsize=(2, 2)),FullyConnectedLayer(n_in=20*12*12, n_out=100),SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
    net.SGD(training_data, 60, mini_batch_size, 0.1, 
                validation_data, test_data)   

I bump into the following error:

    Problem occurred during compilation with the command line below:
    /MYHOME/anaconda3/envs/behi/bin/x86_64-conda-linux-gnu-c++ -shared -g -O3 -fno-math-errno -Wno-unused-label -Wno-unused-variable -Wno-write-strings -Wl,-rpath,/usr/local/cuda-11.7/lib64 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -m64 -fPIC -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/pygpu -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/numpy/core/include -I/MYHOME/anaconda3/envs/behi/include -I/usr/local/cuda-11.7/include -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/theano/gpuarray/c_code -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/numpy/core/include -I/MYHOME/anaconda3/envs/behi/include/python3.10 -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/theano/gof/c_code -L/MYHOME/anaconda3/envs/behi/lib -L/usr/local/cuda-11.7/lib64 -L/MYHOME/anaconda3/envs/behi/lib -fvisibility=hidden -o /MYHOME/.theano/compiledir_Linux-5.15--generic-x86_64-with-glibc2.35-x86_64-3.10.4-64/tmpf3qvese9/mc1cd627e616bc8abc33fbfac83f3f655fef0d5f1dc7018ab317b39356ccb88a5.so /MYHOME/.theano/compiledir_Linux-5.15--generic-x86_64-with-glibc2.35-x86_64-3.10.4-64/tmpf3qvese9/mod.cpp -lgpuarray -lcudnn -lpython3.10dnn_fwd.c: In member function 'int {anonymous}::__struct_compiled_op_mc1cd627e616bc8abc33fbfac83f3f655fef0d5f1dc7018ab317b39356ccb88a5::conv_fwd_node_mc1cd627e616bc8abc33fbfac83f3f655fef0d5f1dc7018ab317b39356ccb88a5_0(PyGpuArrayObject*, PyGpuArrayObject*, PyGpuArrayObject*, cudnnConvolutionDescriptor_t, double, double, PyGpuArrayObject**, _Params_c1c8b4ff173698da406f46c90c8211df3674b0478655765dc1ebaa6eb2c3f1af_1348bc2b76f74f3ee4c82e363ec6eacd0522bb7d4a29dd246904b25b733edb4a*)':
    dnn_fwd.c:326:60: error: invalid conversion from 'size_t {aka long unsigned int}' to 'int*' [-fpermissive]
    dnn_fwd.c:326:60: error: cannot convert 'cudnnConvolutionFwdAlgo_t*' to 'cudnnConvolutionFwdAlgoPerf_t* {aka cudnnConvolutionFwdAlgoPerfStruct*}' for argument '8' to 'cudnnStatus_t cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t, cudnnTensorDescriptor_t, cudnnFilterDescriptor_t, cudnnConvolutionDescriptor_t, cudnnTensorDescriptor_t, int, int*, cudnnConvolutionFwdAlgoPerf_t*)'

I understand that incompatibility of packages should be the reason, however I was wondering if there is any way to resolve this by modify the module or dnn_fwd.c or the theano's config file without downgrading the packages?

When working with pymc3 (3.11.2) and theano (1.1.2) I found that dimension expansion of theano tensors shows some unexpected behaviour, compared to numpy style advanced indexing.

This code works:

import theano.tensor as T
A = T.zeros((3,4))
B = A[None,:,:]

While leaving out the last axis indexer raises a ValueError:

import theano.tensor as T
A = T.zeros((3,4))
B = A[None,:]

ValueError: ('You cannot drop a non-broadcastable dimension.', ([False, False], ['x', 0]))

In numpy you can usually leave out trailing ",:" axis indexers. In theano it seems they are necessary so the dimshuffle functions works correctly.

I don't know if this is fixed in any new version or if it is necessary to fix it at all. I just want to drop this somewhere on the internet for other people to find it, because that error annoyed me for a long time.

I am trying to use AutomatedRecommendationTool - A machine learning Automated Recommendation Tool for guiding synthetic biology. It uses a package named pymc3. But there are some issues regarding the compiler. Following is the Error:

Exception: ('Compilation failed (return status=1): C:\\Users\\vaibh\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19044-SP0-AMD64_Family_23_Model_96_Stepping_1_AuthenticAMD-3.9.12-64\\tmpqepy79wz\\mod.cpp:1:0: sorry, unimplemented: 64-bit mode not compiled in\r.  #include <Python.h>\r.  \r. ', 'FunctionGraph(Elemwise{mul,no_inplace}(TensorConstant{3.141592653589793}, TensorConstant{0.01}))')

Following is the code cell that I am trying to run -

%%time
if run_art:
    art = RecommendationEngine(df, **art_params)
else:
    with open(os.path.join(art_params['output_directory'], 'art.pkl'), 'rb') as output:
        art = pickle.load(output)

I am using a jupyter notebook for executing the code. Following are the system specifications - Processor: AMD Ryzen 5 4600H with Radeon Graphics 3.00 GHz System type: 64-bit Operating System, x64-based processor Operating System: Windows 10 I have also installed a c++ compiler (MingW) and have added its bin path to system variables:

C:\Users\vaibh>g++ --version
g++ (MinGW.org GCC-6.3.0-1) 6.3.0
Copyright (C) 2016 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Following are the packages that I have installed -

pipenv: 2022.5.2 depinfo: 1.7.0 python-libsbml(or just libsbml): 5.19.5 rfc3986: 2.0.0 h11: 0.13.0 rich: 12.4.4 pydantic: 1.9.1 diskcache: 5.4.0 importlib_resources: 5.7.1 Semver: 2.13.0 Pathvalidate: 2.5.0 pydoe: 0.3.8 tpot: 0.11.7 edd-utils: 0.0.12 pytorch: 1.11.0 mpi4py: 3.1.3 pymc3: 3.11.4 blas: 1.0

Anybody who has been able to solve this issue, please help.

Update: Theano-PyMC:1.1.2 was also installed as a dependency

Theano does not run properly on Python 3.9 and Numpy 1.22.2 for me.

I get this error:

Traceback (most recent call last):

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/configparser.py", line 168, in fetch_val_for_key

    return theano_cfg.get(section, option)

  File "/usr/lib/python3.9/configparser.py", line 781, in get

    d = self._unify_values(section, vars)

  File "/usr/lib/python3.9/configparser.py", line 1152, in _unify_values

    raise NoSectionError(section) from None

configparser.NoSectionError: No section: 'blas'

 

During handling of the above exception, another exception occurred:

 

Traceback (most recent call last):

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/configparser.py", line 327, in __get__

    val_str = fetch_val_for_key(self.fullname,

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/configparser.py", line 172, in fetch_val_for_key

    raise KeyError(key)

KeyError: 'blas.ldflags'

 

During handling of the above exception, another exception occurred:

 

Traceback (most recent call last):

  File "/home/nutzer/projects-2022-04-06/subtitle2go/punctuator2/punctuator.py", line 4, in <module>

    import models

  File "/home/nutzer/projects-2022-04-06/subtitle2go/punctuator2/models.py", line 4, in <module>

    import theano

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/__init__.py", line 124, in <module>

    from theano.scan_module import (scan, map, reduce, foldl, foldr, clone,

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/scan_module/__init__.py", line 41, in <module>

    from theano.scan_module import scan_opt

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/scan_module/scan_opt.py", line 60, in <module>

    from theano import tensor, scalar

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/__init__.py", line 17, in <module>

    from theano.tensor import blas

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/blas.py", line 155, in <module>

    from theano.tensor.blas_headers import blas_header_text

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/blas_headers.py", line 987, in <module>

    if not config.blas.ldflags:

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/configparser.py", line 332, in __get__

    val_str = self.default()

  File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/configdefaults.py", line 1284, in default_blas_ldflags

    blas_info = np.distutils.__config__.blas_opt_info

AttributeError: module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'

As the error says, there is no blas_opt_info in my numpy config, but replacing line 1284 with:

         blas_info = np.distutils.__config__.blas_opt_info

        =>

        blas_info = np.distutils.__config__.blas_ilp64_opt_info

works and then Theano works as expected! If someone else confirms this error I can do a PR where I'd use a try/except to catch the attribute error on line 1284, with another try to look for blas_ilp64_opt_info if blas_opt_info isn't available. That should work on older and newer numpy versions alike.

When I try to import theano (or pymc3) I get:

from theano.gof.toolbox import is_same_graph

ImportError: cannot import name 'is_same_graph'

Using python 3.6, Theano 1.0.5 and pymc3 3.10.0 installed via pip without errors.