added regtests for pytorch module

regtest/.gitignore

@@ -31,6 +31,7 @@
 !/tools
 !/sasa
 !/s2cm
+!/pytorch
 # These files we just want to ignore completely
 tmp
 report.txt

regtest/pytorch/Makefile

@@ -0,0 +1,2 @@
+include ../scripts/module.make
+

regtest/pytorch/rt-pytorch_model/COLVAR.reference

@@ -0,0 +1,14 @@
+#! FIELDS time phi model.node-0
+#! SET min_phi -pi
+#! SET max_phi pi
+ 0.000000 -2.85656 -0.281187
+ 5.000000 -2.29224 -0.750853
+ 10.000000 -2.13168 -0.846786
+ 15.000000 -2.92489 -0.215007
+ 20.000000 -2.56135 -0.548228
+ 25.000000 -1.10925 -0.895364
+ 30.000000 1.47871 0.995763
+ 35.000000 -1.27313 -0.956024
+ 40.000000 -1.10608 -0.893949
+ 45.000000 -3.03879 -0.102622
+ 50.000000 -1.69099 -0.992786

regtest/pytorch/rt-pytorch_model/DERIVATIVES.reference

@@ -0,0 +1,12 @@
+#! FIELDS time parameter model.node-0
+ 0.000000 0   -0.9596529007
+ 5.000000 0   -0.6604691148
+ 10.000000 0   -0.5319328904
+ 15.000000 0   -0.9766126275
+ 20.000000 0   -0.8363288045
+ 25.000000 0    0.4453351200
+ 30.000000 0    0.0919610485
+ 35.000000 0    0.2932883203
+ 40.000000 0    0.4481694996
+ 45.000000 0   -0.9947203994
+ 50.000000 0   -0.1199020892

regtest/pytorch/rt-pytorch_model/Makefile

@@ -0,0 +1 @@
+include ../../scripts/test.make

regtest/pytorch/rt-pytorch_model/config

@@ -0,0 +1,10 @@
+plumed_needs=libtorch
+plumed_modules=pytorch
+type=driver
+arg="--plumed plumed.dat --mf_xtc alanine.xtc"
+
+# note: model has been previously created with create-pytorch-model.py
+# the following crashes in CI
+# function plumed_regtest_before(){
+#  python create-pytorch-model.py
+#}

regtest/pytorch/rt-pytorch_model/create-pytorch-model.py

@@ -0,0 +1,43 @@
+import torch
+print(torch.__version__)
+
+def my_torch_cv(x):
+    '''
+    Here goes the definition of the CV.
+
+    Inputs:
+        x (torch.tensor): input, either scalar or 1-D array
+    Return:
+        y (torch.tensor): collective variable (scalar)
+    '''
+    # CV definition
+    y = torch.sin(x)
+
+    return y
+
+input_size = 1
+
+# -- DEFINE INPUT -- 
+#random 
+#x = torch.rand(input_size, dtype=torch.float32, requires_grad=True).unsqueeze(0)
+#or by choosing the value(s) of the array
+x = torch.tensor([0.], dtype=torch.float32, requires_grad=True)
+
+# -- CALCULATE CV -- 
+y = my_torch_cv(x)
+
+# -- CALCULATE DERIVATIVES -- 
+for yy in y:
+    dy = torch.autograd.grad(yy, x, create_graph=True)
+    # -- PRINT -- 
+    print('CV TEST')
+    print('n_input\t: {}'.format(input_size))
+    print('x\t: {}'.format(x))
+    print('cv\t: {}'.format(yy))
+    print('der\t: {}'.format(dy))
+
+# Compile via tracing
+traced_cv   = torch.jit.trace ( my_torch_cv, example_inputs=x )
+filename='torch_model.pt'
+traced_cv.save(filename)
+

regtest/pytorch/rt-pytorch_model/plumed.dat

@@ -0,0 +1,14 @@
+# vim:ft=plumed
+
+#define input x
+phi: TORSION ATOMS=5,7,9,15
+
+#load model computing y=sin(x)
+model: PYTORCH_MODEL FILE=torch_model.ptc ARG=phi
+
+#output derivatives dy/dx
+DUMPDERIVATIVES ARG=model.* STRIDE=5 FILE=DERIVATIVES
+
+#print colvar
+PRINT FMT=%g STRIDE=5 FILE=COLVAR ARG=phi,model.*
+ENDPLUMED

regtest/pytorch/rt-pytorch_model_2d/COLVAR.reference

@@ -0,0 +1,16 @@
+#! FIELDS time phi psi model.node-0 model.node-1
+#! SET min_phi -pi
+#! SET max_phi pi
+#! SET min_psi -pi
+#! SET max_psi pi
+ 0.000000 -2.85656 2.7909 -0.281187 0.343545
+ 5.000000 -2.29224 1.14276 -0.750853 0.909781
+ 10.000000 -2.13168 1.89325 -0.846786 0.94846
+ 15.000000 -2.92489 2.86227 -0.215007 0.275709
+ 20.000000 -2.56135 0.91674 -0.548228 0.793622
+ 25.000000 -1.10925 1.61448 -0.895364 0.999046
+ 30.000000 1.47871 -1.34139 0.995763 -0.973801
+ 35.000000 -1.27313 0.919978 -0.956024 0.795588
+ 40.000000 -1.10608 0.941929 -0.893949 0.808695
+ 45.000000 -3.03879 -2.43909 -0.102622 -0.646133
+ 50.000000 -1.69099 1.06944 -0.992786 0.876932

regtest/pytorch/rt-pytorch_model_2d/DERIVATIVES.reference

@@ -0,0 +1,23 @@
+#! FIELDS time parameter model.node-0 model.node-1
+ 0.000000 0   -0.9596529007    0.0000000000
+ 0.000000 1    0.0000000000   -0.9391362667
+ 5.000000 0   -0.6604691148    0.0000000000
+ 5.000000 1    0.0000000000    0.4150882959
+ 10.000000 0   -0.5319328904    0.0000000000
+ 10.000000 1    0.0000000000   -0.3168977797
+ 15.000000 0   -0.9766126275    0.0000000000
+ 15.000000 1    0.0000000000   -0.9612411261
+ 20.000000 0   -0.8363288045    0.0000000000
+ 20.000000 1    0.0000000000    0.6084106565
+ 25.000000 0    0.4453351200    0.0000000000
+ 25.000000 1    0.0000000000   -0.0436719432
+ 30.000000 0    0.0919610485    0.0000000000
+ 30.000000 1    0.0000000000    0.2274037302
+ 35.000000 0    0.2932883203    0.0000000000
+ 35.000000 1    0.0000000000    0.6058377624
+ 40.000000 0    0.4481694996    0.0000000000
+ 40.000000 1    0.0000000000    0.5882287621
+ 45.000000 0   -0.9947203994    0.0000000000
+ 45.000000 1    0.0000000000   -0.7632246017
+ 50.000000 0   -0.1199020892    0.0000000000
+ 50.000000 1    0.0000000000    0.4806151688

regtest/pytorch/rt-pytorch_model_2d/Makefile

@@ -0,0 +1 @@
+include ../../scripts/test.make

regtest/pytorch/rt-pytorch_model_2d/config

@@ -0,0 +1,10 @@
+plumed_needs=libtorch
+plumed_modules=pytorch
+type=driver
+arg="--plumed plumed.dat --mf_xtc alanine.xtc"
+
+# note: model has been previously created with create-pytorch-model.py
+# the following crashes in CI
+# function plumed_regtest_before(){
+#  python create-pytorch-model.py
+#}

regtest/pytorch/rt-pytorch_model_2d/create-pytorch-model.py

@@ -0,0 +1,45 @@
+import torch
+print(torch.__version__)
+
+def my_torch_cv(x):
+    '''
+    Here goes the definition of the CV.
+
+    Inputs:
+        x (torch.tensor): input, either scalar or 1-D array
+    Return:
+        y (torch.tensor): collective variable (scalar)
+    '''
+    # CV definition
+    #y1 = torch.sin(x)
+    #y2 = torch.cos(x)
+    #y = torch.cat((y1,y2))
+    y = torch.sin(x)
+    return y
+
+input_size = 2
+
+# -- DEFINE INPUT -- 
+#random 
+#x = torch.rand(input_size, dtype=torch.float32, requires_grad=True).unsqueeze(0)
+#or by choosing the value(s) of the array
+x = torch.tensor([0.,1.57], dtype=torch.float32, requires_grad=True)
+
+# -- CALCULATE CV -- 
+y = my_torch_cv(x)
+
+# -- CALCULATE DERIVATIVES -- 
+for yy in y:
+    dy = torch.autograd.grad(yy, x, create_graph=True)
+    # -- PRINT -- 
+    print('CV TEST')
+    print('n_input\t: {}'.format(input_size))
+    print('x\t: {}'.format(x))
+    print('cv\t: {}'.format(yy))
+    print('der\t: {}'.format(dy))
+
+# Compile via tracing
+traced_cv   = torch.jit.trace ( my_torch_cv, example_inputs=x )
+filename='torch_model.pt'
+traced_cv.save(filename)
+

regtest/pytorch/rt-pytorch_model_2d/plumed.dat

@@ -0,0 +1,15 @@
+# vim:ft=plumed
+
+#define input x
+phi: TORSION ATOMS=5,7,9,15
+psi: TORSION ATOMS=7,9,15,17
+
+#load model computing [sin(x),cos(x)]
+model: PYTORCH_MODEL FILE=torch_model.ptc ARG=phi,psi
+
+#output derivatives dy/dx
+DUMPDERIVATIVES ARG=model.* STRIDE=5 FILE=DERIVATIVES
+
+#print colvar
+PRINT FMT=%g STRIDE=5 FILE=COLVAR ARG=phi,psi,model.*
+ENDPLUMED

regtest/pytorch/rt-pytorch_model_derivatives/COLVAR.reference

@@ -0,0 +1,14 @@
+#! FIELDS time phi model.node-0 sinphi
+#! SET min_phi -pi
+#! SET max_phi pi
+ 0.000000 -2.85656 -0.281187 -0.281187
+ 5.000000 -2.29224 -0.750853 -0.750853
+ 10.000000 -2.13168 -0.846786 -0.846787
+ 15.000000 -2.92489 -0.215007 -0.215006
+ 20.000000 -2.56135 -0.548228 -0.548228
+ 25.000000 -1.10925 -0.895364 -0.895364
+ 30.000000 1.47871 0.995763 0.995763
+ 35.000000 -1.27313 -0.956024 -0.956024
+ 40.000000 -1.10608 -0.893949 -0.893949
+ 45.000000 -3.03879 -0.102622 -0.102622
+ 50.000000 -1.69099 -0.992786 -0.992786

regtest/pytorch/rt-pytorch_model_derivatives/DERIVATIVES.reference

@@ -0,0 +1,12 @@
+#! FIELDS time parameter model.node-0
+ 0.000000 0   -0.95965
+ 5.000000 0   -0.66047
+ 10.000000 0   -0.53193
+ 15.000000 0   -0.97661
+ 20.000000 0   -0.83633
+ 25.000000 0    0.44534
+ 30.000000 0    0.09196
+ 35.000000 0    0.29329
+ 40.000000 0    0.44817
+ 45.000000 0   -0.99472
+ 50.000000 0   -0.11990

regtest/pytorch/rt-pytorch_model_derivatives/Makefile

@@ -0,0 +1 @@
+include ../../scripts/test.make

regtest/pytorch/rt-pytorch_model_derivatives/config

@@ -0,0 +1,10 @@
+plumed_needs=libtorch
+plumed_modules=pytorch
+type=driver
+arg="--plumed plumed.dat --mf_xtc alanine.xtc"
+
+# note: model has been previously created with create-pytorch-model.py
+# the following crashes in CI
+# function plumed_regtest_before(){
+#  python create-pytorch-model.py
+#}

regtest/pytorch/rt-pytorch_model_derivatives/create-pytorch-model.py

@@ -0,0 +1,43 @@
+import torch
+print(torch.__version__)
+
+def my_torch_cv(x):
+    '''
+    Here goes the definition of the CV.
+
+    Inputs:
+        x (torch.tensor): input, either scalar or 1-D array
+    Return:
+        y (torch.tensor): collective variable (scalar)
+    '''
+    # CV definition
+    y = torch.sin(x)
+
+    return y
+
+input_size = 1
+
+# -- DEFINE INPUT -- 
+#random 
+#x = torch.rand(input_size, dtype=torch.float32, requires_grad=True).unsqueeze(0)
+#or by choosing the value(s) of the array
+x = torch.tensor([0.], dtype=torch.float32, requires_grad=True)
+
+# -- CALCULATE CV -- 
+y = my_torch_cv(x)
+
+# -- CALCULATE DERIVATIVES -- 
+for yy in y:
+    dy = torch.autograd.grad(yy, x, create_graph=True)
+    # -- PRINT -- 
+    print('CV TEST')
+    print('n_input\t: {}'.format(input_size))
+    print('x\t: {}'.format(x))
+    print('cv\t: {}'.format(yy))
+    print('der\t: {}'.format(dy))
+
+# Compile via tracing
+traced_cv   = torch.jit.trace ( my_torch_cv, example_inputs=x )
+filename='torch_model.pt'
+traced_cv.save(filename)
+

regtest/pytorch/rt-pytorch_model_derivatives/plumed.dat

@@ -0,0 +1,17 @@
+# vim:ft=plumed
+
+#define input x
+phi: TORSION ATOMS=5,7,9,15
+
+#load model computing y=sin(x)
+model: PYTORCH_MODEL FILE=torch_model.ptc ARG=phi
+#compute sin(x) with python
+sinphi: CUSTOM ARG=phi FUNC=sin(x) PERIODIC=NO
+
+#output derivatives dy/dx (copy the latter to reference file and change header)
+DUMPDERIVATIVES ARG=model.* STRIDE=5 FILE=DERIVATIVES FMT=%10.5f
+DUMPDERIVATIVES ARG=sinphi STRIDE=5 FILE=DERIVATIVES_REF FMT=%10.5f
+
+#print colvar
+PRINT FMT=%g STRIDE=5 FILE=COLVAR ARG=phi,model.*,sinphi
+ENDPLUMED

regtest/pytorch/rt-pytorch_model_script/COLVAR.reference

@@ -0,0 +1,14 @@
+#! FIELDS time phi model.node-0
+#! SET min_phi -pi
+#! SET max_phi pi
+ 0.000000 -2.85656 0.29301
+ 5.000000 -2.29224 1.13685
+ 10.000000 -2.13168 1.5919
+ 15.000000 -2.92489 0.220155
+ 20.000000 -2.56135 0.655518
+ 25.000000 -1.10925 -2.01054
+ 30.000000 1.47871 0.995763
+ 35.000000 -1.27313 -3.25967
+ 40.000000 -1.10608 -1.99467
+ 45.000000 -3.03879 0.103167
+ 50.000000 -1.69099 8.27997

regtest/pytorch/rt-pytorch_model_script/DERIVATIVES.reference

@@ -0,0 +1,12 @@
+#! FIELDS time parameter model.node-0
+ 0.000000 0    1.08585
+ 5.000000 0    2.29242
+ 10.000000 0    3.53416
+ 15.000000 0    1.04847
+ 20.000000 0    1.42970
+ 25.000000 0    5.04227
+ 30.000000 0    0.09196
+ 35.000000 0   11.62547
+ 40.000000 0    4.97869
+ 45.000000 0    1.01064
+ 50.000000 0   69.55791

regtest/pytorch/rt-pytorch_model_script/Makefile

@@ -0,0 +1 @@
+include ../../scripts/test.make

regtest/pytorch/rt-pytorch_model_script/config

@@ -0,0 +1,10 @@
+plumed_needs=libtorch
+plumed_modules=pytorch
+type=driver
+arg="--plumed plumed.dat --mf_xtc alanine.xtc"
+
+# note: model has been previously created with create-pytorch-model.py
+# the following crashes in CI
+# function plumed_regtest_before(){
+#  python create-pytorch-model.py
+#}

regtest/pytorch/rt-pytorch_model_script/create-pytorch-model.py

@@ -0,0 +1,28 @@
+import torch
+print(torch.__version__)
+
+def my_torch_cv(x):
+    '''
+    Here goes the definition of the CV.
+
+    Inputs:
+        x (torch.tensor): input, either scalar or 1-D array
+    Return:
+        y (torch.tensor): collective variable (scalar)
+    '''
+    if x > 0:
+        # CV definition
+        y = torch.sin(x)
+    else:
+        y = torch.tan(x)
+
+    return y
+
+input_size = 1
+
+# Compile via scripting
+scripted_cv = torch.jit.script( my_torch_cv )
+
+filename='torch_model.pt'
+scripted_cv.save(filename)
+