Merge branch 'master' into neuralsde

.github/workflows/codestyle.yml

@@ -16,9 +16,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v1
+    - uses: actions/checkout@v3
     - name: Set up Python 3.8
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v4
       with:
         python-version: 3.8
     - name: Install dependencies

.github/workflows/os-coverage.yml

@@ -7,38 +7,72 @@ jobs:
     strategy:
       fail-fast: true
       max-parallel: 15
-
       matrix:
-        os: [ubuntu-18.04, ubuntu-20.04, ubuntu-22.04, macos-latest]
-        python-version: [3.7, 3.8, 3.9]
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        torch-version: ["1.8.1", "1.9.1", "1.10.0", "1.11.0", "1.12.0", "1.13.1", "2.0.0"]
         exclude:
-          # temporary exclusion until py3.8 deps work on Windows
-          - python-version: 3.7
-            os: windows-2019
-          - python-version: 3.8
-            os: windows-2019
+          # python >= 3.10 does not support pytorch < 1.11.0
+          - torch-version: "1.8.1"
+            python-version: "3.10"
+          - torch-version: "1.9.1"
+            python-version: "3.10"
+          - torch-version: "1.10.0"
+            python-version: "3.10"
+          # python >= 3.11 does not support pytorch < 1.13.0
+          - torch-version: "1.8.1"
+            python-version: "3.11"
+          - torch-version: "1.9.1"
+            python-version: "3.11"
+          - torch-version: "1.10.0"
+            python-version: "3.11"
+          - torch-version: "1.11.0"
+            python-version: "3.11"
+          - torch-version: "1.12.0"
+            python-version: "3.11"
+          - torch-version: "1.13.1"
+            python-version: "3.11"
 
+    defaults:
+      run:
+        shell: bash
     steps:
-    - uses: actions/checkout@v2
+    - name: Check out repository
+      uses: actions/checkout@v3
+
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
-
-    - name: Install dependencies
+
+    - name: Install Poetry
+      uses: snok/install-poetry@v1
+      with:
+        virtualenvs-create: true
+        virtualenvs-in-project: true
+
+    - name: Load cached venv
+      id: cached-pip-wheels
+      uses: actions/cache@v3
+      with:
+        path: ~/.cache
+        key: venv-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.torch-version }}-${{ hashFiles('**/poetry.lock') }}
+
+    - name: Install dependencies # hack for 🐛: don't let poetry try installing Torch https://github.com/pytorch/pytorch/issues/88049
       run: |
-        curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python - 
-        source $HOME/.poetry/env
-        python -m pip install --upgrade --user pip
-        pip install pytest pytest-cov
-        poetry lock
-        poetry build
-        poetry install
-        poetry run pip install 'setuptools==59.5.0'
-      # pinning setuptools is a temporary fix to: pytorch-lightning 1.5.10 requires setuptools==59.5.0
-      # supposedly poetry allows pinning ver. of setuptools in pyproject.ml files but it is not working atm https://github.com/python-poetry/poetry/issues/4511
-
-    - name: Run tests
+        pip install pytest pytest-cov papermill poethepoet>=0.10.0
+        pip install torch==${{ matrix.torch-version }} pytorch-lightning scikit-learn torchsde torchcde>=0.2.3 scipy matplotlib ipykernel ipywidgets 
+        poetry install --only-root
+        poetry run pip install setuptools
+
+    - name: List dependencies
+      run: | 
+        pip list
+    
+    - name: Run pytest checks
       run: |
-        source $HOME/.poetry/env
+        source $VENV
         poetry run coverage run --source=torchdyn -m pytest
+    
+    - name: Report coverage
+      uses: codecov/[email protected]

.github/workflows/publish.yaml

@@ -5,26 +5,24 @@ on:
     types:
       - created
       - edited
-
+      
 jobs:
   build:
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/checkout@v2
-    - uses: actions/setup-python@v2
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v3
       with:
         python-version: 3.8
 
     - name: Build
       run: |
-        curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python - 
-        source $HOME/.poetry/env
+        curl -sSL https://install.python-poetry.org | python3 -
         poetry lock
         poetry build
 
     - name: Publish distribution 📦 to PyPI
       if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
       run: |
-        source $HOME/.poetry/env
         poetry config pypi-token.pypi ${{ secrets.pypi_token }}
         poetry publish

README.md

@@ -13,7 +13,7 @@ Torchdyn is a PyTorch library dedicated to **numerical deep learning**: differen
 [![Slack](https://img.shields.io/badge/slack-chat-blue.svg?logo=slack)](https://join.slack.com/t/diffeqml/shared_invite/zt-trwgahq8-zgDqFmwS2gHYX6hsRvwDvg)
 [![codecov](https://codecov.io/gh/DiffEqML/torchdyn/branch/master/graph/badge.svg)](https://codecov.io/gh/DiffEqML/torchdyn)
 [![Docs](https://img.shields.io/badge/docs-passing-green.svg?)](https://torchdyn.readthedocs.io/)
-[![python_sup](https://img.shields.io/badge/python-3.7+-black.svg?)](https://www.python.org/downloads/release/python-370/)
+[![python_sup](https://img.shields.io/badge/python-3.8+-black.svg?)](https://www.python.org/downloads/release/python-370/)
 
 </div>
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "torchdyn"
-version = "1.0.3"
+version = "1.0.6"
 license = "Apache License, Version 2.0"
 description = "A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods."
 authors = ["Michael Poli", "Stefano Massaroli", "DiffEqML"]
@@ -9,11 +9,11 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = "^3.7"
-torch = "^1.8.1"
+python = "^3.8"
+torch = ">=1.8.1"
 torchsde="*"
 torchcde="^0.2.3"
-sklearn = "*"
+scikit-learn = "*"
 pytorch-lightning = "*"
 torchvision = "*"
 scipy = "*"
@@ -38,6 +38,15 @@ build-backend = "poetry.masonry.api"
 requires = ["poetry", "wheel", "setuptools-cpp"]
 
 [tool.pytest.ini_options]
+log_cli = true
+log_cli_level = "CRITICAL"
+log_cli_format = "%(message)s"
+
+log_file = "pytest.log"
+log_file_level = "DEBUG"
+log_file_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)"
+log_file_date_format = "%Y-%m-%d %H:%M:%S"
+
 filterwarnings = [
         "ignore:Call to deprecated create function FieldDescriptor",  # pytorch lightning needs tensorboard which has a conflict with python 3.9
         "ignore:Call to deprecated create function Descriptor", # pytorch lightning needs tensorboard which has a conflict with python 3.9

setup.py

@@ -15,12 +15,12 @@
 
 setup(
     name="torchdyn",
-    version="1.0.3",
+    version="1.0.6",
     author="Michael Poli and Stefano Massaroli",
     description="PyTorch package for all things neural differential equations.",
     url="https://github.com/DiffEqML/torchdyn",
     install_requires=[
-        "torch>=1.6.0",
+        "torch>=1.8.1",
         "pytorch-lightning>=0.8.4",
         "matplotlib",
         "scikit-learn",
@@ -31,4 +31,5 @@
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: Apache Software License",
     ],
+    packages=["torchdyn"],
 )

test/models/test_ode.py

@@ -10,6 +10,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from packaging.version import parse
 import pytest
 import torch
 import torch.nn as nn
@@ -19,7 +20,7 @@
 from torchdyn.datasets import ToyDataset
 from torchdyn.core import NeuralODE
 from torchdyn.nn import GalLinear, GalConv2d, DepthCat, Augmenter, DataControl
-from torchdyn.numerics import odeint, Euler
+from torchdyn.numerics import odeint, odeint_mshooting, Lorenz, Euler
 
 from functools import partial
 import copy
@@ -258,4 +259,60 @@ def forward(self, t, x, u, v, z, args={}):
     t_eval, sol2 = odeprob(x0, t_span=torch.linspace(0, 5, 10))
 
     assert (sol1==sol2).all()
-    grad(sol2.sum(), x0)
+    grad(sol2.sum(), x0)
+
+
+@pytest.mark.skipif(parse(torch.__version__) < parse("1.11.0"),
+        reason="adjoint support added in torch 1.11.0")
+def test_complex_ode():
+    """Test odeint for complex numbers with a simple complex-valued ODE, corresponding
+    to Rabi oscillations of quantum two-level system."""
+    class Rabi(nn.Module):
+        def __init__(self, omega):
+            super().__init__()
+            self.sx = torch.tensor([[0, 1], [1, 0]], dtype=torch.complex128)
+            self.omega = omega
+            return
+        def forward(self, t, x):
+            dx = -1.0j * self.omega * self.sx @ x
+            dx += dx.adjoint()
+            return dx
+
+    # Odeint parameters
+    omega = torch.randn(1)
+    rabi = Rabi(omega)
+    tspan = torch.linspace(0., 2., 10)
+
+    # Random initial state
+    x0 = torch.rand(2, 2, dtype=torch.complex128)
+    x0 = 0.5 * (x0 + x0.adjoint()) / torch.real(x0.trace())
+    # Solve the ODE problem
+    t_eval, sol = odeint(f=rabi, x=x0, t_span=tspan, solver="dopri5", atol=1e-8, rtol=1e-6)
+
+    # Expected solution
+    sx = torch.tensor([[0, 1], [1, 0]], dtype=torch.complex128)
+    si = torch.tensor([[1, 0], [0, 1]], dtype=torch.complex128)
+    U_t = torch.cos(omega * t_eval)[:, None, None] * si 
+    U_t += -1j * torch.sin(omega * t_eval)[:, None, None] * sx
+    sol_exp = U_t @ x0 @ U_t.adjoint()
+
+    # Check result
+    assert torch.allclose(sol, sol_exp, rtol=1e-5, atol=1e-5)
+
+
+@pytest.mark.parametrize('solver', ['mszero'])
+def test_odeint_mshooting(solver):
+    x0 = torch.randn(8, 3) + 15
+    t_span = torch.linspace(0, 3, 10)
+    sys = Lorenz()
+
+    odeint_mshooting(sys, x0, t_span, solver=solver, fine_steps=2, maxiter=4)
+
+
+@pytest.mark.parametrize('solver', ['euler', 'rk4', 'dopri5'])
+def test_odeint(solver):
+    x0 = torch.randn(8, 3) + 15
+    t_span = torch.linspace(0., 2., 10)
+    sys = Lorenz()
+
+    odeint(sys, x0, t_span, solver=solver)

test/test_sensitivity.py

@@ -10,8 +10,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# Test adjoint and perform a rough benchmarking of wall-clock time
+
 import time
 from copy import deepcopy
+import logging
 
 import pytest
 import torch
@@ -27,9 +30,10 @@
 batch_size = 128
 torch.manual_seed(1415112413244349)
 
-
 t_span = torch.linspace(0, 1, 100)
 
+logger = logging.getLogger("out")
+
 
 # TODO(numerics): log wall-clock times and other torch.grad tests
 # TODO(bug): `tsit5` + `adjoint` peak error
@@ -38,17 +42,21 @@
 @pytest.mark.parametrize('stiffness', [0.1, 0.5])
 @pytest.mark.parametrize('interpolator', [None])
 def test_odeint_adjoint(sensitivity, solver, interpolator, stiffness):
+
     f = VanDerPol(stiffness)
     x = torch.randn(1024, 2, requires_grad=True)
-    t0 = time.time()
+
     prob = ODEProblem(f, sensitivity=sensitivity, interpolator=interpolator, solver=solver, atol=1e-4, rtol=1e-4, atol_adjoint=1e-4, rtol_adjoint=1e-4)
+    t0 = time.time()
     t_eval, sol_torchdyn = prob.odeint(x, t_span)
     t_end1 = time.time() - t0
 
     t0 = time.time()
     sol_torchdiffeq = torchdiffeq.odeint_adjoint(f, x, t_span, method='dopri5', atol=1e-4, rtol=1e-4)
     t_end2 = time.time() - t0
 
+    logger.info(f"Fwd times: {t_end1:.3f}, {t_end2:.3f}")
+
     true_sol = torchdiffeq.odeint_adjoint(f, x, t_span, method='dopri5', atol=1e-9, rtol=1e-9)
 
     t0 = time.time()
@@ -59,6 +67,8 @@ def test_odeint_adjoint(sensitivity, solver, interpolator, stiffness):
     grad2 = torch.autograd.grad(sol_torchdiffeq[-1].sum(), x)[0]
     t_end2 = time.time() - t0
 
+    logger.info(f"Bwd times: {t_end1:.3f}, {t_end2:.3f}")
+
     grad_true = torch.autograd.grad(true_sol[-1].sum(), x)[0]
 
     err1 = (grad1-grad_true).abs().sum(1)

torchdyn/__init__.py

@@ -10,11 +10,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '1.0'
+__version__ = '1.0.6'
 __author__  = 'Michael Poli, Stefano Massaroli et al.'
 
 from torch import Tensor
 from typing import Tuple
 
 TTuple = Tuple[Tensor, Tensor]
-

torchdyn/numerics/odeint.py

@@ -14,7 +14,6 @@
 	Functional API of ODE integration routines, with specialized functions for different options
 	`odeint` and `odeint_mshooting` prepare and redirect to more specialized routines, detected automatically.
 """
-from inspect import getargspec
 from typing import List, Tuple, Union, Callable, Dict, Iterable
 from warnings import warn
 
@@ -65,11 +64,6 @@ def odeint(f:Callable, x:Tensor, t_span:Union[List, Tensor], solver:Union[str, n
 	x, t_span = solver.sync_device_dtype(x, t_span)
 	stepping_class = solver.stepping_class
 
-	# instantiate save_at tensor
-	if len(save_at) == 0: save_at = t_span
-	if not isinstance(save_at, torch.Tensor):
-		save_at = torch.tensor(save_at)
-
 	# instantiate the interpolator similar to the solver steps above
 	if isinstance(solver, Tsitouras45):
 		if verbose: warn("Running interpolation not yet implemented for `tsit5`")
@@ -87,6 +81,7 @@ def odeint(f:Callable, x:Tensor, t_span:Union[List, Tensor], solver:Union[str, n
 		if stepping_class == 'fixed':
 			if atol != odeint.__defaults__[0] or rtol != odeint.__defaults__[1]:
 				warn("Setting tolerances has no effect on fixed-step methods")
+			# instantiate save_at tensor
 			return _fixed_odeint(f_, x, t_span, solver, save_at=save_at, args=args)
 		elif stepping_class == 'adaptive':
 			t = t_span[0]
@@ -415,6 +410,10 @@ def _adaptive_odeint(f, k1, x, dt, t_span, solver, atol=1e-4, rtol=1e-4, args=No
 
 def _fixed_odeint(f, x, t_span, solver, save_at=(), args={}):
 	"""Solves IVPs with same `t_span`, using fixed-step methods"""
+	if len(save_at) == 0: save_at = t_span
+	if not isinstance(save_at, torch.Tensor):
+		save_at = torch.tensor(save_at)
+
 	assert all(torch.isclose(t, save_at).sum() == 1 for t in save_at),\
 		"each element of save_at [torch.Tensor] must be contained in t_span [torch.Tensor] once and only once"