Bump minimum SymPy version and add Python 3.9 to CI

0330b3ca · Markus Holzer · Michael Kuron · 6374f339 · 0330b3ca · 0330b3ca
Commit 0330b3ca authored Feb 04, 2021 by Markus Holzer Committed by Michael Kuron Feb 04, 2021
8 changed files
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -36,6 +36,31 @@ tests-and-coverage:
      cobertura: coverage.xml
      junit: report.xml

+# pipeline with latest python version
+latest-python:
+  stage: test
+  except:
+    variables:
+      - $ENABLE_NIGHTLY_BUILDS
+  image: i10git.cs.fau.de:5005/pycodegen/pycodegen/latest_python
+  before_script:
+    - pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
+  script:
+    - env
+    - pip list
+    - export NUM_CORES=$(nproc --all)
+    - mkdir -p ~/.config/matplotlib
+    - echo "backend:template" > ~/.config/matplotlib/matplotlibrc
+    - mkdir public
+    - py.test -v -n $NUM_CORES -m "not longrun" --junitxml=report.xml
+  tags:
+    - docker
+    - AVX
+  artifacts:
+    when: always
+    reports:
+      junit: report.xml
+
 # Nightly test  - runs "long run" jobs only
 test-longrun:
  stage: test
@@ -85,15 +110,18 @@ ubuntu:
    variables:
      - $ENABLE_NIGHTLY_BUILDS
  image: i10git.cs.fau.de:5005/pycodegen/pycodegen/ubuntu
-  script:
+  before_script:
+    - apt-get -y remove python3-sympy
+    - ln -s /usr/include/locale.h /usr/include/xlocale.h
    - pip3 install `grep -Eo 'sympy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
-    - pip3 install `grep -Eo 'numpy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
+    - pip3 install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
+  script:
+    - export NUM_CORES=$(nproc --all)
    - mkdir -p ~/.config/matplotlib
    - echo "backend:template" > ~/.config/matplotlib/matplotlibrc
-    - pip3 install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
    - env
    - pip3 list
-    - pytest-3 -v -m "not longrun" --junitxml=report.xml
+    - pytest-3 -v -n $NUM_CORES -m "not longrun" --junitxml=report.xml
  tags:
    - docker
    - cuda11

--- a/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
+++ b/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1-final"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pytest\n",
+    "pytest.importorskip('pycuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
@@ -34,7 +33,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -53,15 +52,15 @@
   ]
  },
  {
+   "cell_type": "markdown",
+   "metadata": {},
   "source": [
    "## Pipe Flow Scenario"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -202,15 +201,15 @@
   ]
  },
  {
+   "cell_type": "markdown",
+   "metadata": {},
   "source": [
    "## General Setup"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -226,15 +225,15 @@
   ]
  },
  {
+   "cell_type": "markdown",
+   "metadata": {},
   "source": [
    "## 1. Reference: SRT Method"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -254,29 +253,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
-     "output_type": "execute_result",
     "data": {
      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f8678eccbe0>"
+       "<matplotlib.colorbar.Colorbar at 0x7f20b2f75bb0>"
      ]
     },
+     "execution_count": 7,
     "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
    },
    {
-     "output_type": "display_data",
     "data": {
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\n"
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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
     },
     "metadata": {
      "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
    }
   ],
   "source": [
@@ -286,6 +286,8 @@
   ]
  },
  {
+   "cell_type": "markdown",
+   "metadata": {},
   "source": [
    "## 2. Centered Cumulant Method with Implicit Forcing\n",
    "\n",
@@ -294,13 +296,11 @@
    "    \\kappa_{100}^* = - \\kappa_{100}.\n",
    "$$\n",
    "Thus, $\\kappa_{100}^* = F_x /2$. In total, the entire acceleration given by the force is added onto the momentum."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -323,7 +323,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -334,7 +334,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -344,29 +344,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
-     "output_type": "execute_result",
     "data": {
      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f8678e35130>"
+       "<matplotlib.colorbar.Colorbar at 0x7f20aa846670>"
      ]
     },
+     "execution_count": 11,
     "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
    },
    {
-     "output_type": "display_data",
     "data": {
-      "text/plain": "<Figure size 1152x432 with 2 Axes>",
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\n"
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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
     },
     "metadata": {
      "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
    }
   ],
   "source": [
@@ -377,7 +378,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -385,15 +386,15 @@
   ]
  },
  {
+   "cell_type": "markdown",
+   "metadata": {},
   "source": [
    "## 3. Centered Cumulant Method with Explicit Forcing"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -418,7 +419,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -429,7 +430,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -439,29 +440,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
-     "output_type": "execute_result",
     "data": {
      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f8678b73c10>"
+       "<matplotlib.colorbar.Colorbar at 0x7f20aa641070>"
      ]
     },
+     "execution_count": 16,
     "metadata": {},
-     "execution_count": 15
+     "output_type": "execute_result"
    },
    {
-     "output_type": "display_data",
     "data": {
-      "text/plain": "<Figure size 1152x432 with 2 Axes>",
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\n"
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\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
     },
     "metadata": {
      "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
    }
   ],
   "source": [
@@ -472,7 +474,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -480,5 +482,26 @@
    "assert_allclose(cm_expl_f_u, cm_impl_f_u, rtol=1, atol=1e-4)"
   ]
  }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb
+++ b/lbmpy_tests/phasefield/test_numerical_1D_nphase_model.ipynb
--- a/lbmpy_tests/test_code_hashequivalence.py
+++ b/lbmpy_tests/test_code_hashequivalence.py
 from hashlib import sha256

 from lbmpy.creationfunctions import create_lb_ast
-from pystencils.llvm.llvmjit import generate_llvm
-

 def test_hash_equivalence_llvm():
+
+    import pytest
+    pytest.importorskip("llvmlite")
+    from pystencils.llvm.llvmjit import generate_llvm
+
+
    ref_value = "6db6ed9e2cbd05edae8fcaeb8168e3178dd578c2681133f3ae9228b23d2be432"
    ast = create_lb_ast(stencil='D3Q19', method='srt', optimization={'target': 'llvm'})
    code = generate_llvm(ast)

--- a/lbmpy_tests/test_cumulant_methods.py
+++ b/lbmpy_tests/test_cumulant_methods.py
+import pytest
+
 from lbmpy.methods import create_srt
 from lbmpy.stencils import get_stencil
 from lbmpy.methods.creationfunctions import create_with_default_polynomial_cumulants

-
-def test_weights():
-    for stencil_name in ('D2Q9', 'D3Q19', 'D3Q27'):
-        stencil = get_stencil(stencil_name)
-        cumulant_method = create_with_default_polynomial_cumulants(stencil, [1])
-        moment_method = create_srt(stencil, 1, cumulant=False, compressible=True, maxwellian_moments=True)
-        assert cumulant_method.weights == moment_method.weights
+@pytest.mark.parametrize('stencil_name', ['D2Q9', 'D3Q19', 'D3Q27'])
+def test_weights(stencil_name):
+    stencil = get_stencil(stencil_name)
+    cumulant_method = create_with_default_polynomial_cumulants(stencil, [1])
+    moment_method = create_srt(stencil, 1, cumulant=False, compressible=True, maxwellian_moments=True)
+    assert cumulant_method.weights == moment_method.weights
--- a/lbmpy_tests/test_gpu_block_size_limiting.py
+++ b/lbmpy_tests/test_gpu_block_size_limiting.py
 from lbmpy.creationfunctions import create_lb_ast
+import pytest


 def test_gpu_block_size_limiting():
+    pytest.importorskip("pycuda")
    too_large = 2048*2048
    opt = {'target': 'gpu', 'gpu_indexing_params': {'block_size': (too_large, too_large, too_large)}}
    ast = create_lb_ast(stencil='D3Q19', cumulant=True, relaxation_rate=1.8, optimization=opt,

--- a/lbmpy_tests/test_sparse_lbm.ipynb
+++ b/lbmpy_tests/test_sparse_lbm.ipynb
@@ -4,6 +4,27 @@
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pytest\n",
+    "pytest.importorskip('pycuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
   "outputs": [],
   "source": [
    "from lbmpy.session import *\n",
@@ -23,7 +44,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -47,12 +68,12 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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\n",
+      "image/png": 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