Merge branch 'mir-group:main' into stratified_metrics

.github/workflows/tests.yml

@@ -31,6 +31,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install setuptools wheel
+        if [ ${TORCH} = "1.13.1" ]; then pip install numpy==1.*; fi  # older torch versions fail with numpy 2
         pip install torch==${TORCH} -f https://download.pytorch.org/whl/cpu/torch_stable.html
         pip install h5py scikit-learn  # install packages that aren't required dependencies but that the tests do need
         pip install  --upgrade-strategy only-if-needed .

CHANGELOG.md

@@ -7,7 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 Most recent change on the bottom.
 
 
-## Unreleased - 0.6.1
+## Unreleased
+
+
+## [0.6.1] - 2024-7-9
 ### Added
 - add support for equivariance testing of arbitrary Cartesian tensor outputs
 - [Breaking] use entry points for `nequip.extension`s (e.g. for field registration)

nequip/__init__.py

@@ -11,16 +11,14 @@
 torch_version = packaging.version.parse(torch.__version__)
 
 # only allow 1.11*, 1.13* or higher (no 1.12.*)
-assert (torch_version > packaging.version.parse("1.11.0")) and not (
-    packaging.version.parse("1.12.0")
-    <= torch_version
-    < packaging.version.parse("1.13.0")
+assert (torch_version == packaging.version.parse("1.11")) or (
+    torch_version >= packaging.version.parse("1.13")
 ), f"NequIP supports PyTorch 1.11.* or 1.13.* or later, but {torch_version} found"
 
 # warn if using 1.13* or 2.0.*
-if packaging.version.parse("1.13.0") <= torch_version < packaging.version.parse("2.1"):
+if packaging.version.parse("1.13.0") <= torch_version:
     warnings.warn(
-        f"!! PyTorch version {torch_version} found. Upstream issues in PyTorch versions 1.13.* and 2.0.* have been seen to cause unusual performance degredations on some CUDA systems that become worse over time; see https://github.com/mir-group/nequip/discussions/311. The best tested PyTorch version to use with CUDA devices is 1.11; while using other versions if you observe this problem, an unexpected lack of this problem, or other strange behavior, please post in the linked GitHub issue."
+        f"!! PyTorch version {torch_version} found. Upstream issues in PyTorch versions 1.13.* and 2.* have been seen to cause unusual performance degredations on some CUDA systems that become worse over time; see https://github.com/mir-group/nequip/discussions/311. The best tested PyTorch version to use with CUDA devices is 1.11; while using other versions if you observe this problem, an unexpected lack of this problem, or other strange behavior, please post in the linked GitHub issue."
     )
 
 

nequip/_version.py

@@ -2,4 +2,4 @@
 # See Python packaging guide
 # https://packaging.python.org/guides/single-sourcing-package-version/
 
-__version__ = "0.6.0"
+__version__ = "0.6.1"

nequip/utils/test.py

@@ -2,7 +2,7 @@
 
 import torch
 from e3nn import o3
-from e3nn.util.test import equivariance_error, FLOAT_TOLERANCE
+from e3nn.util.test import equivariance_error
 
 from nequip.nn import GraphModuleMixin, GraphModel
 from nequip.data import (
@@ -12,7 +12,17 @@
     _EDGE_FIELDS,
     _CARTESIAN_TENSOR_FIELDS,
 )
-
+from nequip.utils.misc import dtype_from_name
+
+# The default float tolerance
+FLOAT_TOLERANCE = {
+    t: torch.as_tensor(v, dtype=dtype_from_name(t))
+    for t, v in {"float32": 1e-3, "float64": 1e-10}.items()
+}
+# Allow lookup by name or dtype object:
+for t, v in list(FLOAT_TOLERANCE.items()):
+    FLOAT_TOLERANCE[dtype_from_name(t)] = v
+del t, v
 
 # This has to be somewhat large because of float32 sum reductions over many edges/atoms
 PERMUTATION_FLOAT_TOLERANCE = {torch.float32: 1e-4, torch.float64: 1e-10}

nequip/utils/unittests/conftest.py

@@ -12,12 +12,11 @@
 
 import torch
 
-from nequip.utils.test import set_irreps_debug
+from nequip.utils.test import set_irreps_debug, FLOAT_TOLERANCE
 from nequip.data import AtomicData, ASEDataset
 from nequip.data.transforms import TypeMapper
 from nequip.utils.torch_geometric import Batch
 from nequip.utils._global_options import _set_global_options
-from nequip.utils.misc import dtype_from_name
 
 # Sometimes we run parallel using pytest-xdist, and want to be able to use
 # as many GPUs as are available
@@ -42,12 +41,6 @@
     # Test parallelization, but don't waste time spawning tons of workers if lots of cores available
     os.environ["NEQUIP_NUM_TASKS"] = "2"
 
-# The default float tolerance
-FLOAT_TOLERANCE = {
-    t: torch.as_tensor(v, dtype=dtype_from_name(t))
-    for t, v in {"float32": 1e-3, "float64": 1e-10}.items()
-}
-
 
 @pytest.fixture(scope="session", autouse=True, params=["float32", "float64"])
 def float_tolerance(request):

nequip/utils/unittests/model_tests.py

@@ -228,7 +228,13 @@ def test_equivariance(self, model, atomic_batch, device):
         instance, out_fields = model
         instance = instance.to(device=device)
         atomic_batch = atomic_batch.to(device=device)
-        assert_AtomicData_equivariant(func=instance, data_in=atomic_batch)
+        assert_AtomicData_equivariant(
+            func=instance,
+            data_in=atomic_batch,
+            e3_tolerance={torch.float32: 1e-3, torch.float64: 1e-8}[
+                torch.get_default_dtype()
+            ],
+        )
 
     def test_embedding_cutoff(self, model, config, device):
         instance, out_fields = model