drop torch

Author: kratsg

.zenodo.json

@@ -28,7 +28,6 @@
       "fitting",
       "scipy",
       "numpy",
-      "pytorch",
       "jax",
       "auto-differentiation"
     ],

CITATION.cff

@@ -26,7 +26,6 @@ keywords:
   - fitting
   - scipy
   - numpy
-  - pytorch
   - jax
   - auto-differentiation
 license: "Apache-2.0"
@@ -37,7 +36,7 @@ abstract: |
   of that statistical model for multi-bin histogram-based analysis and its
   interval estimation is based on the asymptotic formulas of "Asymptotic
   formulae for likelihood-based tests of new physics". pyhf supports modern
-  computational graph libraries such as PyTorch and JAX in order
+  computational graph libraries such as JAX in order
   to make use of features such as autodifferentiation and GPU acceleration.
 references:
   - type: article

docker/gpu/install_backend.sh

@@ -18,10 +18,7 @@ function get_JAXLIB_GPU_WHEEL {
 function install_backend() {
   # 1: the backend option name in setup.py
   local backend="${1}"
-  if [[ "${backend}" == "torch" ]]; then
-    # shellcheck disable=SC2102
-    python3 -m pip install --no-cache-dir .[xmlio,torch]
-  elif [[ "${backend}" == "jax" ]]; then
+  if [[ "${backend}" == "jax" ]]; then
     python3 -m pip install --no-cache-dir .[xmlio]
     python3 -m pip install --no-cache-dir "$(get_JAXLIB_GPU_WHEEL)"
     python3 -m pip install --no-cache-dir jax

docs/api.rst

@@ -65,7 +65,6 @@ The computational backends that :code:`pyhf` provides interfacing for the vector
    :nosignatures:
 
    numpy_backend.numpy_backend
-   pytorch_backend.pytorch_backend
    jax_backend.jax_backend
 
 Optimizers

docs/conf.py

@@ -146,7 +146,6 @@ def setup(app):
 # today_fmt = '%B %d, %Y'
 
 autodoc_mock_imports = [
-    'torch',
     'jax',
     'iminuit',
 ]

docs/installation.rst

@@ -27,13 +27,6 @@ Install latest stable release from `PyPI <https://pypi.org/project/pyhf/>`__...
 
     python -m pip install pyhf
 
-... with PyTorch backend
-++++++++++++++++++++++++
-
-.. code-block:: console
-
-    python -m pip install 'pyhf[torch]'
-
 ... with JAX backend
 ++++++++++++++++++++
 
@@ -67,13 +60,6 @@ Install latest development version from `GitHub <https://github.com/scikit-hep/p
 
     python -m pip install --upgrade 'pyhf@git+https://github.com/scikit-hep/pyhf.git'
 
-... with PyTorch backend
-++++++++++++++++++++++++
-
-.. code-block:: console
-
-    python -m pip install --upgrade 'pyhf[torch]@git+https://github.com/scikit-hep/pyhf.git'
-
 ... with JAX backend
 ++++++++++++++++++++++
 

pyproject.toml

@@ -23,7 +23,6 @@ keywords = [
     "jax",
     "numpy",
     "physics",
-    "pytorch",
     "scipy",
 ]
 classifiers = [
@@ -67,10 +66,6 @@ Homepage = "https://github.com/scikit-hep/pyhf"
 
 [project.optional-dependencies]
 shellcomplete = ["click_completion"]
-torch = [
-    "torch>=1.10.0",  # c.f. PR #1657
-    "numpy<2.0"  # c.f. https://github.com/pytorch/pytorch/issues/157973
-]
 jax = [
     "jax>=0.4.1",  # c.f. PR #2079
     "jaxlib>=0.4.1",  # c.f. PR #2079
@@ -181,18 +176,12 @@ markers = [
     "fail_jax",
     "fail_numpy",
     "fail_numpy_minuit",
-    "fail_pytorch",
-    "fail_pytorch64",
     "only_jax",
     "only_numpy",
     "only_numpy_minuit",
-    "only_pytorch",
-    "only_pytorch64",
     "skip_jax",
     "skip_numpy",
     "skip_numpy_minuit",
-    "skip_pytorch",
-    "skip_pytorch64",
 ]
 filterwarnings = [
     "error",
@@ -201,8 +190,6 @@ filterwarnings = [
     'ignore: Exception ignored in:pytest.PytestUnraisableExceptionWarning',  #FIXME: Exception ignored in: <_io.FileIO [closed]>
     'ignore:invalid value encountered in (true_)?divide:RuntimeWarning',  #FIXME
     'ignore:invalid value encountered in add:RuntimeWarning',  #FIXME
-    "ignore:In future, it will be an error for 'np.bool_' scalars to be interpreted as an index:DeprecationWarning",  #FIXME: tests/test_tensor.py::test_pdf_eval[pytorch]
-    'ignore:Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with:UserWarning',  #FIXME: tests/test_optim.py::test_minimize[no_grad-scipy-pytorch-no_stitch]
     'ignore:divide by zero encountered in (true_)?divide:RuntimeWarning',  #FIXME: pytest tests/test_tensor.py::test_pdf_calculations[numpy]
     'ignore:[A-Z]+ is deprecated and will be removed in Pillow 10:DeprecationWarning',  # keras
     "ignore:ml_dtypes.float8_e4m3b11 is deprecated.",  #FIXME: Can remove when jaxlib>=0.4.12
@@ -212,8 +199,6 @@ filterwarnings = [
     "ignore:'MultiCommand' is deprecated and will be removed in Click 9.0. Use 'Group' instead.:DeprecationWarning",  # Click
     "ignore:Jupyter is migrating its paths to use standard platformdirs:DeprecationWarning",  # papermill
     "ignore:datetime.datetime.utcnow\\(\\) is deprecated:DeprecationWarning",  # papermill
-    "ignore:In future, it will be an error for 'np.bool' scalars to be interpreted as an index:DeprecationWarning",  # PyTorch
-    "ignore:__array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments.:DeprecationWarning",  # PyTorch interacting with NumPy
 ]
 
 [tool.coverage.run]
@@ -243,7 +228,6 @@ module = [
   'jax.*',
   'matplotlib.*',
   'scipy.*',
-  'torch.*',
   'uproot.*',
 ]
 ignore_missing_imports = true
@@ -273,7 +257,6 @@ module = [
   'pyhf.tensor.common.*',
   'pyhf.tensor',
   'pyhf.tensor.jax_backend.*',
-  'pyhf.tensor.pytorch_backend.*',
 ]
 ignore_errors = true
 

src/pyhf/cli/infer.py

@@ -37,7 +37,7 @@ def cli():
 )
 @click.option(
     "--backend",
-    type=click.Choice(["numpy", "pytorch", "jax", "np", "torch"]),
+    type=click.Choice(["numpy", "jax", "np"]),
     help="The tensor backend used for the calculation.",
     default="numpy",
 )
@@ -82,9 +82,7 @@ def fit(
         }
     """
     # set the backend if not NumPy
-    if backend in ["pytorch", "torch"]:
-        set_backend("pytorch", precision="64b")
-    elif backend in ["jax"]:
+    if backend in ["jax"]:
         set_backend("jax")
     tensorlib, _ = get_backend()
 
@@ -149,7 +147,7 @@ def fit(
 )
 @click.option(
     '--backend',
-    type=click.Choice(['numpy', 'pytorch', 'jax', 'np', 'torch']),
+    type=click.Choice(['numpy', 'jax', 'np']),
     help='The tensor backend used for the calculation.',
     default='numpy',
 )
@@ -212,9 +210,7 @@ def cls(
     )
 
     # set the backend if not NumPy
-    if backend in ['pytorch', 'torch']:
-        set_backend("pytorch", precision="64b")
-    elif backend in ['jax']:
+    if backend in ['jax']:
         set_backend("jax")
     tensorlib, _ = get_backend()
 

src/pyhf/optimize/common.py

@@ -42,11 +42,6 @@ def _get_tensor_shim():
 
         return numpy_shim
 
-    if tensorlib.name == 'pytorch':
-        from pyhf.optimize.opt_pytorch import wrap_objective as pytorch_shim
-
-        return pytorch_shim
-
     if tensorlib.name == 'jax':
         from pyhf.optimize.opt_jax import wrap_objective as jax_shim