Make safetensors an optional dependency

README.md

@@ -19,8 +19,9 @@ any other PyTensor graph — a PyMC model included — as there is nothing else
 pip install pytensor-ml
 ```
 
-The only hard dependencies are `pytensor`, `numpy`, and `safetensors`. A backend beyond the default (`numba`,
-`jax`, `torch`, `mlx`) is installed separately, and only loads when you actually compile against it.
+The only hard dependencies are `pytensor` and `numpy`. Saving and loading weights needs `safetensors`
+(`pip install safetensors`). A backend beyond the default (`numba`, `jax`, `torch`, `mlx`) is installed
+separately, and only loads when you actually compile against it.
 
 ## Quickstart
 

pyproject.toml

@@ -37,7 +37,6 @@ keywords = [
 dependencies = [
   "pytensor>=3.2.3,<4.0.0",
   "numpy",
-  "safetensors"
 ]
 
 [project.urls]
@@ -48,6 +47,7 @@ Tracker = "https://github.com/pymc-devs/pytensor-ml/issues"
 
 [project.optional-dependencies]
 dev = [
+    "safetensors",
     "pre-commit",
     "pytest",
     "pytest-cov",

pytensor_ml/checkpoint.py

@@ -10,15 +10,24 @@
 
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.tensor.random.type import RandomGeneratorType
-from safetensors import safe_open
-from safetensors.numpy import save_file
 
 # Generator state rides in the archive's metadata, which is a flat str-to-str map shared with whatever
 # wrote the file, so our entries are namespaced away from a foreign writer's (HuggingFace stores a
 # "format" key there).
 _RNG_KEY_PREFIX = "rng/"
 
 
+def import_safetensors():
+    try:
+        import safetensors
+        import safetensors.numpy
+    except ModuleNotFoundError as error:
+        raise ModuleNotFoundError(
+            "pytensor-ml requires safetensors for checkpointing. Install it with `pip install safetensors`."
+        ) from error
+    return safetensors
+
+
 def holds_generator(variable: SharedVariable) -> bool:
     """Report whether a shared variable holds a random generator rather than a tensor."""
     return isinstance(variable.type, RandomGeneratorType)
@@ -154,9 +163,8 @@ def save_state(shared_variables: Sequence[SharedVariable], path: str | Path) ->
     optimizer state to capture a complete training checkpoint; both are ordinary shared variables and
     carry self-describing names (e.g. ``"fc1/weight"``, ``"fc1/weight/adam/first_moment"``).
     :func:`~pytensor_ml.pytensorf.collect_optimizer_state` finds the half no walk of the graph reaches.
-    A random
-    generator has no tensor to store, so its state is written to the archive's metadata under the same
-    key, which is what lets a stochastic network checkpoint at all.
+    A random generator has no tensor to store, so its state is written to the archive's metadata under
+    the same key, which is what lets a stochastic network checkpoint at all.
 
     Parameters
     ----------
@@ -194,7 +202,8 @@ def save_state(shared_variables: Sequence[SharedVariable], path: str | Path) ->
             generator_states[_RNG_KEY_PREFIX + key] = json.dumps(jsonable_rng_state(state))
         else:
             tensors[key] = _as_saveable_array(variable.get_value(), variable.type.dtype)
-    save_file(tensors, fspath(path), metadata=generator_states or None)
+    safetensors = import_safetensors()
+    safetensors.numpy.save_file(tensors, fspath(path), metadata=generator_states or None)
 
 
 def _as_saveable_array(value: Any, dtype: str) -> np.ndarray:
@@ -274,7 +283,8 @@ def load_state(
         source_by_key[key] = source
         target_by_key[key] = variable
 
-    with safe_open(fspath(path), framework="numpy") as archive:
+    safetensors = import_safetensors()
+    with safetensors.safe_open(fspath(path), framework="numpy") as archive:
         values = {key: archive.get_tensor(key) for key in archive.keys()}
         metadata = archive.metadata() or {}
     archived_states = {

pytensor_ml/pretrained.py

@@ -11,12 +11,12 @@
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.graph.basic import Variable
 from pytensor.tensor.random.type import RandomGeneratorType
-from safetensors import safe_open
 
 from pytensor_ml.checkpoint import (
     bit_generator_kind,
     generator_from_state,
     holds_generator,
+    import_safetensors,
     jsonable_rng_state,
     load_state,
     save_state,
@@ -479,7 +479,10 @@ def from_pretrained(
         # raises, so drawing them first is work thrown away.
         with initial_values_from(EmptyInitializer()):
             data_inputs, outputs, keys = build_from_config(config)
-        with safe_open(_huggingface_weights(directory, variant), framework="numpy") as weights:
+        safetensors = import_safetensors()
+        with safetensors.safe_open(
+            _huggingface_weights(directory, variant), framework="numpy"
+        ) as weights:
             keys.load(weights.get_tensor, weights.keys())
         return data_inputs, outputs