chore: Linted Physical folder

benchmarks/speed_benchmarks/jax_fns.py

@@ -66,7 +66,7 @@ def setup(self):
     def __call__(self, inputs):
         x = inputs
         for lyr, actv in zip(self.layers, self.jax_activations, strict=False):
-            x = lyr(x)  # type: ignore
+            x = lyr(x)
             x = actv(x)  # type: ignore
         return x
 

examples/model_api/linear_regression_jax_training.py

@@ -58,5 +58,5 @@
 for i in range(num_epochs):
     outputs, gradients = pm.evaluate_all(params)
     updates, opt_state = optimizer.update(gradients, opt_state)
-    params = optax.apply_updates(params, updates)  # type: ignore
+    params = optax.apply_updates(params, updates)
     print(f"Epoch: {i} / {num_epochs} -> ", outputs["final_cost"])

examples/model_api/variable_length_many_to_one_lstm.py

@@ -67,8 +67,8 @@
     target_end = int(input_end + target_lengths[idx])
 
     # NOTE: Pylance sees int, int type arguments but throws an error.
-    single_input = backend.arange(start, input_end).reshape(-1, input_dim)  # type: ignore
-    single_target = backend.arange(input_end, target_end).reshape(-1, output_dim)  # type: ignore
+    single_input = backend.arange(start, input_end).reshape(-1, input_dim)
+    single_target = backend.arange(input_end, target_end).reshape(-1, output_dim)
 
     single_data = (single_input, single_target)
     train_data.append(single_data)
@@ -150,7 +150,7 @@
 # Prepare the test input data.
 test_input = backend.arange(
     starting_number,
-    starting_number + inference_max_input,  # type: ignore
+    starting_number + inference_max_input,
 ).reshape(-1, input_dim)
 
 # Prepare the test data.
@@ -172,7 +172,7 @@
 
 # Prepare target values.
 test_target_values = backend.arange(
-    starting_number + inference_max_input,  # type: ignore
+    starting_number + inference_max_input,
     starting_number + inference_max_input + inference_max_target_length,
 )
 
@@ -204,4 +204,4 @@
 )
 
 # Measure test error.
-error = backend.abs(unpacked_output_data.squeeze() - test_target_values).sum()  # type: ignore
+error = backend.abs(unpacked_output_data.squeeze() - test_target_values).sum()

mithril/backends/backend.py

@@ -57,26 +57,27 @@ def __init__(self, precision: int = 32, device: str = "cpu") -> None:
         #     setattr(self, key, value)
 
     @property
-    def precision(self):
+    def precision(self) -> int:
         return self._precision
 
+    #!!
     @property
-    def device(self):
+    def device(self) -> Any:
         return self._device
 
-    def get_device(self):
+    def get_device(self) -> str:
         return self._device
 
     @property
     def inf(self) -> DataType | float:
         raise NotImplementedError("inf is not implemented")
 
     @property
-    def pi(self):
+    def pi(self) -> float:
         return math.pi
 
     @property
-    def e(self):
+    def e(self) -> float:
         return math.e
 
     @property
@@ -104,7 +105,7 @@ def to_device(
     def block_until_ready(self, data: DataType) -> DataType | None:
         raise RuntimeError("Backend does not support block_until_ready method!")
 
-    def empty_cache(self):  # noqa: B027
+    def empty_cache(self) -> None:  # noqa: B027
         pass
         # print("Warning: empty_cache is not supported!")
 
@@ -126,7 +127,7 @@ def cast(self, value: Any) -> Any:
 
         return value
 
-    def __del__(self):
+    def __del__(self) -> None:
         self.empty_cache()
 
     @overload

mithril/backends/parallel.py

@@ -37,10 +37,10 @@ def run_callable(self, *primals: Any, fn_name: str) -> dict[str, Any]:
     @abstractmethod
     def parallelize(
         self, tensor: DataType, device_mesh: tuple[int, ...] | None = None
-    ) -> dict[str, Any]:
+    ) -> DataType:
         raise NotImplementedError()
 
-    def clean_up(self):
+    def clean_up(self) -> None:
         self.callables = dict()
         self.device_mesh = None
         self.n_devices = -1

mithril/backends/with_autograd/common_primitives.py

@@ -63,80 +63,80 @@
 ]
 
 
-def greater(left: DataType, right: DataType):
+def greater(left: DataType, right: DataType) -> DataType:
     return left > right
 
 
-def greater_equal(left: DataType, right: DataType):
+def greater_equal(left: DataType, right: DataType) -> DataType:
     return left >= right
 
 
-def less(left: DataType, right: DataType):
+def less(left: DataType, right: DataType) -> DataType:
     return left < right
 
 
-def less_equal(left: DataType, right: DataType):
+def less_equal(left: DataType, right: DataType) -> DataType:
     return left <= right
 
 
-def equal(left: DataType, right: DataType):
-    return left == right
+def equal(left: DataType, right: DataType) -> DataType:
+    return left == right  # type: ignore
 
 
-def not_equal(left: DataType, right: DataType):
-    return left != right
+def not_equal(left: DataType, right: DataType) -> DataType:
+    return left != right  # type: ignore
 
 
-def logical_not(input: DataType):
+def logical_not(input: DataType) -> DataType:
     return ~input
 
 
-def logical_or(left: DataType, right: DataType):
-    return left | right
+def logical_or(left: DataType, right: DataType) -> DataType:
+    return left | right  # type: ignore
 
 
-def logical_and(left: DataType, right: DataType):
-    return left & right
+def logical_and(left: DataType, right: DataType) -> DataType:
+    return left & right  # type: ignore
 
 
-def matrix_multiplication(left: DataType, right: DataType):
-    return left @ right
+def matrix_multiplication(left: DataType, right: DataType) -> DataType:
+    return left @ right  # type: ignore
 
 
-def add(left: DataType, right: DataType):
-    return left + right
+def add(left: DataType, right: DataType) -> DataType:
+    return left + right  # type: ignore
 
 
-def subtract(left: DataType, right: DataType):
-    return left - right
+def subtract(left: DataType, right: DataType) -> DataType:
+    return left - right  # type: ignore
 
 
-def multiplication(left: DataType, right: DataType):
-    return left * right
+def multiplication(left: DataType, right: DataType) -> DataType:
+    return left * right  # type: ignore
 
 
-def divide(numerator: DataType, denominator: DataType):
-    return numerator / denominator
+def divide(numerator: DataType, denominator: DataType) -> DataType:
+    return numerator / denominator  # type: ignore
 
 
-def floor_divide(numerator: DataType, denominator: DataType):
-    return numerator // denominator
+def floor_divide(numerator: DataType, denominator: DataType) -> DataType:
+    return numerator // denominator  # type: ignore
 
 
-def shift_left(input: DataType, shift: DataType):
-    return input << shift
+def shift_left(input: DataType, shift: DataType) -> DataType:
+    return input << shift  # type: ignore
 
 
-def shift_right(input: DataType, shift: DataType):
-    return input >> shift
+def shift_right(input: DataType, shift: DataType) -> DataType:
+    return input >> shift  # type: ignore
 
 
-def power(base: DataType, exponent: DataType):
-    return base**exponent
+def power(base: DataType, exponent: DataType) -> DataType:
+    return base**exponent  # type: ignore
 
 
-def squared_error(input: DataType, target: DataType):
-    return (input - target) ** 2
+def squared_error(input: DataType, target: DataType) -> DataType:
+    return (input - target) ** 2  # type: ignore
 
 
 def minus(input: DataType) -> DataType:
@@ -148,18 +148,18 @@ def transpose(
 ) -> DataType:
     if not axes:
         return input.T
-    return input.transpose(*axes)
+    return input.transpose(*axes)  # type: ignore
 
 
-def swapaxes(input: DataType, axis1: int, axis2: int):
+def swapaxes(input: DataType, axis1: int, axis2: int) -> DataType:
     return input.swapaxes(axis1, axis2)
 
 
-def square(input: DataType):
-    return input * input
+def square(input: DataType) -> DataType:
+    return input * input  # type: ignore
 
 
-def buffer(input: DataType):
+def buffer(input: DataType) -> DataType:
     return input
 
 
@@ -168,27 +168,29 @@ def permute_tensor(input: DataType, indices: DataType) -> DataType:
 
 
 def reshape(input: DataType, shape: tuple[int, ...]) -> DataType:
-    return input.reshape(shape)
+    return input.reshape(shape)  # type: ignore
 
 
 def item(input: DataType) -> int | float | bool:
     return input.item()  # type: ignore
 
 
-def tensor_item(input: DataType, index: int | slice | tuple[int | slice, ...]):
-    return input[index]
+def tensor_item(
+    input: DataType, index: int | slice | tuple[int | slice, ...]
+) -> DataType:
+    return input[index]  # type: ignore
 
 
-def primitive_slice(start: int | None, stop: int | None, step: int | None):
+def primitive_slice(start: int | None, stop: int | None, step: int | None) -> slice:
     return slice(start, stop, step)
 
 
 def length(input: DataType) -> int:
     return len(input)
 
 
-def cartesian_diff(left: DataType, right: DataType):
-    return left[:, None, :] - right[None, :, :]
+def cartesian_diff(left: DataType, right: DataType) -> DataType:
+    return left[:, None, :] - right[None, :, :]  # type: ignore
 
 
 def primitive_embedding(input: DataType, weight: DataType) -> DataType:
@@ -218,11 +220,11 @@ def union(*inputs: int | float | tuple[int | float, ...]) -> tuple[int | float,
     return result
 
 
-def to_tuple(*args: tuple[int | float | bool, ...]):
+def to_tuple(*args: int | float | bool) -> tuple[int | float | bool, ...]:
     return tuple(args)
 
 
-def to_list(*args: tuple[int | float | bool, ...]):
+def to_list(*args: int | float | bool) -> list[int | float | bool]:
     return list(args)
 
 
@@ -291,7 +293,7 @@ def padding_converter_2d(
 def stride_converter(
     input: int | PaddingType | tuple[int, int] | None,
     kernel_size: int | tuple[int, int],
-):
+) -> int | tuple[int, int] | PaddingType:
     if input is None:
         return kernel_size
     else:
@@ -303,7 +305,7 @@ def tuple_converter(
     | PaddingType
     | tuple[int, int]
     | tuple[tuple[int, int], tuple[int, int]],
-):
+) -> tuple[int, int] | tuple[tuple[int, int], tuple[int, int]] | PaddingType:
     if isinstance(input, int):
         return (input, input)
     else:

mithril/backends/with_autograd/jax_backend/backend.py

@@ -75,14 +75,14 @@ def is_manualgrad(self) -> bool:
         return False
 
     @property
-    def inf(self):
+    def inf(self) -> float:
         return jax.numpy.inf
 
     @property
-    def nan(self):
+    def nan(self) -> float:
         return jax.numpy.nan
 
-    def get_backend_array_type(self):
+    def get_backend_array_type(self) -> type[jax.Array]:
         return jax.Array
 
     @property
@@ -98,7 +98,7 @@ def DataType(self):  # noqa: N802
         return utils.ArrayType
 
     @staticmethod
-    def get_available_devices():
+    def get_available_devices() -> list[str]:
         """Static method to get a list of available devices.
 
         Parameters
@@ -112,7 +112,7 @@ def get_available_devices():
     def register_primitive(fn: Callable[..., Any]) -> None:
         JaxBackend.registered_primitives[fn.__name__] = fn
 
-    def set_seed(self, seed: int):
+    def set_seed(self, seed: int) -> None:
         self.seed = seed
         self.prng_key = jax.random.PRNGKey(seed)
 
@@ -145,23 +145,23 @@ def block_until_ready(self, data: jax.Array) -> jax.Array | None:
 
     def register_callable(
         self, fn: Callable[..., Any], fn_name: str, jit: bool = False
-    ):
+    ) -> None:
         assert (
             self._parallel_manager is not None
         ), "Parallel manager is not initialized!"
 
         fn_name = str(id(self)) + fn_name
         return self._parallel_manager.register_callable(fn, fn_name, jit)
 
-    def _run_callable(self, *primals: jax.Array, fn_name: str):
+    def _run_callable(self, *primals: jax.Array, fn_name: str) -> Any:
         assert (
             self._parallel_manager is not None
         ), "Parallel manager is not initialized!"
 
         fn_name = str(id(self)) + fn_name
         return self._parallel_manager.run_callable(*primals, fn_name=fn_name)
 
-    def _create_parallel(self, device_mesh: tuple[int, ...]):
+    def _create_parallel(self, device_mesh: tuple[int, ...]) -> None:
         self._parallel_manager = JaxParallel(math.prod(device_mesh), self._device)
 
     def array(
@@ -538,7 +538,9 @@ def multinomial(
 
         return samples
 
-    def jit(self, *args: Any, **kwargs: Any):
+    def jit(  # type: ignore[override]
+        self, *args: Any, **kwargs: Any
+    ) -> Callable[..., jax.Array | tuple[jax.Array, ...]] | dict[str, jax.Array]:
         return jax.jit(*args, **kwargs)
 
     def grad(