Add LazyLoader for delayed imports of slow modules

cirq-core/cirq/_import.py

@@ -171,3 +171,55 @@ def wrap_func(module: ModuleType) -> Optional[ModuleType]:
     for module in execute_list:
         if module.__loader__ is not None and hasattr(module.__loader__, 'exec_module'):
             cast(Loader, module.__loader__).exec_module(module)  # Calls back into wrap_func
+
+
+class LazyLoader(ModuleType):
+    """Lazily import a module, mainly to avoid pulling in large dependencies.
+
+    This class is a modified version of a similar class in TensorFlow.
+
+    To use, instead of importing the module normally
+        ```
+        import heavy_module
+        ```
+    define the module
+        ```
+        heavy_module = LazyLoader("heavy_module", globals(), "mypackage.heavy_module")
+        ```
+    """
+
+    def __init__(self, local_name, parent_module_globals, name):
+        """Create the LazyLoader module.
+
+        Args:
+            local_name: The local name that the module will be refered to as.
+            parent_module_globals: The globals of the module where this should be imported.
+                Typically this will be globals().
+            name: The full qualified name of the module.
+        """
+        self._local_name = local_name
+        self._parent_module_globals = parent_module_globals
+        self._module = None
+        super().__init__(name)
+
+    def _load(self):
+        """Load the module and insert it into the parent's globals."""
+        # Import the target module and insert it into the parent's namespace
+        if self._module:
+            return self._module
+        self._module = importlib.import_module(self.__name__)
+        self._parent_module_globals[self._local_name] = self._module
+
+        # Update this object's dict so that if someone keeps a reference to the LazyLoader,
+        # lookups are efficient (__getattr__ is only called on lookups that fail).
+        self.__dict__.update(self._module.__dict__)
+
+        return self._module
+
+    def __getattr__(self, item):
+        module = self._load()
+        return getattr(module, item)
+
+    def __dir__(self):
+        module = self._load()
+        return dir(module)

cirq-core/cirq/_import_test.py

@@ -0,0 +1,30 @@
+# Copyright 2021 The Cirq Developers
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from cirq import _import
+
+
+def test_lazy_loader():
+    linalg = _import.LazyLoader("linalg", globals(), "scipy.linalg")
+    linalg.fun = 1
+    assert linalg._module is None
+    assert "linalg" not in linalg.__dict__
+
+    linalg.det([[1]])
+
+    assert linalg._module is not None
+    assert globals()["linalg"] == linalg._module
+    assert "fun" in linalg.__dict__
+    assert "LinAlgError" in dir(linalg)
+    assert linalg.fun == 1

cirq-core/cirq/experiments/t1_decay_experiment.py

@@ -21,12 +21,15 @@
 import numpy as np
 
 
-from cirq import circuits, ops, study, value
+from cirq import circuits, ops, study, value, _import
 from cirq._compat import proper_repr
 
 if TYPE_CHECKING:
     import cirq
 
+# We initialize optimize lazily, otherwise it slows global import speed.
+optimize = _import.LazyLoader("optimize", globals(), "scipy.optimize")
+
 
 # TODO(#3388) Add documentation for Raises.
 # pylint: disable=missing-raises-doc
@@ -130,10 +133,7 @@ def exp_decay(x, t1):
 
         # Fit to exponential decay to find the t1 constant
         try:
-            # Import scipy.optimize here to avoid costly module level import.
-            import scipy.optimize
-
-            popt, _ = scipy.optimize.curve_fit(exp_decay, xs, probs, p0=[t1_guess])
+            popt, _ = optimize.curve_fit(exp_decay, xs, probs, p0=[t1_guess])
             t1 = popt[0]
             return t1
         except RuntimeError:

cirq-core/cirq/experiments/xeb_fitting.py

@@ -27,7 +27,7 @@
 import numpy as np
 import pandas as pd
 import sympy
-from cirq import ops, protocols
+from cirq import ops, protocols, _import
 from cirq.circuits import Circuit
 from cirq.experiments.xeb_simulation import simulate_2q_xeb_circuits
 
@@ -36,6 +36,10 @@
     import multiprocessing
     import scipy.optimize
 
+# We initialize these lazily, otherwise they slow global import speed.
+optimize = _import.LazyLoader("optimize", globals(), "scipy.optimize")
+stats = _import.LazyLoader("stats", globals(), "scipy.stats")
+
 THETA_SYMBOL, ZETA_SYMBOL, CHI_SYMBOL, GAMMA_SYMBOL, PHI_SYMBOL = sympy.symbols(
     'theta zeta chi gamma phi'
 )
@@ -410,10 +414,7 @@ def _mean_infidelity(angles):
             print(f"Loss: {loss:7.3g}", flush=True)
         return loss
 
-    # Import scipy.optimize here to avoid costly top level moule import.
-    import scipy.optimize
-
-    optimization_result = scipy.optimize.minimize(
+    optimization_result = optimize.minimize(
         _mean_infidelity,
         x0=x0,
         options={
@@ -574,15 +575,12 @@ def _fit_exponential_decay(
     cycle_depths_pos = cycle_depths[positives]
     log_fidelities = np.log(fidelities[positives])
 
-    # We import here to avoid costly module level load time dependency on scipy.stats.
-    import scipy.stats
-
-    slope, intercept, _, _, _ = scipy.stats.linregress(cycle_depths_pos, log_fidelities)
+    slope, intercept, _, _, _ = stats.linregress(cycle_depths_pos, log_fidelities)
     layer_fid_0 = np.clip(np.exp(slope), 0, 1)
     a_0 = np.clip(np.exp(intercept), 0, 1)
 
     try:
-        (a, layer_fid), pcov = scipy.optimize.curve_fit(
+        (a, layer_fid), pcov = optimize.curve_fit(
             exponential_decay,
             cycle_depths,
             fidelities,

cirq-core/cirq/qis/measures.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 """Measures on and between quantum states and operations."""
 
+
 from typing import Optional, TYPE_CHECKING, Tuple
 
 import numpy as np
-import scipy
 
-from cirq import protocols, value
+from cirq import protocols, value, _import
 from cirq.qis.states import (
     QuantumState,
     infer_qid_shape,
@@ -27,13 +27,18 @@
     validate_normalized_state_vector,
 )
 
+# We initialize these lazily, otherwise they slow global import speed.
+stats = _import.LazyLoader("stats", globals(), "scipy.stats")
+linalg = _import.LazyLoader("linalg", globals(), "scipy.linalg")
+
+
 if TYPE_CHECKING:
     import cirq
 
 
 def _sqrt_positive_semidefinite_matrix(mat: np.ndarray) -> np.ndarray:
     """Square root of a positive semidefinite matrix."""
-    eigs, vecs = scipy.linalg.eigh(mat)
+    eigs, vecs = linalg.eigh(mat)
     return vecs @ (np.sqrt(np.abs(eigs)) * vecs).T.conj()
 
 
@@ -237,7 +242,7 @@ def _fidelity_state_vectors_or_density_matrices(state1: np.ndarray, state2: np.n
     elif state1.ndim == 2 and state2.ndim == 2:
         # Both density matrices
         state1_sqrt = _sqrt_positive_semidefinite_matrix(state1)
-        eigs = scipy.linalg.eigvalsh(state1_sqrt @ state2 @ state1_sqrt)
+        eigs = linalg.eigvalsh(state1_sqrt @ state2 @ state1_sqrt)
         trace = np.sum(np.sqrt(np.abs(eigs)))
         return trace ** 2
     raise ValueError(
@@ -277,11 +282,7 @@ def von_neumann_entropy(
                 qid_shape = (state.shape[0],)
             validate_density_matrix(state, qid_shape=qid_shape, dtype=state.dtype, atol=atol)
         eigenvalues = np.linalg.eigvalsh(state)
-
-        # We import here to avoid a costly module level load time dependency on scipy.stats.
-        import scipy.stats
-
-        return scipy.stats.entropy(np.abs(eigenvalues), base=2)
+        return stats.entropy(np.abs(eigenvalues), base=2)
     if validate:
         _ = quantum_state(state, qid_shape=qid_shape, copy=False, validate=True, atol=atol)
     return 0.0