Automate inference and make models arguments to inference engines (#181)

* Enable tracing of QuantumCircuit __add__ (#131)

* Weighted average utility (#134)

* Add new style QHBMs (#125)

* Replace custom code with TF op (#141)

* Enable EBM seed (#143)

* Add modular Hamiltonian expectation (#135)

* Energy expectation (#150)

* Update TF, TFP, and TFQ versions (#152)

* try updating

* update python versions

* Tests passing

* increase tolerance for flaky test

* update setup.py

* Utility function tests (#153)

* Eager toggle decoration completion (#155)

* generalize QuantumInference expectation

* removed reduced tests

* inferece change

* revert

* genralize expectation

* circuit infer tests passing

* update infer

* checkout energy files

* format

* circuit tests passing

* updated h infer test passing

* circuit tests passing

* inference tests passing

* format

* remove lint

* vqt tests passing

* format

* remove lint

* start updating circuit infer

* infere

* update infere

* energy tests passing

* update infer

* move arg

* hamuiltonian inference tests passing

* format

* circuit infer test

* remove lint

* update vqt tets

* format

* tolerance update

* lint

Co-authored-by: Faris Sbahi <[email protected]>

qhbmlib/circuit_infer.py

@@ -30,19 +30,23 @@ class QuantumInference(tf.keras.layers.Layer):
   """Methods for inference on QuantumCircuit objects."""
 
   def __init__(self,
+               circuit: circuit_model.QuantumCircuit,
                backend: Union[str, cirq.Sampler] = "noiseless",
                differentiator: Union[None,
                                      tfq.differentiators.Differentiator] = None,
                name: Union[None, str] = None):
     """Initialize a QuantumInference layer.
 
     Args:
+      circuit: The parameterized quantum circuit on which to do inference.
       backend: Specifies what backend TFQ will use to compute expectation
         values. `str` options are {"noisy", "noiseless"}; users may also specify
         a preconfigured cirq execution object to use instead.
       differentiator: Specifies how to take the derivative of a quantum circuit.
       name: Identifier for this inference engine.
     """
+    circuit.build([])
+    self._circuit = circuit
     self._differentiator = differentiator
     self._backend = backend
     self._sample_layer = tfq.layers.Sample(backend=backend)
@@ -79,17 +83,19 @@ def _expectation_function(circuits, symbol_names, symbol_values,
   def backend(self):
     return self._backend
 
+  @property
+  def circuit(self):
+    return self._circuit
+
   @property
   def differentiator(self):
     return self._differentiator
 
-  def expectation(self, qnn: circuit_model.QuantumCircuit,
-                  initial_states: tf.Tensor,
+  def expectation(self, initial_states: tf.Tensor,
                   observables: Union[tf.Tensor, hamiltonian_model.Hamiltonian]):
     """Returns the expectation values of the observables against the QNN.
 
     Args:
-      qnn: The parameterized quantum circuit on which to do inference.
       initial_states: Shape [batch_size, num_qubits] of dtype `tf.int8`.
         Each entry is an initial state for the set of qubits.  For each state,
         `qnn` is applied and the pure state expectation value is calculated.
@@ -104,11 +110,11 @@ def expectation(self, qnn: circuit_model.QuantumCircuit,
       transformed initial state.
     """
     if isinstance(observables, tf.Tensor):
-      u = qnn
+      u = self.circuit
       ops = observables
       post_process = lambda x: x
     elif isinstance(observables.energy, energy_model.PauliMixin):
-      u = qnn + observables.circuit_dagger
+      u = self.circuit + observables.circuit_dagger
       ops = observables.operator_shards
       post_process = lambda y: tf.map_fn(
           lambda x: tf.expand_dims(
@@ -134,12 +140,10 @@ def expectation(self, qnn: circuit_model.QuantumCircuit,
     )
     return utils.expand_unique_results(post_process(expectations), idx)
 
-  def sample(self, qnn: circuit_model.QuantumCircuit, initial_states: tf.Tensor,
-             counts: tf.Tensor):
+  def sample(self, initial_states: tf.Tensor, counts: tf.Tensor):
     """Returns bitstring samples from the QNN.
 
       Args:
-        qnn: The parameterized quantum circuit on which to do inference.
         initial_states: Shape [batch_size, num_qubits] of dtype `tf.int8`.
           These are the initial states of each qubit in the circuit.
         counts: Shape [batch_size] of dtype `tf.int32` such that `counts[i]` is
@@ -150,16 +154,16 @@ def sample(self, qnn: circuit_model.QuantumCircuit, initial_states: tf.Tensor,
           that `ragged_samples[i]` contains `counts[i]` bitstrings drawn from
           `(qnn)|initial_states[i]>`.
     """
-    circuits = qnn(initial_states)
+    circuits = self.circuit(initial_states)
     num_circuits = tf.shape(circuits)[0]
     tiled_values = tf.tile(
-        tf.expand_dims(qnn.symbol_values, 0), [num_circuits, 1])
+        tf.expand_dims(self.circuit.symbol_values, 0), [num_circuits, 1])
     num_samples_mask = tf.cast((tf.ragged.range(counts) + 1).to_tensor(),
                                tf.bool)
     num_samples_mask = tf.map_fn(tf.random.shuffle, num_samples_mask)
     samples = self._sample_layer(
         circuits,
-        symbol_names=qnn.symbol_names,
+        symbol_names=self.circuit.symbol_names,
         symbol_values=tiled_values,
         repetitions=tf.expand_dims(tf.math.reduce_max(counts), 0))
     return tf.ragged.boolean_mask(samples, num_samples_mask)

qhbmlib/energy_infer.py

@@ -57,17 +57,35 @@ class EnergyInferenceBase(tf.keras.layers.Layer, abc.ABC):
   """
 
   def __init__(self,
+               input_energy: energy_model.BitstringEnergy,
                initial_seed: Union[None, tf.Tensor] = None,
                name: Union[None, str] = None):
     """Initializes an EnergyInferenceBase.
 
     Args:
+      input_energy: The parameterized energy function which defines this
+        distribution via the equations of an energy based model.  This class
+        assumes that all parameters of `energy` are `tf.Variable`s and that
+        they are all returned by `energy.variables`.
       initial_seed: PRNG seed; see tfp.random.sanitize_seed for details. This
         seed will be used in the `sample` method.  If None, the seed is updated
         after every inference call.  Otherwise, the seed is fixed.
       name: Optional name for the model.
     """
     super().__init__(name=name)
+    self._energy = input_energy
+    self._energy.build([None, self._energy.num_bits])
+
+    self._tracked_variables = input_energy.variables
+    if len(self._tracked_variables) == 0:
+      self._checkpoint = False
+    else:
+      self._tracked_variables_checkpoint = [
+          tf.Variable(v.read_value(), trainable=False)
+          for v in self._tracked_variables
+      ]
+      self._checkpoint = True
+
     if initial_seed is None:
       self._update_seed = tf.Variable(True, trainable=False)
     else:
@@ -104,22 +122,52 @@ def seed(self, initial_seed: Union[None, tf.Tensor]):
       self._update_seed.assign(False)
     self._seed.assign(tfp.random.sanitize_seed(initial_seed))
 
+  @property
+  def variables_updated(self):
+    """Returns True if tracked variables do not have the checkpointed values."""
+    if self._checkpoint:
+      variables_not_equal_list = tf.nest.map_structure(
+          lambda v, vc: tf.math.reduce_any(tf.math.not_equal(v, vc)),
+          self._tracked_variables, self._tracked_variables_checkpoint)
+      return tf.math.reduce_any(tf.stack(variables_not_equal_list))
+    else:
+      return False
+
+  def _checkpoint_variables(self):
+    """Checkpoints the currently tracked variables."""
+    if self._checkpoint:
+      tf.nest.map_structure(lambda v, vc: vc.assign(v), self._tracked_variables,
+                            self._tracked_variables_checkpoint)
+
   def _preface_inference(self):
     """Things all energy inference methods do before proceeding.
 
     Called by `preface_inference` before the wrapped inference method.
     Currently includes:
-      - run `self.infer` if this is the first call of a wrapped function
+      - run `self._ready_inference` if this is first call of a wrapped function
       - change the seed if not set by the user during initialization
+      - run `self._ready_inference` if tracked energy parameters changed
 
     Note: subclasses should take care to call the superclass method.
     """
     if self._first_inference:
-      self.infer(self.energy)
+      self._checkpoint_variables()
+      self._ready_inference()
       self._first_inference.assign(False)
     if self._update_seed:
       new_seed, _ = tfp.random.split_seed(self.seed)
       self._seed.assign(new_seed)
+    if self.variables_updated:
+      self._checkpoint_variables()
+      self._ready_inference()
+
+  @abc.abstractmethod
+  def _ready_inference(self):
+    """Performs computations common to all inference methods.
+
+    Contains inference code that must be run first if the variables of
+    `self.energy` have been updated since the last time inference was performed.
+    """
 
   @preface_inference
   def call(self, inputs, *args, **kwargs):
@@ -181,37 +229,30 @@ def _sample(self, num_samples: int):
     """Default implementation wrapped by `self.sample`."""
     raise NotImplementedError()
 
-  @abc.abstractmethod
-  def infer(self, energy: energy_model.BitstringEnergy):
-    """Do the work to ready this layer for use.
-
-    This should be called each time the underlying model is updated.
-
-    Args:
-      energy: The parameterized energy function which defines this distribution
-        via the equations of an energy based model.
-    """
-    raise NotImplementedError()
-
 
 class EnergyInference(EnergyInferenceBase):
   """Provides some default method implementations."""
 
   def __init__(self,
+               input_energy: energy_model.BitstringEnergy,
                num_expectation_samples: int,
                initial_seed: Union[None, tf.Tensor] = None,
                name: Union[None, str] = None):
     """Initializes an EnergyInference.
 
     Args:
+      input_energy: The parameterized energy function which defines this
+        distribution via the equations of an energy based model.  This class
+        assumes that all parameters of `energy` are `tf.Variable`s and that
+        they are all returned by `energy.variables`.
       num_expectation_samples: Number of samples to draw and use for estimating
         the expectation value.
       initial_seed: PRNG seed; see tfp.random.sanitize_seed for details. This
         seed will be used in the `sample` method.  If None, the seed is updated
         after every inference call.  Otherwise, the seed is fixed.
       name: Optional name for the model.
     """
-    super().__init__(initial_seed, name)
+    super().__init__(input_energy, initial_seed, name)
     self.num_expectation_samples = num_expectation_samples
 
   def _expectation(self, function):
@@ -330,7 +371,7 @@ class AnalyticEnergyInference(EnergyInference):
   """Uses an explicit categorical distribution to implement parent functions."""
 
   def __init__(self,
-               num_bits: int,
+               input_energy: energy_model.BitstringEnergy,
                num_expectation_samples: int,
                initial_seed: Union[None, tf.Tensor] = None,
                name: Union[None, str] = None):
@@ -341,19 +382,23 @@ def __init__(self,
     and other inference tasks.
 
     Args:
-      num_bits: Number of bits on which this layer acts.
+      input_energy: The parameterized energy function which defines this
+        distribution via the equations of an energy based model.  This class
+        assumes that all parameters of `energy` are `tf.Variable`s and that
+        they are all returned by `energy.variables`.
       num_expectation_samples: Number of samples to draw and use for estimating
         the expectation value.
       initial_seed: PRNG seed; see tfp.random.sanitize_seed for details. This
         seed will be used in the `sample` method.  If None, the seed is updated
         after every inference call.  Otherwise, the seed is fixed.
       name: Optional name for the model.
     """
-    super().__init__(num_expectation_samples, initial_seed, name)
+    super().__init__(input_energy, num_expectation_samples, initial_seed, name)
     self._all_bitstrings = tf.constant(
-        list(itertools.product([0, 1], repeat=num_bits)), dtype=tf.int8)
+        list(itertools.product([0, 1], repeat=input_energy.num_bits)),
+        dtype=tf.int8)
     self._logits_variable = tf.Variable(
-        tf.zeros([tf.shape(self._all_bitstrings)[0]]), trainable=False)
+        -input_energy(self.all_bitstrings), trainable=False)
     self._distribution = tfd.Categorical(logits=self._logits_variable)
 
   @property
@@ -368,9 +413,13 @@ def all_energies(self):
 
   @property
   def distribution(self):
-    """Categorical distribution set during last call to `self.infer`."""
+    """Categorical distribution set during `self._ready_inference`."""
     return self._distribution
 
+  def _ready_inference(self):
+    """See base class docstring."""
+    self._logits_variable.assign(-self.all_energies)
+
   def _call(self, inputs, *args, **kwargs):
     """See base class docstring."""
     if inputs is None:
@@ -394,41 +443,43 @@ def _sample(self, num_samples: int):
         self.distribution.sample(num_samples, seed=self.seed),
         axis=0)
 
-  def infer(self, energy: energy_model.BitstringEnergy):
-    """See base class docstring."""
-    self._energy = energy
-    self._logits_variable.assign(-1.0 * self.all_energies)
-
 
 class BernoulliEnergyInference(EnergyInference):
   """Manages inference for a Bernoulli defined by spin energies."""
 
   def __init__(self,
-               num_bits: int,
+               input_energy: energy_model.BernoulliEnergy,
                num_expectation_samples: int,
                initial_seed: Union[None, tf.Tensor] = None,
                name: Union[None, str] = None):
     """Initializes a BernoulliEnergyInference.
 
     Args:
-      num_bits: Number of bits on which this layer acts.
+      input_energy: The parameterized energy function which defines this
+        distribution via the equations of an energy based model.  This class
+        assumes that all parameters of `energy` are `tf.Variable`s and that
+        they are all returned by `energy.variables`.
       num_expectation_samples: Number of samples to draw and use for estimating
         the expectation value.
       initial_seed: PRNG seed; see tfp.random.sanitize_seed for details. This
         seed will be used in the `sample` method.  If None, the seed is updated
         after every inference call.  Otherwise, the seed is fixed.
       name: Optional name for the model.
     """
-    super().__init__(num_expectation_samples, initial_seed, name)
-    self._logits_variable = tf.Variable(tf.zeros([num_bits]), trainable=False)
+    super().__init__(input_energy, num_expectation_samples, initial_seed, name)
+    self._logits_variable = tf.Variable(input_energy.logits, trainable=False)
     self._distribution = tfd.Bernoulli(
         logits=self._logits_variable, dtype=tf.int8)
 
   @property
   def distribution(self):
-    """Bernoulli distribution set during last call to `self.infer`."""
+    """Bernoulli distribution set during `self._ready_inference`."""
     return self._distribution
 
+  def _ready_inference(self):
+    """See base class docstring."""
+    self._logits_variable.assign(self.energy.logits)
+
   def _call(self, inputs, *args, **kwargs):
     """See base class docstring."""
     if inputs is None:
@@ -454,14 +505,9 @@ def _log_partition_forward_pass(self):
     """
     thetas = 0.5 * self.energy.logits
     single_log_partitions = tf.math.log(
-        tf.math.exp(thetas) + tf.math.exp(-1.0 * thetas))
+        tf.math.exp(thetas) + tf.math.exp(-thetas))
     return tf.math.reduce_sum(single_log_partitions)
 
   def _sample(self, num_samples: int):
     """See base class docstring"""
     return self.distribution.sample(num_samples, seed=self.seed)
-
-  def infer(self, energy: energy_model.BitstringEnergy):
-    """See base class docstring."""
-    self._energy = energy
-    self._logits_variable.assign(self.energy.logits)