import numpy as np import tensorflow as tf from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice from keras.src import tree from keras.src.backend.common import KerasVariable from keras.src.backend.common import global_state from keras.src.backend.common import standardize_dtype from keras.src.backend.common.keras_tensor import KerasTensor from keras.src.backend.common.name_scope import name_scope as base_name_scope from keras.src.backend.common.stateless_scope import StatelessScope from keras.src.backend.common.stateless_scope import in_stateless_scope from keras.src.backend.tensorflow.sparse import sparse_to_dense from keras.src.utils.naming import auto_name SUPPORTS_SPARSE_TENSORS = True class Variable( KerasVariable, tf.__internal__.types.Tensor, tf.__internal__.tracking.Trackable, ): _should_act_as_resource_variable = True @property def handle(self): return self.value.handle def _initialize(self, value): self._value = tf.Variable( value, dtype=self._dtype, trainable=self.trainable, name=self.name ) def _deferred_initialize(self): if self._value is not None: raise ValueError(f"Variable {self.path} is already initialized.") if in_stateless_scope(): raise ValueError( "You are attempting to initialize a variable " "while in a stateless scope. This is disallowed. " "Make sure that all variables are initialized " "before you start using your layer/model objects." ) with tf.init_scope(): value = self._initializer(self._shape, dtype=self._dtype) self._initialize(value) def _direct_assign(self, value): self._value.assign(tf.cast(value, self._value.dtype)) def _convert_to_tensor(self, value, dtype=None): return convert_to_tensor(value, dtype=dtype) def numpy(self): # noqa: F811 return self.value.numpy() @property def shape(self): return tf.TensorShape(super().shape) # Overload native accessor. def __tf_tensor__(self, dtype=None, name=None): return tf.convert_to_tensor(self.value, dtype=dtype, name=name) # Methods below are for SavedModel support @property def _shared_name(self): return self.value._shared_name def _serialize_to_tensors(self): try: return self.value._serialize_to_tensors() except NotImplementedError: return {"VARIABLE_VALUE": self.value} def _restore_from_tensors(self, restored_tensors): try: return self.value._restore_from_tensors(restored_tensors) except NotImplementedError: self.assign(restored_tensors["VARIABLE_VALUE"]) return self.value def _copy_trackable_to_cpu(self, object_map): self.value._copy_trackable_to_cpu(object_map) object_map[self] = tf.Variable(object_map[self.value]) def _export_to_saved_model_graph( self, object_map, tensor_map, options, **kwargs ): resource_list = self.value._export_to_saved_model_graph( object_map, tensor_map, options, **kwargs ) object_map[self] = tf.Variable(object_map[self.value]) return resource_list def _write_object_proto(self, proto, options): return self.value._write_object_proto(proto, options) def convert_to_tensor(x, dtype=None, sparse=None): if isinstance(x, tf.SparseTensor) and sparse is not None and not sparse: x = sparse_to_dense(x) if dtype is not None: dtype = standardize_dtype(dtype) if not tf.is_tensor(x): if dtype == "bool": # TensorFlow boolean conversion is stricter than other backends. # It does not allow ints. We convert without dtype and cast instead. x = tf.convert_to_tensor(x) return tf.cast(x, dtype) return tf.convert_to_tensor(x, dtype=dtype) elif dtype is not None and not x.dtype == dtype: if isinstance(x, tf.SparseTensor): x_shape = x.shape x = tf.cast(x, dtype) x.set_shape(x_shape) return x return tf.cast(x, dtype=dtype) return x def convert_to_numpy(x): if isinstance(x, tf.SparseTensor): x = sparse_to_dense(x) elif isinstance(x, tf.IndexedSlices): x = tf.convert_to_tensor(x) elif isinstance(x, tf.RaggedTensor): x = x.to_tensor() return np.asarray(x) def is_tensor(x): return tf.is_tensor(x) def shape(x): """Always return a tuple shape. `tf.shape` will return a `tf.Tensor`, which differs from the tuple return type on the torch and jax backends. We write our own method instead which always returns a tuple, with integer values when the shape is known, and tensor values when the shape is unknown (this is tf specific, as dynamic shapes do not apply in other backends). """ if isinstance(x, KerasTensor): return x.shape if not tf.is_tensor(x): x = tf.convert_to_tensor(x) dynamic = tf.shape(x) if x.shape == tf.TensorShape(None): raise ValueError( "All tensors passed to `ops.shape` must have a statically known " f"rank. Received: x={x} with unknown rank." ) static = x.shape.as_list() return tuple(dynamic[i] if s is None else s for i, s in enumerate(static)) def cast(x, dtype): dtype = standardize_dtype(dtype) if isinstance(x, tf.SparseTensor): x_shape = x.shape x = tf.cast(x, dtype) x.set_shape(x_shape) return x else: return tf.cast(x, dtype=dtype) def compute_output_spec(fn, *args, **kwargs): with StatelessScope(): graph_name = auto_name("scratch_graph") with tf.__internal__.FuncGraph(graph_name).as_default(): def convert_keras_tensor_to_tf(x): if isinstance(x, KerasTensor): if x.sparse: return tf.compat.v1.sparse_placeholder( shape=x.shape, dtype=x.dtype ) else: return tf.compat.v1.placeholder( shape=x.shape, dtype=x.dtype ) return x args, kwargs = tree.map_structure( convert_keras_tensor_to_tf, (args, kwargs) ) tf_out = fn(*args, **kwargs) def convert_tf_to_keras_tensor(x): if tf.is_tensor(x): return KerasTensor( x.shape, x.dtype, sparse=isinstance(x, tf.SparseTensor) ) return x output_spec = tree.map_structure(convert_tf_to_keras_tensor, tf_out) return output_spec def cond(pred, true_fn, false_fn): return tf.cond(pred, true_fn=true_fn, false_fn=false_fn) def vectorized_map(function, elements): return tf.vectorized_map(function, elements) def scatter(indices, values, shape): return tf.scatter_nd(indices, values, shape) def scatter_update(inputs, indices, updates): return tf.tensor_scatter_nd_update(inputs, indices, updates) def slice(inputs, start_indices, shape): return tf.slice(inputs, start_indices, shape) def slice_update(inputs, start_indices, updates): return dynamic_update_slice(inputs, updates, start_indices) def while_loop( cond, body, loop_vars, maximum_iterations=None, ): is_tuple = isinstance(loop_vars, (tuple, list)) loop_vars = tuple(loop_vars) if is_tuple else (loop_vars,) def _body(*args): outputs = body(*args) return tuple(outputs) if is_tuple else (outputs,) outputs = tf.while_loop( cond, _body, loop_vars, maximum_iterations=maximum_iterations, ) return outputs if is_tuple else outputs[0] def fori_loop(lower, upper, body_fun, init_val): return tf.while_loop( lambda i, val: i < upper, lambda i, val: (i + 1, body_fun(i, val)), (lower, init_val), )[1] def stop_gradient(variable): return tf.stop_gradient(variable) def unstack(x, num=None, axis=0): return tf.unstack(x, num=num, axis=axis) def custom_gradient(fun): return tf.custom_gradient(f=fun) class name_scope(base_name_scope): def __init__(self, name, **kwargs): super().__init__(name, **kwargs) self._tf_name_scope = tf.name_scope(name) def __enter__(self): name_scope_stack = global_state.get_global_attribute( "name_scope_stack", default=[], set_to_default=True ) if self.deduplicate and name_scope_stack: parent_caller = name_scope_stack[-1].caller parent_name = name_scope_stack[-1].name if ( self.caller is not None and self.caller is parent_caller and self.name == parent_name ): return self name_scope_stack.append(self) self._pop_on_exit = True self._tf_name_scope.__enter__() return self def __exit__(self, *args, **kwargs): super().__exit__(*args, **kwargs) if self._pop_on_exit: self._tf_name_scope.__exit__(*args, **kwargs) def device_scope(device_name): return tf.device(device_name)