EVOLUTION-MANAGER

Edit File: constant_op.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # 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 # # http://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. # ============================================================================== """Operations that generate constants. See the [constants guide](https://tensorflow.org/api_guides/python/constant_op). """ # Must be separate from array_ops to avoid a cyclic dependency. from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.framework import dtypes from tensorflow.python.framework import op_callbacks from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.profiler import trace from tensorflow.python.util.tf_export import tf_export def _eager_reshape(tensor, shape, ctx): """Eager-only version of Reshape op; requires tensor is an eager Tensor.""" attr_t = tensor._datatype_enum() # pylint: disable=protected-access attr_tshape, (shape,) = execute.args_to_matching_eager( [shape], ctx, [dtypes.int32, dtypes.int64], dtypes.int32) inputs_flat = [tensor, shape] attrs = ("T", attr_t, "Tshape", attr_tshape) result, = execute.execute( b"Reshape", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx) return result def _eager_fill(dims, value, ctx): """Eager-only version of Fill op; requires value is an eager Tensor.""" attr_t = value.dtype.as_datatype_enum dims = convert_to_eager_tensor(dims, ctx, dtypes.int32) inputs_flat = [dims, value] attrs = ("T", attr_t, "index_type", types_pb2.DT_INT32) result, = execute.execute( b"Fill", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx) return result def _eager_identity(tensor, ctx): """Eager-only version of Identity op; requires tensor is an eager Tensor.""" attrs = ("T", tensor.dtype.as_datatype_enum) result, = execute.execute( b"Identity", 1, inputs=[tensor], attrs=attrs, ctx=ctx) return result def convert_to_eager_tensor(value, ctx, dtype=None): """Converts the given `value` to an `EagerTensor`. Note that this function could return cached copies of created constants for performance reasons. Args: value: value to convert to EagerTensor. ctx: value of context.context(). dtype: optional desired dtype of the converted EagerTensor. Returns: EagerTensor created from value. Raises: TypeError: if `dtype` is not compatible with the type of t. """ if isinstance(value, ops.EagerTensor): if dtype is not None and value.dtype != dtype: raise TypeError("Expected tensor with type %r not %r" % ( dtype, value.dtype)) return value if dtype is not None: try: dtype = dtype.as_datatype_enum except AttributeError: dtype = dtypes.as_dtype(dtype).as_datatype_enum ctx.ensure_initialized() return ops.EagerTensor(value, ctx.device_name, dtype) @tf_export(v1=["constant"]) def constant_v1( value, dtype=None, shape=None, name="Const", verify_shape=False): """Creates a constant tensor. The resulting tensor is populated with values of type `dtype`, as specified by arguments `value` and (optionally) `shape` (see examples below). The argument `value` can be a constant value, or a list of values of type `dtype`. If `value` is a list, then the length of the list must be less than or equal to the number of elements implied by the `shape` argument (if specified). In the case where the list length is less than the number of elements specified by `shape`, the last element in the list will be used to fill the remaining entries. The argument `shape` is optional. If present, it specifies the dimensions of the resulting tensor. If not present, the shape of `value` is used. If the argument `dtype` is not specified, then the type is inferred from the type of `value`. For example: ```python # Constant 1-D Tensor populated with value list. tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7] # Constant 2-D tensor populated with scalar value -1. tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.] [-1. -1. -1.]] ``` `tf.constant` differs from `tf.fill` in a few ways: * `tf.constant` supports arbitrary constants, not just uniform scalar Tensors like `tf.fill`. * `tf.constant` creates a `Const` node in the computation graph with the exact value at graph construction time. On the other hand, `tf.fill` creates an Op in the graph that is expanded at runtime. * Because `tf.constant` only embeds constant values in the graph, it does not support dynamic shapes based on other runtime Tensors, whereas `tf.fill` does. Args: value: A constant value (or list) of output type `dtype`. dtype: The type of the elements of the resulting tensor. shape: Optional dimensions of resulting tensor. name: Optional name for the tensor. verify_shape: Boolean that enables verification of a shape of values. Returns: A Constant Tensor. Raises: TypeError: if shape is incorrectly specified or unsupported. """ return _constant_impl(value, dtype, shape, name, verify_shape=verify_shape, allow_broadcast=False) @tf_export("constant", v1=[]) def constant(value, dtype=None, shape=None, name="Const"): """Creates a constant tensor from a tensor-like object. Note: All eager `tf.Tensor` values are immutable (in contrast to `tf.Variable`). There is nothing especially _constant_ about the value returned from `tf.constant`. This function it is not fundamentally different from `tf.convert_to_tensor`. The name `tf.constant` comes from the `value` being embeded in a `Const` node in the `tf.Graph`. `tf.constant` is useful for asserting that the value can be embedded that way. If the argument `dtype` is not specified, then the type is inferred from the type of `value`. >>> # Constant 1-D Tensor from a python list. >>> tf.constant([1, 2, 3, 4, 5, 6]) <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)> >>> # Or a numpy array >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> tf.constant(a) <tf.Tensor: shape=(2, 3), dtype=int64, numpy= array([[1, 2, 3], [4, 5, 6]])> If `dtype` is specified the resulting tensor values are cast to the requested `dtype`. >>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64) <tf.Tensor: shape=(6,), dtype=float64, numpy=array([1., 2., 3., 4., 5., 6.])> If `shape` is set, the `value` is reshaped to match. Scalars are expanded to fill the `shape`: >>> tf.constant(0, shape=(2, 3)) <tf.Tensor: shape=(2, 3), dtype=int32, numpy= array([[0, 0, 0], [0, 0, 0]], dtype=int32)> >>> tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) <tf.Tensor: shape=(2, 3), dtype=int32, numpy= array([[1, 2, 3], [4, 5, 6]], dtype=int32)> `tf.constant` has no effect if an eager Tensor is passed as the `value`, it even transmits gradients: >>> v = tf.Variable([0.0]) >>> with tf.GradientTape() as g: ... loss = tf.constant(v + v) >>> g.gradient(loss, v).numpy() array([2.], dtype=float32) But, since `tf.constant` embeds the value in the `tf.Graph` this fails for symbolic tensors: >>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.constant(i) Traceback (most recent call last): ... TypeError: ... `tf.constant` will _always_ create CPU (host) tensors. In order to create tensors on other devices, use `tf.identity`. (If the `value` is an eager Tensor, however, the tensor will be returned unmodified as mentioned above.) Related Ops: * `tf.convert_to_tensor` is similar but: * It has no `shape` argument. * Symbolic tensors are allowed to pass through. >>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.convert_to_tensor(i) * `tf.fill`: differs in a few ways: * `tf.constant` supports arbitrary constants, not just uniform scalar Tensors like `tf.fill`. * `tf.fill` creates an Op in the graph that is expanded at runtime, so it can efficiently represent large tensors. * Since `tf.fill` does not embed the value, it can produce dynamically sized outputs. Args: value: A constant value (or list) of output type `dtype`. dtype: The type of the elements of the resulting tensor. shape: Optional dimensions of resulting tensor. name: Optional name for the tensor. Returns: A Constant Tensor. Raises: TypeError: if shape is incorrectly specified or unsupported. ValueError: if called on a symbolic tensor. """ return _constant_impl(value, dtype, shape, name, verify_shape=False, allow_broadcast=True) def _constant_impl( value, dtype, shape, name, verify_shape, allow_broadcast): """Implementation of constant.""" ctx = context.context() if ctx.executing_eagerly(): if trace.enabled: with trace.Trace("tf.constant"): return _constant_eager_impl(ctx, value, dtype, shape, verify_shape) return _constant_eager_impl(ctx, value, dtype, shape, verify_shape) g = ops.get_default_graph() tensor_value = attr_value_pb2.AttrValue() tensor_value.tensor.CopyFrom( tensor_util.make_tensor_proto( value, dtype=dtype, shape=shape, verify_shape=verify_shape, allow_broadcast=allow_broadcast)) dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype) attrs = {"value": tensor_value, "dtype": dtype_value} const_tensor = g._create_op_internal( # pylint: disable=protected-access "Const", [], [dtype_value.type], attrs=attrs, name=name).outputs[0] if op_callbacks.should_invoke_op_callbacks(): # TODO(b/147670703): Once the special-op creation code paths # are unified. Remove this `if` block. callback_outputs = op_callbacks.invoke_op_callbacks( "Const", tuple(), attrs, (const_tensor,), op_name=name, graph=g) if callback_outputs is not None: const_tensor, = callback_outputs return const_tensor def _constant_eager_impl(ctx, value, dtype, shape, verify_shape): """Implementation of eager constant.""" t = convert_to_eager_tensor(value, ctx, dtype) if shape is None: return t shape = tensor_shape.as_shape(shape) if shape == t.shape: return t if verify_shape: raise TypeError("Expected Tensor's shape: %s, got %s." % (tuple(shape), tuple(t.shape))) num_t = t.shape.num_elements() # TODO(josh11b): Implement shape -> eager tensor conversion. if num_t == shape.num_elements(): return _eager_reshape(t, shape.as_list(), ctx) if num_t == 1: if t.dtype == dtypes.bool: # We don't have a Fill kernel for bool dtype on GPU. So we first run # Fill on CPU and then copy to GPU if needed. with ops.device("/device:CPU:0"): x = _eager_fill(shape.as_list(), _eager_identity(t, ctx), ctx) return _eager_identity(x, ctx) else: return _eager_fill(shape.as_list(), t, ctx) raise TypeError("Eager execution of tf.constant with unsupported shape " "(value has %d elements, shape is %s with %d elements)." % (num_t, shape, shape.num_elements())) def is_constant(tensor_or_op): if isinstance(tensor_or_op, ops.Tensor): op = tensor_or_op.op else: op = tensor_or_op return op.type == "Const" def _constant_tensor_conversion_function(v, dtype=None, name=None, as_ref=False): _ = as_ref return constant(v, dtype=dtype, name=name) ops.register_tensor_conversion_function( (list, tuple), _constant_tensor_conversion_function, 100) ops.register_tensor_conversion_function( object, _constant_tensor_conversion_function, 200) def _tensor_shape_tensor_conversion_function(s, dtype=None, name=None, as_ref=False): """Function to convert TensorShape to Tensor.""" _ = as_ref if not s.is_fully_defined(): raise ValueError( "Cannot convert a partially known TensorShape to a Tensor: %s" % s) s_list = s.as_list() int64_value = 0 for dim in s_list: if dim >= 2**31: int64_value = dim break if dtype is not None: if dtype not in (dtypes.int32, dtypes.int64): raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype) if dtype == dtypes.int32 and int64_value: raise ValueError("Cannot convert a TensorShape to dtype int32; " "a dimension is too large (%s)" % int64_value) else: dtype = dtypes.int64 if int64_value else dtypes.int32 if name is None: name = "shape_as_tensor" return constant(s_list, dtype=dtype, name=name) ops.register_tensor_conversion_function( tensor_shape.TensorShape, _tensor_shape_tensor_conversion_function, 100) def _dimension_tensor_conversion_function(d, dtype=None, name=None, as_ref=False): """Function to convert Dimension to Tensor.""" _ = as_ref if d.value is None: raise ValueError("Cannot convert an unknown Dimension to a Tensor: %s" % d) if dtype is not None: if dtype not in (dtypes.int32, dtypes.int64): raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype) else: dtype = dtypes.int32 if name is None: name = "shape_as_tensor" return constant(d.value, dtype=dtype, name=name) ops.register_tensor_conversion_function( tensor_shape.Dimension, _dimension_tensor_conversion_function, 100)