459 lines
16 KiB
Python
459 lines
16 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Operations that generate constants.
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See the [constants guide](https://tensorflow.org/api_guides/python/constant_op).
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"""
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# Must be separate from array_ops to avoid a cyclic dependency.
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from typing import Union
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import numpy as np
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from tensorflow.core.framework import types_pb2
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from tensorflow.core.protobuf import struct_pb2
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from tensorflow.python.eager import context
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from tensorflow.python.eager import execute
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# Import constant_tensor_conversion.py to register tensor conversion functions
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# for builtins. These functions were previously in this file, but were
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# refactored out so they can be registered at TF import time without importing
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# all of constant_op.py.
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from tensorflow.python.framework import constant_tensor_conversion # pylint: disable=unused-import
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor as tensor_lib
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from tensorflow.python.framework import tensor_conversion_registry
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.profiler import trace
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from tensorflow.python.saved_model import nested_structure_coder
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from tensorflow.python.util.tf_export import tf_export
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def _eager_reshape(tensor, shape, ctx):
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"""Eager-only version of Reshape op; requires tensor is an eager Tensor."""
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attr_t = tensor._datatype_enum() # pylint: disable=protected-access
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attr_tshape, (shape,) = execute.args_to_matching_eager(
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[shape], ctx, [dtypes.int32, dtypes.int64], dtypes.int32)
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inputs_flat = [tensor, shape]
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attrs = ("T", attr_t, "Tshape", attr_tshape)
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[result] = execute.execute(
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b"Reshape", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx)
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return result
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def _eager_fill(dims, value, ctx):
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"""Eager-only version of Fill op; requires value is an eager Tensor."""
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attr_t = value.dtype.as_datatype_enum
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dims = convert_to_eager_tensor(dims, ctx, dtypes.int32)
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inputs_flat = [dims, value]
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attrs = ("T", attr_t, "index_type", types_pb2.DT_INT32)
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[result] = execute.execute(
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b"Fill", 1, inputs=inputs_flat, attrs=attrs, ctx=ctx)
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return result
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def _eager_identity(tensor, ctx):
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"""Eager-only version of Identity op; requires tensor is an eager Tensor."""
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attrs = ("T", tensor.dtype.as_datatype_enum)
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[result] = execute.execute(
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b"Identity", 1, inputs=[tensor], attrs=attrs, ctx=ctx)
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return result
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def convert_to_eager_tensor(value, ctx, dtype=None) -> ops._EagerTensorBase:
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"""Converts the given `value` to an `EagerTensor`.
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Note that this function could return cached copies of created constants for
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performance reasons.
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Args:
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value: value to convert to EagerTensor.
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ctx: value of context.context().
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dtype: optional desired dtype of the converted EagerTensor.
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Returns:
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EagerTensor created from value.
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Raises:
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TypeError: if `dtype` is not compatible with the type of t.
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"""
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if isinstance(value, np.ndarray):
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# Make a copy explicitly because the EagerTensor might share the underlying
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# memory with the input array. Without this copy, users will be able to
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# modify the EagerTensor after its creation by changing the input array.
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value = value.copy()
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if isinstance(value, ops.EagerTensor):
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if dtype is not None and value.dtype != dtype:
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raise TypeError(f"Expected tensor {value} with dtype {dtype!r}, but got "
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f"dtype {value.dtype!r}.")
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return value
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if dtype is not None:
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try:
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dtype = dtype.as_datatype_enum
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except AttributeError:
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dtype = dtypes.as_dtype(dtype).as_datatype_enum
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ctx.ensure_initialized()
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return ops.EagerTensor(value, ctx.device_name, dtype)
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@tf_export(v1=["constant"])
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def constant_v1(
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value, dtype=None, shape=None, name="Const", verify_shape=False
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) -> Union[ops.Operation, ops._EagerTensorBase]:
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"""Creates a constant tensor.
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The resulting tensor is populated with values of type `dtype`, as
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specified by arguments `value` and (optionally) `shape` (see examples
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below).
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The argument `value` can be a constant value, or a list of values of type
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`dtype`. If `value` is a list, then the length of the list must be less
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than or equal to the number of elements implied by the `shape` argument (if
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specified). In the case where the list length is less than the number of
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elements specified by `shape`, the last element in the list will be used
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to fill the remaining entries.
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The argument `shape` is optional. If present, it specifies the dimensions of
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the resulting tensor. If not present, the shape of `value` is used.
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If the argument `dtype` is not specified, then the type is inferred from
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the type of `value`.
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For example:
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```python
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# Constant 1-D Tensor populated with value list.
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tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
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# Constant 2-D tensor populated with scalar value -1.
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tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
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[-1. -1. -1.]]
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```
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`tf.constant` differs from `tf.fill` in a few ways:
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* `tf.constant` supports arbitrary constants, not just uniform scalar
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Tensors like `tf.fill`.
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* `tf.constant` creates a `Const` node in the computation graph with the
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exact value at graph construction time. On the other hand, `tf.fill`
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creates an Op in the graph that is expanded at runtime.
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* Because `tf.constant` only embeds constant values in the graph, it does
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not support dynamic shapes based on other runtime Tensors, whereas
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`tf.fill` does.
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Args:
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value: A constant value (or list) of output type `dtype`.
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dtype: The type of the elements of the resulting tensor.
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shape: Optional dimensions of resulting tensor.
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name: Optional name for the tensor.
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verify_shape: Boolean that enables verification of a shape of values.
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Returns:
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A Constant Tensor.
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Raises:
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TypeError: if shape is incorrectly specified or unsupported.
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"""
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return _constant_impl(value, dtype, shape, name, verify_shape=verify_shape,
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allow_broadcast=False)
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@tf_export("constant", v1=[])
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def constant(
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value, dtype=None, shape=None, name="Const"
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) -> Union[ops.Operation, ops._EagerTensorBase]:
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"""Creates a constant tensor from a tensor-like object.
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Note: All eager `tf.Tensor` values are immutable (in contrast to
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`tf.Variable`). There is nothing especially _constant_ about the value
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returned from `tf.constant`. This function is not fundamentally different from
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`tf.convert_to_tensor`. The name `tf.constant` comes from the `value` being
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embedded in a `Const` node in the `tf.Graph`. `tf.constant` is useful
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for asserting that the value can be embedded that way.
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If the argument `dtype` is not specified, then the type is inferred from
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the type of `value`.
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>>> # Constant 1-D Tensor from a python list.
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>>> tf.constant([1, 2, 3, 4, 5, 6])
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<tf.Tensor: shape=(6,), dtype=int32,
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numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
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>>> # Or a numpy array
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>>> a = np.array([[1, 2, 3], [4, 5, 6]])
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>>> tf.constant(a)
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<tf.Tensor: shape=(2, 3), dtype=int64, numpy=
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array([[1, 2, 3],
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[4, 5, 6]])>
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If `dtype` is specified, the resulting tensor values are cast to the requested
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`dtype`.
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>>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64)
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<tf.Tensor: shape=(6,), dtype=float64,
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numpy=array([1., 2., 3., 4., 5., 6.])>
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If `shape` is set, the `value` is reshaped to match. Scalars are expanded to
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fill the `shape`:
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>>> tf.constant(0, shape=(2, 3))
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<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
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array([[0, 0, 0],
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[0, 0, 0]], dtype=int32)>
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>>> tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
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<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
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array([[1, 2, 3],
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[4, 5, 6]], dtype=int32)>
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`tf.constant` has no effect if an eager Tensor is passed as the `value`, it
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even transmits gradients:
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>>> v = tf.Variable([0.0])
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>>> with tf.GradientTape() as g:
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... loss = tf.constant(v + v)
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>>> g.gradient(loss, v).numpy()
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array([2.], dtype=float32)
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But, since `tf.constant` embeds the value in the `tf.Graph` this fails for
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symbolic tensors:
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>>> with tf.compat.v1.Graph().as_default():
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... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
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... t = tf.constant(i)
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Traceback (most recent call last):
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...
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TypeError: ...
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`tf.constant` will create tensors on the current device. Inputs which are
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already tensors maintain their placements unchanged.
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Related Ops:
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* `tf.convert_to_tensor` is similar but:
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* It has no `shape` argument.
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* Symbolic tensors are allowed to pass through.
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>>> with tf.compat.v1.Graph().as_default():
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... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32)
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... t = tf.convert_to_tensor(i)
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* `tf.fill`: differs in a few ways:
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* `tf.constant` supports arbitrary constants, not just uniform scalar
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Tensors like `tf.fill`.
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* `tf.fill` creates an Op in the graph that is expanded at runtime, so it
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can efficiently represent large tensors.
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* Since `tf.fill` does not embed the value, it can produce dynamically
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sized outputs.
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Args:
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value: A constant value (or list) of output type `dtype`.
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dtype: The type of the elements of the resulting tensor.
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shape: Optional dimensions of resulting tensor.
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name: Optional name for the tensor.
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Returns:
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A Constant Tensor.
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Raises:
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TypeError: if shape is incorrectly specified or unsupported.
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ValueError: if called on a symbolic tensor.
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"""
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return _constant_impl(value, dtype, shape, name, verify_shape=False,
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allow_broadcast=True)
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def _constant_impl(
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value, dtype, shape, name, verify_shape, allow_broadcast
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) -> Union[ops.Operation, ops._EagerTensorBase]:
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"""Implementation of constant."""
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ctx = context.context()
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if ctx.executing_eagerly():
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if trace.enabled:
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with trace.Trace("tf.constant"):
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return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
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return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
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const_tensor = ops._create_graph_constant( # pylint: disable=protected-access
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value, dtype, shape, name, verify_shape, allow_broadcast
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)
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return const_tensor
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def _constant_eager_impl(
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ctx, value, dtype, shape, verify_shape
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) -> ops._EagerTensorBase:
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"""Creates a constant on the current device."""
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t = convert_to_eager_tensor(value, ctx, dtype)
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if shape is None:
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return t
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shape = tensor_shape.as_shape(shape)
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if shape == t.shape:
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return t
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if verify_shape:
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raise TypeError(f"Expected Tensor {t} (converted from {value}) with shape "
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f"{tuple(shape)}, but got shape {tuple(t.shape)}.")
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num_t = t.shape.num_elements()
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# TODO(josh11b): Implement shape -> eager tensor conversion.
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if num_t == shape.num_elements():
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return _eager_reshape(t, shape.as_list(), ctx)
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if num_t == 1:
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if t.dtype == dtypes.bool:
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# We don't have a Fill kernel for bool dtype on GPU. So we first run
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# Fill on CPU and then copy to GPU if needed.
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with ops.device("/device:CPU:0"):
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x = _eager_fill(shape.as_list(), _eager_identity(t, ctx), ctx)
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return _eager_identity(x, ctx)
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else:
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return _eager_fill(shape.as_list(), t, ctx)
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raise TypeError("Eager execution of tf.constant with unsupported shape. "
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f"Tensor {t} (converted from {value}) has {num_t:d} "
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f"elements, but got `shape` {shape} with "
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f"{shape.num_elements()} elements).")
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def is_constant(tensor_or_op):
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if isinstance(tensor_or_op, tensor_lib.Tensor):
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op = tensor_or_op.op
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else:
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op = tensor_or_op
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return op.type == "Const"
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def _tensor_shape_tensor_conversion_function(s,
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dtype=None,
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name=None,
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as_ref=False):
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"""Function to convert TensorShape to Tensor."""
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_ = as_ref
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if not s.is_fully_defined():
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raise ValueError(
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f"Cannot convert a partially known TensorShape {s} to a Tensor.")
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s_list = s.as_list()
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int64_value = 0
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for dim in s_list:
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if dim >= 2**31:
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int64_value = dim
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break
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if dtype is not None:
|
||
|
|
if dtype not in (dtypes.int32, dtypes.int64):
|
||
|
|
raise TypeError(f"Cannot convert TensorShape {s} to dtype {dtype}. "
|
||
|
|
"Allowed dtypes are tf.int32 and tf.int64.")
|
||
|
|
if dtype == dtypes.int32 and int64_value:
|
||
|
|
raise ValueError(f"Cannot convert TensorShape {s} to dtype int32; "
|
||
|
|
f"a dimension is too large. Consider using tf.int64.")
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
tensor_conversion_registry.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(f"Cannot convert unknown Dimension {d} to a Tensor.")
|
||
|
|
if dtype is not None:
|
||
|
|
if dtype not in (dtypes.int32, dtypes.int64):
|
||
|
|
raise TypeError(f"Cannot convert Dimension {d} to dtype {dtype}. "
|
||
|
|
"Allowed dtypes are tf.int32 and tf.int64.")
|
||
|
|
else:
|
||
|
|
dtype = dtypes.int32
|
||
|
|
if name is None:
|
||
|
|
name = "shape_as_tensor"
|
||
|
|
return constant(d.value, dtype=dtype, name=name)
|
||
|
|
|
||
|
|
|
||
|
|
tensor_conversion_registry.register_tensor_conversion_function(
|
||
|
|
tensor_shape.Dimension, _dimension_tensor_conversion_function, 100)
|
||
|
|
|
||
|
|
|
||
|
|
class _ConstantTensorCodec:
|
||
|
|
"""Codec for Tensor."""
|
||
|
|
|
||
|
|
def can_encode(self, pyobj):
|
||
|
|
return isinstance(pyobj, tensor_lib.Tensor)
|
||
|
|
|
||
|
|
def do_encode(self, tensor_value, encode_fn):
|
||
|
|
"""Returns an encoded `TensorProto` for the given `tf.Tensor`."""
|
||
|
|
del encode_fn
|
||
|
|
encoded_tensor = struct_pb2.StructuredValue()
|
||
|
|
if isinstance(tensor_value, ops.EagerTensor):
|
||
|
|
encoded_tensor.tensor_value.CopyFrom(
|
||
|
|
tensor_util.make_tensor_proto(tensor_value.numpy())
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
if tensor_value.op.type == "Const":
|
||
|
|
encoded_tensor.tensor_value.CopyFrom(tensor_value.op.get_attr("value"))
|
||
|
|
else:
|
||
|
|
raise nested_structure_coder.NotEncodableError(
|
||
|
|
f"No encoder for object {str(tensor_value)} of type"
|
||
|
|
f" {type(tensor_value)}."
|
||
|
|
)
|
||
|
|
return encoded_tensor
|
||
|
|
|
||
|
|
def can_decode(self, value):
|
||
|
|
return value.HasField("tensor_value")
|
||
|
|
|
||
|
|
def do_decode(self, value, decode_fn):
|
||
|
|
"""Returns the `tf.Tensor` encoded by the proto `value`."""
|
||
|
|
del decode_fn
|
||
|
|
tensor_proto = value.tensor_value
|
||
|
|
tensor = constant(tensor_util.MakeNdarray(tensor_proto))
|
||
|
|
return tensor
|
||
|
|
|
||
|
|
|
||
|
|
nested_structure_coder.register_codec(_ConstantTensorCodec())
|
||
|
|
|
||
|
|
|
||
|
|
class _NumpyCodec:
|
||
|
|
"""Codec for Numpy."""
|
||
|
|
|
||
|
|
def can_encode(self, pyobj):
|
||
|
|
return isinstance(pyobj, np.ndarray)
|
||
|
|
|
||
|
|
def do_encode(self, numpy_value, encode_fn):
|
||
|
|
"""Returns an encoded `TensorProto` for `np.ndarray`."""
|
||
|
|
del encode_fn
|
||
|
|
encoded_numpy = struct_pb2.StructuredValue()
|
||
|
|
encoded_numpy.numpy_value.CopyFrom(
|
||
|
|
tensor_util.make_tensor_proto(numpy_value)
|
||
|
|
)
|
||
|
|
return encoded_numpy
|
||
|
|
|
||
|
|
def can_decode(self, value):
|
||
|
|
return value.HasField("numpy_value")
|
||
|
|
|
||
|
|
def do_decode(self, value, decode_fn):
|
||
|
|
"""Returns the `np.ndarray` encoded by the proto `value`."""
|
||
|
|
del decode_fn
|
||
|
|
tensor_proto = value.numpy_value
|
||
|
|
numpy = tensor_util.MakeNdarray(tensor_proto)
|
||
|
|
return numpy
|
||
|
|
|
||
|
|
|
||
|
|
nested_structure_coder.register_codec(_NumpyCodec())
|