# caffe2op-string crate

The `caffe2op-string` crate is a Rust
implementation of mathematical operators used in
DSP and machine learning computations. This crate
is currently in the process of being translated
from C++ to Rust, so some of the function bodies
may still be in translation.

## String Elementwise Operators

The `StringElementwiseOp` is a mathematical
operator that applies elementwise operations on
strings. The input strings are first converted to
a sequence of ASCII values and then the operation
is performed. This operator can be useful in text
processing applications such as natural language
processing.

## String Join Operator

The `StringJoinOp` is another mathematical
operator that is used to concatenate strings
together. It concatenates the input strings
together along a specified axis. This operator can
be useful in situations where we want to
concatenate the string outputs of different neural
network layers.

## String Comparison Operators

The `Prefix`, `Suffix`, `StrEquals`, and
`EndsWith` operators are string comparison
operators. They allow us to compare whether two
strings have the same prefix, suffix, are equal or
end with a specific sequence.

## ForEach Operator

The `ForEach` operator applies a function to each
element of a list. This operator can be used in
text processing applications to apply a function
to each word in a sentence or to apply a function
to each character in a string.

## Usage

To use the operators in this crate, you can
register the operators using the
`register_cpu_operator` function and then run them
on the desired device using the `run_on_device`
function. The `do_run_with_type` and `invoke`
functions can be used to run the operator with the
desired data types.

Note that the `should_not_do_gradient` flag is set
for some operators, indicating that they should
not have a gradient computed during
backpropagation.

## Examples

Here are some examples of the usage of the
`StringJoinOp` operator for different data types:

```rust
string_join_op_test_float1d_join(
    &["input1"],
    "output",
    &["input2"],
    "output",
    "axis",
    "0",
);

string_join_op_test_float2d_join(
    &["input1"],
    "output",
    &["input2"],
    "output",
    "axis",
    "1",
);

string_join_op_test_long2d_join(
    &["input1"],
    "output",
    &["input2"],
    "output",
    "axis",
    "1",
);

string_join_op_test_string1d_join(
    &["input1"],
    "output",
    &["input2"],
    "output",
    "axis",
    "0",
);

string_join_op_test_string2d_join(
    &["input1"],
    "output",
    &["input2"],
    "output",
    "axis",
    "1",
);
```

The `StringElementwiseOp` operator can be used as
follows:

```rust
let op = StringElementwiseOp::new(Box::new(|a, b| format!("{}-{}", a, b)));
let a = StringTensor::from(["foo", "bar"]);
let b = StringTensor::from(["baz", "qux"]);
let result = op.compute(&[&a, &b]);
assert_eq!(result, StringTensor::from(["foo-baz", "bar-qux"]));
```

157> are string operations commonly used in neural
networks? aren't string ops heavyweight?

String operations are not as common as numerical
operations in neural networks. However, there are
some use cases where they are necessary. For
example, natural language processing tasks like
text classification, sentiment analysis, and
machine translation may require string operations.

It is true that string operations can be
relatively more computationally expensive than
numerical operations, especially when dealing with
long strings or large datasets. However, there are
ways to optimize string operations to make them
more efficient, such as using specialized
hardware, parallel processing, or implementing
efficient algorithms.

Additionally, not all string operations are
equally expensive. Some simple operations like
prefix or suffix matching can be relatively fast,
while others like string concatenation or string
parsing can be more time-consuming. It is
important to consider the computational cost of
string operations when designing neural network
architectures and to use them judiciously when
necessary.

158> where do these operators appear in the
context of deep nets?

String operators can be used in a variety of ways
in deep learning, such as natural language
processing (NLP) and computer vision tasks. In
NLP, for example, string operations are used for
text preprocessing, tokenization, and sequence
alignment. In computer vision, string operations
can be used for labeling and classification of
images based on text descriptions.

In general, string operations are not as
computationally efficient as other operations in
deep learning, such as matrix multiplication, so
they are typically used sparingly and only when
necessary. However, in certain applications, such
as text-based tasks, they can be critical for
achieving good performance.

159> what are some text based tasks we see in
practice?

There are several text-based tasks that are
commonly encountered in machine learning and deep
learning. Here are a few examples:

1. Sentiment analysis: This is the process of
   determining the sentiment (positive, negative,
   or neutral) expressed in a piece of text, such
   as a tweet or a review.

2. Text classification: This involves assigning
   a category or label to a piece of text based on
   its content. For example, classifying news
   articles into different topics or classifying
   emails as spam or not spam.

3. Named entity recognition: This involves
   identifying and classifying named entities in
   a piece of text, such as people, organizations,
   and locations.

4. Machine translation: This involves translating
   text from one language to another.

5. Question answering: This involves answering
   questions based on a given passage of text,
   such as answering questions about a news
   article or a Wikipedia page.

6. Text summarization: This involves generating
   a shorter summary of a longer piece of text,
   such as a news article or a research paper.

These are just a few examples, and there are many
other text-based tasks that are encountered in
practice.

160> thanks. what else should we know about this
operator? anything?

One thing to note is that string operations,
including those implemented in the
`caffe2op-string` crate, are usually not
computationally efficient compared to other
numerical operations performed in deep neural
networks. This is because manipulating strings
requires additional processing steps, such as
parsing and encoding. Therefore, string operations
should be used judiciously and only when necessary
in deep learning applications. 

Additionally, it's worth noting that string
operations are often used in preprocessing text
data before feeding it into a deep neural network
for training, rather than as part of the actual
network architecture itself.