Kye Gomez
7 months ago
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This section of the documentation is dedicated to examples highlighting Swarms functionality.
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We try to keep all examples up to date, but if you think there is a bug please [submit a pull request](https://github.com/kyegomez/swarms-docs/tree/main/docs/examples). We are also more than happy to include new examples)
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# check_device
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# Module/Function Name: check_device
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The `check_device` is a utility function in PyTorch designed to identify and return the appropriate device(s) for CUDA processing. If CUDA is not available, a CPU device is returned. If CUDA is available, the function returns a list of all available GPU devices.
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The function examines the CUDA availability, checks for multiple GPUs, and finds additional properties for each device.
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## Function Signature and Arguments
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**Signature:**
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```python
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def check_device(
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log_level: Any = logging.INFO,
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memory_threshold: float = 0.8,
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capability_threshold: float = 3.5,
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return_type: str = "list",
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) -> Union[torch.device, List[torch.device]]
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```
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| Parameter | Data Type | Default Value | Description |
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| ------------- | ------------- | ------------- | ------------- |
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| `log_level` | Any | logging.INFO | The log level. |
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| `memory_threshold` | float | 0.8 | It is used to check the threshold of memory used on the GPU(s). |
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| `capability_threshold` | float | 3.5 | It is used to consider only those GPU(s) which have higher compute capability compared to the threshold. |
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| `return_type` | str | "list" | Depending on the `return_type` either a list of devices can be returned or a single device. |
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This function does not take any mandatory argument. However, it supports optional arguments such as `log_level`, `memory_threshold`, `capability_threshold`, and `return_type`.
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**Returns:**
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- A single torch.device if one device or list of torch.devices if multiple CUDA devices are available, else returns the CPU device if CUDA is not available.
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## Usage and Examples
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### Example 1: Basic Usage
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```python
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import logging
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import torch
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from swarms.utils import check_device
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# Basic usage
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device = check_device(
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log_level=logging.INFO,
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memory_threshold=0.8,
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capability_threshold=3.5,
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return_type="list",
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)
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```
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### Example 2: Using CPU when CUDA is not available
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```python
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import torch
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from swarms.utils import check_device
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# When CUDA is not available
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device = check_device()
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print(device) # If CUDA is not available it should return torch.device('cpu')
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```
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### Example 3: Multiple GPU Available
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```python
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import torch
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from swarms.utils import check_device
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# When multiple GPUs are available
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device = check_device()
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print(device) # Should return a list of available GPU devices
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```
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## Tips and Additional Information
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- This function is useful when a user wants to exploit CUDA capabilities for faster computation but unsure of the available devices. This function abstracts all the necessary checks and provides a list of CUDA devices to the user.
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- The `memory_threshold` and `capability_threshold` are utilized to filter the GPU devices. The GPUs which have memory usage above the `memory_threshold` and compute capability below the `capability_threshold` are not considered.
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- As of now, CPU does not have memory or capability values, therefore, in the respective cases, it will be returned as default without any comparison.
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## Relevant Resources
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- For more details about the CUDA properties functions used (`torch.cuda.get_device_capability, torch.cuda.get_device_properties`), please refer to the official PyTorch [CUDA semantics documentation](https://pytorch.org/docs/stable/notes/cuda.html).
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- For more information about Torch device objects, you can refer to the official PyTorch [device documentation](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device).
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- For a better understanding of how the `logging` module works in Python, see the official Python [logging documentation](https://docs.python.org/3/library/logging.html).
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# display_markdown_message
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# Module Name: `display_markdown_message`
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## Introduction
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`display_markdown_message` is a useful utility function for creating visually-pleasing markdown messages within Python scripts. This function automatically manages multiline strings with lots of indentation and makes single-line messages with ">" tags easy to read, providing users with convenient and elegant logging or messaging capacity.
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## Function Definition and Arguments
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Function Definition:
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```python
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def display_markdown_message(message: str, color: str = "cyan"):
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```
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This function accepts two parameters:
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|Parameter |Type |Default Value |Description |
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|--- |--- |--- |--- |
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|message |str |None |This is the message that is to be displayed. This should be a string. It can contain markdown syntax.|
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|color |str |"cyan" |This allows you to choose the color of the message. Default is "cyan". Accepts any valid color name.|
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## Functionality and Usage
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This utility function is used to display a markdown formatted message on the console. It accepts a message as a string and an optional color for the message. The function is ideal for generating stylized print outputs such as headers, status updates or pretty notifications.
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By default, any text within the string which is enclosed within `>` tags or `---` is treated specially:
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- Lines encased in `>` tags are rendered as a blockquote in markdown.
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- Lines consisting of `---` are rendered as horizontal rules.
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The function automatically strips off leading and trailing whitespaces from any line within the message, maintaining aesthetic consistency in your console output.
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### Usage Examples
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#### Basic Example
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```python
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display_markdown_message("> This is an important message", color="red")
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```
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Output:
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```md
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> **This is an important message**
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```
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This example will print out the string "This is an important message" in red color, enclosed in a blockquote tag.
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#### Multiline Example
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```python
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message = """
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> Header
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My normal message here.
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---
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Another important information
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"""
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display_markdown_message(message, color="green")
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```
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Output:
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```md
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> **Header**
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My normal message here.
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_____
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Another important information
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```
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The output is a green colored markdown styled text with the "Header" enclosed in a blockquote, followed by the phrase "My normal message here", a horizontal rule, and finally another phrase, "Another important information".
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## Additional Information
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Use newline characters `\n` to separate the lines of the message. Remember, each line of the message is stripped of leading and trailing whitespaces. If you have special markdown requirements, you may need to revise the input message string accordingly.
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Also, keep in mind the console or terminal's ability to display the chosen color. If a particular console does not support the chosen color, the output may fallback to the default console color.
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For a full list of color names supported by the `Console` module, refer to the official [Console documentation](http://console.readthedocs.io/).
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## References and Resources
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- Python Strings: https://docs.python.org/3/tutorial/introduction.html#strings
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- Python Markdown: https://pypi.org/project/markdown/
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- Console module: https://console.readthedocs.io/
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# extract_code_from_markdown
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# swarms.utils Module
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The `swarms.utils` module provides utility functions designed to facilitate specific tasks within the main Swarm codebase. The function `extract_code_from_markdown` is a critical function within this module that we will document in this example.
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## Overview and Introduction
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Many software projects use Markdown extensively for writing documentation, tutorials, and other text documents that can be easily rendered and viewed in different formats, including HTML.
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The `extract_code_from_markdown` function plays a crucial role within the swarms.utils library. As developers write large volumes of Markdown, they often need to isolate code snippets from the whole Markdown file body. These isolated snippets can be used to generate test cases, transform into other languages, or analyze for metrics.
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## Function Definition: `extract_code_from_markdown`
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```python
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def extract_code_from_markdown(markdown_content: str) -> str:
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"""
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Extracts code blocks from a Markdown string and returns them as a single string.
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Args:
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- markdown_content (str): The Markdown content as a string.
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Returns:
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- str: A single string containing all the code blocks separated by newlines.
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"""
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# Regular expression for fenced code blocks
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pattern = r"```(?:\w+\n)?(.*?)```"
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matches = re.findall(pattern, markdown_content, re.DOTALL)
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# Concatenate all code blocks separated by newlines
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return "\n".join(code.strip() for code in matches)
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```
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### Arguments
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The function `extract_code_from_markdown` takes one argument:
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| Argument | Description | Type | Default Value |
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|-----------------------|----------------------------------------|-------------|-------------------|
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| markdown_content | The input markdown content as a string | str | N/A |
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## Function Explanation and Usage
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This function uses a regular expression to find all fenced code blocks in a Markdown string. The pattern `r"```(?:\w+\n)?(.*?)```"` matches strings that start and end with three backticks, optionally followed by a newline and then any number of any characters (the `.*?` part) until the first occurrence of another triple backtick set.
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Once we have the matches, we join all the code blocks into a single string, each block separated by a newline.
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The method's functionality is particularly useful when we need to extract code blocks from markdown content for secondary processing, such as syntax highlighting or execution in a different environment.
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### Usage Examples
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Below are three examples of how you might use this function:
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#### Example 1:
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Extracting code blocks from a simple markdown string.
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```python
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from swarms.utils import extract_code_from_markdown
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markdown_string = """# Example
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This is an example of a code block:
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```python
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print("Hello World!")
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``` """
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print(extract_code_from_markdown(markdown_string))
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```
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#### Example 2:
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Extracting code blocks from a markdown file.
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```python
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import re
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def extract_code_from_markdown(markdown_content: str) -> str:
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pattern = r"```(?:\w+\n)?(.*?)```"
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matches = re.findall(pattern, markdown_content, re.DOTALL)
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return "\n".join(code.strip() for code in matches)
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# Assume that 'example.md' contains multiple code blocks
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with open("example.md") as file:
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markdown_content = file.read()
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print(extract_code_from_markdown(markdown_content))
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```
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#### Example 3:
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Using the function in a pipeline to extract and then analyze code blocks.
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```python
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import re
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def extract_code_from_markdown(markdown_content: str) -> str:
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pattern = r"```(?:\w+\n)?(.*?)```"
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matches = re.findall(pattern, markdown_content, re.DOTALL)
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return "\n".join(code.strip() for code in matches)
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def analyze_code_blocks(code: str):
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# Add your analysis logic here
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pass
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# Assume that 'example.md' contains multiple code blocks
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with open("example.md") as file:
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markdown_content = file.read()
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code_blocks = extract_code_from_markdown(markdown_content)
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analyze_code_blocks(code_blocks)
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```
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## Conclusion
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This concludes the detailed documentation of the `extract_code_from_markdown` function from the swarms.utils module. With this documentation, you should be able to understand the function's purpose, how it works, its parameters, and see examples of how to use it effectively.
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# find_image_path
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Firstly, we will divide this documentation into multiple sections.
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# Overview
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The module **swarms.utils** has the main goal of providing necessary utility functions that are crucial during the creation of the swarm intelligence frameworks. These utility functions can include common operations such as handling input-output operations for files, handling text parsing, and handling basic mathematical computations necessary during the creation of swarm intelligence models.
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The current function `find_image_path` in the module is aimed at extracting an image path from a given text document.
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# Function Detailed Explanation
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## Definition
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The function `find_image_path` takes a singular argument as an input:
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```python
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def find_image_path(text):
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# function body
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```
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## Parameter
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The parameter `text` in the function is a string that represents the document or text from which the function is trying to extract all paths to the images present. The function scans the given text, looking for <em>absolute</em> or <em>relative</em> paths to image files (.png, .jpg, .jpeg) on the disk.
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| Parameter Name | Data Type | Default Value | Description |
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|:--------------:|:---------:|:-------------:|:--------:|
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| `text` | `str` | - | The text content to scan for image paths |
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## Return Value
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The return value of the function `find_image_path` is a string that represents the longest existing image path extracted from the input text. If no image paths exist within the text, the function returns `None`.
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| Return Value | Data Type | Description |
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|:------------:|:-----------:|:-----------:|
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| Path | `str` | Longest image path found in the text or `None` if no path found |
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# Function's Code
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The function `find_image_path` performs text parsing and pattern recognition to find image paths within the provided text. The function uses `regular expressions (re)` module to detect all potential paths.
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```python
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def find_image_path(text):
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pattern = r"([A-Za-z]:\\[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))|(/[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))"
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matches = [match.group() for match in re.finditer(pattern, text) if match.group()]
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matches += [match.replace("\\", "") for match in matches if match]
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existing_paths = [match for match in matches if os.path.exists(match)]
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return max(existing_paths, key=len) if existing_paths else None
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```
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# Usage Examples
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Let's consider examples of how the function `find_image_path` can be used in different scenarios.
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**Example 1:**
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Consider the case where a text without any image path is provided.
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```python
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from swarms.utils import find_image_path
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text = "There are no image paths in this text"
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print(find_image_path(text)) # Outputs: None
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```
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**Example 2:**
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Consider the case where the text has multiple image paths.
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```python
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from swarms.utils import find_image_path
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text = "Here is an image path: /home/user/image1.png. Here is another one: C:\\Users\\User\\Documents\\image2.jpeg"
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print(
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find_image_path(text)
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) # Outputs: the longest image path (depends on your file system and existing files)
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```
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**Example 3:**
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In the final example, we consider a case where the text has an image path, but the file does not exist.
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```python
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from swarms.utils import find_image_path
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text = "Here is an image path: /home/user/non_existant.png"
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print(find_image_path(text)) # Outputs: None
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```
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# Closing Notes
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In conclusion, the `find_image_path` function is crucial in the `swarms.utils` module as it supports a key operation of identifying image paths within given input text. This allows users to automate the extraction of such data from larger documents/text. However, it's important to note the function returns only existing paths in your file system and only the longest if multiple exist.
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# limit_tokens_from_string
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## Introduction
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The `Swarms.utils` library contains utility functions used across codes that handle machine learning and other operations. The `Swarms.utils` library includes a notable function named `limit_tokens_from_string()`. This function particularly limits the number of tokens in a given string.
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# Function: limit_tokens_from_string()
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Within the `Swarms.utils` library, there is a method `limit_tokens_from_string(string: str, model: str = "gpt-4", limit: int = 500) -> str:`
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## Description
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The function `limit_tokens_from_string()` limits the number of tokens in a given string based on the specified threshold. It is primarily useful when you are handling large text data and need to chunk or limit your text to a certain length. Limiting token length could be useful in various scenarios such as when working with data with limited computational resources, or when dealing with models that accept a specific maximum limit of text.
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## Parameters
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| Parameter | Type | Default Value | Description
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| :-----------| :----------- | :------------ | :------------|
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| `string` | `str` | `None` | The input string from which the tokens need to be limited. |
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| `model` | `str` | `"gpt-4"` | The model used to encode and decode the token. The function defaults to `gpt-4` but you can specify any model supported by `tiktoken`. If a model is not found, it falls back to use `gpt2` |
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| `limit` | `int` | `500` | The limit up to which the tokens have to be sliced. Default limit is 500.|
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## Returns
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| Return | Type | Description
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| :-----------| :----------- | :------------
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| `out` | `str` | A string that is constructed back from the encoded tokens that have been limited to a count of `limit` |
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## Method Detail and Usage Examples
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The method `limit_tokens_from_string()` takes in three parameters - `string`, `model`, and `limit`.
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First, it tries to get the encoding for the model specified in the `model` argument using `tiktoken.encoding_for_model(model)`. In case the specified model is not found, the function uses `gpt2` model encoding as a fallback.
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Next, the input `string` is tokenized using the `encode` method on the `encoding` tensor. This results in the `encoded` tensor.
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Then, the function slices the `encoded` tensor to get the first `limit` number of tokens.
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Finally, the function converts back the tokens into the string using the `decode` method of the `encoding` tensor. The resulting string `out` is returned.
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### Example 1:
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```python
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from swarms.utils import limit_tokens_from_string
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# longer input string
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string = "This is a very long string that needs to be tokenized. This string might exceed the maximum token limit, so it will need to be truncated."
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# lower token limit
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limit = 10
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output = limit_tokens_from_string(string, limit=limit)
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```
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### Example 2:
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```python
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from swarms.utils import limit_tokens_from_string
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# longer input string with different model
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string = "This string will be tokenized using gpt2 model. If the string is too long, it will be truncated."
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# model
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model = "gpt2"
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output = limit_tokens_from_string(string, model=model)
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```
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### Example 3:
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```python
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from swarms.utils import limit_tokens_from_string
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|
||||
# try with a random model string
|
||||
string = "In case the method does not find the specified model, it will fall back to gpt2 model."
|
||||
|
||||
# model
|
||||
model = "gpt-4"
|
||||
|
||||
output = limit_tokens_from_string(string, model=model)
|
||||
```
|
||||
|
||||
**Note:** If specifying a model not supported by `tiktoken` intentionally, it will fall back to `gpt2` model for encoding.
|
||||
|
||||
@ -1,105 +0,0 @@
|
||||
# load_model_torch
|
||||
|
||||
# load_model_torch: Utility Function Documentation
|
||||
|
||||
## Introduction:
|
||||
|
||||
`load_model_torch` is a utility function in the `swarms.utils` library that is designed to load a saved PyTorch model and move it to the designated device. It provides flexibility allowing the user to specify the model file location, the device where the loaded model should be moved to, whether to strictly enforce the keys in the state dictionary to match the keys returned by the model's `state_dict()`, and many more.
|
||||
|
||||
Moreover, if the saved model file only contains the state dictionary, but not the model architecture, you can pass the model architecture as an argument.
|
||||
|
||||
## Function Definition and Parameters:
|
||||
|
||||
```python
|
||||
def load_model_torch(
|
||||
model_path: str = None,
|
||||
device: torch.device = None,
|
||||
model: nn.Module = None,
|
||||
strict: bool = True,
|
||||
map_location=None,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> nn.Module:
|
||||
```
|
||||
|
||||
The following table describes the parameters in detail:
|
||||
|
||||
| Name | Type | Default Value | Description |
|
||||
| ------ | ------ | ------------- | ------------|
|
||||
| model_path | str | None | A string specifying the path to the saved model file on disk. _Required_ |
|
||||
| device | torch.device | None | A `torch.device` object that specifies the target device for the loaded model. If not provided, the function checks for the availability of a GPU and uses it if available. If not, it defaults to CPU. |
|
||||
| model | nn.Module | None | An instance of `torch.nn.Module` representing the model's architecture. This parameter is required if the model file only contains the model's state dictionary and not the model architecture. |
|
||||
| strict | bool | True | A boolean that determines whether to strictly enforce that the keys in the state dictionary match the keys returned by the model's `state_dict()` function. If set to `True`, the function will raise a KeyError when the state dictionary and `state_dict()` keys do not match. |
|
||||
| map_location | callable | None | A function to remap the storage locations of the loaded model's parameters. Useful for loading models saved on a device type that is different from the current one. |
|
||||
| *args, **kwargs | - | - | Additional arguments and keyword arguments to be passed to `torch.load`.
|
||||
|
||||
Returns:
|
||||
|
||||
- `torch.nn.Module` - The loaded model after moving it to the desired device.
|
||||
|
||||
Raises:
|
||||
|
||||
- `FileNotFoundError` - If the saved model file is not found at the specified path.
|
||||
- `RuntimeError` - If there was an error while loading the model.
|
||||
|
||||
## Example of Usage:
|
||||
|
||||
This function can be used directly inside your code as shown in the following examples:
|
||||
|
||||
### Example 1:
|
||||
Loading a model without specifying a device results in the function choosing the most optimal available device automatically.
|
||||
|
||||
```python
|
||||
import torch.nn as nn
|
||||
|
||||
from swarms.utils import load_model_torch
|
||||
|
||||
# Assume `mymodel.pth` is in the current directory
|
||||
model_path = "./mymodel.pth"
|
||||
|
||||
|
||||
# Define your model architecture if the model file only contains state dict
|
||||
class MyModel(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.linear = nn.Linear(10, 2)
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear(x)
|
||||
|
||||
|
||||
model = MyModel()
|
||||
|
||||
# Load the model
|
||||
loaded_model = load_model_torch(model_path, model=model)
|
||||
|
||||
# Now you can use the loaded model for prediction or further training
|
||||
```
|
||||
### Example 2:
|
||||
Explicitly specifying a device.
|
||||
|
||||
```python
|
||||
# Assume `mymodel.pth` is in the current directory
|
||||
model_path = "./mymodel.pth"
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
# Load the model
|
||||
loaded_model = load_model_torch(model_path, device=device)
|
||||
```
|
||||
|
||||
### Example 3:
|
||||
Using a model file that contains only the state dictionary, not the model architecture.
|
||||
|
||||
```python
|
||||
# Assume `mymodel_state_dict.pth` is in the current directory
|
||||
model_path = "./mymodel_state_dict.pth"
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
# Define your model architecture
|
||||
model = MyModel()
|
||||
|
||||
# Load the model
|
||||
loaded_model = load_model_torch(model_path, device=device, model=model)
|
||||
```
|
||||
|
||||
This gives you an insight on how to use `load_model_torch` utility function from `swarms.utils` library efficiently. Always remember to pass the model path argument while the other arguments can be optional based on your requirements. Furthermore, handle exceptions properly for smooth functioning of your PyTorch related projects.
|
||||
@ -1,79 +0,0 @@
|
||||
# math_eval
|
||||
|
||||
|
||||
The `math_eval` function is a python decorator that wraps around a function to run two functions on the same inputs and compare their results. The decorator can be used for testing functions that are expected to have equivalent functionality, or in situations where two different methods are used to calculate or retrieve a value, and the results need to be compared.
|
||||
|
||||
The `math_eval` function in this case accepts two functions as parameters: `func1` and `func2`, and returns a decorator. This returned decorator, when applied to a function, enhances that function to execute both `func1` and `func2`, and compare the results.
|
||||
|
||||
This can be particularly useful in situations when you are implementing a new function and wants to compare its behavior and results with that of an existing one under the same set of input parameters. It also logs the results if they do not match which could be quite useful during the debug process.
|
||||
|
||||
## Usage Example
|
||||
|
||||
Let's say you have two functions: `ground_truth` and `generated_func`, that have similar functionalities or serve the same purpose. You are writing a new function called `test_func`, and you'd like to compare the results of `ground_truth` and `generated_func` when `test_func` is run. Here is how you would use the `math_eval` decorator:
|
||||
|
||||
```python
|
||||
@math_eval(ground_truth, generated_func)
|
||||
def test_func(x):
|
||||
return x
|
||||
|
||||
|
||||
result1, result2 = test_func(5)
|
||||
print(f"Result from ground_truth: {result1}")
|
||||
print(f"Result from generated_func: {result2}")
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
| Parameter | Data Type | Description |
|
||||
| ---- | ---- | ---- |
|
||||
| func1 | Callable | The first function whose result you want to compare. |
|
||||
| func2 | Callable | The second function whose result you want to compare. |
|
||||
|
||||
The data types for `func1` and `func2` cannot be specified as they can be any python function (or callable object). The decorator verifies that they are callable and exceptions are handled within the decorator function.
|
||||
|
||||
## Return Values
|
||||
|
||||
The `math_eval` function does not return a direct value, since it is a decorator. When applied to a function, it alters the behavior of the wrapped function to return two values:
|
||||
|
||||
1. `result1`: The result of running `func1` with the given input parameters.
|
||||
2. `result2`: The result of running `func2` with the given input parameters.
|
||||
|
||||
These two return values are provided in that order as a tuple.
|
||||
|
||||
## Source Code
|
||||
|
||||
Here's how to implement the `math_eval` decorator:
|
||||
|
||||
```python
|
||||
import functools
|
||||
import logging
|
||||
|
||||
|
||||
def math_eval(func1, func2):
|
||||
"""Math evaluation decorator."""
|
||||
|
||||
def decorator(func):
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
try:
|
||||
result1 = func1(*args, **kwargs)
|
||||
except Exception as e:
|
||||
logging.error(f"Error in func1: {e}")
|
||||
result1 = None
|
||||
|
||||
try:
|
||||
result2 = func2(*args, **kwargs)
|
||||
except Exception as e:
|
||||
logging.error(f"Error in func2: {e}")
|
||||
result2 = None
|
||||
|
||||
if result1 != result2:
|
||||
logging.warning(f"Outputs do not match: {result1} != {result2}")
|
||||
|
||||
return result1, result2
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
```
|
||||
Please note that the code is logging exceptions to facilitate debugging, but the actual processing and handling of the exception would depend on how you want your application to respond to exceptions. Therefore, you may want to customize the error handling depending upon your application's requirements.
|
||||
@ -1,87 +0,0 @@
|
||||
# metrics_decorator
|
||||
|
||||
This documentation explains the use and functionality of the `metrics_decorator` function in the LLM (Large Language Models).
|
||||
|
||||
The `metrics_decorator` function is a standard Python decorator that augments a specific function by wrapping extra functionality around it. It is commonly used for things like timing, logging or memoization.
|
||||
--
|
||||
The `metrics_decorator` in LLM is specially designed to measure and calculate three key performance metrics when generating language models:
|
||||
|
||||
1. `Time to First Token`: Measures the elapsed time from the start of function execution until the generation of the first token.
|
||||
2. `Generation Latency`: It measures the total time taken for a complete run.
|
||||
3. `Throughput`: Calculates the rate of production of tokens per unit of time.
|
||||
|
||||
```python
|
||||
def metrics_decorator(func: Callable):
|
||||
"""
|
||||
|
||||
Metrics decorator for LLM
|
||||
|
||||
Args:
|
||||
func (Callable): The function to be decorated.
|
||||
|
||||
"""
|
||||
|
||||
@wraps(func)
|
||||
def wrapper(self, *args, **kwargs):
|
||||
"""
|
||||
An inner function that wraps the decorated function. It calculates 'Time to First Token',
|
||||
'Generation Latency' and 'Throughput' metrics.
|
||||
|
||||
Args:
|
||||
self : The object instance.
|
||||
*args : Variable length argument list of the decorated function.
|
||||
**kwargs : Arbitrary keyword arguments of the decorated function.
|
||||
"""
|
||||
|
||||
# Measure Time to First Token
|
||||
start_time = time.time()
|
||||
result = func(self, *args, **kwargs)
|
||||
first_token_time = time.time()
|
||||
|
||||
# Measure Generation Latency
|
||||
end_time = time.time()
|
||||
|
||||
# Calculate Throughput (assuming the function returns a list of tokens)
|
||||
throughput = len(result) / (end_time - start_time)
|
||||
|
||||
return f"""
|
||||
Time to First Token: {first_token_time - start_time}
|
||||
Generation Latency: {end_time - start_time}
|
||||
Throughput: {throughput}
|
||||
"""
|
||||
|
||||
return wrapper
|
||||
```
|
||||
## Example Usage
|
||||
Now let's discuss the usage of the `metrics_decorator` function with an example.
|
||||
|
||||
Assuming that we have a language generation function called `text_generator()` that generates a list of tokens.
|
||||
|
||||
```python
|
||||
@metrics_decorator
|
||||
def text_generator(self, text: str):
|
||||
"""
|
||||
Args:
|
||||
text (str): The input text.
|
||||
|
||||
Returns:
|
||||
A list of tokens generated from the input text.
|
||||
"""
|
||||
# language generation implementation goes here
|
||||
return tokens
|
||||
|
||||
|
||||
# Instantiate the class and call the decorated function
|
||||
obj = ClassName()
|
||||
obj.text_generator("Hello, world!")
|
||||
```
|
||||
|
||||
When the decorated `text_generator()` function is called, it will measure and return:
|
||||
|
||||
- Time elapsed until the first token is generated.
|
||||
- The total execution time of the function.
|
||||
- The rate of tokens generation per unit time.
|
||||
|
||||
This example provides a basic overview of how a function can be decorated with the `metrics_decorator`. The provided `func` argument could be any method from any class, as long as it complies with the structure defined in `metrics_decorator`. It is worth noting that the decorated function must return a list of tokens for the `Throughput` metric to work correctly.
|
||||
|
||||
Remember, applying the `metrics_decorator` does not affect the original functionality of the decorated function, it just adds additional measurement and logging capabilities to it. It's a great utility for tracking and optimizing the performance of your language models.
|
||||
@ -1,71 +0,0 @@
|
||||
# pdf_to_text
|
||||
|
||||
## Introduction
|
||||
The function `pdf_to_text` is a Python utility for converting a PDF file into a string of text content. It leverages the `pypdf` library, an excellent Python library for processing PDF files. The function takes in a PDF file's path and reads its content, subsequently returning the extracted textual data.
|
||||
|
||||
This function can be very useful when you want to extract textual information from PDF files automatically. For instance, when processing a large number of documents, performing textual analysis, or when you're dealing with text data that is only available in PDF format.
|
||||
|
||||
## Class / Function Definition
|
||||
|
||||
`pdf_to_text` is a standalone function defined as follows:
|
||||
|
||||
```python
|
||||
def pdf_to_text(pdf_path: str) -> str:
|
||||
```
|
||||
|
||||
## Parameters
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|:-:|---|---|
|
||||
| pdf_path | str | The path to the PDF file to be converted |
|
||||
|
||||
## Returns
|
||||
|
||||
| Return Value | Type | Description |
|
||||
|:-:|---|---|
|
||||
| text | str | The text extracted from the PDF file. |
|
||||
|
||||
## Raises
|
||||
|
||||
| Exception | Description |
|
||||
|---|---|
|
||||
| FileNotFoundError | If the PDF file is not found at the specified path. |
|
||||
| Exception | If there is an error in reading the PDF file. |
|
||||
|
||||
## Function Description
|
||||
|
||||
`pdf_to_text` utilises the `PdfReader` function from the `pypdf` library to read the PDF file. If the PDF file does not exist at the specified path or there was an error while reading the file, appropriate exceptions will be raised. It then iterates through each page in the PDF and uses the `extract_text` function to extract the text content from each page. These contents are then concatenated into a single variable and returned as the result.
|
||||
|
||||
## Usage Examples
|
||||
|
||||
To use this function, you first need to install the `pypdf` library. It can be installed via pip:
|
||||
|
||||
```python
|
||||
!pip install pypdf
|
||||
```
|
||||
|
||||
Then, you should import the `pdf_to_text` function:
|
||||
|
||||
```python
|
||||
from swarms.utils import pdf_to_text
|
||||
```
|
||||
|
||||
Here is an example of how to use `pdf_to_text`:
|
||||
|
||||
```python
|
||||
# Define the path to the pdf file
|
||||
pdf_path = "sample.pdf"
|
||||
|
||||
# Use the function to extract text
|
||||
text = pdf_to_text(pdf_path)
|
||||
|
||||
# Print the extracted text
|
||||
print(text)
|
||||
```
|
||||
|
||||
## Tips and Additional Information
|
||||
- Ensure that the PDF file path is valid and that the file exists at the specified location. If the file does not exist, a `FileNotFoundError` will be raised.
|
||||
- This function reads the text from the PDF. It does not handle images, graphical elements, or any non-text content.
|
||||
- If the PDF contains scanned images rather than textual data, the `extract_text` function may not be able to extract any text. In such cases, you would require OCR (Optical Character Recognition) tools to extract the text.
|
||||
- Be aware of the possibility that the output string might contain special characters or escape sequences because they were part of the PDF's content. You might need to clean the resulting text according to your requirements.
|
||||
- The function uses the pypdf library to facilitate the PDF reading and text extraction. For any issues related to PDF manipulation, consult the [pypdf library documentation](https://pypdf.readthedocs.io/en/stable/).
|
||||
@ -1,103 +0,0 @@
|
||||
# prep_torch_inference
|
||||
|
||||
```python
|
||||
def prep_torch_inference(
|
||||
model_path: str = None,
|
||||
device: torch.device = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Prepare a Torch model for inference.
|
||||
|
||||
Args:
|
||||
model_path (str): Path to the model file.
|
||||
device (torch.device): Device to run the model on.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The prepared model.
|
||||
"""
|
||||
try:
|
||||
model = load_model_torch(model_path, device)
|
||||
model.eval()
|
||||
return model
|
||||
except Exception as e:
|
||||
# Add error handling code here
|
||||
print(f"Error occurred while preparing Torch model: {e}")
|
||||
return None
|
||||
```
|
||||
This method is part of the 'swarms.utils' module. It accepts a model file path and a torch device as input and returns a model that is ready for inference.
|
||||
|
||||
## Detailed Functionality
|
||||
|
||||
The method loads a PyTorch model from the file specified by `model_path`. This model is then moved to the specified `device` if it is provided. Subsequently, the method sets the model to evaluation mode by calling `model.eval()`. This is a crucial step when preparing a model for inference, as certain layers like dropout or batch normalization behave differently during training vs during evaluation.
|
||||
In the case of any exception (e.g., the model file not found or the device unavailable), it prints an error message and returns `None`.
|
||||
|
||||
## Parameters
|
||||
|
||||
| Parameter | Type | Description | Default |
|
||||
|-----------|------|-------------|---------|
|
||||
| model_path | str | Path to the model file. | None |
|
||||
| device | torch.device | Device to run the model on. | None |
|
||||
| args | tuple | Additional positional arguments. | None |
|
||||
| kwargs | dict | Additional keyword arguments. | None |
|
||||
|
||||
## Returns
|
||||
|
||||
| Type | Description |
|
||||
|------|-------------|
|
||||
| torch.nn.Module | The prepared model ready for inference. Returns `None` if any exception occurs. |
|
||||
|
||||
## Usage Examples
|
||||
|
||||
Here are some examples of how you can use the `prep_torch_inference` method. Before that, you need to import the necessary modules as follows:
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
from swarms.utils import load_model_torch, prep_torch_inference
|
||||
```
|
||||
|
||||
### Example 1: Load a model for inference on CPU
|
||||
|
||||
```python
|
||||
model_path = "saved_model.pth"
|
||||
model = prep_torch_inference(model_path)
|
||||
|
||||
if model is not None:
|
||||
print("Model loaded successfully and is ready for inference.")
|
||||
else:
|
||||
print("Failed to load the model.")
|
||||
```
|
||||
|
||||
### Example 2: Load a model for inference on CUDA device
|
||||
|
||||
```python
|
||||
model_path = "saved_model.pth"
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
model = prep_torch_inference(model_path, device)
|
||||
|
||||
if model is not None:
|
||||
print(f"Model loaded successfully on device {device} and is ready for inference.")
|
||||
else:
|
||||
print("Failed to load the model.")
|
||||
```
|
||||
|
||||
### Example 3: Load a model with additional arguments for `load_model_torch`
|
||||
|
||||
```python
|
||||
model_path = "saved_model.pth"
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
# Suppose load_model_torch accepts an additional argument, map_location
|
||||
model = prep_torch_inference(model_path, device, map_location=device)
|
||||
|
||||
if model is not None:
|
||||
print(f"Model loaded successfully on device {device} and is ready for inference.")
|
||||
else:
|
||||
print("Failed to load the model.")
|
||||
```
|
||||
|
||||
Please note, you need to ensure the given model path does exist and the device is available on your machine, else `prep_torch_inference` method will return `None`. Depending on the complexity and size of your models, loading them onto a specific device might take a while. So it's important that you take this into consideration when designing your machine learning workflows.
|
||||
@ -1,110 +0,0 @@
|
||||
# print_class_parameters
|
||||
|
||||
# Module Function Name: print_class_parameters
|
||||
|
||||
The `print_class_parameters` function is a utility function developed to help developers and users alike in retrieving and printing the parameters of a class constructor in Python, either in standard output or returned as a dictionary if the `api_format` is set to `True`.
|
||||
|
||||
This utility function utilizes the `inspect` module to fetch the signature of the class constructor and fetches the parameters from the obtained signature. The parameter values and their respective types are then outputted.
|
||||
|
||||
This function allows developers to easily inspect and understand the class' constructor parameters without the need to individually go through the class structure. This eases the testing and debugging process for developers and users alike, aiding in generating more efficient and readable code.
|
||||
|
||||
__Function Definition:__
|
||||
|
||||
```python
|
||||
def print_class_parameters(cls, api_format: bool = False):
|
||||
```
|
||||
__Parameters:__
|
||||
|
||||
| Parameter | Type | Description | Default value |
|
||||
|---|---|---|---|
|
||||
| cls | type | The Python class to inspect. | None |
|
||||
| api_format | bool | Flag to determine if the output should be returned in dictionary format (if set to True) or printed out (if set to False) | False |
|
||||
|
||||
__Functionality and Usage:__
|
||||
|
||||
Inside the `print_class_parameters` function, it starts by getting the signature of the constructor of the inputted class by invoking `inspect.signature(cls.__init__)`. It then extracts the parameters from the signature and stores it in the `params` variable.
|
||||
|
||||
If the `api_format` argument is set to `True`, instead of printing the parameters and their types, it stores them inside a dictionary where each key-value pair is a parameter name and its type. It then returns this dictionary.
|
||||
|
||||
If `api_format` is set to `False` or not set at all (defaulting to False), the function iterates over the parameters and prints the parameter name and its type. "self" parameters are excluded from the output as they are inherent to all class methods in Python.
|
||||
|
||||
A possible exception that may occur during the execution of this function is during the invocation of the `inspect.signature()` function call. If the inputted class does not have an `__init__` method or any error occurs during the retrieval of the class constructor's signature, an exception will be triggered. In that case, an error message that includes the error details is printed out.
|
||||
|
||||
__Usage and Examples:__
|
||||
|
||||
Assuming the existence of a class:
|
||||
|
||||
```python
|
||||
class Agent:
|
||||
def __init__(self, x: int, y: int):
|
||||
self.x = x
|
||||
self.y = y
|
||||
```
|
||||
|
||||
One could use `print_class_parameters` in its typical usage:
|
||||
|
||||
```python
|
||||
print_class_parameters(Agent)
|
||||
```
|
||||
|
||||
Results in:
|
||||
|
||||
```
|
||||
Parameter: x, Type: <class 'int'>
|
||||
Parameter: y, Type: <class 'int'>
|
||||
```
|
||||
|
||||
Or, with `api_format` set to `True`
|
||||
|
||||
```python
|
||||
output = print_class_parameters(Agent, api_format=True)
|
||||
print(output)
|
||||
```
|
||||
|
||||
Results in:
|
||||
|
||||
```
|
||||
{'x': "<class 'int'>", 'y': "<class 'int'>"}
|
||||
```
|
||||
|
||||
__Note:__
|
||||
|
||||
The function `print_class_parameters` is not limited to custom classes. It can inspect built-in Python classes such as `list`, `dict`, and others. However, it is most useful when inspecting custom-defined classes that aren't inherently documented in Python or third-party libraries.
|
||||
|
||||
__Source Code__
|
||||
|
||||
```python
|
||||
def print_class_parameters(cls, api_format: bool = False):
|
||||
"""
|
||||
Print the parameters of a class constructor.
|
||||
|
||||
Parameters:
|
||||
cls (type): The class to inspect.
|
||||
|
||||
Example:
|
||||
>>> print_class_parameters(Agent)
|
||||
Parameter: x, Type: <class 'int'>
|
||||
Parameter: y, Type: <class 'int'>
|
||||
"""
|
||||
try:
|
||||
# Get the parameters of the class constructor
|
||||
sig = inspect.signature(cls.__init__)
|
||||
params = sig.parameters
|
||||
|
||||
if api_format:
|
||||
param_dict = {}
|
||||
for name, param in params.items():
|
||||
if name == "self":
|
||||
continue
|
||||
param_dict[name] = str(param.annotation)
|
||||
return param_dict
|
||||
|
||||
# Print the parameters
|
||||
for name, param in params.items():
|
||||
if name == "self":
|
||||
continue
|
||||
print(f"Parameter: {name}, Type: {param.annotation}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"An error occurred while inspecting the class: {e}")
|
||||
```
|
||||
Loading…
Reference in new issue