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csg-robomaster / examples /python-operator-dataflow /llm_op.py

TomPei

Duplicate from opencsg/csg-robomaster

0bfb62b 2 days ago

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from dora import DoraStatus
import pylcs
import os
import pyarrow as pa
from transformers import AutoModelForCausalLM, AutoTokenizer
import json

import re
import time

MODEL_NAME_OR_PATH = "/home/peiji/deepseek-coder-6.7B-instruct-GPTQ"
# MODEL_NAME_OR_PATH = "hanspeterlyngsoeraaschoujensen/deepseek-math-7b-instruct-GPTQ"

CODE_MODIFIER_TEMPLATE = """
### Instruction
Respond with the small modified code only. No explanation.
```python
{code}
```
{user_message}
### Response:
"""


MESSAGE_SENDER_TEMPLATE = """
### Instruction
You're a json expert. Format your response as a json with a topic and a data field in a ```json block.  No explanation needed. No code needed.
The schema for those json are:
- line: Int[4]
The response should look like this: 
```json
  {{ "topic": "line", "data": [10, 10, 90, 10] }}
```
{user_message}
### Response:
"""

ASSISTANT_TEMPLATE = """
### Instruction
You're a helpuf assistant named dora. 
Reply with a short message. No code needed. 
User {user_message}
### Response:
"""


model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME_OR_PATH,
    device_map="auto",
    trust_remote_code=True,
    revision="main",
)


tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)


def extract_python_code_blocks(text):
    """
    Extracts Python code blocks from the given text that are enclosed in triple backticks with a python language identifier.
    Parameters:
    - text: A string that may contain one or more Python code blocks.
    Returns:
    - A list of strings, where each string is a block of Python code extracted from the text.
    """
    pattern = r"```python\n(.*?)\n```"
    matches = re.findall(pattern, text, re.DOTALL)
    if len(matches) == 0:
        pattern = r"```python\n(.*?)(?:\n```|$)"
        matches = re.findall(pattern, text, re.DOTALL)
        if len(matches) == 0:
            return [text]
        else:
            matches = [remove_last_line(matches[0])]

    return matches


def extract_json_code_blocks(text):
    """
    Extracts json code blocks from the given text that are enclosed in triple backticks with a json language identifier.
    Parameters:
    - text: A string that may contain one or more json code blocks.
    Returns:
    - A list of strings, where each string is a block of json code extracted from the text.
    """
    pattern = r"```json\n(.*?)\n```"
    matches = re.findall(pattern, text, re.DOTALL)
    if len(matches) == 0:
        pattern = r"```json\n(.*?)(?:\n```|$)"
        matches = re.findall(pattern, text, re.DOTALL)
        if len(matches) == 0:
            return [text]

    return matches


def remove_last_line(python_code):
    """
    Removes the last line from a given string of Python code.
    Parameters:
    - python_code: A string representing Python source code.
    Returns:
    - A string with the last line removed.
    """
    lines = python_code.split("\n")  # Split the string into lines
    if lines:  # Check if there are any lines to remove
        lines.pop()  # Remove the last line
    return "\n".join(lines)  # Join the remaining lines back into a string


def calculate_similarity(source, target):
    """
    Calculate a similarity score between the source and target strings.
    This uses the edit distance relative to the length of the strings.
    """
    edit_distance = pylcs.edit_distance(source, target)
    max_length = max(len(source), len(target))
    # Normalize the score by the maximum possible edit distance (the length of the longer string)
    similarity = 1 - (edit_distance / max_length)
    return similarity


def find_best_match_location(source_code, target_block):
    """
    Find the best match for the target_block within the source_code by searching line by line,
    considering blocks of varying lengths.
    """
    source_lines = source_code.split("\n")
    target_lines = target_block.split("\n")

    best_similarity = 0
    best_start_index = 0
    best_end_index = -1

    # Iterate over the source lines to find the best matching range for all lines in target_block
    for start_index in range(len(source_lines) - len(target_lines) + 1):
        for end_index in range(start_index + len(target_lines), len(source_lines) + 1):
            current_window = "\n".join(source_lines[start_index:end_index])
            current_similarity = calculate_similarity(current_window, target_block)
            if current_similarity > best_similarity:
                best_similarity = current_similarity
                best_start_index = start_index
                best_end_index = end_index

    # Convert line indices back to character indices for replacement
    char_start_index = len("\n".join(source_lines[:best_start_index])) + (
        1 if best_start_index > 0 else 0
    )
    char_end_index = len("\n".join(source_lines[:best_end_index]))

    return char_start_index, char_end_index


def replace_code_in_source(source_code, replacement_block: str):
    """
    Replace the best matching block in the source_code with the replacement_block, considering variable block lengths.
    """
    replacement_block = extract_python_code_blocks(replacement_block)[0]
    start_index, end_index = find_best_match_location(source_code, replacement_block)
    if start_index != -1 and end_index != -1:
        # Replace the best matching part with the replacement block
        new_source = (
            source_code[:start_index] + replacement_block + source_code[end_index:]
        )
        return new_source
    else:
        return source_code


class Operator:

    def on_event(
        self,
        dora_event,
        send_output,
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT" and dora_event["id"] == "code_modifier":
            input = dora_event["value"][0].as_py()

            with open(input["path"], "r", encoding="utf8") as f:
                code = f.read()

            user_message = input["user_message"]
            start_llm = time.time()
            output = self.ask_llm(
                CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message)
            )

            source_code = replace_code_in_source(code, output)
            print("response time:", time.time() - start_llm, flush=True)
            send_output(
                "modified_file",
                pa.array(
                    [
                        {
                            "raw": source_code,
                            "path": input["path"],
                            "response": output,
                            "prompt": input["user_message"],
                        }
                    ]
                ),
                dora_event["metadata"],
            )
            print("response: ", output, flush=True)
            send_output(
                "assistant_message",
                pa.array([output]),
                dora_event["metadata"],
            )
        elif dora_event["type"] == "INPUT" and dora_event["id"] == "message_sender":
            user_message = dora_event["value"][0].as_py()
            output = self.ask_llm(
                MESSAGE_SENDER_TEMPLATE.format(user_message=user_message)
            )
            outputs = extract_json_code_blocks(output)[0]
            try:
                output = json.loads(outputs)
                if not isinstance(output["data"], list):
                    output["data"] = [output["data"]]

                if output["topic"] in [
                    "line",
                ]:
                    send_output(
                        output["topic"],
                        pa.array(output["data"]),
                        dora_event["metadata"],
                    )
                else:
                    print("Could not find the topic: {}".format(output["topic"]))
            except:
                print("Could not parse json")
            # if data is not iterable, put data in a list
        elif dora_event["type"] == "INPUT" and dora_event["id"] == "assistant":
            user_message = dora_event["value"][0].as_py()
            output = self.ask_llm(ASSISTANT_TEMPLATE.format(user_message=user_message))
            send_output(
                "assistant_message",
                pa.array([output]),
                dora_event["metadata"],
            )
        return DoraStatus.CONTINUE

    def ask_llm(self, prompt):

        # Generate output
        # prompt = PROMPT_TEMPLATE.format(system_message=system_message, prompt=prompt))
        input = tokenizer(prompt, return_tensors="pt")
        input_ids = input.input_ids.cuda()

        # add attention mask here
        attention_mask = input["attention_mask"].cuda()

        output = model.generate(
            inputs=input_ids,
            temperature=0.7,
            do_sample=True,
            top_p=0.95,
            top_k=40,
            max_new_tokens=512,
            attention_mask=attention_mask,
            eos_token_id=tokenizer.eos_token_id,
        )
        # Get the tokens from the output, decode them, print them

        # Get text between im_start and im_end
        return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :]


if __name__ == "__main__":
    op = Operator()

    # Path to the current file
    current_file_path = __file__

    # Directory of the current file
    current_directory = os.path.dirname(current_file_path)

    path = current_directory + "object_detection.py"
    with open(path, "r", encoding="utf8") as f:
        raw = f.read()

    op.on_event(
        {
            "type": "INPUT",
            "id": "message_sender",
            "value": pa.array(
                [
                    {
                        "path": path,
                        "user_message": "send a star ",
                    },
                ]
            ),
            "metadata": [],
        },
        print,
    )

	from dora import DoraStatus
	import pylcs
	import os
	import pyarrow as pa
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import json

	import re
	import time

	MODEL_NAME_OR_PATH = "/home/peiji/deepseek-coder-6.7B-instruct-GPTQ"
	# MODEL_NAME_OR_PATH = "hanspeterlyngsoeraaschoujensen/deepseek-math-7b-instruct-GPTQ"

	CODE_MODIFIER_TEMPLATE = """
	### Instruction
	Respond with the small modified code only. No explanation.

	```python
	{code}
	```

	{user_message}

	### Response:
	"""


	MESSAGE_SENDER_TEMPLATE = """
	### Instruction
	You're a json expert. Format your response as a json with a topic and a data field in a ```json block. No explanation needed. No code needed.
	The schema for those json are:
	- line: Int[4]

	The response should look like this:
	```json
	{{ "topic": "line", "data": [10, 10, 90, 10] }}
	```

	{user_message}

	### Response:
	"""

	ASSISTANT_TEMPLATE = """
	### Instruction
	You're a helpuf assistant named dora.
	Reply with a short message. No code needed.

	User {user_message}

	### Response:
	"""


	model = AutoModelForCausalLM.from_pretrained(
	MODEL_NAME_OR_PATH,
	device_map="auto",
	trust_remote_code=True,
	revision="main",
	)


	tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)


	def extract_python_code_blocks(text):
	"""
	Extracts Python code blocks from the given text that are enclosed in triple backticks with a python language identifier.

	Parameters:
	- text: A string that may contain one or more Python code blocks.

	Returns:
	- A list of strings, where each string is a block of Python code extracted from the text.
	"""
	pattern = r"```python\n(.*?)\n```"
	matches = re.findall(pattern, text, re.DOTALL)
	if len(matches) == 0:
	pattern = r"```python\n(.*?)(?:\n```\|$)"
	matches = re.findall(pattern, text, re.DOTALL)
	if len(matches) == 0:
	return [text]
	else:
	matches = [remove_last_line(matches[0])]

	return matches


	def extract_json_code_blocks(text):
	"""
	Extracts json code blocks from the given text that are enclosed in triple backticks with a json language identifier.

	Parameters:
	- text: A string that may contain one or more json code blocks.

	Returns:
	- A list of strings, where each string is a block of json code extracted from the text.
	"""
	pattern = r"```json\n(.*?)\n```"
	matches = re.findall(pattern, text, re.DOTALL)
	if len(matches) == 0:
	pattern = r"```json\n(.*?)(?:\n```\|$)"
	matches = re.findall(pattern, text, re.DOTALL)
	if len(matches) == 0:
	return [text]

	return matches


	def remove_last_line(python_code):
	"""
	Removes the last line from a given string of Python code.

	Parameters:
	- python_code: A string representing Python source code.

	Returns:
	- A string with the last line removed.
	"""
	lines = python_code.split("\n") # Split the string into lines
	if lines: # Check if there are any lines to remove
	lines.pop() # Remove the last line
	return "\n".join(lines) # Join the remaining lines back into a string


	def calculate_similarity(source, target):
	"""
	Calculate a similarity score between the source and target strings.
	This uses the edit distance relative to the length of the strings.
	"""
	edit_distance = pylcs.edit_distance(source, target)
	max_length = max(len(source), len(target))
	# Normalize the score by the maximum possible edit distance (the length of the longer string)
	similarity = 1 - (edit_distance / max_length)
	return similarity


	def find_best_match_location(source_code, target_block):
	"""
	Find the best match for the target_block within the source_code by searching line by line,
	considering blocks of varying lengths.
	"""
	source_lines = source_code.split("\n")
	target_lines = target_block.split("\n")

	best_similarity = 0
	best_start_index = 0
	best_end_index = -1

	# Iterate over the source lines to find the best matching range for all lines in target_block
	for start_index in range(len(source_lines) - len(target_lines) + 1):
	for end_index in range(start_index + len(target_lines), len(source_lines) + 1):
	current_window = "\n".join(source_lines[start_index:end_index])
	current_similarity = calculate_similarity(current_window, target_block)
	if current_similarity > best_similarity:
	best_similarity = current_similarity
	best_start_index = start_index
	best_end_index = end_index

	# Convert line indices back to character indices for replacement
	char_start_index = len("\n".join(source_lines[:best_start_index])) + (
	1 if best_start_index > 0 else 0
	)
	char_end_index = len("\n".join(source_lines[:best_end_index]))

	return char_start_index, char_end_index


	def replace_code_in_source(source_code, replacement_block: str):
	"""
	Replace the best matching block in the source_code with the replacement_block, considering variable block lengths.
	"""
	replacement_block = extract_python_code_blocks(replacement_block)[0]
	start_index, end_index = find_best_match_location(source_code, replacement_block)
	if start_index != -1 and end_index != -1:
	# Replace the best matching part with the replacement block
	new_source = (
	source_code[:start_index] + replacement_block + source_code[end_index:]
	)
	return new_source
	else:
	return source_code


	class Operator:

	def on_event(
	self,
	dora_event,
	send_output,
	) -> DoraStatus:
	if dora_event["type"] == "INPUT" and dora_event["id"] == "code_modifier":
	input = dora_event["value"][0].as_py()

	with open(input["path"], "r", encoding="utf8") as f:
	code = f.read()

	user_message = input["user_message"]
	start_llm = time.time()
	output = self.ask_llm(
	CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message)
	)

	source_code = replace_code_in_source(code, output)
	print("response time:", time.time() - start_llm, flush=True)
	send_output(
	"modified_file",
	pa.array(
	[
	{
	"raw": source_code,
	"path": input["path"],
	"response": output,
	"prompt": input["user_message"],
	}
	]
	),
	dora_event["metadata"],
	)
	print("response: ", output, flush=True)
	send_output(
	"assistant_message",
	pa.array([output]),
	dora_event["metadata"],
	)
	elif dora_event["type"] == "INPUT" and dora_event["id"] == "message_sender":
	user_message = dora_event["value"][0].as_py()
	output = self.ask_llm(
	MESSAGE_SENDER_TEMPLATE.format(user_message=user_message)
	)
	outputs = extract_json_code_blocks(output)[0]
	try:
	output = json.loads(outputs)
	if not isinstance(output["data"], list):
	output["data"] = [output["data"]]

	if output["topic"] in [
	"line",
	]:
	send_output(
	output["topic"],
	pa.array(output["data"]),
	dora_event["metadata"],
	)
	else:
	print("Could not find the topic: {}".format(output["topic"]))
	except:
	print("Could not parse json")
	# if data is not iterable, put data in a list
	elif dora_event["type"] == "INPUT" and dora_event["id"] == "assistant":
	user_message = dora_event["value"][0].as_py()
	output = self.ask_llm(ASSISTANT_TEMPLATE.format(user_message=user_message))
	send_output(
	"assistant_message",
	pa.array([output]),
	dora_event["metadata"],
	)
	return DoraStatus.CONTINUE

	def ask_llm(self, prompt):

	# Generate output
	# prompt = PROMPT_TEMPLATE.format(system_message=system_message, prompt=prompt))
	input = tokenizer(prompt, return_tensors="pt")
	input_ids = input.input_ids.cuda()

	# add attention mask here
	attention_mask = input["attention_mask"].cuda()

	output = model.generate(
	inputs=input_ids,
	temperature=0.7,
	do_sample=True,
	top_p=0.95,
	top_k=40,
	max_new_tokens=512,
	attention_mask=attention_mask,
	eos_token_id=tokenizer.eos_token_id,
	)
	# Get the tokens from the output, decode them, print them

	# Get text between im_start and im_end
	return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :]


	if __name__ == "__main__":
	op = Operator()

	# Path to the current file
	current_file_path = __file__

	# Directory of the current file
	current_directory = os.path.dirname(current_file_path)

	path = current_directory + "object_detection.py"
	with open(path, "r", encoding="utf8") as f:
	raw = f.read()

	op.on_event(
	{
	"type": "INPUT",
	"id": "message_sender",
	"value": pa.array(
	[
	{
	"path": path,
	"user_message": "send a star ",
	},
	]
	),
	"metadata": [],
	},
	print,
	)