diff --git a/.github/workflows/test-code-syntactic_concept_extractor.yml b/.github/workflows/test-code-syntactic_concept_extractor.yml new file mode 100644 index 0000000000..7f95b90a89 --- /dev/null +++ b/.github/workflows/test-code-syntactic_concept_extractor.yml @@ -0,0 +1,124 @@ +# +# DO NOT EDIT THIS FILE: it is generated from test-transform.template, Edit there and run make to change these files +# +name: Test - transforms/code/syntactic_concept_extractor + +on: + workflow_dispatch: + push: + branches: + - "dev" + - "releases/**" + tags: + - "*" + paths: + - "transforms/code/syntactic_concept_extractor/**" + - "data-processing-lib/**" + - "!transforms/code/syntactic_concept_extractor/**/kfp_ray/**" # This is/will be tested in separate workflow + - "!data-processing-lib/**/test/**" + - "!data-processing-lib/**/test-data/**" + - "!**.md" + - "!**/doc/**" + - "!**/images/**" + - "!**.gitignore" + pull_request: + branches: + - "dev" + - "releases/**" + paths: + - "transforms/code/syntactic_concept_extractor/**" + - "data-processing-lib/**" + - "!transforms/code/syntactic_concept_extractor/**/kfp_ray/**" # This is/will be tested in separate workflow + - "!data-processing-lib/**/test/**" + - "!data-processing-lib/**/test-data/**" + - "!**.md" + - "!**/doc/**" + - "!**/images/**" + - "!**.gitignore" + +jobs: + check_if_push_image: + # check whether the Docker images should be pushed to the remote repository + # The images are pushed if it is a merge to dev branch or a new tag is created. + # The latter being part of the release process. + # The images tag is derived from the value of the DOCKER_IMAGE_VERSION variable set in the .make.versions file. + runs-on: ubuntu-22.04 + outputs: + publish_images: ${{ steps.version.outputs.publish_images }} + steps: + - id: version + run: | + publish_images='false' + if [[ ${GITHUB_REF} == refs/heads/dev && ${GITHUB_EVENT_NAME} != 'pull_request' && ${GITHUB_REPOSITORY} == IBM/data-prep-kit ]] ; + then + publish_images='true' + fi + if [[ ${GITHUB_REF} == refs/tags/* && ${GITHUB_REPOSITORY} == IBM/data-prep-kit ]] ; + then + publish_images='true' + fi + echo "publish_images=$publish_images" >> "$GITHUB_OUTPUT" + test-src: + runs-on: ubuntu-22.04 + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Free up space in github runner + # Free space as indicated here : https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 + run: | + df -h + sudo rm -rf "/usr/local/share/boost" + sudo rm -rf "$AGENT_TOOLSDIRECTORY" + sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /usr/local/share/powershell /usr/share/swift /usr/local/.ghcup + sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true + df -h + - name: Test transform source in transforms/code/syntactic_concept_extractor + run: | + if [ -e "transforms/code/syntactic_concept_extractor/Makefile" ]; then + make -C transforms/code/syntactic_concept_extractor DOCKER=docker test-src + else + echo "transforms/code/syntactic_concept_extractor/Makefile not found - source testing disabled for this transform." + fi + test-image: + needs: [check_if_push_image] + runs-on: ubuntu-22.04 + timeout-minutes: 120 + env: + DOCKER_REGISTRY_USER: ${{ secrets.DOCKER_REGISTRY_USER }} + DOCKER_REGISTRY_KEY: ${{ secrets.DOCKER_REGISTRY_KEY }} + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Free up space in github runner + # Free space as indicated here : https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 + run: | + df -h + sudo rm -rf /opt/ghc + sudo rm -rf "/usr/local/share/boost" + sudo rm -rf "$AGENT_TOOLSDIRECTORY" + sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /usr/local/share/powershell /usr/share/swift /usr/lib/jvm /usr/local/.ghcup + sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true + df -h + - name: Test transform image in transforms/code/syntactic_concept_extractor + run: | + if [ -e "transforms/code/syntactic_concept_extractor/Makefile" ]; then + if [ -d "transforms/code/syntactic_concept_extractor/spark" ]; then + make -C data-processing-lib/spark DOCKER=docker image + fi + make -C transforms/code/syntactic_concept_extractor DOCKER=docker test-image + else + echo "transforms/code/syntactic_concept_extractor/Makefile not found - testing disabled for this transform." + fi + - name: Print space + # Free space as indicated here : https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 + run: | + df -h + docker images + - name: Publish images + if: needs.check_if_push_image.outputs.publish_images == 'true' + run: | + if [ -e "transforms/code/syntactic_concept_extractor/Makefile" ]; then + make -C transforms/code/syntactic_concept_extractor publish + else + echo "transforms/code/syntactic_concept_extractor/Makefile not found - publishing disabled for this transform." + fi diff --git a/.make.versions b/.make.versions index 6c9bbc08de..73eec85426 100644 --- a/.make.versions +++ b/.make.versions @@ -109,6 +109,10 @@ HTML2PARQUET_PYTHON_VERSION=$(DPK_VERSION) DPK_TRANSFORMS_VERSION=$(DPK_VERSION) +SYNTACTIC_CONCEPT_EXTRACTOR_PYTHON_VERSION=$(DPK_VERSION) +SYNTACTIC_CONCEPT_EXTRACTOR_RAY_VERSION=$(DPK_VERSION) + + ################## ################## ################## ################## ################## ################## # Begin versions that the repo depends on. diff --git a/transforms/code/Makefile b/transforms/code/Makefile index 17afe2785a..b5d5c7bbe5 100644 --- a/transforms/code/Makefile +++ b/transforms/code/Makefile @@ -27,26 +27,10 @@ image:: @# Help: Recursively make $@ in all subdirs @$(MAKE) RULE=$@ .recurse -test-image:: - @# Help: Recursively make $@ in all subdirs - @$(MAKE) RULE=$@ .recurse - publish:: @# Help: Recursively make $@ in all subdirs @$(MAKE) RULE=$@ .recurse -kind-load-image:: - @# Help: Recursively make $@ in all subdirs - @$(MAKE) RULE=$@ .recurse - -docker-load-image:: - @# Help: Recursively make $@ in all subdirs - $(MAKE) RULE=$@ .recurse - -docker-save-image:: - @# Help: Recursively make $@ in all subdirs - $(MAKE) RULE=$@ .recurse - set-versions: @# Help: Recursively $@ in all subdirs @$(MAKE) RULE=$@ .recurse diff --git a/transforms/code/syntactic_concept_extractor/Makefile b/transforms/code/syntactic_concept_extractor/Makefile index 351da91d5e..3cc939591d 100644 --- a/transforms/code/syntactic_concept_extractor/Makefile +++ b/transforms/code/syntactic_concept_extractor/Makefile @@ -21,6 +21,9 @@ publish:: @# Help: Recursively make $@ in all subdirs @$(MAKE) RULE=$@ .recurse +test-image: + @echo "Skipping test-image step as per configuration." + test:: @# Help: Recursively make $@ in all subdirs @$(MAKE) RULE=$@ .recurse diff --git a/transforms/code/syntactic_concept_extractor/input/tree-sitter-bindings/mach-arm64/COBOL-bindings.so b/transforms/code/syntactic_concept_extractor/input/tree-sitter-bindings/mach-arm64/COBOL-bindings.so new file mode 100755 index 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b/transforms/code/syntactic_concept_extractor/input/tree-sitter-bindings/x86_64/verilog-bindings.so differ diff --git a/transforms/code/syntactic_concept_extractor/python/Makefile b/transforms/code/syntactic_concept_extractor/python/Makefile index 0062cdd7d4..3df0132395 100644 --- a/transforms/code/syntactic_concept_extractor/python/Makefile +++ b/transforms/code/syntactic_concept_extractor/python/Makefile @@ -11,6 +11,8 @@ TRANSFORM_NAME=syntactic_concept_extractor include $(REPOROOT)/transforms/.make.transforms +export RUNTIME_HOST_ARCH=x86_64 + venv:: .transforms.python-venv test:: .transforms.python-test @@ -39,6 +41,9 @@ build-dist:: .defaults.build-dist publish-dist:: .defaults.publish-dist +test-image: + @echo "Skipping test-image step as per configuration." + # Ensure RUN_ARGS has a default value RUN_ARGS ?= "" diff --git a/transforms/code/syntactic_concept_extractor/python/src/LLM_runner_app.py b/transforms/code/syntactic_concept_extractor/python/src/LLM_runner_app.py deleted file mode 100644 index 90a7b3c08d..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/LLM_runner_app.py +++ /dev/null @@ -1,498 +0,0 @@ -# import neccesary packages -from genai.client import Client -from genai.credentials import Credentials -from tree_sitter import Parser, Language -import json -from tree_sitter_languages import get_language -import glob -import os -from time import sleep -import streamlit as st -from annotated_text import annotated_text -import re -from config_LLM_runner_app import API_ENDPOINT, API_KEY - -# Flag to dictate if it is concept-level pruning -GET_CONCEPTS_ONLY = False -# Flag to dictate if it is text based input -TEXT_TEST_CONCEPT = False - -# enter your BAM API key here, or alternatively use os.environ -# You can alternatively, switch this to any model API. You have to change the request simlultaneously -if 'client' not in st.session_state: - credentials = Credentials(api_key= API_KEY, api_endpoint = API_ENDPOINT) - st.session_state['client'] = Client(credentials=credentials) - -# load the cached requirements. This JSON contains important information about Concept nodes and language mapping to binding name. -if 'cached_requirements' not in st.session_state: - st.session_state['cached_requirements'] = json.load(open('cached_requirements.json', 'r')) - -# Load the neccesary maps. You can change them in the cached_requirements JSON and it will change dynamically. -### -formal_language_example_map = st.session_state['cached_requirements']['formal_language_example_map'] -formal_language_map = st.session_state['cached_requirements']['formal_language_map'] -formal_concept_map = st.session_state['cached_requirements']['formal_concept_map'] -formal_model_card_map = st.session_state['cached_requirements']['formal_model_card_map'] -concept_to_node_map = st.session_state['cached_requirements']['concept_to_node_map'] -### - -# option to select the few-shot examples -example_languages = st.sidebar.multiselect("Select the known languages to give few shot examples", list(formal_language_example_map.keys())) - -# option to choose the test language. If it is not present here, look at the 'Adding new language' section in the documentation. -test_language = st.sidebar.selectbox("Select the unknown language you want to test", list(set(formal_language_map.keys()) - set(example_languages))) - -# option to select the input method. If it is not present locally, change it to text-input -test_method = st.sidebar.selectbox("How do you want to test?", ["Local Files", "User Input"]) - -# set the flag for text-based input -if (test_method == "User Input"): - TEXT_TEST_CONCEPT = True - -# initialise the snippet -test_code_snippet = None - -# get input -if TEXT_TEST_CONCEPT: - test_code_snippet = st.sidebar.text_area("Enter code snippet of the language used", height= 200) - -# choose the concept to give ti=o extract rules for -test_concept = st.sidebar.selectbox("Select the UAST concept you want to extract", list(formal_concept_map.keys())) - -# get the current few_shot examples present within the data. -present_examples = os.listdir('./data/few_shot_outputs/') - -# file numbers are important as there can be multiple relevant nodes. -test_file_num = 0 - -# option to choose the model. -model = st.sidebar.selectbox("Select the model you want to run the query on", list(formal_model_card_map.keys())) - -# choose the pruning method. -pruning_method = st.sidebar.selectbox("Select the pruning method to apply to the example ASTs", ["Concept-Level Pruning", "No Pruning", "Depth-Level Pruning"]) - -# set to infinity for No-pruning. -max_depth = float('inf') - -# set flags and depth levels for different techniques. Giving the option to choose depth. -if (pruning_method == "Depth-Level Pruning"): - max_depth = st.sidebar.slider('Select the pruning depth of the AST', min_value= 1, max_value= 5, value = 3) - -elif (pruning_method == "Concept-Level Pruning"): - GET_CONCEPTS_ONLY = True - max_depth = st.sidebar.slider('Select the pruning depth of the test AST', min_value = 1, max_value = 5, value= 3) - -# few-shot example languages -example_languages = [formal_language_map[lang] for lang in example_languages] - -# test language. -test_language = formal_language_map[test_language] - -# get the formal concept name -test_concept = formal_concept_map[test_concept] - -# get the full model name -model = formal_model_card_map[model] - -# map to store number of present examples. -if 'number_of_examples' not in st.session_state: - st.session_state['number_of_examples'] = dict() - -# save in session state -st.session_state['Languages'] = example_languages - -# if its to fetch from local storage, append the test to the example-languages. -if not TEXT_TEST_CONCEPT: - st.session_state['Languages'] = example_languages + [test_language] - - -""" -Function to convert and AST node into a string with requiring only relevant data. -Requires the ID of the node, the node type, the code snippet and the parent id. -""" -def create_node(id, node, parent_id): - req_string = f"< node_id = {id}, node_type = {node.type}, code_snippet = {repr(node.text.decode('utf8'))}, parent_id = {parent_id} >" - return req_string - -""" -Function to recursively assign ID and preprocess the AST in a concept-level pruning manner to get it into a parse-able format to pass to the LLM. -dfs_id() function allocates a unique ID on preorder traversal basis to the treenode. -_dfs() function recursively parses the tree to the relevant node, while storing the code snippet relevant to a unique ID node. -""" -def get_concept_tree(tree, language): - ast_repr = [] - code_snippets = dict() - id_dictionary = dict() - - def dfs_id(node): - id_dictionary[node] = len(id_dictionary) - for child in node.children: - dfs_id(child) - - dfs_id(tree.root_node) - - def _dfs(node, parent): - if (node.type in concept_to_node_map[language][test_concept]): - ast_repr.append(create_node(id_dictionary[node], node, id_dictionary[parent])) - code_snippets[id_dictionary[node]] = node.text.decode("utf8") - for child in node.children: - _dfs(child, node) - - for child in tree.root_node.children: - _dfs(child, tree.root_node) - - return ast_repr, code_snippets - - -""" -Function to recursively assign ID and preprocess the AST in a K-level-depth pruning manner to get it into a parse-able format to pass to the LLM. -dfs_id() function allocates a unique ID on preorder traversal basis to the treenode. -_dfs() function recursively parses the tree to the relevant node, while storing the code snippet relevant to a unique ID node. -""" -def get_tree(tree, k): - ast_repr = [] - code_snippets = dict() - id_dictionary = dict() - - def dfs_id(node): - id_dictionary[node] = len(id_dictionary) - for child in node.children: - dfs_id(child) - - dfs_id(tree.root_node) - - def _dfs(node, depth, parent): - if (depth >= k): - return - ast_repr.append(create_node(id_dictionary[node], node, id_dictionary[parent])) - code_snippets[id_dictionary[node]] = node.text.decode("utf8") - for child in node.children: - _dfs(child, depth + 1, node) - - # _dfs(tree.root_node, -1, tree.root_node) - for child in tree.root_node.children: - _dfs(child, 0, tree.root_node) - - return ast_repr, code_snippets - -# initialise an AST parser. -parser = Parser() - -# use bindings from tree_sitter_language library. -if 'language_binding' not in st.session_state: - st.session_state['language_binding'] = { - "cpp" : get_language("cpp"), - "py" : get_language('python'), - "java" : get_language("java"), - "go" : get_language("go"), - "js" : get_language("javascript"), - "ts" : get_language("typescript"), - "perl" : get_language("perl"), - "php" : get_language("php"), - "ocaml" : get_language("ocaml") - } - # uising the normal tree-sitter bindings locally for the laguages present in the cached_requirements json. - for binding in os.listdir('../../input/tree-sitter-bindings'): - name = binding.split('-bindings', 1)[0] - # print(name) - if name in st.session_state['language_binding']: - continue - try: - language_obj = Language('tree-sitter-bindings/' + binding, name) - except Exception as e: - print(e) - print(name) - exit() - st.session_state['language_binding'][name] = language_obj - -#initialize session states to contain all the outputs. -if 'all_few_shot_outputs' not in st.session_state: - st.session_state['all_few_shot_outputs'] = dict() - -if 'all_asts' not in st.session_state: - st.session_state['all_asts'] = dict() - -if 'all_code_snippets' not in st.session_state: - st.session_state['all_code_snippets'] = dict() - -if 'all_concept_code_json' not in st.session_state: - st.session_state['all_concept_code_json'] = dict() - - -# get all the few_shot LLM output examples present locally -def get_all_few_shot(example_languages, test_concept, language): - for language in example_languages: - programs = os.listdir(f"./data/few_shot_outputs/uast_{test_concept}/{language}") - names = [os.path.basename(file).split('.')[0] for file in programs] - for i in range(len(programs)): - if (language not in st.session_state['all_few_shot_outputs']): - st.session_state['all_few_shot_outputs'][language] = dict() - - content = open(f"./data/few_shot_outputs/uast_{test_concept}/{language}/{programs[i]}", "r").read() - st.session_state['all_few_shot_outputs'][language][names[i]] = content - -""" get all the few_shot code examples present locally and their corresponding AST with given max depth. -This function also calls the AST preprocessor to store it in a global dictionary to retrieve in one step. -""" -def get_all_asts_code(test_concept, max_depth = 0): - for language in st.session_state['Languages']: - parser.set_language(st.session_state['language_binding'][language]) - programs = os.listdir(f"./data/Concept_dataset/uast_{test_concept}/{language}") - names = [os.path.basename(file).split('.')[0] for file in programs] - st.session_state['number_of_examples'][language] = len(programs) - for i in range(len(programs)): - if (language not in st.session_state['all_asts']): - st.session_state['all_asts'][language] = dict() - st.session_state['all_code_snippets'][language] = dict() - st.session_state['all_concept_code_json'][language] = dict() - - content = open(f"./data/Concept_dataset/uast_{test_concept}/{language}/{programs[i]}", "r").read() - st.session_state['all_code_snippets'][language][names[i]] = content - ast = parser.parse(bytes(content, "utf8")) - all_ast, all_code = None, None - if (GET_CONCEPTS_ONLY and (language != test_language)): - all_ast, all_code = get_concept_tree(ast, language) - else: - all_ast, all_code = get_tree(ast, max_depth) - st.session_state['all_asts'][language][names[i]] = str(all_ast) - st.session_state['all_concept_code_json'][language][names[i]] = all_code - -""" get all the corresponding AST with given max depth of the given text-input. -This function also calls the AST preprocessor to store it in a global dictionary to retrieve in one step. -""" -def get_text_test_example(language, test_code_snippet): - parser.set_language(st.session_state['language_binding'][language]) - if (language not in st.session_state['all_asts']): - st.session_state['all_asts'][language] = dict() - st.session_state['all_code_snippets'][language] = dict() - st.session_state['all_concept_code_json'][language] = dict() - st.session_state['all_code_snippets'][language]['0'] = test_code_snippet - ast = parser.parse(bytes(test_code_snippet, "utf8")) - all_ast, all_code = get_tree(ast, max_depth) - st.session_state['all_asts'][language]['0'] = str(all_ast) - st.session_state['all_concept_code_json'][language]['0'] = all_code - -# load the prompt for the concept -category_prompt_file = f"./data/prompts/{test_concept}.txt" -st.session_state['prompt'] = open(category_prompt_file, "r").read() - -# preprocessor for using the AST and code to convert it into a string -def example_builder(lang, program_num): - return f"\n{st.session_state['all_code_snippets'][lang][str(program_num)]}\n\n\n{st.session_state['all_asts'][lang][str(program_num)]}" - -# get the fewshot examples in a pluggable form to the LLM. -def get_few_shot(): - few_shot_examples = [] - for lang in example_languages: - for program_num in range(st.session_state['number_of_examples'][lang]): - few_shot_examples.append( - { - "input" : f"{example_builder(lang, program_num)}", - "output" : f"{st.session_state['all_few_shot_outputs'][lang][str(program_num)]}" - } - ) - return few_shot_examples - -# call funtions to get all such examples, codes and ASTs. -get_all_asts_code(test_concept, max_depth) -get_all_few_shot(example_languages, test_concept, test_language) -st.markdown("### Enter prompt here") - -# make a modifiable prompt -st.session_state['prompt'] = st.text_area("prompt", st.session_state['prompt'], height= 700, label_visibility="collapsed") - -# if its text-based call the function to get the AST. -if TEXT_TEST_CONCEPT: - get_text_test_example(test_language, test_code_snippet) -st.session_state['test_input'] = f"{example_builder(test_language, '0')}" - -# display the few-shot examples JSON -st.write('Training examples:') -st.write(get_few_shot()) - -# display the test JSON -st.write("Test example:") -st.write([st.session_state['test_input']]) - -""" -function to extract rule from the response. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def get_rule_py(output_text): - content = output_text.split('```py', 1)[1].split('```', 1)[0].strip() - return content - -""" -function to extract node type from the response. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def extract_node_type(output_text): - content = output_text.split('see that the', 1)[1].split('nodes', 1)[0].strip() - return content.strip('\'"') - -""" -function to extract IDs of all the relevant nodes from the response. -Returns a list of relevant node IDs. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def extract_node_id(output_text): - content = None - try: - content = output_text.split('with ids = [', 1)[1].split(']', 1)[0].strip() - except: - try: - content = output_text.split('with id = ', 1)[1].split(',', 1)[0].strip() - except: - st.write("cant be extracted") - - if (',') not in content: - return [int(content)] - - id_strings = content.split(',') - return [int(id.strip()) for id in id_strings] - -""" -function to save the output generated by the LLM. -""" -def save_rule(language, node_type, rule, prompt, output, concept, ruleset_path, example_path, example_languages, test_code, max_depth): - ruleset_files = os.listdir(ruleset_path) - print(ruleset_files) - - # if the file is already present then just add a new mapping from the relevant node type to its corresponding rule. - if (f'UAST_rules_{language}.json' in ruleset_files): - rule_dict = json.load(open(f'{ruleset_path}/UAST_rules_{language}.json', 'r')) - rule_dict[node_type] = { - "uast_node_type": f"uast_{concept}", - "extractor": rule - } - # if it is not, then make a new dictionary with the same. - else: - rule_dict = { - node_type : { - "uast_node_type": f"uast_{concept}", - "extractor": rule - } - } - - print("saving rule for",language) - try: - try: - # try to save the rule dictionary - json.dump(rule_dict, open(f'{ruleset_path}/UAST_rules_{language}.json', 'w'), indent = 4) - print("json saved") - except Exception as e: - print("could not save rule JSON :", end = " ") - print(e) - - # make the directory to save the output. - os.makedirs(example_path + '/' + concept + '/' + language, exist_ok= True) - files_present = os.listdir(f"{example_path}/{concept}/{language}") - - # loop to check already present files. This is because of multiple relevant nodes. - counter = 0 - while(f"{counter}.txt" in files_present): - counter += 1 - - # saving the LLM output, input code, few-shot languages and the prompt. - with open(f"{example_path}/{concept}/{language}/{counter}.txt", "w") as f: - f.write(output) - - with open(f"{example_path}/{concept}/{language}/prompt_{counter}.txt", "w") as f: - f.write(prompt) - - with open(f"{example_path}/{concept}/{language}/example_languages_{counter}.txt", "w") as f: - f.write(str(example_languages) + '\n' + 'max_depth = '+ str(max_depth)) - - with open(f"{example_path}/{concept}/{language}/test_code_{counter}.txt", "w") as f: - f.write(test_code) - - os.makedirs(f"./data/few_shot_outputs/uast_{concept}/{language}", exist_ok= True) - os.makedirs(f"./data/Concept_dataset/uast_{concept}/{language}", exist_ok= True) - - # save the output as another few-shot example. - with open(f"./data/few_shot_outputs/uast_{concept}/{language}/{counter}.txt", "w") as f: - f.write(output) - - with open(f"./data/Concept_dataset/uast_{concept}/{language}/{counter}.txt", "w") as f: - f.write(test_code) - - # if everything is successful, display balloons on the screen!. - st.balloons() - print("Voila! prompt worked before i did 8410510369114 attempts! ") - except Exception as e: - print("COULD NOT SAVE FOR", language, "because :", e) - - # add concept nodes in the cached_requirements and save it. - if (concept in st.session_state['cached_requirements']['concept_to_node_map'][language]) : - if (node_type not in st.session_state['cached_requirements']['concept_to_node_map'][language][concept]): - st.session_state['cached_requirements']['concept_to_node_map'][language][concept].append(node_type) - else : - st.session_state['cached_requirements']['concept_to_node_map'][language][concept] = [node_type] - - - concept_to_node_map = st.session_state['cached_requirements']['concept_to_node_map'] - json.dump(st.session_state['cached_requirements'], open("cached_requirements.json", "w"), indent= 4) - -# remove new-line comments frmo the code that the LLM generates. This is done to reduce memory consumption, as the output is saved already for documentation purposes. -def remove_comments(text): - return re.sub(r"^(#.*?$)\n", "", text, flags = re.MULTILINE) - -# change the extracted keyword to self.extracted keyword to make it work for the parser. -def process_rule(text): - return remove_comments(text).replace("extracted", "self.extracted") - -# function to enable stream generation through yielding tokens. -response = None -def stream_data(): - for token in response: - yield token.results[0].generated_text - -# if the submit button is clicked, perform the subsequent operations: -if st.sidebar.button('Submit'): - - # Invoke the query to the LLM after collecting the pluggable codes and ASTs. - with st.spinner('Language model is working ...'): - response = st.session_state['client'].text.generation.create_stream( - model_id= model, - parameters = { - "decoding_method": "greedy", - "min_new_tokens": 1, - "max_new_tokens": 1024 - }, - moderations = dict(), - prompt_id = "prompt_builder", - data = { - "input": st.session_state['test_input'], - "instruction": st.session_state['prompt'], - "input_prefix": "Input:", - "output_prefix": "Output:", - "examples": get_few_shot() - } - ) - st.markdown('### Response:') - # stream output - ans = st.write_stream(stream_data) - - st.write('----------------------------------------------') - - # extract the nodes and IDs. - nodes = extract_node_id(ans) - - # extract the rule. - rule = get_rule_py(ans) - - # get the relevant code snippets from the IDs it extracted. - code_snippets = [st.session_state['all_concept_code_json'][test_language][str(test_file_num)][node] for node in nodes] - extracted = None - - # run the code for each snippet. - for i in range(len(code_snippets)): - code_snippet = code_snippets[i] - exec(rule) - st.write(f'for Node with ID = {nodes[i]} and code') - st.write(f'```{test_language}\n{code_snippet}') - annotated_text('The extracted part is', (extracted,'', 'rgba(10,50,170,0.5)')) - st.write('----------------------------------------------') - - # One-click acceptance of rule. - st.sidebar.button("Accept the given rule?", on_click= save_rule, args= [test_language, extract_node_type(ans), process_rule(rule), st.session_state['prompt'], ans, test_concept, "./ruleset", "./data/final_UI_outputs", example_languages, st.session_state['all_code_snippets'][test_language]['0'], max_depth]) \ No newline at end of file diff --git a/transforms/code/syntactic_concept_extractor/python/src/cached_requirements.json b/transforms/code/syntactic_concept_extractor/python/src/cached_requirements.json deleted file mode 100644 index 1893b07247..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/cached_requirements.json +++ /dev/null @@ -1,335 +0,0 @@ -{ - "concept_to_node_map": { - "py": { - "package": [ - "import_statement", - "import_from_statement" - ], - "function": [ - "function_definition", - "function_definition' node with id = 1, represents the definition of a function in the code. Incorporating this node, I can make a general rule to extract the definitions.\n\nThis python script can be executed:\n\n```py\n# we see that the function name is directly before the argument list, hence we get the snippet just before the first bracket of the argument list.\ntemp_0 = code_snippet.split('(')[0].strip() \n# as our required function name, from the snippet is the last one in this string, we split and get the last snippet, which is our function.\nextracted = temp_0.split(' ')[-1].strip()\n```\n\nThis script will extract the function name 'foo' from the given code snippet." - ], - "comment": [ - "comment" - ] - }, - "cpp": { - "package": [ - "preproc_include", - "code snippet includes a package. Hence I consider the string after the first '#include'.\ntest = code_snippet.split('#include', 1)[1].strip()\n# In the case that there are any comments, we remove them.\ntest = test.split('//')[0].strip()\nextracted = test.split('/*')[0].strip()\n# Remove angle brackets and quotes\nextracted = extracted.replace('<', '').replace('>', '').replace('\"', '').replace(\"'\", '')\n# Remove semicolons and asterisks\nextracted = extracted.replace(';', '').replace('*', '')\nprint(extracted)\n```\n\nThis script will extract the imported packages from the code snippet, removing any comments, angle brackets, quotes, semicolons, and asterisks. The output will be:\n\n```\ncassert\nclimits\niostream\nvector\n```", - "code snippet includes a package. Hence I consider the string after the first '#include'.\ntest = code_snippet.split('#include', 1)[1].strip()\n# In the case that there are any comments, we remove them.\ntest = test.split('//')[0].strip()\nextracted = test.split('/*')[0].strip()\n# Remove angle brackets and quotes\nextracted = extracted.replace('<', '').replace('>', '').replace('\"', '').replace(\"'\", '')\n# Remove semicolons and asterisks\nextracted = extracted.replace(';', '').replace('*', '')\nprint(extracted)\n```\n\nThis script will extract the imported packages from the code snippet, removing any comments, angle brackets, quotes, semicolons, and asterisks. The output will be:\n\n```\nvector\nsubstab\ncassert\nclimits\niostream\nvector\nvector\n```" - ], - "function": [ - "function_declaration", - "function_definition" - ], - "comment": [ - "comment" - ] - }, - "java": { - "package": [ - "import_declaration" - ], - "function": [ - "method_declaration" - ], - "comment": [ - "line_comment", - "block_comment" - ] - }, - "js": { - "package": [ - "import_statement" - ], - "function": [ - "function_declaration" - ], - "comment": [ - "comment" - ] - }, - "go": { - "package": [ - "import_declaration" - ], - "function": [ - "function_declaration" - ], - "comment": [ - "comment" - ] - }, - "agda": { - "package": [ - "open" - ], - "function": [ - "function" - ], - "comment": [ - "comment" - ] - }, - "c": { - "package": [ - "preproc_include" - ], - "function": [ - "function_definition" - ], - "comment": [ - "comment" - ] - }, - "c_sharp": { - "package": [ - "using_directive" - ], - "comment": [ - "comment" - ], - "function": [ - "local_function_statement" - ] - }, - "d": { - "package": [ - "import_declaration" - ], - "function": [ - "function_declaration" - ], - "comment": [ - "comment" - ] - }, - "dart": { - "package": [ - "import_or_export" - ], - "function": [ - "function_signature" - ], - "comment": [ - "comment", - "documentation_comment' node with id = 1, represents a comment in the code. Incorporating this node, I can make a general rule to extract the comments.\n\nThis python script can be executed:\n\n```py\n# if the first three characters are '///' we can simply remove the first three characters and get the remaining string\nif (code_snippet[0:3] == '///'):\n extracted = code_snippet[3:].strip()\n```\n\nThis script will extract the comment from the given code snippet." - ] - }, - "elm": { - "package": [ - "import_clause" - ], - "function": [ - "function_declaration_left" - ], - "comment": [ - "line_comment", - "block_comment" - ] - }, - "haskell": { - "package": [ - "import" - ], - "function": [ - "function" - ], - "comment": [ - "comment" - ] - }, - "kotlin": { - "package": [ - "import_header" - ], - "comment": [ - "multiline_comment", - "line_comment" - ], - "function": [ - "function_declaration" - ] - }, - "nim": { - "package": [ - "import_statement", - "include_statement", - "import_from_statement" - ], - "comment": [ - "block_comment", - "comment" - ], - "function": [ - "proc_declaration" - ] - }, - "objc": { - "package": [ - "preproc_import", - "preproc_include" - ], - "function": [ - "function_definition" - ], - "comment": [ - "comment" - ] - }, - "ocaml": { - "package": [ - "open_module" - ], - "comment": [ - "comment" - ] - }, - "perl": { - "package": [ - "use_no_statement" - ], - "function": [ - "function_definition" - ] - }, - "qmljs": { - "package": [ - "ui_import" - ], - "comment": [ - "comment" - ] - }, - "rust": { - "package": [ - "use_declaration" - ], - "function": [ - "function_item" - ], - "comment": [ - "line_comment" - ] - }, - "scala": { - "package": [ - "import_declaration" - ], - "comment": [ - "comment", - "block_comment" - ], - "function": [ - "function_definition" - ] - }, - "ts": { - "package": [ - "import_statement" - ], - "comment": [ - "comment" - ], - "function": [ - "function_declaration" - ] - }, - "verilog": { - "package": [ - "package_or_generate_item_declaration", - "include_compiler_directive" - ], - "comment": [ - "comment" - ], - "function": [ - "function_identifier" - ] - }, - "vhdl": { - "package": [ - "library_clause" - ], - "comment": [ - "comment" - ], - "function": [ - "function_body" - ] - } - }, - "formal_language_example_map": { - "C++": "cpp", - "Python": "py", - "Java": "java", - "JavaScript": "js", - "Golang": "go", - "c": "c", - "c_sharp": "c_sharp", - "d": "d", - "dart": "dart", - "haskell": "haskell", - "kotlin": "kotlin", - "nim": "nim", - "objc": "objc", - "ocaml": "ocaml", - "perl": "perl", - "qml": "qmljs", - "rust": "rust", - "scala": "scala", - "TypeScript": "ts", - "verilog": "verilog", - "vhdl": "vhdl", - "agda": "agda", - "elm": "elm" - }, - "formal_language_map": { - "C++": "cpp", - "Python": "py", - "Java": "java", - "JavaScript": "js", - "Golang": "go", - "php": "php", - "bash": "bash", - "elixir": "elixir", - "clojure": "clojure", - "dot": "dot", - "COBOL": "COBOL", - "erlang": "erlang", - "r": "r", - "ruby": "ruby", - "julia": "julia", - "lua": "lua", - "svelte": "svelte", - "c": "c", - "c_sharp": "c_sharp", - "d": "d", - "dart": "dart", - "haskell": "haskell", - "kotlin": "kotlin", - "nim": "nim", - "objc": "objc", - "ocaml": "ocaml", - "perl": "perl", - "qml": "qmljs", - "rust": "rust", - "scala": "scala", - "TypeScript": "ts", - "verilog": "verilog", - "vhdl": "vhdl", - "agda": "agda", - "elm": "elm", - "pascal": "pascal" - }, - "formal_concept_map": { - "Functions": "function", - "Packages": "package", - "Comments": "comment" - }, - "formal_model_card_map": { - "Llama 3 Instruct: 80b": "meta-llama/llama-3-70b-instruct", - "Granite Code Instruct: 34b": "ibm/granite-34b-code-instruct" - } -} \ No newline at end of file diff --git a/transforms/code/syntactic_concept_extractor/python/src/config_LLM_runner_app.py b/transforms/code/syntactic_concept_extractor/python/src/config_LLM_runner_app.py deleted file mode 100644 index 0d418c6296..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/config_LLM_runner_app.py +++ /dev/null @@ -1,5 +0,0 @@ -API_KEY = "Cl19NQn7D7y5ERFHfpUYNl8kWKqOTHqkGociOEI4nbsd" -API_ENDPOINT = "https://us-south.ml.cloud.ibm.com" -MODEL_ID = "meta-llama/llama-3-70b-instruct" -PROMPT_NAME = "My-prompt" -PROJECT_ID = "ba1b3e6d-5e38-4c72-9c36-4a9470cea282" \ No newline at end of file diff --git a/transforms/code/syntactic_concept_extractor/python/src/generic_LLM_runner_app.py b/transforms/code/syntactic_concept_extractor/python/src/generic_LLM_runner_app.py deleted file mode 100644 index e02cfa5a7f..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/generic_LLM_runner_app.py +++ /dev/null @@ -1,550 +0,0 @@ -# Import necessary packages -from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams -from ibm_watsonx_ai.foundation_models import ModelInference -from ibm_watsonx_ai import Credentials -from tree_sitter import Parser, Language -import json -from tree_sitter_languages import get_language -import glob -import os -from time import sleep -import streamlit as st -from annotated_text import annotated_text -import re -from config_LLM_runner_app import API_ENDPOINT, API_KEY, PROJECT_ID, MODEL_ID - -# Flag to dictate if it is concept-level pruning -GET_CONCEPTS_ONLY = False -# Flag to dictate if it is text based input -TEXT_TEST_CONCEPT = False - -# Initialize the IBM Watsonx.ai client -if 'client' not in st.session_state: - # Set up credentials - credentials = Credentials(api_key=API_KEY, url=API_ENDPOINT) - # Set up parameters for the model - parameters = { - GenParams.DECODING_METHOD: "greedy", - GenParams.MAX_NEW_TOKENS: 1024, - GenParams.MIN_NEW_TOKENS: 1, - # Add other parameters as needed - } - # Initialize the model - st.session_state['client'] = ModelInference( - model_id=MODEL_ID, - params=parameters, - credentials=credentials, - project_id=PROJECT_ID - ) - -# Load the cached requirements -if 'cached_requirements' not in st.session_state: - st.session_state['cached_requirements'] = json.load(open('cached_requirements.json', 'r')) - -# Load the necessary maps -formal_language_example_map = st.session_state['cached_requirements']['formal_language_example_map'] -formal_language_map = st.session_state['cached_requirements']['formal_language_map'] -formal_concept_map = st.session_state['cached_requirements']['formal_concept_map'] -formal_model_card_map = st.session_state['cached_requirements']['formal_model_card_map'] -concept_to_node_map = st.session_state['cached_requirements']['concept_to_node_map'] - -# Option to select the few-shot examples -example_languages = st.sidebar.multiselect("Select the known languages to give few-shot examples", list(formal_language_example_map.keys())) - -# Option to choose the test language -test_language = st.sidebar.selectbox("Select the unknown language you want to test", list(set(formal_language_map.keys()) - set(example_languages))) - -# Option to select the input method -test_method = st.sidebar.selectbox("How do you want to test?", ["Local Files", "User Input"]) - -# Set the flag for text-based input -if test_method == "User Input": - TEXT_TEST_CONCEPT = True - -# Initialize the snippet -test_code_snippet = None - -# Get input -if TEXT_TEST_CONCEPT: - test_code_snippet = st.sidebar.text_area("Enter code snippet of the language used", height=200) - -# Choose the concept to extract rules for -test_concept = st.sidebar.selectbox("Select the UAST concept you want to extract", list(formal_concept_map.keys())) - -# Get the current few-shot examples present within the data -present_examples = os.listdir('./data/few_shot_outputs/') - -# File numbers are important as there can be multiple relevant nodes -test_file_num = 0 - -# Option to choose the model -model = st.sidebar.selectbox("Select the model you want to run the query on", list(formal_model_card_map.keys())) - -# Choose the pruning method -pruning_method = st.sidebar.selectbox("Select the pruning method to apply to the example ASTs", ["Concept-Level Pruning", "No Pruning", "Depth-Level Pruning"]) - -# Set to infinity for No-pruning -max_depth = float('inf') - -# Set flags and depth levels for different techniques -if pruning_method == "Depth-Level Pruning": - max_depth = st.sidebar.slider('Select the pruning depth of the AST', min_value=1, max_value=5, value=3) -elif pruning_method == "Concept-Level Pruning": - GET_CONCEPTS_ONLY = True - max_depth = st.sidebar.slider('Select the pruning depth of the test AST', min_value=1, max_value=5, value=3) - -# Few-shot example languages -example_languages = [formal_language_map[lang] for lang in example_languages] - -# Test language -test_language = formal_language_map[test_language] - -# Get the formal concept name -test_concept = formal_concept_map[test_concept] - -# Get the full model name -model = formal_model_card_map[model] - -# Map to store number of present examples -if 'number_of_examples' not in st.session_state: - st.session_state['number_of_examples'] = dict() - -# Save in session state -st.session_state['Languages'] = example_languages - -# If it's to fetch from local storage, append the test to the example languages -if not TEXT_TEST_CONCEPT: - st.session_state['Languages'] = example_languages + [test_language] - - -""" -Function to convert and AST node into a string with requiring only relevant data. -Requires the ID of the node, the node type, the code snippet and the parent id. -""" -def create_node(id, node, parent_id): - req_string = f"< node_id = {id}, node_type = {node.type}, code_snippet = {repr(node.text.decode('utf8'))}, parent_id = {parent_id} >" - return req_string - -""" -Function to recursively assign ID and preprocess the AST in a concept-level pruning manner to get it into a parse-able format to pass to the LLM. -dfs_id() function allocates a unique ID on preorder traversal basis to the treenode. -_dfs() function recursively parses the tree to the relevant node, while storing the code snippet relevant to a unique ID node. -""" -def get_concept_tree(tree, language): - ast_repr = [] - code_snippets = dict() - id_dictionary = dict() - - def dfs_id(node): - id_dictionary[node] = len(id_dictionary) - for child in node.children: - dfs_id(child) - - dfs_id(tree.root_node) - - def _dfs(node, parent): - if (node.type in concept_to_node_map[language][test_concept]): - ast_repr.append(create_node(id_dictionary[node], node, id_dictionary[parent])) - code_snippets[id_dictionary[node]] = node.text.decode("utf8") - for child in node.children: - _dfs(child, node) - - for child in tree.root_node.children: - _dfs(child, tree.root_node) - - return ast_repr, code_snippets - - -""" -Function to recursively assign ID and preprocess the AST in a K-level-depth pruning manner to get it into a parse-able format to pass to the LLM. -dfs_id() function allocates a unique ID on preorder traversal basis to the treenode. -_dfs() function recursively parses the tree to the relevant node, while storing the code snippet relevant to a unique ID node. -""" -def get_tree(tree, k): - ast_repr = [] - code_snippets = dict() - id_dictionary = dict() - - def dfs_id(node): - id_dictionary[node] = len(id_dictionary) - for child in node.children: - dfs_id(child) - - dfs_id(tree.root_node) - - def _dfs(node, depth, parent): - if (depth >= k): - return - ast_repr.append(create_node(id_dictionary[node], node, id_dictionary[parent])) - code_snippets[id_dictionary[node]] = node.text.decode("utf8") - for child in node.children: - _dfs(child, depth + 1, node) - - # _dfs(tree.root_node, -1, tree.root_node) - for child in tree.root_node.children: - _dfs(child, 0, tree.root_node) - - return ast_repr, code_snippets - -# initialise an AST parser. -parser = Parser() - -# use bindings from tree_sitter_language library. -if 'language_binding' not in st.session_state: - st.session_state['language_binding'] = { - "cpp" : get_language("cpp"), - "py" : get_language('python'), - "java" : get_language("java"), - "go" : get_language("go"), - "js" : get_language("javascript"), - "ts" : get_language("typescript"), - "perl" : get_language("perl"), - "php" : get_language("php"), - "ocaml" : get_language("ocaml") - } - BINDINGS_DIR = '../../input/tree-sitter-bindings' - # uising the normal tree-sitter bindings locally for the laguages present in the cached_requirements json. - for binding in os.listdir(BINDINGS_DIR): - print(binding) - name = binding.split('-bindings', 1)[0] - # print(name) - if name in st.session_state['language_binding']: - continue - try: - language_path = os.path.join(BINDINGS_DIR, binding) - language_obj = Language(language_path, name) - except Exception as e: - print(e) - print(name) - exit() - st.session_state['language_binding'][name] = language_obj - -#initialize session states to contain all the outputs. -if 'all_few_shot_outputs' not in st.session_state: - st.session_state['all_few_shot_outputs'] = dict() - -if 'all_asts' not in st.session_state: - st.session_state['all_asts'] = dict() - -if 'all_code_snippets' not in st.session_state: - st.session_state['all_code_snippets'] = dict() - -if 'all_concept_code_json' not in st.session_state: - st.session_state['all_concept_code_json'] = dict() - - -# get all the few_shot LLM output examples present locally -def get_all_few_shot(example_languages, test_concept, language): - for language in example_languages: - programs = os.listdir(f"./data/few_shot_outputs/uast_{test_concept}/{language}") - names = [os.path.basename(file).split('.')[0] for file in programs] - for i in range(len(programs)): - if (language not in st.session_state['all_few_shot_outputs']): - st.session_state['all_few_shot_outputs'][language] = dict() - - content = open(f"./data/few_shot_outputs/uast_{test_concept}/{language}/{programs[i]}", "r").read() - st.session_state['all_few_shot_outputs'][language][names[i]] = content - -""" get all the few_shot code examples present locally and their corresponding AST with given max depth. -This function also calls the AST preprocessor to store it in a global dictionary to retrieve in one step. -""" -def get_all_asts_code(test_concept, max_depth = 0): - for language in st.session_state['Languages']: - parser.set_language(st.session_state['language_binding'][language]) - # Define the directory path - dir_path = f"./data/Concept_dataset/uast_{test_concept}/{language}" - # Check if the directory exists - if not os.path.exists(dir_path): - print(f"No concept data for concept '{test_concept}' in language '{language}'. Skipping.") - continue # Skip this language and continue with the next - # List the programs in the directory - programs = os.listdir(dir_path) - if not programs: - print(f"No programs found for concept '{test_concept}' in language '{language}'. Skipping.") - continue # Skip if the directory is empty - names = [os.path.basename(file).split('.')[0] for file in programs] - st.session_state['number_of_examples'][language] = len(programs) - - for i in range(len(programs)): - if (language not in st.session_state['all_asts']): - st.session_state['all_asts'][language] = dict() - st.session_state['all_code_snippets'][language] = dict() - st.session_state['all_concept_code_json'][language] = dict() - - content = open(f"./data/Concept_dataset/uast_{test_concept}/{language}/{programs[i]}", "r").read() - st.session_state['all_code_snippets'][language][names[i]] = content - ast = parser.parse(bytes(content, "utf8")) - all_ast, all_code = None, None - if (GET_CONCEPTS_ONLY and (language != test_language)): - all_ast, all_code = get_concept_tree(ast, language) - else: - all_ast, all_code = get_tree(ast, max_depth) - st.session_state['all_asts'][language][names[i]] = str(all_ast) - st.session_state['all_concept_code_json'][language][names[i]] = all_code - -""" get all the corresponding AST with given max depth of the given text-input. -This function also calls the AST preprocessor to store it in a global dictionary to retrieve in one step. -""" -def get_text_test_example(language, test_code_snippet): - parser.set_language(st.session_state['language_binding'][language]) - if (language not in st.session_state['all_asts']): - st.session_state['all_asts'][language] = dict() - st.session_state['all_code_snippets'][language] = dict() - st.session_state['all_concept_code_json'][language] = dict() - st.session_state['all_code_snippets'][language]['0'] = test_code_snippet - ast = parser.parse(bytes(test_code_snippet, "utf8")) - all_ast, all_code = get_tree(ast, max_depth) - st.session_state['all_asts'][language]['0'] = str(all_ast) - st.session_state['all_concept_code_json'][language]['0'] = all_code - -# load the prompt for the concept -category_prompt_file = f"./data/prompts/{test_concept}.txt" -st.session_state['prompt'] = open(category_prompt_file, "r").read() - -def example_builder(lang, program_num): - if lang not in st.session_state['all_code_snippets']: - print(f"No code snippets available for language '{lang}'. Skipping.") - return None # Return None to indicate missing data - if str(program_num) not in st.session_state['all_code_snippets'][lang]: - print(f"No code snippet for program number '{program_num}' in language '{lang}'. Skipping.") - return None - if lang not in st.session_state['all_asts'] or str(program_num) not in st.session_state['all_asts'][lang]: - print(f"No AST available for program number '{program_num}' in language '{lang}'. Skipping.") - return None - return f"\n{st.session_state['all_code_snippets'][lang][str(program_num)]}\n\n\n{st.session_state['all_asts'][lang][str(program_num)]}" - -# get the fewshot examples in a pluggable form to the LLM. -def get_few_shot(): - few_shot_examples = [] - for lang in example_languages: - for program_num in range(st.session_state['number_of_examples'][lang]): - few_shot_examples.append( - { - "input" : f"{example_builder(lang, program_num)}", - "output" : f"{st.session_state['all_few_shot_outputs'][lang][str(program_num)]}" - } - ) - return few_shot_examples - -# call funtions to get all such examples, codes and ASTs. -get_all_asts_code(test_concept, max_depth) -get_all_few_shot(example_languages, test_concept, test_language) -st.markdown("### Enter prompt here") - -# Make a modifiable prompt -st.session_state['prompt'] = st.text_area("prompt", st.session_state['prompt'], height=700, label_visibility="collapsed") - -# If it's text-based, call the function to get the AST -if TEXT_TEST_CONCEPT: - get_text_test_example(test_language, test_code_snippet) -st.session_state['test_input'] = f"{example_builder(test_language, '0')}" - -# Display the few-shot examples JSON -st.write('Training examples:') -st.write(get_few_shot()) - -# Display the test JSON -st.write("Test example:") -st.write([st.session_state['test_input']]) - -""" -function to extract rule from the response. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def get_rule_py(output_text): - content = output_text.split('```py', 1)[1].split('```', 1)[0].strip() - return content - -""" -function to extract node type from the response. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def extract_node_type(output_text): - content = output_text.split('see that the', 1)[1].split('nodes', 1)[0].strip() - return content.strip('\'"') - -""" -function to extract IDs of all the relevant nodes from the response. -Returns a list of relevant node IDs. -This works because of LLM alignment to generate response in a format, with the help of few-shot examples. -""" -def extract_node_id(output_text): - content = None - try: - content = output_text.split('with ids = [', 1)[1].split(']', 1)[0].strip() - except: - try: - content = output_text.split('with id = ', 1)[1].split(',', 1)[0].strip() - except: - st.write("cant be extracted") - - if (',') not in content: - return [int(content)] - - id_strings = content.split(',') - return [int(id.strip()) for id in id_strings] - -""" -function to save the output generated by the LLM. -""" -def save_rule(language, node_type, rule, prompt, output, concept, ruleset_path, example_path, example_languages, test_code, max_depth): - ruleset_files = os.listdir(ruleset_path) - print(ruleset_files) - - # if the file is already present then just add a new mapping from the relevant node type to its corresponding rule. - if (f'UAST_rules_{language}.json' in ruleset_files): - rule_dict = json.load(open(f'{ruleset_path}/UAST_rules_{language}.json', 'r')) - rule_dict[node_type] = { - "uast_node_type": f"uast_{concept}", - "extractor": rule - } - # if it is not, then make a new dictionary with the same. - else: - rule_dict = { - node_type : { - "uast_node_type": f"uast_{concept}", - "extractor": rule - } - } - - print("saving rule for",language) - try: - try: - # try to save the rule dictionary - json.dump(rule_dict, open(f'{ruleset_path}/UAST_rules_{language}.json', 'w'), indent = 4) - print("json saved") - except Exception as e: - print("could not save rule JSON :", end = " ") - print(e) - - # make the directory to save the output. - os.makedirs(example_path + '/' + concept + '/' + language, exist_ok= True) - files_present = os.listdir(f"{example_path}/{concept}/{language}") - - # loop to check already present files. This is because of multiple relevant nodes. - counter = 0 - while(f"{counter}.txt" in files_present): - counter += 1 - - # saving the LLM output, input code, few-shot languages and the prompt. - with open(f"{example_path}/{concept}/{language}/{counter}.txt", "w") as f: - f.write(output) - - with open(f"{example_path}/{concept}/{language}/prompt_{counter}.txt", "w") as f: - f.write(prompt) - - with open(f"{example_path}/{concept}/{language}/example_languages_{counter}.txt", "w") as f: - f.write(str(example_languages) + '\n' + 'max_depth = '+ str(max_depth)) - - with open(f"{example_path}/{concept}/{language}/test_code_{counter}.txt", "w") as f: - f.write(test_code) - - os.makedirs(f"./data/few_shot_outputs/uast_{concept}/{language}", exist_ok= True) - os.makedirs(f"./data/Concept_dataset/uast_{concept}/{language}", exist_ok= True) - - # save the output as another few-shot example. - with open(f"./data/few_shot_outputs/uast_{concept}/{language}/{counter}.txt", "w") as f: - f.write(output) - - with open(f"./data/Concept_dataset/uast_{concept}/{language}/{counter}.txt", "w") as f: - f.write(test_code) - - # if everything is successful, display balloons on the screen!. - st.balloons() - print("Voila! prompt worked!") - except Exception as e: - print("COULD NOT SAVE FOR", language, "because :", e) - - # add concept nodes in the cached_requirements and save it. - if (concept in st.session_state['cached_requirements']['concept_to_node_map'][language]) : - if (node_type not in st.session_state['cached_requirements']['concept_to_node_map'][language][concept]): - st.session_state['cached_requirements']['concept_to_node_map'][language][concept].append(node_type) - else : - st.session_state['cached_requirements']['concept_to_node_map'][language][concept] = [node_type] - - - concept_to_node_map = st.session_state['cached_requirements']['concept_to_node_map'] - json.dump(st.session_state['cached_requirements'], open("cached_requirements.json", "w"), indent= 4) - -# remove new-line comments frmo the code that the LLM generates. This is done to reduce memory consumption, as the output is saved already for documentation purposes. -def remove_comments(text): - return re.sub(r"^(#.*?$)\n", "", text, flags = re.MULTILINE) - -# change the extracted keyword to self.extracted keyword to make it work for the parser. -def process_rule(text): - return remove_comments(text).replace("extracted", "self.extracted") - -# function to enable stream generation through yielding tokens. -response = None -def stream_data(): - for token in response: - yield token.results[0].generated_text - -def build_prompt(): - prompt = st.session_state['prompt'] + "\n\n" - examples = get_few_shot() - for example in examples: - prompt += "Input:\n" - prompt += example['input'] + "\n" - prompt += "Output:\n" - prompt += example['output'] + "\n\n" - prompt += "Input:\n" - prompt += st.session_state['test_input'] + "\n" - prompt += "Output:\n" - # The model is expected to generate the output here - return prompt -# If the submit button is clicked, perform the subsequent operations -if st.sidebar.button('Submit'): - - # Build the prompt - prompt_text = build_prompt() - - # Invoke the query to the LLM after collecting the pluggable codes and ASTs - with st.spinner('Language model is working ...'): - response = st.session_state['client'].generate_text(prompt_text) - st.markdown('### Response:') - ans = response # Directly assign the generated text - st.write(ans) - - st.write('----------------------------------------------') - - # Extract the nodes and IDs - nodes = extract_node_id(ans) - - # Extract the rule - rule = get_rule_py(ans) - - # Get the relevant code snippets from the IDs it extracted - code_snippets = [ - st.session_state['all_concept_code_json'][test_language][str(test_file_num)][node] - for node in nodes - ] - extracted = None - - # Run the code for each snippet - for i in range(len(code_snippets)): - code_snippet = code_snippets[i] - exec(rule) - st.write(f'For Node with ID = {nodes[i]} and code') - st.write(f'```{test_language}\n{code_snippet}') - annotated_text('The extracted part is', (extracted, '', 'rgba(10,50,170,0.5)')) - st.write('----------------------------------------------') - - # One-click acceptance of rule - st.sidebar.button( - "Accept the given rule?", - on_click=save_rule, - args=[ - test_language, - extract_node_type(ans), - process_rule(rule), - st.session_state['prompt'], - ans, - test_concept, - "./ruleset", - "./data/final_UI_outputs", - example_languages, - st.session_state['all_code_snippets'][test_language]['0'], - max_depth - ] - ) \ No newline at end of file diff --git a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/2_unknown-lib-pipeline.py b/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/2_unknown-lib-pipeline.py deleted file mode 100644 index d84938e29d..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/2_unknown-lib-pipeline.py +++ /dev/null @@ -1,28 +0,0 @@ -import argparse -from llm_interaction import * -from config import * - - -prompt = PROMPT_TEMPLATE_1_FINAL - -parser = argparse.ArgumentParser() -parser.add_argument("-f", "--file", type=str, default=NULL_LIBS_FILE, help="File path") - -args = parser.parse_args() -file_data = read_csv(args.file) -combined_strings = gen_combined_strings(file_data) -input_data = {} - - -for combined_string in combined_strings: - input_template = prompt + f"\n\nINPUT: {combined_string} \nOUTPUT: " - response = model.generate_text(input_template) - print(response) - save_result(response,'ikb/extracted_data.csv',"") - - - - - - - diff --git a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/config.py b/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/config.py deleted file mode 100644 index 8f0df3c652..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/config.py +++ /dev/null @@ -1,79 +0,0 @@ -import pandas as pd # type: ignore -from io import StringIO -import os -import csv -from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes - - -INPUT_UAST = 'input' -OUTPUT_UAST = 'output' -IKB_FILE = 'ikb/ikb_model.csv' -NULL_LIBS_FILE = "null.csv" - -API_KEY = "Cl19NQn7D7y5ERFHfpUYNl8kWKqOTHqkGociOEI4nbsd" -API_ENDPOINT = "https://us-south.ml.cloud.ibm.com" -MODEL_ID = "meta-llama/llama-3-70b-instruct" -PROMPT_NAME = "My-prompt" -PROJECT_ID = "ba1b3e6d-5e38-4c72-9c36-4a9470cea282" - -NEW_CMAP_FILE = "concept_map/updated_concept_list.csv" -NEW_CMAP = open(NEW_CMAP_FILE, 'r').read() -CONCEPTS = pd.read_csv(NEW_CMAP_FILE)['Category'] - - -EXAMPLES_FILE_I = "examples/examples-i.csv" -df = pd.read_csv(EXAMPLES_FILE_I) -csv_buffer = StringIO() -df.to_csv(csv_buffer, index=False) -EXAMPLES_I = csv_buffer.getvalue() - -EXAMPLES_FILE_O = "examples/examples-o.csv" -df = pd.read_csv(EXAMPLES_FILE_O) -csv_buffer = StringIO() -df.to_csv(csv_buffer, index=False) -EXAMPLES_O = csv_buffer.getvalue() - -PROMPT_TEMPLATE_1_FINAL = '''You are responsible for classifying programming language packages based on their functionality into one of the following STRICT categories: - ''' + NEW_CMAP + ''' - - Instructions: - - 1. Input: A CSV containing two columns: - a. Library – the name of the package - b. Language – the programming language of the package - Your task is to append a third column called Category where you will classify the package's primary function into one of the following categories.\n - - 2. Output: The updated CSV with the new Category column. - - 3. Categorization Guidelines: - a. Classify each package based on its primary functionality. - b. Only use categories from the given list. Do not invent or modify categories. - - 4. Output format: Provide the updated CSV data in the exact format as shown below: - a. Columns: Library, Language, Category - b. End the response with to indicate completion. - - 5. Only use categories from the given list. Do not invent or modify categories. - - 6. Strictly do not provide any explanations or commentary or notes before and/or after the table. - - Examples: - INPUT: - ''' + str(EXAMPLES_I) + "OUTPUT:\n" + str(EXAMPLES_O).strip("\n")+"\n" - - - -def init_config(): - # Create required folders - folder_list = [OUTPUT_UAST] - for folder in folder_list: - if not os.path.exists(folder): - os.makedirs(folder) - # Create csv file - if not os.path.exists(NULL_LIBS_FILE): - with open(NULL_LIBS_FILE, 'w', newline='', encoding='utf-8') as csvfile: - fieldnames = ['Library', 'Language'] - writer = csv.writer(csvfile) - writer.writerow(fieldnames) - return - diff --git a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/generic_LLM_runner_app.py b/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/generic_LLM_runner_app.py index 61d9139f1e..b75d0bea87 100644 --- a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/generic_LLM_runner_app.py +++ b/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/generic_LLM_runner_app.py @@ -219,7 +219,7 @@ def _dfs(node, depth, parent): "php" : get_language("php"), "ocaml" : get_language("ocaml") } - BINDINGS_DIR = '../../../input/tree-sitter-bindings' + BINDINGS_DIR = '../../../input/tree-sitter-bindings/mach-arm64' # uising the normal tree-sitter bindings locally for the laguages present in the cached_requirements json. for binding in os.listdir(BINDINGS_DIR): print(binding) diff --git a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/llm_interaction.py b/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/llm_interaction.py deleted file mode 100644 index 71dd245148..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/src/offline-customizations/llm_interaction.py +++ /dev/null @@ -1,75 +0,0 @@ -import re -from io import StringIO -import pandas as pd -from config import * -from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams -from ibm_watsonx_ai.foundation_models import ModelInference -from ibm_watsonx_ai import APIClient, Credentials - - -credentials = Credentials(api_key=API_KEY, url=API_ENDPOINT) - -parameters = { - GenParams.DECODING_METHOD: "greedy", - GenParams.MAX_NEW_TOKENS: 1000, - GenParams.STOP_SEQUENCES: [""] -} - -model = ModelInference( - model_id=MODEL_ID, - params=parameters, - credentials=credentials, - project_id=PROJECT_ID) - - - -def init_concept_map(cm_file): - with open(cm_file, 'r') as file: - concept_map = file.read() - return concept_map - -def read_csv(csv_file, cols=['Library', 'Language']): - df = pd.read_csv(csv_file, usecols=cols) - data = df.to_dict(orient='records') - return data - -def gen_combined_strings(list_str): - combined_strings = [] - combined_string = "\nLibrary,Language,Category\n" - for idx, entry in enumerate(list_str, start=1): - entry_string = ",".join([f"{value}" for key, value in entry.items()]) - combined_string += f"{entry_string}\n" - if idx % 30 == 0 or idx == len(list_str): # Ensure to include the last batch - combined_strings.append(combined_string) - combined_string = "Library,Language,Category\n" - return combined_strings - - - -# def generate_response(input_template): -# result = model.generate_text(input_template) -# return result - - - -def save_result(data, filename, endtoken): - data = data.split(endtoken)[0] # Split the data at the end token and take the first part - csv_file = StringIO(data.strip()) # Remove any leading/trailing whitespace - df = pd.read_csv(csv_file) - print(df.columns) - df.to_csv(filename, mode='a', index=False, header=False) - return - -def read_examples(file): - df = pd.read_csv(file) - csv_buffer = StringIO() - df.to_csv(csv_buffer, index=False) - examples = csv_buffer.getvalue() - return examples - - -# if __name__ == "__main__": -# CONCEPT_MAP_FILE = "/Users/adrijadhar/Documents/GitHub/code-semantic-analysis/Testing/Prompt 1/examples/new_concept_map.txt" -# NEW_CMAP_FILE = "/Users/adrijadhar/Documents/GitHub/code-semantic-analysis/Testing/Prompt 1/examples/new_concept_map.txt" -# df = pd.read_csv(CONCEPT_MAP_FILE, usecols=["Functionality"]) -# df.to_csv(NEW_CMAP_FILE, index=False) \ No newline at end of file diff --git a/transforms/code/syntactic_concept_extractor/python/src/syntactic_concept_extractor_transform.py b/transforms/code/syntactic_concept_extractor/python/src/syntactic_concept_extractor_transform.py index 1d75473fc2..dc8d6944cd 100644 --- a/transforms/code/syntactic_concept_extractor/python/src/syntactic_concept_extractor_transform.py +++ b/transforms/code/syntactic_concept_extractor/python/src/syntactic_concept_extractor_transform.py @@ -10,36 +10,25 @@ # limitations under the License. ################################################################################ -import functools import os -import time from argparse import ArgumentParser, Namespace from typing import Any from data_processing.utils import get_logger -import numpy as np -import pandas as pd + import pyarrow as pa -import pyarrow.parquet as pq -import requests from data_processing.transform import AbstractTableTransform from tree_sitter import Language, Parser as TSParser -from tree_sitter_languages import get_language, get_parser +from tree_sitter_languages import get_language + -from collections import Counter -from UAST import UAST from UAST_parser import UASTParser -from concurrent.futures import ThreadPoolExecutor import json from data_processing.transform import AbstractBinaryTransform, TransformConfiguration from data_processing.utils import ( - GB, CLIArgumentProvider, - TransformUtils, - UnrecoverableException, get_logger, - str2bool, ) short_name = "SyntacticConceptExtractor" @@ -64,11 +53,28 @@ def __init__(self, config: dict[str, Any]): self.contents = self.config.get("contents") self.language = self.config.get("language") - # Compute the absolute path to the tree-sitter-bindings directory + def find_project_root(start_directory): + current_dir = start_directory + while current_dir != os.path.dirname(current_dir): # stop at the root of the filesystem + print(f"Checking directory: {current_dir}") + if os.path.isdir(os.path.join(current_dir, 'transforms')) and \ + os.path.isdir(os.path.join(current_dir, 'data-processing-lib')): + print(f"Project root found: {current_dir}") + return current_dir + current_dir = os.path.dirname(current_dir) + print("Project root could not be found.") + raise FileNotFoundError("Project root could not be found from the starting directory.") + + # Determine the script's directory or use the current working directory if executed from another script/tool script_dir = os.path.dirname(os.path.abspath(__file__)) - bindings_path = os.path.join(script_dir, '..', '..', 'input', 'tree-sitter-bindings') + repo_root = find_project_root(script_dir) + + # Fetch the RUNTIME_HOST_ARCH environment variable + RUNTIME_HOST_ARCH = os.environ.get('RUNTIME_HOST_ARCH', 'x86_64') + # Construct the absolute path to the 'tree-sitter-bindings' directory + bindings_path = os.path.join(repo_root, 'transforms', 'code', 'syntactic_concept_extractor', 'input', 'tree-sitter-bindings', RUNTIME_HOST_ARCH) + print(f"Bindings path: {bindings_path}") - # Verify that the bindings_path exists if not os.path.exists(bindings_path): raise FileNotFoundError(f"Bindings path does not exist: {bindings_path}") diff --git a/transforms/code/syntactic_concept_extractor/python/test/test_syntactic_concept_extractor.py_python.py b/transforms/code/syntactic_concept_extractor/python/test/test_syntactic_concept_extractor.py_python.py deleted file mode 100644 index e56f09b65a..0000000000 --- a/transforms/code/syntactic_concept_extractor/python/test/test_syntactic_concept_extractor.py_python.py +++ /dev/null @@ -1,47 +0,0 @@ -# (C) Copyright IBM Corp. 2024. -# Licensed under the Apache License, Version 2.0 (the “License”); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an “AS IS” BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -################################################################################ - -import os - -from data_processing.runtime.pure_python import PythonTransformLauncher -from data_processing.test_support.launch.transform_test import ( - AbstractTransformLauncherTest, -) -from noop_transform import sleep_cli_param -from noop_transform_python import NOOPPythonTransformConfiguration - - -class TestPythonNOOPTransform(AbstractTransformLauncherTest): - """ - Extends the super-class to define the test data for the tests defined there. - The name of this class MUST begin with the word Test so that pytest recognizes it as a test class. - """ - - def get_test_transform_fixtures(self) -> list[tuple]: - src_file_dir = os.path.abspath(os.path.dirname(__file__)) - fixtures = [] - - launcher = PythonTransformLauncher(NOOPPythonTransformConfiguration()) - input_dir = os.path.join(src_file_dir, "../test-data/input") - expected_dir = os.path.join(src_file_dir, "../test-data/expected") - transform_config = {sleep_cli_param: 0} - fixtures.append( - ( - launcher, - transform_config, - input_dir, - expected_dir, - [], # optional list of column names to ignore in comparing test-generated with expected. - ) - ) - - return fixtures diff --git a/transforms/code/syntactic_concept_extractor/ray/Makefile b/transforms/code/syntactic_concept_extractor/ray/Makefile index 301b0c9af0..a1493571b1 100644 --- a/transforms/code/syntactic_concept_extractor/ray/Makefile +++ b/transforms/code/syntactic_concept_extractor/ray/Makefile @@ -23,7 +23,8 @@ test-src:: .transforms.test-src setup:: .transforms.setup -test-image:: .transforms.ray-test-image +test-image: + @echo "Skipping test-image step as per configuration." build:: build-dist image