feat: Dagster Data pipeline

python/tabby/tabby-data-pipeline/predict.py → python/tabby/tabby-data-pipeline/modal/predict.py

@@ -6,17 +6,19 @@
 
 
 import asyncio
+from collections import namedtuple
+from datetime import datetime
 
 GPU_CONFIG = gpu.A10G()
 
-MODEL_ID = os.popen("cat /tmp/tabby_model_id").read().strip()
+MODEL_ID = os.environ.get("MODEL_ID")
 LAUNCH_FLAGS = ["serve", "--model", MODEL_ID, "--port", "8000", "--device", "cuda"]
 
 
 def download_model():
     import subprocess
     import os
-    MODEL_ID = os.popen("cat /tmp/tabby_model_id").read().strip()
+    MODEL_ID = os.environ.get("MODEL_ID")
     print(f'MODEL_ID={MODEL_ID}')
     subprocess.run(
         [
@@ -33,12 +35,13 @@ def download_model():
         "tabbyml/tabby:0.5.5",
         add_python="3.11",
     )
+    .env({"MODEL_ID": os.environ.get("MODEL_ID")})
     .dockerfile_commands("ENTRYPOINT []")
+    .copy_local_dir(local_path='./modal/tabby_python_client/tabby_python_client', remote_path='/root/tabby_python_client')
     .pip_install(
-        "git+https://github.com/TabbyML/tabby.git#egg=tabby-python-client&subdirectory=experimental/eval/tabby-python-client",
+        "httpx",
         "pandas"
     )
-    .copy_local_file(local_path="/tmp/tabby_model_id", remote_path="/tmp/tabby_model_id")
     .run_function(download_model)
 )
 
@@ -62,16 +65,17 @@ def __enter__(self):
 
         my_env = os.environ.copy()
         my_env["TABBY_DISABLE_USAGE_COLLECTION"] = "1"
-        MODEL_ID = os.popen("cat /tmp/tabby_model_id").read().strip()
+        MODEL_ID = os.environ.get("MODEL_ID")
         print(f'MODEL_ID={MODEL_ID}')
+
         LAUNCH_FLAGS = ["serve", "--model", MODEL_ID, "--port", "8000", "--device", "cuda"]
         self.launcher = subprocess.Popen(["/opt/tabby/bin/tabby"] + LAUNCH_FLAGS, env=my_env)
         self.client = Client("http://127.0.0.1:8000", timeout=240)
 
         # Poll until webserver at 127.0.0.1:8000 accepts connections before running inputs.
         def webserver_ready():
             try:
-                socket.create_connection(("127.0.0.1", 8000), timeout=1).close()
+                socket.create_connection(("127.0.0.1", 8000), timeout=30).close()
                 return True
             except (socket.timeout, ConnectionRefusedError):
                 # Check if launcher webserving process has exited.
@@ -99,7 +103,7 @@ async def health(self):
         return resp.to_dict()
 
     @method()
-    async def complete(self, language, crossfile_context, index, row):
+    async def complete(self, language: str, index: int, prompt: str, prediction: bool):
         from tabby_python_client.api.v1 import completion
         from tabby_python_client.models import (
             CompletionRequest,
@@ -111,15 +115,10 @@ async def complete(self, language, crossfile_context, index, row):
         from tabby_python_client import errors
         import pandas as pd
 
-        if 'prediction' in row and not pd.isnull(row['prediction']):
+        # if prediction exists, just skip
+        if prediction:
             return None, None, None
-
-        if crossfile_context:
-            prompt = row["crossfile_context"]["text"] + row["prompt"]
-        else:
-            prompt = row["prompt"]
-
-        groundtruth = row["groundtruth"]
+
 
         request = CompletionRequest(
             language=language, debug_options=DebugOptions(raw_prompt=prompt)
@@ -141,10 +140,14 @@ async def complete(self, language, crossfile_context, index, row):
         except Exception as e:
             return index, None, f"error type: {type(e)}"
 
-
+def write_log(log: str):
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    with open('./modal/log.txt', 'a') as f:
+        f.write(f"{now} : {log}")
+        f.write("\n")
 
 @stub.local_entrypoint()
-async def main(language, file):
+async def main(language: str):
     import json
     import pandas as pd
 
@@ -157,45 +160,60 @@ async def main(language, file):
     print(health_resp)
     assert(health_resp['model'] == MODEL_ID)
 
-    whole_path_file = "./data/" + MODEL_ID.split("/")[-1] + "/" + language + "/" + file
-
-    if file == 'line_completion.jsonl':
-        crossfile_context = False
-    else:
-        crossfile_context = True
-
-    objs = []
-    with open(whole_path_file) as fin:
-        for line in fin:
-            obj = json.loads(line)
-            objs.append(obj)
-
-    df = pd.DataFrame(objs)
-
-    outputs = await asyncio.gather(*[model.complete.remote.aio(language, crossfile_context, index, row) for index, row in df.iterrows()])
-
-    skipped = 0
-    success = 0
-    error = 0
-
-    for index, prediction, error_msg in outputs:
-        if index is None:
-            skipped += 1
-        elif prediction is not None:
-            df.loc[index, 'prediction'] = prediction
-            success += 1
-        else:
-            df.loc[index, 'error'] = error_msg
-            error += 1
-    print(f"Skipped {skipped} rows, {success} rows with predictions, {error} rows with errors")
-
-    with open(whole_path_file, 'w') as fout:
-        for index, row in df.iterrows():
-            json.dump(row.to_dict(), fout)
-            fout.write('\n')
-
-
-    with open("log.txt", "a") as flog:
-        flog.write(f"model: {MODEL_ID}; language: {language}; file: {file}")
-        flog.write(f"Skipped {skipped} rows, {success} rows with predictions, {error} rows with errors")
-        flog.write("\n\n")
+
+    for file in ['line_completion.jsonl', 'line_completion_rg1_bm25.jsonl', 'line_completion_oracle_bm25.jsonl']:
+
+        whole_path_file = "./data/" + MODEL_ID.split("/")[-1] + "/" + language + "/" + file
+        objs = []
+        with open(whole_path_file) as fin:
+            for line in fin:
+                obj = json.loads(line)
+                if file == 'line_completion.jsonl':
+                    obj['raw_prompt'] = obj['prompt']
+                else:
+                    obj['raw_prompt'] = obj['crossfile_context']['text']
+                objs.append(obj)
+
+        df = pd.DataFrame(objs)
+
+        write_log(f"model: {MODEL_ID}; language: {language}; file: {file}: length = {len(df)}")
+
+        def chunker(seq, size):
+            return (seq[pos:pos + size] for pos in range(0, len(seq), size))
+
+        def get_prediction(row):
+            if 'prediction' in row and not pd.isnull(row['prediction']):
+                return True
+            else:
+                return False
+
+        skipped = 0
+        success = 0
+        error = 0
+
+        for group in chunker(df, 30):
+            outputs = await asyncio.gather(*[model.complete.remote.aio(language, index, row['raw_prompt'], get_prediction(row)) for index, row in group.iterrows()])
+
+            for index, prediction, error_msg in outputs:
+                if index is None:
+                    skipped += 1
+                elif prediction is not None:
+                    df.loc[index, 'prediction'] = prediction
+                    success += 1
+                else:
+                    df.loc[index, 'error'] = error_msg
+                    error += 1
+
+        write_log(f"Skipped {skipped} rows, {success} rows with predictions, {error} rows with errors")
+
+        whole_path_file = "./data/" + MODEL_ID.split("/")[-1] + "/" + language + "/" + file
+
+        with open(whole_path_file, 'w') as fout:
+            for index, row in df.iterrows():
+                row_dict = row.to_dict()
+                json.dump(row_dict, fout)
+                fout.write('\n')
+
+
+
+        write_log(f"model: {MODEL_ID}; language: {language}; file: {file}: end!\n")

python/tabby/tabby-data-pipeline/modal/tabby_python_client/.gitignore

@@ -0,0 +1,23 @@
+__pycache__/
+build/
+dist/
+*.egg-info/
+.pytest_cache/
+
+# pyenv
+.python-version
+
+# Environments
+.env
+.venv
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# JetBrains
+.idea/
+
+/coverage.xml
+/.coverage

python/tabby/tabby-data-pipeline/modal/tabby_python_client/README.md

@@ -0,0 +1,89 @@
+# tabby-python-client
+A client library for accessing Tabby Server
+
+## Usage
+First, create a client:
+
+```python
+from tabby_python_client import Client
+
+client = Client(base_url="https://api.example.com")
+```
+
+If the endpoints you're going to hit require authentication, use `AuthenticatedClient` instead:
+
+```python
+from tabby_python_client import AuthenticatedClient
+
+client = AuthenticatedClient(base_url="https://api.example.com", token="SuperSecretToken")
+```
+
+Now call your endpoint and use your models:
+
+```python
+from tabby_python_client.models import MyDataModel
+from tabby_python_client.api.my_tag import get_my_data_model
+from tabby_python_client.types import Response
+
+my_data: MyDataModel = get_my_data_model.sync(client=client)
+# or if you need more info (e.g. status_code)
+response: Response[MyDataModel] = get_my_data_model.sync_detailed(client=client)
+```
+
+Or do the same thing with an async version:
+
+```python
+from tabby_python_client.models import MyDataModel
+from tabby_python_client.api.my_tag import get_my_data_model
+from tabby_python_client.types import Response
+
+my_data: MyDataModel = await get_my_data_model.asyncio(client=client)
+response: Response[MyDataModel] = await get_my_data_model.asyncio_detailed(client=client)
+```
+
+By default, when you're calling an HTTPS API it will attempt to verify that SSL is working correctly. Using certificate verification is highly recommended most of the time, but sometimes you may need to authenticate to a server (especially an internal server) using a custom certificate bundle.
+
+```python
+client = AuthenticatedClient(
+    base_url="https://internal_api.example.com", 
+    token="SuperSecretToken",
+    verify_ssl="/path/to/certificate_bundle.pem",
+)
+```
+
+You can also disable certificate validation altogether, but beware that **this is a security risk**.
+
+```python
+client = AuthenticatedClient(
+    base_url="https://internal_api.example.com", 
+    token="SuperSecretToken", 
+    verify_ssl=False
+)
+```
+
+There are more settings on the generated `Client` class which let you control more runtime behavior, check out the docstring on that class for more info.
+
+Things to know:
+1. Every path/method combo becomes a Python module with four functions:
+    1. `sync`: Blocking request that returns parsed data (if successful) or `None`
+    1. `sync_detailed`: Blocking request that always returns a `Request`, optionally with `parsed` set if the request was successful.
+    1. `asyncio`: Like `sync` but async instead of blocking
+    1. `asyncio_detailed`: Like `sync_detailed` but async instead of blocking
+
+1. All path/query params, and bodies become method arguments.
+1. If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)
+1. Any endpoint which did not have a tag will be in `tabby_python_client.api.default`
+
+## Building / publishing this Client
+This project uses [Poetry](https://python-poetry.org/) to manage dependencies  and packaging.  Here are the basics:
+1. Update the metadata in pyproject.toml (e.g. authors, version)
+1. If you're using a private repository, configure it with Poetry
+    1. `poetry config repositories.<your-repository-name> <url-to-your-repository>`
+    1. `poetry config http-basic.<your-repository-name> <username> <password>`
+1. Publish the client with `poetry publish --build -r <your-repository-name>` or, if for public PyPI, just `poetry publish --build`
+
+If you want to install this client into another project without publishing it (e.g. for development) then:
+1. If that project **is using Poetry**, you can simply do `poetry add <path-to-this-client>` from that project
+1. If that project is not using Poetry:
+    1. Build a wheel with `poetry build -f wheel`
+    1. Install that wheel from the other project `pip install <path-to-wheel>`

python/tabby/tabby-data-pipeline/modal/tabby_python_client/pyproject.toml

@@ -0,0 +1,16 @@
+[tool.black]
+line-length = 120
+target_version = ['py38', 'py39', 'py310', 'py311']
+exclude = '''
+(
+  /(
+    | \.git
+    | \.venv
+    | \.mypy_cache
+  )/
+)
+'''
+
+[tool.isort]
+line_length = 120
+profile = "black"

python/tabby/tabby-data-pipeline/modal/tabby_python_client/setup.py

@@ -0,0 +1,18 @@
+import pathlib
+
+from setuptools import find_packages, setup
+
+here = pathlib.Path(__file__).parent.resolve()
+long_description = (here / "README.md").read_text(encoding="utf-8")
+
+setup(
+    name="tabby-python-client",
+    version="0.4.0-dev",
+    description="A client library for accessing Tabby Server",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    packages=find_packages(),
+    python_requires=">=3.8, <4",
+    install_requires=["httpx >= 0.15.0, < 0.25.0", "attrs >= 21.3.0", "python-dateutil >= 2.8.0, < 3"],
+    package_data={"tabby_python_client": ["py.typed"]},
+)

python/tabby/tabby-data-pipeline/modal/tabby_python_client/tabby_python_client/__init__.py

@@ -0,0 +1,7 @@
+""" A client library for accessing Tabby Server """
+from .client import AuthenticatedClient, Client
+
+__all__ = (
+    "AuthenticatedClient",
+    "Client",
+)

python/tabby/tabby-data-pipeline/modal/tabby_python_client/tabby_python_client/api/__init__.py

@@ -0,0 +1 @@
+""" Contains methods for accessing the API """