From 51be6cef8fa9f43b4d45cbc9e905c80202e8bd91 Mon Sep 17 00:00:00 2001 From: Nikhil Thorat Date: Thu, 7 Dec 2023 11:17:01 -0500 Subject: [PATCH] Small tweaks to improve the glaive dataset blog post. (#938) - Reduce the amount of times we log events for progress to 0.5, and then multiply by shard_count, reducing the overall amount of logs by 24 when you are running a 12-core multiproc map. This is a hack, we should really use dask properly with progress once we switch to streaming. - Restart the dask client when executing a new task if there are no tasks pending. This helps with making the second run much faster as workers dont GC intermittently. Coding dataset notebook: - Adds a limit example to the notebook to show how you determine whether it's what you want. Also: - Add the glaive dataset to the huggingface demo with the map outputs. This currently is a bit weird as I ran it locally and pushed, so it's not completely reproducible. - Improve the notebook for curating a coding dataset. https://lilacai-lilac.hf.space/datasets#lilac/glaive&expandedStats=%7B%22answer_formatted.has_edit%22%3Atrue%7D&query=%7B%22filters%22%3A%5B%7B%22path%22%3A%5B%22answer_formatted%22%2C%22has_edit%22%5D%2C%22op%22%3A%22equals%22%2C%22value%22%3A1%7D%5D%7D&compareColumns=%5B%7B%22column%22%3A%5B%22answer%22%5D%2C%22compareToColumn%22%3A%5B%22answer_formatted%22%2C%22answer%22%5D%2C%22swapDirection%22%3Afalse%7D%5D&rowId=%22fffc265c-845e-4a2b-b3ce-2caa61fed0f4%22 Fixes https://github.com/lilacai/lilac/issues/928 --- .../curate_coding_dataset/glaive_preview.png | Bin 0 -> 253992 bytes docs/blog/curate-coding-dataset.md | 51 ++-- lilac/data/dataset_duckdb.py | 1 + lilac/tasks.py | 42 +++- lilac_hf_space.yml | 17 ++ notebooks/CurateCodingDataset.ipynb | 225 ++++++++++++++++-- 6 files changed, 286 insertions(+), 50 deletions(-) create mode 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zS+MA>wyl*U+S9q$vzWzPujQFcFXHihmut@olAC@Rc z;eJIJ5FK@8oTK0T{F7!DKa*;IhVJ|lw5v1RMp%!N2?lRUV+#lfxY$~G=J(~u56hb< zu1(=Zq-;}3>?cM>y?~L9m&snybZ&ZWKm17wUw-%jAvBaxFJ*RS#-Od1@6RN_C^KT3 zdBlD#hdVo*eM$41u1%TdarGx=^ev&nXjHknabL+wQ7K@DdpeS;qNzMA0+BqsylnA* z7)?Ep5GSh1`E!zk+PyAGy0f!3T%Hkw z`Q(I{Tw>45&sWaqV?%3eP$O8GGthrY5Bd0keN}m$VG&6@uu9#sP`4&4hBOGDg%C7SgB-syj|t{g1yRj5$bxq z+IP!0SH|P6pS&sm`v+$Q^nN7o_&0@zb)8fP0Yg@*g&MwIm(=H5jh~t^Nv>Y_4~0d| zRhsH+3s2=sC&l3Y{X|)o#=W6>oE7^;kJ(^N-1oKU%?buymna{It>XvDMl_wt@960p4ogN4WIZ*h2*$%yGKk4VP%48{P zb92~TOUj?-`s+zu-k^4Mx&_VlE8mT0M{q6g_gCx~R}!C;18G(gKclMgi(cEb?p0R4 zJ{_Ln!9mmFdo-uUd4g)hvz^v~3$2~_N3qLs!|-&KhOP{~eirM&otBoX!Y3^VYetGc zkg(V=Kh)pOP~xPuRH8&(>b{{o#qlwXxt}Ag8#N-s$NJ=@>l9h3!~>f+ia|vgvWEJ? zdQD=djFTY)-gGPNte#qgQmWA_rT$BhG(U=y=Cpeah;1?KKjg2eHu{CB$>dzqmje^0 zwa)j}%kGy?KJi%;bMmZAV!x7X+z9Lpy94g1(iUyCT<;~cf`ZwV6;?GhwWPJRwI_UE_aycf7nyjtxf2^38=qNPu9QnmOifXjmXt8~hb^^k zY;3f%OCGxx9SjpX)qa-GF%Mpr@|gGEqxqKb%08>E`0iqKr`!1MjhG`#c;W0Ku`!a* zh66RDm+NbGYl + ## A First Look at the Glaive Dataset Let's load the Glaive dataset into Lilac from the HuggingFace hub. In this example, we're going to be using a Jupyter notebook (follow along -[here](https://github.com/lilacai/lilac/blob/main/notebooks/CurateCodingDataset.ipynb)). +[here](https://github.com/lilacai/lilac/blob/main/notebooks/CurateCodingDataset.ipynb)) or +[view the live demo on HuggingFace](https://lilacai-lilac.hf.space/datasets#lilac/glaive&expandedStats=%7B%22answer_formatted.has_edit%22%3Atrue%7D&query=%7B%22filters%22%3A%5B%7B%22path%22%3A%5B%22answer_formatted%22%2C%22has_edit%22%5D%2C%22op%22%3A%22equals%22%2C%22value%22%3A1%7D%5D%7D&compareColumns=%5B%7B%22column%22%3A%5B%22answer%22%5D%2C%22compareToColumn%22%3A%5B%22answer_formatted%22%2C%22answer%22%5D%2C%22swapDirection%22%3Afalse%7D%5D&rowId=%22fffc265c-845e-4a2b-b3ce-2caa61fed0f4%22). ```python import lilac as ll @@ -51,7 +54,7 @@ INFO: Uvicorn running on http://127.0.0.1:5432 (Press CTRL+C to quit) You can see that the dataset consists of `question` and `answer` pairs, where the answer is in markdown format, often containing python code blocks. Immediately we can see that the python -formatting is not consistent with our style, which will result in an AI product producing +formatting is not consistent with our desired style, which will result in an AI product producing inconsistent code. Let's standardize the model's code output by running the excellent @@ -63,8 +66,8 @@ In our Jupyter notebook, we'll define a simple function that takes one row from returns a new `answer_formatted` column that has two sub-fields: 1. `answer`: the rewritten output with formatted python code -2. `has_edit`: a bit that is true if the code formatter made any changes. We will use the bit in the - UI to filter on the rows that got updated. +2. `has_edit`: true when the code formatter made a change. We will use the bit in the UI to filter + on the rows that got updated. To modify the dataset in Lilac, we will use [](#Dataset.map). To learn more about `Dataset.map`, see the guide on [](../datasets/dataset_edit.md). @@ -104,7 +107,7 @@ ds.map(format_code, output_column='answer_formatted', num_jobs=-1, execution_typ ## Dataset.map -`Dataset.map` is the main vehicle of making edits to the data. It's similar to HuggingFace's +`Dataset.map` is the main vehicle for making edits to data. It's similar to HuggingFace's [`Dataset.map()`](https://huggingface.co/docs/datasets/process#map) with a few key differences: - The output of Lilac's `Dataset.map` is always stored in a separate column. This enables tracking @@ -139,10 +142,10 @@ different examples by using the left and right arrow keys. The process of refining data is iterative. If the diff is not exactly what we like, we can change the parameters to the formatter, re-run the map with `overwrite=True`, and see the new results. -If some of the edits are not what we want, we can manually label them as "bad" to clicking on the -label in the top left corner of the example. Then we can apply a filter on the "bad" examples to -make sure the new version of our map improved those examples. Conversely, we can also label good -examples as "good" and filter on those in future versions to make sure we didn't regress. +If some of the edits are not ideal, we can click on the label in the top left corner of the example +and tag it as "bad". Then we can apply a filter for "bad" examples and make sure that new versions +of our map improved on those examples. Conversely, we can tag "good" examples and see if we regress +as we iterate on the dataset. @@ -152,13 +155,13 @@ using the download dialog or the python API. See ## Going forward -In this blog post, we've shown how to use Lilac to curate a dataset for a code assistant model. We -used a formatter to standardize the python code outputted by our AI product. We then visualized the -changes to the dataset to understand the behavior of the formatter and any side-effects. +We believe that text is becoming new programming language. It is the source code of LLMs. + +At Lilac, we are building the tooling to work with this new programming language, bringing the +tooling, rigor, and best practices from software engineering to the development of the data behind +AI systems. -Looking ahead, the landscape of programming is undergoing a paradigm shift with the emergence of -text as a new programming language. Lilac plays a big part in this transformation as the traditional -boundaries between code and data dissolve. +There is much more to come! -We hope you enjoyed this blog post. If you have any questions or feedback, please reach out to us on +If you have any questions or feedback, please reach out to us on [Discord](https://discord.gg/jNzw9mC8pp) or [Github](https://github.com/lilacai/lilac). diff --git a/lilac/data/dataset_duckdb.py b/lilac/data/dataset_duckdb.py index 77b6cc72b..673f07644 100644 --- a/lilac/data/dataset_duckdb.py +++ b/lilac/data/dataset_duckdb.py @@ -879,6 +879,7 @@ def _compute_disk_cached( task_step_id=task_step_id, initial_id=start_idx, shard_id=shard_id, + shard_count=shard_count, estimated_len=estimated_len, step_description=task_step_description, ) diff --git a/lilac/tasks.py b/lilac/tasks.py index c4450f2c5..c5b244251 100644 --- a/lilac/tasks.py +++ b/lilac/tasks.py @@ -4,6 +4,7 @@ import builtins import functools import multiprocessing +import random import time import traceback import uuid @@ -163,15 +164,16 @@ def __init__(self, dask_client: Optional[Client] = None) -> None: async def _update_tasks(self) -> None: adapter = TypeAdapter(list[TaskStepInfo]) for task_id, task in list(self._tasks.items()): + task_progress_topic = _progress_event_topic(task_id) if task.status == TaskStatus.COMPLETED: if task_id in self._task_threadpools: threadpool = self._task_threadpools[task_id] threadpool.shutdown() # Clean up threaded events. - del THREADED_EVENTS[_progress_event_topic(task_id)] + if task_progress_topic in THREADED_EVENTS: + del THREADED_EVENTS[task_progress_topic] continue - task_progress_topic = _progress_event_topic(task_id) if task_id in self._dask_futures: try: step_events = cast(Any, self._dask_client.get_events(task_progress_topic)) @@ -322,6 +324,12 @@ def _set_task_completed(self, task_id: TaskId, task_future: Union[DaskFuture, Fu if task_id in self._dask_futures: del self._dask_futures[task_id] + def _restart_client_if_no_tasks(self) -> None: + # Check if any tasks are not completed. If not, we restart the dask client to free up memory. + tasks_pending = any(task.status == TaskStatus.PENDING for task in self._tasks.values()) + if not tasks_pending: + self._dask_client.restart() + def _set_task_shard_completed( self, task_id: TaskId, task_future: Union[DaskFuture, Future], num_shards: int ) -> None: @@ -336,6 +344,9 @@ def execute(self, task_id: str, type: TaskExecutionType, task: TaskFn, *args: An task_info = self._tasks[task_id] if type == 'processes': + # Restart the workers to avoid GC slowing down the workers. + self._restart_client_if_no_tasks() + dask_task_id = _dask_task_id(task_id, None) task_future = self._dask_client.submit( functools.partial(_execute_task, task, task_info, dask_task_id), @@ -376,8 +387,10 @@ def execute_sharded( for i, (task, args) in enumerate(subtasks): if type == 'processes': - dask_task_id = _dask_task_id(task_id, i) + # Restart the workers to avoid GC slowing down the workers. + self._restart_client_if_no_tasks() + dask_task_id = _dask_task_id(task_id, i) task_future = self._dask_client.submit( functools.partial(_execute_task, task, task_info, dask_task_id), *args, key=dask_task_id ) @@ -543,14 +556,18 @@ def show_progress( pbar.update(total_len - pbar.n) +# The interval to emit progress events. +EMIT_EVERY_SEC = 0.5 + + def report_progress( it: Union[Iterator[TProgress], Iterable[TProgress]], task_step_id: Optional[TaskStepId], shard_id: Optional[int] = None, + shard_count: Optional[int] = None, initial_id: Optional[int] = None, estimated_len: Optional[int] = None, step_description: Optional[str] = None, - emit_every_s: float = 0.25, ) -> Generator[TProgress, None, None]: """An iterable wrapper that emits progress and yields the original iterable.""" if not task_step_id or task_step_id[0] == '': @@ -576,11 +593,15 @@ def report_progress( it_idx = initial_id if initial_id else 0 start_time = time.time() - last_emit = time.time() - emit_every_s + # Reduce the emit frequency if there are multiple shards to reduce the size of the event stream. + emit_every_sec = EMIT_EVERY_SEC if not shard_count else EMIT_EVERY_SEC * shard_count + # Add jitter to the emit frequency to avoid all workers emitting at the same time. + jitter_sec = random.uniform(0, emit_every_sec) + last_emit = time.time() - EMIT_EVERY_SEC - jitter_sec for t in it: - cur_time = time.time() - if estimated_len and cur_time - last_emit > emit_every_s: + cur_time = time.time() + jitter_sec + if estimated_len and cur_time - last_emit > emit_every_sec: elapsed_sec = cur_time - start_time it_per_sec = ((it_idx or 0) - (initial_id or 0.0)) / elapsed_sec set_worker_task_progress( @@ -693,7 +714,12 @@ def set_worker_task_progress( steps[step_id].it_idx = it_idx steps[step_id].estimated_len = estimated_len - steps[step_id].estimated_total_sec = estimated_total_sec + current_estimated_total_sec = steps[step_id].estimated_total_sec + steps[step_id].estimated_total_sec = ( + max(current_estimated_total_sec, estimated_total_sec) + if current_estimated_total_sec + else estimated_total_sec + ) steps[step_id].elapsed_sec = elapsed_sec steps[step_id].it_per_sec = it_per_sec diff --git a/lilac_hf_space.yml b/lilac_hf_space.yml index b6b01af1b..a6c16e490 100644 --- a/lilac_hf_space.yml +++ b/lilac_hf_space.yml @@ -11,6 +11,23 @@ datasets: source_name: huggingface dataset_name: imdb + - namespace: lilac + name: glaive + source: + dataset_name: glaiveai/glaive-code-assistant + source_name: huggingface + settings: + tags: [machine-learning] + ui: + view_type: 'single_item' + ui: + media_paths: + - question + - answer + - - answer_formatted + - answer + markdown_paths: [] + - name: open-asssistant-conversations namespace: lilac settings: diff --git a/notebooks/CurateCodingDataset.ipynb b/notebooks/CurateCodingDataset.ipynb index 65309816d..fedfc006d 100644 --- a/notebooks/CurateCodingDataset.ipynb +++ b/notebooks/CurateCodingDataset.ipynb @@ -1,19 +1,14 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "import lilac as ll\n", + "# Curating a coding dataset with Lilac\n", + "\n", + "This is the accompanying notebook for the [blog post](https://docs.lilacml.com/blog/curate-coding-dataset.html) on curating a coding dataset with Lilac.\n", "\n", - "config = ll.DatasetConfig(\n", - " namespace='local',\n", - " name='glaive',\n", - " source=ll.HuggingFaceSource(dataset_name='glaiveai/glaive-code-assistant'),\n", - ")\n", - "dataset = ll.create_dataset(config)\n" + "Check out the [live demo](https://lilacai-lilac.hf.space/datasets#lilac/glaive&expandedStats=%7B%22answer_formatted.has_edit%22%3Atrue%7D&query=%7B%22filters%22%3A%5B%7B%22path%22%3A%5B%22answer_formatted%22%2C%22has_edit%22%5D%2C%22op%22%3A%22equals%22%2C%22value%22%3A1%7D%5D%7D&compareColumns=%5B%7B%22column%22%3A%5B%22answer%22%5D%2C%22compareToColumn%22%3A%5B%22answer_formatted%22%2C%22answer%22%5D%2C%22swapDirection%22%3Afalse%7D%5D&rowId=%22fffc265c-845e-4a2b-b3ce-2caa61fed0f4%22).\n" ] }, { @@ -25,34 +20,178 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/dsmilkov/code/lilac/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/Users/nikhil/Code/lilac/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] - }, + } + ], + "source": [ + "import lilac as ll\n", + "\n", + "ll.set_project_dir('./demo_data')\n", + "\n", + "try:\n", + " ds = ll.get_dataset('lilac', 'glaive')\n", + "except Exception:\n", + " # Create the dataset.\n", + " config = ll.DatasetConfig(\n", + " namespace='lilac',\n", + " name='glaive',\n", + " source=ll.HuggingFaceSource(dataset_name='glaiveai/glaive-code-assistant'),\n", + " )\n", + " ds = ll.create_dataset(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO: Started server process [70417]\n", + "INFO: Started server process [70330]\n", "INFO: Waiting for application startup.\n", "INFO: Application startup complete.\n", "INFO: Uvicorn running on http://127.0.0.1:5432 (Press CTRL+C to quit)\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nikhil/Code/lilac/.venv/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 52328 instead\n", + " warnings.warn(\n" + ] } ], "source": [ - "ll.start_server()\n" + "# Start the lilac webserver.\n", + "ll.start_server()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[lilac/glaive][1 shards] map \"format_code\" to \"format_code\": 0%| | 0/136109 [00:00" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run over the whole dataset.\n", + "ds.map(\n", + " format_code,\n", + " output_column='answer_formatted',\n", + " num_jobs=-1, # Use all available CPU cores.\n", + " execution_type='processes', # Run on multiple processes.\n", + " overwrite=True,\n", + ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {