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FROM python:3.11.8-slim | ||
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WORKDIR /spark-test | ||
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COPY requirements.txt requirements.txt | ||
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RUN pip install --no-cache-dir -r requirements.txt | ||
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COPY . . | ||
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CMD ["python3", "main.py"] |
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def create_secrets(): | ||
return { | ||
"AWS_ACCESS_KEY_ID": "minio", | ||
"AWS_SECRET_ACCESS_KEY": "minio123", | ||
"AWS_REGION": "us-east-1", | ||
"S3_ENDPOINT_URL": "http://minio:9000", | ||
"POSTGRES_URL": "jdbc:postgresql://postgres:5432/radicalbit", | ||
"POSTGRES_DB": "radicalbit", | ||
"POSTGRES_HOST": "postgres", | ||
"POSTGRES_PORT": "5432", | ||
"POSTGRES_USER": "postgres", | ||
"POSTGRES_PASSWORD": "postgres", | ||
"POSTGRES_SCHEMA": "public", | ||
} |
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import os | ||
from conf import create_secrets | ||
from uuid import uuid4 | ||
from spark_on_k8s.k8s.sync_client import KubernetesClientManager | ||
from spark_on_k8s.client import SparkOnK8S | ||
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envs = ["KUBECONFIG_FILE_PATH", "JOB_NAME", "SPARK_IMAGE"] | ||
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for var in envs: | ||
if var not in os.environ: | ||
raise EnvironmentError("Failed because {} is not set.".format(var)) | ||
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kube_conf = os.environ["KUBECONFIG_FILE_PATH"] | ||
job_name = os.environ["JOB_NAME"] | ||
spark_image = os.environ["SPARK_IMAGE"] | ||
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k8s_client_manager = KubernetesClientManager(kube_conf) | ||
spark_k8s_client = SparkOnK8S(k8s_client_manager=k8s_client_manager) | ||
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path = "s3a://test-bucket/metrics_one.json" | ||
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spark_k8s_client.submit_app( | ||
image=spark_image, | ||
app_path=f"local:///opt/spark/custom_jobs/{job_name}_job.py", | ||
app_arguments=[ | ||
path, | ||
str(uuid4()), | ||
"completion_dataset_metrics", | ||
"completion_dataset" | ||
], | ||
app_name=f"{spark_image}-completion-job", | ||
namespace="spark", | ||
service_account="spark", | ||
app_waiter="no_wait", | ||
image_pull_policy="IfNotPresent", | ||
secret_values=create_secrets(), | ||
) |
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spark-on-k8s==0.10.1 |
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import pyspark.sql.functions as F | ||
import numpy as np | ||
from pyspark.sql import DataFrame | ||
from pyspark.sql.types import FloatType | ||
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from models.completion_dataset import CompletionMetricsModel | ||
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class CompletionMetrics: | ||
def __init__(self): | ||
pass | ||
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@staticmethod | ||
@F.udf(FloatType()) | ||
def compute_probability_udf(log_prob: float) -> float: | ||
return float(np.exp(log_prob)) | ||
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@staticmethod | ||
@F.udf(FloatType()) | ||
def compute_perplexity(log_probs: list[float]) -> float: | ||
return float(np.exp(-np.mean(log_probs))) | ||
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@staticmethod | ||
@F.udf(FloatType()) | ||
def compute_prob_mean_per_phrase(probs: list[float]) -> float: | ||
return float(np.mean(probs)) | ||
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@staticmethod | ||
def remove_columns(df: DataFrame) -> DataFrame: | ||
df = df.drop( | ||
"model", | ||
"object", | ||
"created", | ||
"system_fingerprint", | ||
"usage", | ||
"service_tier", | ||
) | ||
return df | ||
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def compute_prob(self, df: DataFrame): | ||
df = df.select(F.explode("choices").alias("element"), F.col("id")) | ||
df = df.select( | ||
F.col("id"), F.explode("element.logprobs.content").alias("content") | ||
) | ||
df = df.select("id", "content.logprob", "content.token").withColumn( | ||
"prob", self.compute_probability_udf("logprob") | ||
) | ||
return df | ||
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def extract_metrics(self, df: DataFrame) -> CompletionMetricsModel: | ||
df = self.remove_columns(df) | ||
df = self.compute_prob(df) | ||
df_prob = df.drop("logprob") | ||
df_prob = df_prob.groupBy("id").agg( | ||
F.collect_list(F.struct("token", "prob")).alias("probs") | ||
) | ||
df_mean_values = df.groupBy("id").agg( | ||
self.compute_prob_mean_per_phrase(F.collect_list("prob")).alias( | ||
"prob_per_phrase" | ||
), | ||
self.compute_perplexity(F.collect_list("logprob")).alias( | ||
"perplex_per_phrase" | ||
), | ||
) | ||
df = df_mean_values.agg( | ||
F.mean("prob_per_phrase").alias("prob_tot_mean"), | ||
F.mean("perplex_per_phrase").alias("perplex_tot_mean"), | ||
) | ||
tokens = [ | ||
{ | ||
"id": row["id"], | ||
"probs": [ | ||
{"token": prob["token"], "prob": prob["prob"]} | ||
for prob in row["probs"] | ||
], | ||
} | ||
for row in df_prob.toLocalIterator() | ||
] | ||
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res = { | ||
"tokens": tokens, | ||
"mean_per_phrase": df_mean_values.toPandas().to_dict(orient="records"), | ||
"mean_per_file": df.toPandas().to_dict(orient="records"), | ||
} | ||
return CompletionMetricsModel(**res) |
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from pydantic import BaseModel, confloat, ConfigDict | ||
from typing import List | ||
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class Prob(BaseModel): | ||
token: str | ||
prob: confloat(ge=0, le=1) | ||
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class Probs(BaseModel): | ||
id: str | ||
probs: List[Prob] | ||
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model_config = ConfigDict(ser_json_inf_nan="null") | ||
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class MeanPerPhrase(BaseModel): | ||
id: str | ||
prob_per_phrase: confloat(ge=0, le=1) | ||
perplex_per_phrase: confloat(ge=1) | ||
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model_config = ConfigDict(ser_json_inf_nan="null") | ||
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class MeanPerFile(BaseModel): | ||
prob_tot_mean: confloat(ge=0, le=1) | ||
perplex_tot_mean: confloat(ge=1) | ||
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model_config = ConfigDict(ser_json_inf_nan="null") | ||
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class CompletionMetricsModel(BaseModel): | ||
tokens: List[Probs] | ||
mean_per_phrase: List[MeanPerPhrase] | ||
mean_per_file: List[MeanPerFile] |
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import pytest | ||
import orjson | ||
from jobs.completion_job import compute_metrics | ||
from jobs.metrics.completion_metrics import CompletionMetrics | ||
from jobs.models.completion_dataset import CompletionMetricsModel | ||
from tests.results.completion_metrics_results import completion_metric_results | ||
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@pytest.fixture | ||
def input_file(spark_fixture, test_data_dir): | ||
yield spark_fixture.read.option("multiline", "true").json( | ||
f"{test_data_dir}/completion/metrics.json" | ||
) | ||
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def test_remove_columns(spark_fixture, input_file): | ||
completion_metrics_service = CompletionMetrics() | ||
df = completion_metrics_service.remove_columns(input_file) | ||
assert "id" in df.columns | ||
assert "choices" in df.columns | ||
assert len(df.columns) == 2 | ||
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def test_compute_prob(spark_fixture, input_file): | ||
completion_metrics_service = CompletionMetrics() | ||
df = completion_metrics_service.remove_columns(input_file) | ||
df = completion_metrics_service.compute_prob(df) | ||
assert {"id", "logprob", "token", "prob"} == set(df.columns) | ||
assert not df.rdd.isEmpty() | ||
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def test_extract_metrics(spark_fixture, input_file): | ||
completion_metrics_service = CompletionMetrics() | ||
completion_metrics_model: CompletionMetricsModel = completion_metrics_service.extract_metrics(input_file) | ||
assert len(completion_metrics_model.tokens) > 0 | ||
assert len(completion_metrics_model.mean_per_phrase) > 0 | ||
assert len(completion_metrics_model.mean_per_file) > 0 | ||
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def test_compute_metrics(spark_fixture, input_file): | ||
complete_record = compute_metrics(input_file) | ||
model_quality = complete_record.get("MODEL_QUALITY") | ||
assert model_quality == orjson.dumps(completion_metric_results).decode( | ||
"utf-8" | ||
) |
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[ | ||
{"id": "chatcmpl-AcWID2SsE5iuK6z5AhNCKv3WUcCxN", "choices": [{"finish_reason": "stop", "index": 0, "logprobs": {"content": [{"token": "Sure", "bytes": [83, 117, 114, 101], "logprob": -0.61251247, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -0.102561064, "top_logprobs": []}, {"token": " go", "bytes": [32, 103, 111], "logprob": -2.5411978, "top_logprobs": []}, {"token": " ahead", "bytes": [32, 97, 104, 101, 97, 100], "logprob": -0.0014073749, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -2.0402107, "top_logprobs": []}, {"token": " What's", "bytes": [32, 87, 104, 97, 116, 39, 115], "logprob": -0.8943377, "top_logprobs": []}, {"token": " up", "bytes": [32, 117, 112], "logprob": -0.08216706, "top_logprobs": []}, {"token": "?", "bytes": [63], "logprob": -4.978234e-05, "top_logprobs": []}], "refusal": null}, "message": {"content": "Sure, go ahead. What's up?", "refusal": null, "role": "assistant", "tool_calls": [], "parsed": null}}], "created": 1733743961, "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": "afnfwuinawufwa", "usage": {"completion_tokens": 8, "prompt_tokens": 45, "total_tokens": 53, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}}}, | ||
{"id": "chatcmpl-AcYMMPLnpkksCdLze3M8nnqQbfqVG", "choices": [{"finish_reason": "stop", "index": 0, "logprobs": {"content": [{"token": "Certainly", "bytes": [67, 101, 114, 116, 97, 105, 110, 108, 121], "logprob": -3.8160203, "top_logprobs": []}, {"token": "!", "bytes": [33], "logprob": -0.11697425, "top_logprobs": []}, {"token": " Just", "bytes": [32, 74, 117, 115, 116], "logprob": -5.9011784, "top_logprobs": []}, {"token": " let", "bytes": [32, 108, 101, 116], "logprob": -0.666558, "top_logprobs": []}, {"token": " me", "bytes": [32, 109, 101], "logprob": -5.574252e-05, "top_logprobs": []}, {"token": " know", "bytes": [32, 107, 110, 111, 119], "logprob": -0.0008052219, "top_logprobs": []}, {"token": " how", "bytes": [32, 104, 111, 119], "logprob": -0.7132411, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -0.034184996, "top_logprobs": []}], "refusal": null}, "message": {"content": "Certainly! Just let me know how.", "refusal": null, "role": "assistant", "tool_calls": [], "parsed": null}}], "created": 1733751906, "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": "afnfwuinawufwa", "usage": {"completion_tokens": 8, "prompt_tokens": 45, "total_tokens": 53, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}}} | ||
] |
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[ | ||
{"id": "chatcmpl-AcYPBK1t4QUvRG1oiS2JjoK0xdrGH", "choices": [{"finish_reason": "stop", "index": 0, "logprobs": {"content": [{"token": "Sure", "bytes": [83, 117, 114, 101], "logprob": -0.46845868, "top_logprobs": []}, {"token": "!", "bytes": [33], "logprob": -0.82938546, "top_logprobs": []}, {"token": " Please", "bytes": [32, 80, 108, 101, 97, 115, 101], "logprob": -2.1082883, "top_logprobs": []}, {"token": " ask", "bytes": [32, 97, 115, 107], "logprob": -0.67591065, "top_logprobs": []}, {"token": " your", "bytes": [32, 121, 111, 117, 114], "logprob": -0.022492433, "top_logprobs": []}, {"token": " question", "bytes": [32, 113, 117, 101, 115, 116, 105, 111, 110], "logprob": -0.13909559, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -3.3818815, "top_logprobs": []}, {"token": " I'll", "bytes": [32, 73, 39, 108, 108], "logprob": -0.4059926, "top_logprobs": []}, {"token": " be", "bytes": [32, 98, 101], "logprob": -1.799196, "top_logprobs": []}, {"token": " concise", "bytes": [32, 99, 111, 110, 99, 105, 115, 101], "logprob": -0.23712571, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -0.03838387, "top_logprobs": []}], "refusal": null}, "message": {"content": "Sure! Please ask your question. I'll be concise.", "refusal": null, "role": "assistant", "tool_calls": [], "parsed": null}}], "created": 1733752081, "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": "dwadawdwd", "usage": {"completion_tokens": 11, "prompt_tokens": 45, "total_tokens": 56, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}}}, | ||
{"id": "chatcmpl-AcYQfMsRAIA01ynfZAQb8Zmyc6WKp", "choices": [{"finish_reason": "stop", "index": 0, "logprobs": {"content": [{"token": "Yes", "bytes": [89, 101, 115], "logprob": -5.5577775e-06, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -5.2285613e-05, "top_logprobs": []}, {"token": " Python", "bytes": [32, 80, 121, 116, 104, 111, 110], "logprob": -0.00089550123, "top_logprobs": []}, {"token": " is", "bytes": [32, 105, 115], "logprob": -4.246537e-06, "top_logprobs": []}, {"token": " versatile", "bytes": [32, 118, 101, 114, 115, 97, 116, 105, 108, 101], "logprob": -5.1374755, "top_logprobs": []}, {"token": " and", "bytes": [32, 97, 110, 100], "logprob": -0.22549273, "top_logprobs": []}, {"token": " widely", "bytes": [32, 119, 105, 100, 101, 108, 121], "logprob": -0.06408858, "top_logprobs": []}, {"token": " used", "bytes": [32, 117, 115, 101, 100], "logprob": -0.0007200573, "top_logprobs": []}, {"token": " for", "bytes": [32, 102, 111, 114], "logprob": -0.24617708, "top_logprobs": []}, {"token": " data", "bytes": [32, 100, 97, 116, 97], "logprob": -2.132315, "top_logprobs": []}, {"token": " analysis", "bytes": [32, 97, 110, 97, 108, 121, 115, 105, 115], "logprob": -0.13818723, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -0.10035052, "top_logprobs": []}, {"token": " machine", "bytes": [32, 109, 97, 99, 104, 105, 110, 101], "logprob": -0.33921197, "top_logprobs": []}, {"token": " learning", "bytes": [32, 108, 101, 97, 114, 110, 105, 110, 103], "logprob": 0.0, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -9.138441e-05, "top_logprobs": []}, {"token": " and", "bytes": [32, 97, 110, 100], "logprob": -0.005160108, "top_logprobs": []}, {"token": " monitoring", "bytes": [32, 109, 111, 110, 105, 116, 111, 114, 105, 110, 103], "logprob": -1.1899015, "top_logprobs": []}, {"token": " tasks", "bytes": [32, 116, 97, 115, 107, 115], "logprob": -0.50267833, "top_logprobs": []}, {"token": " due", "bytes": [32, 100, 117, 101], "logprob": -1.4371668, "top_logprobs": []}, {"token": " to", "bytes": [32, 116, 111], "logprob": -8.418666e-06, "top_logprobs": []}, {"token": " its", "bytes": [32, 105, 116, 115], "logprob": -6.9882217e-06, "top_logprobs": []}, {"token": " ease", "bytes": [32, 101, 97, 115, 101], "logprob": -3.047081, "top_logprobs": []}, {"token": " of", "bytes": [32, 111, 102], "logprob": -5.5265704e-05, "top_logprobs": []}, {"token": " use", "bytes": [32, 117, 115, 101], "logprob": -0.000113794704, "top_logprobs": []}, {"token": " and", "bytes": [32, 97, 110, 100], "logprob": -0.0067897392, "top_logprobs": []}, {"token": " robust", "bytes": [32, 114, 111, 98, 117, 115, 116], "logprob": -2.8805246, "top_logprobs": []}, {"token": " libraries", "bytes": [32, 108, 105, 98, 114, 97, 114, 105, 101, 115], "logprob": -0.05405761, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -0.0008492942, "top_logprobs": []}], "refusal": null}, "message": {"content": "Yes, Python is versatile and widely used for data analysis, machine learning, and monitoring tasks due to its ease of use and robust libraries.", "refusal": null, "role": "assistant", "tool_calls": [], "parsed": null}}], "created": 1733752173, "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": "fawfaw", "usage": {"completion_tokens": 28, "prompt_tokens": 45, "total_tokens": 73, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}}}, | ||
{"id": "chatcmpl-AcYR545NY6eo8xAdKuEbE60YgrF1a", "choices": [{"finish_reason": "stop", "index": 0, "logprobs": {"content": [{"token": "Yes", "bytes": [89, 101, 115], "logprob": -0.00012582695, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -2.8206474e-05, "top_logprobs": []}, {"token": " Rust", "bytes": [32, 82, 117, 115, 116], "logprob": -0.0001658757, "top_logprobs": []}, {"token": " is", "bytes": [32, 105, 115], "logprob": -0.0629955, "top_logprobs": []}, {"token": " known", "bytes": [32, 107, 110, 111, 119, 110], "logprob": -0.12907438, "top_logprobs": []}, {"token": " for", "bytes": [32, 102, 111, 114], "logprob": -6.704273e-07, "top_logprobs": []}, {"token": " its", "bytes": [32, 105, 116, 115], "logprob": -0.053365633, "top_logprobs": []}, {"token": " performance", "bytes": [32, 112, 101, 114, 102, 111, 114, 109, 97, 110, 99, 101], "logprob": -0.23435384, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -0.0018123905, "top_logprobs": []}, {"token": " memory", "bytes": [32, 109, 101, 109, 111, 114, 121], "logprob": -0.97972125, "top_logprobs": []}, {"token": " safety", "bytes": [32, 115, 97, 102, 101, 116, 121], "logprob": -1.8908588e-05, "top_logprobs": []}, {"token": ",", "bytes": [44], "logprob": -0.0049610855, "top_logprobs": []}, {"token": " and", "bytes": [32, 97, 110, 100], "logprob": -5.3954464e-05, "top_logprobs": []}, {"token": " concurrency", "bytes": [32, 99, 111, 110, 99, 117, 114, 114, 101, 110, 99, 121], "logprob": -0.08939207, "top_logprobs": []}, {"token": " capabilities", "bytes": [32, 99, 97, 112, 97, 98, 105, 108, 105, 116, 105, 101, 115], "logprob": -2.2125401, "top_logprobs": []}, {"token": ".", "bytes": [46], "logprob": -0.38892052, "top_logprobs": []}], "refusal": null}, "message": {"content": "Yes, Rust is known for its performance, memory safety, and concurrency capabilities.", "refusal": null, "role": "assistant", "tool_calls": [], "parsed": null}}], "created": 1733752199, "model": "gpt-4o-2024-08-06", "object": "chat.completion", "system_fingerprint": "wafwafwf", "usage": {"completion_tokens": 16, "prompt_tokens": 46, "total_tokens": 62, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}}} | ||
] |
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