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Posted to github@beam.apache.org by "Amar3tto (via GitHub)" <gi...@apache.org> on 2025/04/08 11:25:52 UTC
[PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for Benchmarks [beam]
Amar3tto opened a new pull request, #34577:
URL: https://github.com/apache/beam/pull/34577
**Changes:**
1. Added new pipeline `PyTorch DistilBERT Sentiment Analysis streaming` to the `beam_Inference_Python_Benchmarks_Dataflow` workflow.
2. Improved ML pipelines metrics pages on the website.
Successful run: https://github.com/Amar3tto/beam/actions/runs/14305459582/job/40088298051
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis Batch/Streaming pipelines for ML Benchmarks [beam]
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm merged PR #34577:
URL: https://github.com/apache/beam/pull/34577
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #34577:
URL: https://github.com/apache/beam/pull/34577#discussion_r2038275689
##########
sdks/python/apache_beam/examples/inference/pytorch_sentiment_streaming.py:
##########
@@ -0,0 +1,222 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+
+"""A Streaming pipeline that uses RunInference to perform Sentiment analysis
+with DistilBERT model.
+It reads input lines from a Pub/Sub topic (populated from a GCS file),
+performs sentiment classification using a DistilBERT model via RunInference,
+and writes results to BigQuery.
+Resources like Pub/Sub topic/subscription and BigQuery table cleanup are
+handled programmatically.
+"""
+
+import argparse
+import logging
+from collections.abc import Iterable
+
+from google.cloud import pubsub_v1
+
+import apache_beam as beam
+import torch
+import torch.nn.functional as F
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import DistilBertConfig
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizerFast
+
+
+class SentimentPostProcessor(beam.DoFn):
+ """Processes PredictionResult to extract sentiment label and confidence."""
+ def __init__(self, tokenizer: DistilBertTokenizerFast):
+ self.tokenizer = tokenizer
+
+ def process(self, element: tuple[str, PredictionResult]) -> Iterable[dict]:
+ text, prediction_result = element
+ logits = prediction_result.inference['logits']
+ probs = F.softmax(logits, dim=-1)
+ predicted_class = torch.argmax(probs).item()
+ confidence = probs[predicted_class].item()
+ sentiment = 'POSITIVE' if predicted_class == 1 else 'NEGATIVE'
+ yield {
+ 'text': text, 'sentiment': sentiment, 'confidence': float(confidence)
+ }
+
+
+def tokenize_text(text: str,
+ tokenizer: DistilBertTokenizerFast) -> tuple[str, dict]:
+ """Tokenizes input text using the specified tokenizer."""
+ tokenized = tokenizer(
+ text,
+ padding='max_length',
+ truncation=True,
+ max_length=128,
+ return_tensors="pt")
+ return text, {k: torch.squeeze(v) for k, v in tokenized.items()}
+
+
+def parse_known_args(argv):
+ """Parses command-line arguments for pipeline execution."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--output_table', required=True, help='Path to output BigQuery table')
+ parser.add_argument(
+ '--model_path',
+ default='distilbert-base-uncased-finetuned-sst-2-english',
+ help='Hugging Face model name or local path')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help="Path to the model's state_dict.")
+ parser.add_argument(
+ '--input', required=True, help='Path to input file on GCS')
+ parser.add_argument(
+ '--pubsub_topic',
+ default='projects/apache-beam-testing/topics/test_sentiment_topic',
+ help='Pub/Sub topic to publish text lines to')
+ parser.add_argument(
+ '--pubsub_subscription',
+ default='projects/apache-beam-testing/subscriptions/'
+ 'test_sentiment_subscription',
+ help='Pub/Sub subscription to read from')
+ parser.add_argument(
+ '--project', default='apache-beam-testing', help='GCP project ID')
+ return parser.parse_known_args(argv)
+
+
+def ensure_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Ensures that the required Pub/Sub topic and subscription exist."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ publisher.get_topic(request={"topic": full_topic_path})
+ except Exception:
+ publisher.create_topic(name=full_topic_path)
+
+ try:
+ subscriber.get_subscription(
+ request={"subscription": full_subscription_path})
+ except Exception:
+ subscriber.create_subscription(
+ name=full_subscription_path, topic=full_topic_path)
+
+
+def cleanup_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Deletes Pub/Sub topic and subscription created for the pipeline."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ subscriber.delete_subscription(
+ request={"subscription": full_subscription_path})
+ print(f"Deleted subscription: {subscription_name}")
+ except Exception as e:
+ print(f"Failed to delete subscription: {e}")
+
+ try:
+ publisher.delete_topic(request={"topic": full_topic_path})
+ print(f"Deleted topic: {topic_name}")
+ except Exception as e:
+ print(f"Failed to delete topic: {e}")
+
+
+def run(
+ argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+ pipeline_options.view_as(StandardOptions).streaming = True
+
+ model_handler = PytorchModelHandlerKeyedTensor(
+ model_class=DistilBertForSequenceClassification,
+ model_params={'config': DistilBertConfig(num_labels=2)},
+ state_dict_path=known_args.model_state_dict_path)
+ ensure_pubsub_resources(
+ project=known_args.project,
+ topic_path=known_args.pubsub_topic,
+ subscription_path=known_args.pubsub_subscription)
+
+ tokenizer = DistilBertTokenizerFast.from_pretrained(known_args.model_path)
+
+ pipeline = test_pipeline or beam.Pipeline(options=pipeline_options)
+
+ # 1. Load data pipeline: read lines from GCS file and send to Pub/Sub
Review Comment:
I think we need to add a few things to this:
1) These should run as separate pipelines and we should only monitor the results of the streaming portion
2) We need a way of applying a consistent rate to the input elements in pub/sub. A better approach might be to either (a) just do this independently via a script, or (b) within Dataflow use periodic impulse or state/timers to control the rate at which we emit. This will likely work better as a helper which we can use across pipelines
3) For streaming pipelines, we should enable autoscaling
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2848598140
Reminder, please take a look at this pr: @liferoad @Abacn @liferoad
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for Benchmarks [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2786400194
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "Amar3tto (via GitHub)" <gi...@apache.org>.
Amar3tto commented on code in PR #34577:
URL: https://github.com/apache/beam/pull/34577#discussion_r2048983481
##########
sdks/python/apache_beam/examples/inference/pytorch_sentiment_streaming.py:
##########
@@ -0,0 +1,222 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+
+"""A Streaming pipeline that uses RunInference to perform Sentiment analysis
+with DistilBERT model.
+It reads input lines from a Pub/Sub topic (populated from a GCS file),
+performs sentiment classification using a DistilBERT model via RunInference,
+and writes results to BigQuery.
+Resources like Pub/Sub topic/subscription and BigQuery table cleanup are
+handled programmatically.
+"""
+
+import argparse
+import logging
+from collections.abc import Iterable
+
+from google.cloud import pubsub_v1
+
+import apache_beam as beam
+import torch
+import torch.nn.functional as F
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import DistilBertConfig
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizerFast
+
+
+class SentimentPostProcessor(beam.DoFn):
+ """Processes PredictionResult to extract sentiment label and confidence."""
+ def __init__(self, tokenizer: DistilBertTokenizerFast):
+ self.tokenizer = tokenizer
+
+ def process(self, element: tuple[str, PredictionResult]) -> Iterable[dict]:
+ text, prediction_result = element
+ logits = prediction_result.inference['logits']
+ probs = F.softmax(logits, dim=-1)
+ predicted_class = torch.argmax(probs).item()
+ confidence = probs[predicted_class].item()
+ sentiment = 'POSITIVE' if predicted_class == 1 else 'NEGATIVE'
+ yield {
+ 'text': text, 'sentiment': sentiment, 'confidence': float(confidence)
+ }
+
+
+def tokenize_text(text: str,
+ tokenizer: DistilBertTokenizerFast) -> tuple[str, dict]:
+ """Tokenizes input text using the specified tokenizer."""
+ tokenized = tokenizer(
+ text,
+ padding='max_length',
+ truncation=True,
+ max_length=128,
+ return_tensors="pt")
+ return text, {k: torch.squeeze(v) for k, v in tokenized.items()}
+
+
+def parse_known_args(argv):
+ """Parses command-line arguments for pipeline execution."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--output_table', required=True, help='Path to output BigQuery table')
+ parser.add_argument(
+ '--model_path',
+ default='distilbert-base-uncased-finetuned-sst-2-english',
+ help='Hugging Face model name or local path')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help="Path to the model's state_dict.")
+ parser.add_argument(
+ '--input', required=True, help='Path to input file on GCS')
+ parser.add_argument(
+ '--pubsub_topic',
+ default='projects/apache-beam-testing/topics/test_sentiment_topic',
+ help='Pub/Sub topic to publish text lines to')
+ parser.add_argument(
+ '--pubsub_subscription',
+ default='projects/apache-beam-testing/subscriptions/'
+ 'test_sentiment_subscription',
+ help='Pub/Sub subscription to read from')
+ parser.add_argument(
+ '--project', default='apache-beam-testing', help='GCP project ID')
+ return parser.parse_known_args(argv)
+
+
+def ensure_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Ensures that the required Pub/Sub topic and subscription exist."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ publisher.get_topic(request={"topic": full_topic_path})
+ except Exception:
+ publisher.create_topic(name=full_topic_path)
+
+ try:
+ subscriber.get_subscription(
+ request={"subscription": full_subscription_path})
+ except Exception:
+ subscriber.create_subscription(
+ name=full_subscription_path, topic=full_topic_path)
+
+
+def cleanup_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Deletes Pub/Sub topic and subscription created for the pipeline."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ subscriber.delete_subscription(
+ request={"subscription": full_subscription_path})
+ print(f"Deleted subscription: {subscription_name}")
+ except Exception as e:
+ print(f"Failed to delete subscription: {e}")
+
+ try:
+ publisher.delete_topic(request={"topic": full_topic_path})
+ print(f"Deleted topic: {topic_name}")
+ except Exception as e:
+ print(f"Failed to delete topic: {e}")
+
+
+def run(
+ argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+ pipeline_options.view_as(StandardOptions).streaming = True
+
+ model_handler = PytorchModelHandlerKeyedTensor(
+ model_class=DistilBertForSequenceClassification,
+ model_params={'config': DistilBertConfig(num_labels=2)},
+ state_dict_path=known_args.model_state_dict_path)
+ ensure_pubsub_resources(
+ project=known_args.project,
+ topic_path=known_args.pubsub_topic,
+ subscription_path=known_args.pubsub_subscription)
+
+ tokenizer = DistilBertTokenizerFast.from_pretrained(known_args.model_path)
+
+ pipeline = test_pipeline or beam.Pipeline(options=pipeline_options)
+
+ # 1. Load data pipeline: read lines from GCS file and send to Pub/Sub
Review Comment:
Added autoscaling.
Can we merge this PR with the current approach (it is the easiest way to run two pipelines simultaneously)?
Then we can implement reusable independent script that controls input rate.
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on code in PR #34577:
URL: https://github.com/apache/beam/pull/34577#discussion_r2049601161
##########
sdks/python/apache_beam/examples/inference/pytorch_sentiment_streaming.py:
##########
@@ -0,0 +1,222 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+
+"""A Streaming pipeline that uses RunInference to perform Sentiment analysis
+with DistilBERT model.
+It reads input lines from a Pub/Sub topic (populated from a GCS file),
+performs sentiment classification using a DistilBERT model via RunInference,
+and writes results to BigQuery.
+Resources like Pub/Sub topic/subscription and BigQuery table cleanup are
+handled programmatically.
+"""
+
+import argparse
+import logging
+from collections.abc import Iterable
+
+from google.cloud import pubsub_v1
+
+import apache_beam as beam
+import torch
+import torch.nn.functional as F
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import DistilBertConfig
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizerFast
+
+
+class SentimentPostProcessor(beam.DoFn):
+ """Processes PredictionResult to extract sentiment label and confidence."""
+ def __init__(self, tokenizer: DistilBertTokenizerFast):
+ self.tokenizer = tokenizer
+
+ def process(self, element: tuple[str, PredictionResult]) -> Iterable[dict]:
+ text, prediction_result = element
+ logits = prediction_result.inference['logits']
+ probs = F.softmax(logits, dim=-1)
+ predicted_class = torch.argmax(probs).item()
+ confidence = probs[predicted_class].item()
+ sentiment = 'POSITIVE' if predicted_class == 1 else 'NEGATIVE'
+ yield {
+ 'text': text, 'sentiment': sentiment, 'confidence': float(confidence)
+ }
+
+
+def tokenize_text(text: str,
+ tokenizer: DistilBertTokenizerFast) -> tuple[str, dict]:
+ """Tokenizes input text using the specified tokenizer."""
+ tokenized = tokenizer(
+ text,
+ padding='max_length',
+ truncation=True,
+ max_length=128,
+ return_tensors="pt")
+ return text, {k: torch.squeeze(v) for k, v in tokenized.items()}
+
+
+def parse_known_args(argv):
+ """Parses command-line arguments for pipeline execution."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--output_table', required=True, help='Path to output BigQuery table')
+ parser.add_argument(
+ '--model_path',
+ default='distilbert-base-uncased-finetuned-sst-2-english',
+ help='Hugging Face model name or local path')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help="Path to the model's state_dict.")
+ parser.add_argument(
+ '--input', required=True, help='Path to input file on GCS')
+ parser.add_argument(
+ '--pubsub_topic',
+ default='projects/apache-beam-testing/topics/test_sentiment_topic',
+ help='Pub/Sub topic to publish text lines to')
+ parser.add_argument(
+ '--pubsub_subscription',
+ default='projects/apache-beam-testing/subscriptions/'
+ 'test_sentiment_subscription',
+ help='Pub/Sub subscription to read from')
+ parser.add_argument(
+ '--project', default='apache-beam-testing', help='GCP project ID')
+ return parser.parse_known_args(argv)
+
+
+def ensure_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Ensures that the required Pub/Sub topic and subscription exist."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ publisher.get_topic(request={"topic": full_topic_path})
+ except Exception:
+ publisher.create_topic(name=full_topic_path)
+
+ try:
+ subscriber.get_subscription(
+ request={"subscription": full_subscription_path})
+ except Exception:
+ subscriber.create_subscription(
+ name=full_subscription_path, topic=full_topic_path)
+
+
+def cleanup_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Deletes Pub/Sub topic and subscription created for the pipeline."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ subscriber.delete_subscription(
+ request={"subscription": full_subscription_path})
+ print(f"Deleted subscription: {subscription_name}")
+ except Exception as e:
+ print(f"Failed to delete subscription: {e}")
+
+ try:
+ publisher.delete_topic(request={"topic": full_topic_path})
+ print(f"Deleted topic: {topic_name}")
+ except Exception as e:
+ print(f"Failed to delete topic: {e}")
+
+
+def run(
+ argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+ pipeline_options.view_as(StandardOptions).streaming = True
+
+ model_handler = PytorchModelHandlerKeyedTensor(
+ model_class=DistilBertForSequenceClassification,
+ model_params={'config': DistilBertConfig(num_labels=2)},
+ state_dict_path=known_args.model_state_dict_path)
+ ensure_pubsub_resources(
+ project=known_args.project,
+ topic_path=known_args.pubsub_topic,
+ subscription_path=known_args.pubsub_subscription)
+
+ tokenizer = DistilBertTokenizerFast.from_pretrained(known_args.model_path)
+
+ pipeline = test_pipeline or beam.Pipeline(options=pipeline_options)
+
+ # 1. Load data pipeline: read lines from GCS file and send to Pub/Sub
Review Comment:
I think we should at least handle (1) and run these as two pipelines. Otherwise, I do not think we are measuring a meaningful dataset
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "damccorm (via GitHub)" <gi...@apache.org>.
damccorm commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2854852611
waiting on author
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "Amar3tto (via GitHub)" <gi...@apache.org>.
Amar3tto commented on code in PR #34577:
URL: https://github.com/apache/beam/pull/34577#discussion_r2048983481
##########
sdks/python/apache_beam/examples/inference/pytorch_sentiment_streaming.py:
##########
@@ -0,0 +1,222 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+
+"""A Streaming pipeline that uses RunInference to perform Sentiment analysis
+with DistilBERT model.
+It reads input lines from a Pub/Sub topic (populated from a GCS file),
+performs sentiment classification using a DistilBERT model via RunInference,
+and writes results to BigQuery.
+Resources like Pub/Sub topic/subscription and BigQuery table cleanup are
+handled programmatically.
+"""
+
+import argparse
+import logging
+from collections.abc import Iterable
+
+from google.cloud import pubsub_v1
+
+import apache_beam as beam
+import torch
+import torch.nn.functional as F
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import DistilBertConfig
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizerFast
+
+
+class SentimentPostProcessor(beam.DoFn):
+ """Processes PredictionResult to extract sentiment label and confidence."""
+ def __init__(self, tokenizer: DistilBertTokenizerFast):
+ self.tokenizer = tokenizer
+
+ def process(self, element: tuple[str, PredictionResult]) -> Iterable[dict]:
+ text, prediction_result = element
+ logits = prediction_result.inference['logits']
+ probs = F.softmax(logits, dim=-1)
+ predicted_class = torch.argmax(probs).item()
+ confidence = probs[predicted_class].item()
+ sentiment = 'POSITIVE' if predicted_class == 1 else 'NEGATIVE'
+ yield {
+ 'text': text, 'sentiment': sentiment, 'confidence': float(confidence)
+ }
+
+
+def tokenize_text(text: str,
+ tokenizer: DistilBertTokenizerFast) -> tuple[str, dict]:
+ """Tokenizes input text using the specified tokenizer."""
+ tokenized = tokenizer(
+ text,
+ padding='max_length',
+ truncation=True,
+ max_length=128,
+ return_tensors="pt")
+ return text, {k: torch.squeeze(v) for k, v in tokenized.items()}
+
+
+def parse_known_args(argv):
+ """Parses command-line arguments for pipeline execution."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--output_table', required=True, help='Path to output BigQuery table')
+ parser.add_argument(
+ '--model_path',
+ default='distilbert-base-uncased-finetuned-sst-2-english',
+ help='Hugging Face model name or local path')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help="Path to the model's state_dict.")
+ parser.add_argument(
+ '--input', required=True, help='Path to input file on GCS')
+ parser.add_argument(
+ '--pubsub_topic',
+ default='projects/apache-beam-testing/topics/test_sentiment_topic',
+ help='Pub/Sub topic to publish text lines to')
+ parser.add_argument(
+ '--pubsub_subscription',
+ default='projects/apache-beam-testing/subscriptions/'
+ 'test_sentiment_subscription',
+ help='Pub/Sub subscription to read from')
+ parser.add_argument(
+ '--project', default='apache-beam-testing', help='GCP project ID')
+ return parser.parse_known_args(argv)
+
+
+def ensure_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Ensures that the required Pub/Sub topic and subscription exist."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ publisher.get_topic(request={"topic": full_topic_path})
+ except Exception:
+ publisher.create_topic(name=full_topic_path)
+
+ try:
+ subscriber.get_subscription(
+ request={"subscription": full_subscription_path})
+ except Exception:
+ subscriber.create_subscription(
+ name=full_subscription_path, topic=full_topic_path)
+
+
+def cleanup_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Deletes Pub/Sub topic and subscription created for the pipeline."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ subscriber.delete_subscription(
+ request={"subscription": full_subscription_path})
+ print(f"Deleted subscription: {subscription_name}")
+ except Exception as e:
+ print(f"Failed to delete subscription: {e}")
+
+ try:
+ publisher.delete_topic(request={"topic": full_topic_path})
+ print(f"Deleted topic: {topic_name}")
+ except Exception as e:
+ print(f"Failed to delete topic: {e}")
+
+
+def run(
+ argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+ pipeline_options.view_as(StandardOptions).streaming = True
+
+ model_handler = PytorchModelHandlerKeyedTensor(
+ model_class=DistilBertForSequenceClassification,
+ model_params={'config': DistilBertConfig(num_labels=2)},
+ state_dict_path=known_args.model_state_dict_path)
+ ensure_pubsub_resources(
+ project=known_args.project,
+ topic_path=known_args.pubsub_topic,
+ subscription_path=known_args.pubsub_subscription)
+
+ tokenizer = DistilBertTokenizerFast.from_pretrained(known_args.model_path)
+
+ pipeline = test_pipeline or beam.Pipeline(options=pipeline_options)
+
+ # 1. Load data pipeline: read lines from GCS file and send to Pub/Sub
Review Comment:
Added autoscaling.
Can we merge this PR with the current approach (it is the easiest way to run two pipelines simultaneously).
Then we can implement reusable independent script that controls input rate.
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2830260727
Reminder, please take a look at this pr: @liferoad @Abacn @liferoad
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "jrmccluskey (via GitHub)" <gi...@apache.org>.
jrmccluskey commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2830708463
As best I can tell @damccorm's comments haven't been addressed yet so I'll defer here
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis streaming pipeline for ML Benchmarks [beam]
Posted by "liferoad (via GitHub)" <gi...@apache.org>.
liferoad commented on PR #34577:
URL: https://github.com/apache/beam/pull/34577#issuecomment-2830677695
@jrmccluskey please review this PR. Thanks.
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Re: [PR] Add PyTorch DistilBERT Sentiment Analysis Batch/Streaming pipelines for ML Benchmarks [beam]
Posted by "Amar3tto (via GitHub)" <gi...@apache.org>.
Amar3tto commented on code in PR #34577:
URL: https://github.com/apache/beam/pull/34577#discussion_r2079698784
##########
sdks/python/apache_beam/examples/inference/pytorch_sentiment_streaming.py:
##########
@@ -0,0 +1,222 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+
+"""A Streaming pipeline that uses RunInference to perform Sentiment analysis
+with DistilBERT model.
+It reads input lines from a Pub/Sub topic (populated from a GCS file),
+performs sentiment classification using a DistilBERT model via RunInference,
+and writes results to BigQuery.
+Resources like Pub/Sub topic/subscription and BigQuery table cleanup are
+handled programmatically.
+"""
+
+import argparse
+import logging
+from collections.abc import Iterable
+
+from google.cloud import pubsub_v1
+
+import apache_beam as beam
+import torch
+import torch.nn.functional as F
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.base import PredictionResult
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.runners.runner import PipelineResult
+from transformers import DistilBertConfig
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizerFast
+
+
+class SentimentPostProcessor(beam.DoFn):
+ """Processes PredictionResult to extract sentiment label and confidence."""
+ def __init__(self, tokenizer: DistilBertTokenizerFast):
+ self.tokenizer = tokenizer
+
+ def process(self, element: tuple[str, PredictionResult]) -> Iterable[dict]:
+ text, prediction_result = element
+ logits = prediction_result.inference['logits']
+ probs = F.softmax(logits, dim=-1)
+ predicted_class = torch.argmax(probs).item()
+ confidence = probs[predicted_class].item()
+ sentiment = 'POSITIVE' if predicted_class == 1 else 'NEGATIVE'
+ yield {
+ 'text': text, 'sentiment': sentiment, 'confidence': float(confidence)
+ }
+
+
+def tokenize_text(text: str,
+ tokenizer: DistilBertTokenizerFast) -> tuple[str, dict]:
+ """Tokenizes input text using the specified tokenizer."""
+ tokenized = tokenizer(
+ text,
+ padding='max_length',
+ truncation=True,
+ max_length=128,
+ return_tensors="pt")
+ return text, {k: torch.squeeze(v) for k, v in tokenized.items()}
+
+
+def parse_known_args(argv):
+ """Parses command-line arguments for pipeline execution."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--output_table', required=True, help='Path to output BigQuery table')
+ parser.add_argument(
+ '--model_path',
+ default='distilbert-base-uncased-finetuned-sst-2-english',
+ help='Hugging Face model name or local path')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help="Path to the model's state_dict.")
+ parser.add_argument(
+ '--input', required=True, help='Path to input file on GCS')
+ parser.add_argument(
+ '--pubsub_topic',
+ default='projects/apache-beam-testing/topics/test_sentiment_topic',
+ help='Pub/Sub topic to publish text lines to')
+ parser.add_argument(
+ '--pubsub_subscription',
+ default='projects/apache-beam-testing/subscriptions/'
+ 'test_sentiment_subscription',
+ help='Pub/Sub subscription to read from')
+ parser.add_argument(
+ '--project', default='apache-beam-testing', help='GCP project ID')
+ return parser.parse_known_args(argv)
+
+
+def ensure_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Ensures that the required Pub/Sub topic and subscription exist."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ publisher.get_topic(request={"topic": full_topic_path})
+ except Exception:
+ publisher.create_topic(name=full_topic_path)
+
+ try:
+ subscriber.get_subscription(
+ request={"subscription": full_subscription_path})
+ except Exception:
+ subscriber.create_subscription(
+ name=full_subscription_path, topic=full_topic_path)
+
+
+def cleanup_pubsub_resources(
+ project: str, topic_path: str, subscription_path: str):
+ """Deletes Pub/Sub topic and subscription created for the pipeline."""
+ publisher = pubsub_v1.PublisherClient()
+ subscriber = pubsub_v1.SubscriberClient()
+
+ topic_name = topic_path.split("/")[-1]
+ subscription_name = subscription_path.split("/")[-1]
+
+ full_topic_path = publisher.topic_path(project, topic_name)
+ full_subscription_path = subscriber.subscription_path(
+ project, subscription_name)
+
+ try:
+ subscriber.delete_subscription(
+ request={"subscription": full_subscription_path})
+ print(f"Deleted subscription: {subscription_name}")
+ except Exception as e:
+ print(f"Failed to delete subscription: {e}")
+
+ try:
+ publisher.delete_topic(request={"topic": full_topic_path})
+ print(f"Deleted topic: {topic_name}")
+ except Exception as e:
+ print(f"Failed to delete topic: {e}")
+
+
+def run(
+ argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+ pipeline_options.view_as(StandardOptions).streaming = True
+
+ model_handler = PytorchModelHandlerKeyedTensor(
+ model_class=DistilBertForSequenceClassification,
+ model_params={'config': DistilBertConfig(num_labels=2)},
+ state_dict_path=known_args.model_state_dict_path)
+ ensure_pubsub_resources(
+ project=known_args.project,
+ topic_path=known_args.pubsub_topic,
+ subscription_path=known_args.pubsub_subscription)
+
+ tokenizer = DistilBertTokenizerFast.from_pretrained(known_args.model_path)
+
+ pipeline = test_pipeline or beam.Pipeline(options=pipeline_options)
+
+ # 1. Load data pipeline: read lines from GCS file and send to Pub/Sub
Review Comment:
Done
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