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Posted to github@beam.apache.org by "AnandInguva (via GitHub)" <gi...@apache.org> on 2023/02/06 13:34:20 UTC
[GitHub] [beam] AnandInguva commented on a diff in pull request #25226: Add TensorRT runinference example for Text Classification
AnandInguva commented on code in PR #25226:
URL: https://github.com/apache/beam/pull/25226#discussion_r1097381899
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sdks/python/apache_beam/examples/inference/tensorrt_text_classification.py:
##########
@@ -0,0 +1,125 @@
+#
+# 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 pipeline to demonstrate usage of TensorRT with RunInference
+for a text classification model. This pipeline reads data from a text
+file, preprocesses the data, and then uses RunInference to generate
+predictions from the text classification TensorRT engine. Next,
+it postprocesses the RunInference outputs to print the input and
+the predicted class label.
+It also prints metrics provided by RunInference.
+"""
+
+import argparse
+import logging
+
+import numpy as np
+
+import apache_beam as beam
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.tensorrt_inference import TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from transformers import AutoTokenizer
+
+
+class Preprocess(beam.DoFn):
+ """Processes the input sentences to tokenize them.
+
+ The input sentences are tokenized because the
+ model is expecting tokens.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ inputs = self._tokenizer(
+ element, return_tensors="np", padding="max_length", max_length=128)
+ return inputs.input_ids
+
+
+class Postprocess(beam.DoFn):
+ """Processes the PredictionResult to get the predicted class.
+
+ The logits are the output of the TensorRT engine.
+ We can get the class label by getting the index of
+ maximum logit using argmax.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ decoded_input = self._tokenizer.decode(
+ element.example, skip_special_tokens=True)
+ logits = element.inference[0]
+ argmax = np.argmax(logits)
+ output = "Positive" if argmax == 1 else "Negative"
+ print(f"Input: {decoded_input}, \t Sentiment: {output}")
+
+
+def parse_known_args(argv):
+ """Parses args for the workflow."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--input',
+ dest='input',
+ required=True,
+ help='Path to the text file containing sentences.')
+ parser.add_argument(
+ '--trt_model_path',
+ dest='trt_model_path',
+ required=True,
+ help='Path to the pre-built textattack/bert-base-uncased-SST-2'
+ 'TensorRT engine.')
+ parser.add_argument(
+ '--model_id',
+ dest='model_id',
+ default="textattack/bert-base-uncased-SST-2",
+ help="name of model.")
+ return parser.parse_known_args(argv)
+
+
+def run(
+ argv=None,
+ save_main_session=True,
+):
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+
+ model_handler = TensorRTEngineHandlerNumPy(
+ min_batch_size=1,
+ max_batch_size=1,
+ engine_path=known_args.trt_model_path,
+ )
+
+ tokenizer = AutoTokenizer.from_pretrained(known_args.model_id)
+
+ with beam.Pipeline(options=pipeline_options) as pipeline:
+ _ = (
+ pipeline
+ | "ReadSentences" >> beam.io.ReadFromText(known_args.input)
+ | "Preprocess" >> beam.ParDo(Preprocess(tokenizer=tokenizer))
+ | "RunInference" >> RunInference(model_handler=model_handler)
+ | "PostProcess" >> beam.ParDo(Postprocess(tokenizer=tokenizer)))
Review Comment:
```suggestion
| "PostProcess" >> beam.ParDo(Postprocess(tokenizer=tokenizer))
| "LogResult" >> beam.Map(logging.info)
)
```
##########
sdks/python/apache_beam/examples/inference/tensorrt_text_classification.py:
##########
@@ -0,0 +1,125 @@
+#
+# 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 pipeline to demonstrate usage of TensorRT with RunInference
+for a text classification model. This pipeline reads data from a text
+file, preprocesses the data, and then uses RunInference to generate
+predictions from the text classification TensorRT engine. Next,
+it postprocesses the RunInference outputs to print the input and
+the predicted class label.
+It also prints metrics provided by RunInference.
+"""
+
+import argparse
+import logging
+
+import numpy as np
+
+import apache_beam as beam
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.tensorrt_inference import TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from transformers import AutoTokenizer
+
+
+class Preprocess(beam.DoFn):
+ """Processes the input sentences to tokenize them.
+
+ The input sentences are tokenized because the
+ model is expecting tokens.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ inputs = self._tokenizer(
+ element, return_tensors="np", padding="max_length", max_length=128)
+ return inputs.input_ids
+
+
+class Postprocess(beam.DoFn):
+ """Processes the PredictionResult to get the predicted class.
+
+ The logits are the output of the TensorRT engine.
+ We can get the class label by getting the index of
+ maximum logit using argmax.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ decoded_input = self._tokenizer.decode(
+ element.example, skip_special_tokens=True)
+ logits = element.inference[0]
+ argmax = np.argmax(logits)
+ output = "Positive" if argmax == 1 else "Negative"
+ print(f"Input: {decoded_input}, \t Sentiment: {output}")
Review Comment:
Can we yield the required values here and using logging.info in the pipeline to print? (commented below as well)
##########
sdks/python/apache_beam/examples/inference/tensorrt_text_classification.py:
##########
@@ -0,0 +1,125 @@
+#
+# 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 pipeline to demonstrate usage of TensorRT with RunInference
+for a text classification model. This pipeline reads data from a text
+file, preprocesses the data, and then uses RunInference to generate
+predictions from the text classification TensorRT engine. Next,
+it postprocesses the RunInference outputs to print the input and
+the predicted class label.
+It also prints metrics provided by RunInference.
+"""
+
+import argparse
+import logging
+
+import numpy as np
+
+import apache_beam as beam
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.tensorrt_inference import TensorRTEngineHandlerNumPy
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from transformers import AutoTokenizer
+
+
+class Preprocess(beam.DoFn):
+ """Processes the input sentences to tokenize them.
+
+ The input sentences are tokenized because the
+ model is expecting tokens.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ inputs = self._tokenizer(
+ element, return_tensors="np", padding="max_length", max_length=128)
+ return inputs.input_ids
+
+
+class Postprocess(beam.DoFn):
+ """Processes the PredictionResult to get the predicted class.
+
+ The logits are the output of the TensorRT engine.
+ We can get the class label by getting the index of
+ maximum logit using argmax.
+ """
+ def __init__(self, tokenizer: AutoTokenizer):
+ self._tokenizer = tokenizer
+
+ def process(self, element):
+ decoded_input = self._tokenizer.decode(
+ element.example, skip_special_tokens=True)
+ logits = element.inference[0]
+ argmax = np.argmax(logits)
+ output = "Positive" if argmax == 1 else "Negative"
+ print(f"Input: {decoded_input}, \t Sentiment: {output}")
+
+
+def parse_known_args(argv):
+ """Parses args for the workflow."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--input',
+ dest='input',
+ required=True,
+ help='Path to the text file containing sentences.')
+ parser.add_argument(
+ '--trt_model_path',
+ dest='trt_model_path',
+ required=True,
+ help='Path to the pre-built textattack/bert-base-uncased-SST-2'
+ 'TensorRT engine.')
+ parser.add_argument(
+ '--model_id',
+ dest='model_id',
+ default="textattack/bert-base-uncased-SST-2",
+ help="name of model.")
+ return parser.parse_known_args(argv)
+
+
+def run(
+ argv=None,
+ save_main_session=True,
+):
+ known_args, pipeline_args = parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+
+ model_handler = TensorRTEngineHandlerNumPy(
+ min_batch_size=1,
+ max_batch_size=1,
+ engine_path=known_args.trt_model_path,
+ )
+
+ tokenizer = AutoTokenizer.from_pretrained(known_args.model_id)
+
+ with beam.Pipeline(options=pipeline_options) as pipeline:
+ _ = (
+ pipeline
+ | "ReadSentences" >> beam.io.ReadFromText(known_args.input)
+ | "Preprocess" >> beam.ParDo(Preprocess(tokenizer=tokenizer))
+ | "RunInference" >> RunInference(model_handler=model_handler)
+ | "PostProcess" >> beam.ParDo(Postprocess(tokenizer=tokenizer)))
+ metrics = pipeline.result.metrics().query(beam.metrics.MetricsFilter())
+ print(metrics)
Review Comment:
Can we remove the print? If that is necessary, we can use logging.info or logging.debug.
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