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https://issues.apache.org/jira/browse/BEAM-13984?focusedWorklogId=760194&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-760194
]
ASF GitHub Bot logged work on BEAM-13984:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 21/Apr/22 16:45
Start Date: 21/Apr/22 16:45
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on code in PR #17196:
URL: https://github.com/apache/beam/pull/17196#discussion_r855388391
##########
sdks/python/test-suites/tox/py38/build.gradle:
##########
@@ -85,6 +85,15 @@ toxTask "testPy38pandas-14", "py38-pandas-14"
test.dependsOn "testPy38pandas-14"
preCommitPy38.dependsOn "testPy38pandas-14"
+// Create a test task for each minor version of pytorch
+toxTask "testPy38pytorch-19", "py38-pytorch-19"
+test.dependsOn "testPy38pytorch-19"
+preCommitPy38.dependsOn "testPy38pytorch-19"
+
+toxTask "testPy38pytorch-110", "py38-pytorch-110"
+test.dependsOn "testPy38pytorch-110"
+preCommitPy38.dependsOn "testPy38pytorch-110"
Review Comment:
Does pytorch commonly make changes that will break us between minor
versions? We test different minor versions for pandas because our special usage
of pandas in the DataFrame API leads to breakages even between minor versions.
In the case of pyarrow, every release is a major version, which is meant to
communicate that the API can change
(https://arrow.apache.org/docs/format/Versioning.html).
How will we keep this up to date as new version of pytorch come out?
Neither of these are blockers, but these are questions we should consider
##########
sdks/python/apache_beam/ml/inference/pytorch_test.py:
##########
@@ -0,0 +1,246 @@
+#
+# 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.
+#
+
+# pytype: skip-file
+
+import os
+import shutil
+import tempfile
+import unittest
+from collections import OrderedDict
+
+import numpy as np
+import pytest
+
+import apache_beam as beam
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import assert_that
+from apache_beam.testing.util import equal_to
+
+# Protect against environments where pytorch library is not available.
+# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
+try:
+ import torch
+ from apache_beam.ml.inference.api import PredictionResult
+ from apache_beam.ml.inference.base import RunInference
+ from apache_beam.ml.inference.pytorch import PytorchInferenceRunner
+ from apache_beam.ml.inference.pytorch import PytorchModelLoader
+except ImportError:
+ raise unittest.SkipTest('PyTorch dependencies are not installed')
+
+
+def _compare_prediction_result(a, b):
+ return (
+ torch.equal(a.inference, b.inference) and
+ torch.equal(a.example, b.example))
+
+
+class PytorchLinearRegression(torch.nn.Module):
+ def __init__(self, input_dim, output_dim):
+ super().__init__()
+ self.linear = torch.nn.Linear(input_dim, output_dim)
+
+ def forward(self, x):
+ out = self.linear(x)
+ return out
+
+
[email protected]_pytorch
+class PytorchRunInferenceTest(unittest.TestCase):
+ def setUp(self):
+ self.tmpdir = tempfile.mkdtemp()
+
+ def tearDown(self):
+ shutil.rmtree(self.tmpdir)
+
+ def test_inference_runner_single_tensor_feature(self):
+ examples = [
+ torch.from_numpy(np.array([1], dtype="float32")),
+ torch.from_numpy(np.array([5], dtype="float32")),
+ torch.from_numpy(np.array([-3], dtype="float32")),
+ torch.from_numpy(np.array([10.0], dtype="float32")),
+ ]
+ expected_predictions = [
+ PredictionResult(ex, pred) for ex,
+ pred in zip(
+ examples,
+ torch.Tensor([example * 2.0 + 0.5
+ for example in examples]).reshape(-1, 1))
+ ]
+
+ model = PytorchLinearRegression(input_dim=1, output_dim=1)
+ model.load_state_dict(
+ OrderedDict([('linear.weight', torch.Tensor([[2.0]])),
+ ('linear.bias', torch.Tensor([0.5]))]))
+ model.eval()
+
+ inference_runner = PytorchInferenceRunner(torch.device('cpu'))
+ predictions = inference_runner.run_inference(examples, model)
+ for actual, expected in zip(predictions, expected_predictions):
+ self.assertTrue(_compare_prediction_result(actual, expected))
+
+ def test_inference_runner_multiple_tensor_features(self):
+ examples = torch.from_numpy(
+ np.array([1, 5, 3, 10, -14, 0, 0.5, 0.5],
+ dtype="float32")).reshape(-1, 2)
+ examples = [
+ torch.from_numpy(np.array([1, 5], dtype="float32")),
+ torch.from_numpy(np.array([3, 10], dtype="float32")),
+ torch.from_numpy(np.array([-14, 0], dtype="float32")),
+ torch.from_numpy(np.array([0.5, 0.5], dtype="float32")),
+ ]
+ expected_predictions = [
+ PredictionResult(ex, pred) for ex,
+ pred in zip(
+ examples,
+ torch.Tensor([f1 * 2.0 + f2 * 3 + 0.5
+ for f1, f2 in examples]).reshape(-1, 1))
+ ]
+
+ model = PytorchLinearRegression(input_dim=2, output_dim=1)
+ model.load_state_dict(
+ OrderedDict([('linear.weight', torch.Tensor([[2.0, 3]])),
+ ('linear.bias', torch.Tensor([0.5]))]))
+ model.eval()
+
+ inference_runner = PytorchInferenceRunner(torch.device('cpu'))
+ predictions = inference_runner.run_inference(examples, model)
+ for actual, expected in zip(predictions, expected_predictions):
+ self.assertTrue(_compare_prediction_result(actual, expected))
+
+ def test_num_bytes(self):
+ inference_runner = PytorchInferenceRunner(torch.device('cpu'))
+ examples = torch.from_numpy(
+ np.array([1, 5, 3, 10, -14, 0, 0.5, 0.5],
+ dtype="float32")).reshape(-1, 2)
+ self.assertEqual((examples[0].element_size()) * 8,
+ inference_runner.get_num_bytes(examples))
+
+ def test_namespace(self):
+ inference_runner = PytorchInferenceRunner(torch.device('cpu'))
+ self.assertEqual(
+ 'RunInferencePytorch', inference_runner.get_metrics_namespace())
+
+ def test_pipeline_local_model(self):
+ with TestPipeline() as pipeline:
+ examples = torch.from_numpy(
+ np.array([1, 5, 3, 10, -14, 0, 0.5, 0.5],
+ dtype="float32")).reshape(-1, 2)
+ expected_predictions = [
+ PredictionResult(ex, pred) for ex,
+ pred in zip(
+ examples,
+ torch.Tensor([f1 * 2.0 + f2 * 3 + 0.5
+ for f1, f2 in examples]).reshape(-1, 1))
+ ]
+
+ state_dict = OrderedDict([('linear.weight', torch.Tensor([[2.0, 3]])),
+ ('linear.bias', torch.Tensor([0.5]))])
+ path = os.path.join(self.tmpdir, 'my_state_dict_path')
+ torch.save(state_dict, path)
+
+ model_loader = PytorchModelLoader(
+ state_dict_path=path,
+ model_class=PytorchLinearRegression(input_dim=2, output_dim=1))
+
+ pcoll = pipeline | 'start' >> beam.Create(examples)
+ predictions = pcoll | RunInference(model_loader)
+ assert_that(
+ predictions,
+ equal_to(expected_predictions, equals_fn=_compare_prediction_result))
+
+ def test_pipeline_local_model_with_key(self):
+ with TestPipeline() as pipeline:
+ examples = torch.from_numpy(
+ np.array([1, 5, 3, 10], dtype="float32").reshape(-1, 1))
+ keyed_examples = list(zip(range(len(examples)), examples))
+ expected_values = [
+ PredictionResult(ex, pred) for ex,
+ pred in zip(
+ examples,
+ torch.Tensor([example * 2.0 + 0.5
+ for example in examples]).reshape(-1, 1))
+ ]
+ expected_predictions = list(zip(range(len(examples)), expected_values))
+
+ state_dict = OrderedDict([('linear.weight', torch.Tensor([[2.0]])),
+ ('linear.bias', torch.Tensor([0.5]))])
+ path = os.path.join(self.tmpdir, 'my_state_dict_path')
+ torch.save(state_dict, path)
+
+ model_loader = PytorchModelLoader(
+ state_dict_path=path,
+ model_class=PytorchLinearRegression(input_dim=1, output_dim=1))
+
+ pcoll = pipeline | 'start' >> beam.Create(keyed_examples)
+ predictions = pcoll | RunInference(model_loader)
+
+ def _compare_keyed_prediction_result(a, b):
+ key_equal = a[0] == b[0]
+ return (
+ torch.equal(a[1].inference, b[1].inference) and
+ torch.equal(a[1].example, b[1].example) and key_equal)
+
+ assert_that(
+ predictions,
+ equal_to(
+ expected_predictions,
equals_fn=_compare_keyed_prediction_result))
+
+ def test_pipeline_gcs_model(self):
+ with TestPipeline() as pipeline:
+ examples = torch.from_numpy(
+ np.array([1, 5, 3, 10], dtype="float32").reshape(-1, 1))
+ expected_predictions = [
+ PredictionResult(ex, pred) for ex,
+ pred in zip(
+ examples,
+ torch.Tensor([example * 2.0 + 0.5
+ for example in examples]).reshape(-1, 1))
+ ]
+
+ gs_pth =
'gs://apache-beam-ml/pytorch_lin_reg_model_2x+0.5_state_dict.pth'
Review Comment:
I think it's preferable not to require GCP credentials for a unit test, I'm
actually not clear on this, can someone run this unit test and read this GCS
path without setting up GCP credentials?
Issue Time Tracking
-------------------
Worklog Id: (was: 760194)
Time Spent: 4h 10m (was: 4h)
> Implement RunInference for PyTorch
> ----------------------------------
>
> Key: BEAM-13984
> URL: https://issues.apache.org/jira/browse/BEAM-13984
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Andy Ye
> Assignee: Andy Ye
> Priority: P2
> Labels: run-inference
> Time Spent: 4h 10m
> Remaining Estimate: 0h
>
> Implement RunInference for PyTorch as described in the design doc
> [https://s.apache.org/inference-sklearn-pytorch]
> There will be a pytorch_impl.py file that contains PyTorchModelLoader and
> PyTorchInferenceRunner classes.
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