yeandy commented on code in PR #22131: URL: https://github.com/apache/beam/pull/22131#discussion_r922193026
########## sdks/python/apache_beam/ml/inference/tensorrt_inference_test.py: ########## @@ -0,0 +1,218 @@ +# +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed 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 numpy as np +import pytest +import unittest + +import apache_beam as beam +from apache_beam.testing.test_pipeline import TestPipeline +from apache_beam.testing.util import assert_that, equal_to + +# Protect against environments where TensorRT python library is not available. +# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports +try: + import tensorrt as trt + from apache_beam.ml.inference.base import PredictionResult, RunInference + from apache_beam.ml.inference.tensorrt_inference import \ + TensorRTEngineHandlerNumPy +except ImportError: + raise unittest.SkipTest('TensorRT dependencies are not installed') + +try: + from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem +except ImportError: + GCSFileSystem = None # type: ignore + +LOGGER = trt.Logger(trt.Logger.INFO) + +SINGLE_FEATURE_EXAMPLES = np.array([[1], [5], [-3], [10.0]], dtype=np.float32) Review Comment: ```suggestion SINGLE_FEATURE_EXAMPLES = [np.array(1, dtype=np.float32), np.array(5, dtype=np.float32), np.array(-3, dtype=np.float32), np.array(10.0, dtype=np.float32)] ``` `sklearn_inference_test.py` defines test inputs like [this](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/sklearn_inference_test.py#L135). ########## sdks/python/apache_beam/ml/inference/tensorrt_inference.py: ########## @@ -0,0 +1,245 @@ +# +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed 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 logging +import numpy as np +from cuda import cuda +import sys +import tensorrt as trt +from typing import Any, Dict, Iterable, Optional, Sequence, Tuple + +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import ModelHandler, PredictionResult + +TRT_LOGGER = trt.Logger(trt.Logger.INFO) + +logging.basicConfig(level=logging.INFO) +logging.getLogger("TensorRTEngineHandlerNumPy").setLevel(logging.INFO) +log = logging.getLogger("TensorRTEngineHandlerNumPy") + +def _load_engine(engine_path): + file = FileSystems.open(engine_path, 'rb') + runtime = trt.Runtime(TRT_LOGGER) + engine = runtime.deserialize_cuda_engine(file.read()) + assert engine + return engine + + +def _load_onnx(onnx_path): + builder = trt.Builder(TRT_LOGGER) + network = builder.create_network( + flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + parser = trt.OnnxParser(network, TRT_LOGGER) + with FileSystems.open(onnx_path) as f: + if not parser.parse(f.read()): + log.error("Failed to load ONNX file: {}".format(onnx_path)) + for error in range(parser.num_errors): + log.error(parser.get_error(error)) + sys.exit(1) + return network, builder + + +def _build_engine(network, builder): + config = builder.create_builder_config() + runtime = trt.Runtime(TRT_LOGGER) + plan = builder.build_serialized_network(network, config) + engine = runtime.deserialize_cuda_engine(plan) + builder.reset() + return engine + + +def _validate_inference_args(inference_args): + """Confirms that inference_args is None. + + TensorRT engines do not need extra arguments in their execute_v2() call. + However, since inference_args is an argument in the RunInference interface, + we want to make sure it is not passed here in TensorRT's implementation of + RunInference. + """ + if inference_args: + raise ValueError( + 'inference_args were provided, but should be None because TensorRT ' + 'engines do not need extra arguments in their execute_v2() call.') + + +class TensorRTEngine: + def __init__(self, engine: trt.ICudaEngine): + """Implementation of the TensorRTEngine class which handles + allocations associated with TensorRT engine. + + Example Usage:: + + TensorRTEngine(engine) + + Args: + engine: trt.ICudaEngine object that contains TensorRT engine + """ + self.engine = engine + self.context = engine.create_execution_context() + self.inputs = [] + self.outputs = [] + self.gpu_allocations = [] + self.cpu_allocations = [] + + # Setup I/O bindings + for i in range(self.engine.num_bindings): + is_input = False + if self.engine.binding_is_input(i): + is_input = True + name = self.engine.get_binding_name(i) + dtype = self.engine.get_binding_dtype(i) + shape = self.engine.get_binding_shape(i) + if is_input: + batch_size = shape[0] + size = np.dtype(trt.nptype(dtype)).itemsize + for s in shape: + size *= s + err, allocation = cuda.cuMemAlloc(size) + binding = { + 'index': i, + 'name': name, + 'dtype': np.dtype(trt.nptype(dtype)), + 'shape': list(shape), + 'allocation': allocation, + 'size': size + } + self.gpu_allocations.append(allocation) + if self.engine.binding_is_input(i): + self.inputs.append(binding) + else: + self.outputs.append(binding) + + assert self.context + assert batch_size > 0 + assert len(self.inputs) > 0 + assert len(self.outputs) > 0 + assert len(self.gpu_allocations) > 0 + + for output in self.outputs: + self.cpu_allocations.append(np.zeros(output['shape'], output['dtype'])) + + def get_engine_attrs(self): + """Returns TensorRT engine attributes.""" + return self.engine, self.context, self.inputs, self.outputs, self.gpu_allocations, self.cpu_allocations + + +class TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray, + PredictionResult, + TensorRTEngine]): + def __init__(self, min_batch_size: int, max_batch_size: int, **kwargs): + """Implementation of the ModelHandler interface for TensorRT. + + Example Usage:: + + pcoll | RunInference( + TensorRTEngineHandlerNumPy( + min_batch_size=1, + max_batch_size=1, + engine_path="my_uri")) + + Args: + min_batch_size: minimum accepted batch size. + max_batch_size: maximum accepted batch size. + kwargs: Additional arguments like 'engine_path' and 'onnx_path' are + currently supported. + + See https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/ + for details + """ + self.min_batch_size = min_batch_size + self.max_batch_size = max_batch_size + if 'engine_path' in kwargs: + self.engine_path = kwargs.get('engine_path') + elif 'onnx_path' in kwargs: + self.onnx_path = kwargs.get('onnx_path') + + trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="") + + def batch_elements_kwargs(self): + """Sets min_batch_size and max_batch_size of a TensorRT engine.""" + return { + 'min_batch_size': self.min_batch_size, + 'max_batch_size': self.max_batch_size + } + + def load_model(self) -> TensorRTEngine: + """Loads and initializes a TensorRT engine for processing.""" + engine = _load_engine(self.engine_path) + return TensorRTEngine(engine) + + def load_onnx(self) -> Tuple[trt.INetworkDefinition, trt.Builder]: + """Loads and parses an onnx model for processing.""" + return _load_onnx(self.onnx_path) + + def build_engine(self, network: trt.INetworkDefinition, builder: trt.Builder) -> TensorRTEngine: + """Build an engine according to parsed/created network.""" + engine = _build_engine(network, builder) + return TensorRTEngine(engine) + + def run_inference( + self, + batch: np.ndarray, + engine: TensorRTEngine, + inference_args: Optional[Dict[str, Any]] = None + ) -> Iterable[PredictionResult]: Review Comment: I'm seeing this lint error: ``` 19:21:43 apache_beam/ml/inference/tensorrt_inference.py:196: error: Argument 1 of "run_inference" is incompatible with supertype "ModelHandler"; supertype defines the argument type as "Sequence[ndarray[Any, Any]]" [override] 19:21:43 apache_beam/ml/inference/tensorrt_inference.py:196: note: This violates the Liskov substitution principle 19:21:43 apache_beam/ml/inference/tensorrt_inference.py:196: note: See https://mypy.readthedocs.io/en/stable/common_issues.html#incompatible-overrides ``` I think we need to adhere to the [interface](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/base.py#L91) for `run_inference` as found in `base.py`. Could we make it `Sequence[np.ndarray]` like it's done in [sklearn_inference.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/sklearn_inference.py#L105)? This may require some refactor some of the tests. I think particularly how the input examples are defined ########## sdks/python/apache_beam/ml/inference/tensorrt_inference_test.py: ########## @@ -0,0 +1,218 @@ +# +# SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES +# Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed 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 numpy as np +import pytest +import unittest + +import apache_beam as beam +from apache_beam.testing.test_pipeline import TestPipeline +from apache_beam.testing.util import assert_that, equal_to + +# Protect against environments where TensorRT python library is not available. +# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports +try: + import tensorrt as trt + from apache_beam.ml.inference.base import PredictionResult, RunInference + from apache_beam.ml.inference.tensorrt_inference import \ + TensorRTEngineHandlerNumPy +except ImportError: + raise unittest.SkipTest('TensorRT dependencies are not installed') + +try: + from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem +except ImportError: + GCSFileSystem = None # type: ignore + +LOGGER = trt.Logger(trt.Logger.INFO) + +SINGLE_FEATURE_EXAMPLES = np.array([[1], [5], [-3], [10.0]], dtype=np.float32) + +SINGLE_FEATURE_PREDICTIONS = [ + PredictionResult(ex, pred) for ex, + pred in zip( + SINGLE_FEATURE_EXAMPLES, + [[np.array(example * 2.0 + 0.5, dtype=np.float32)] for example in SINGLE_FEATURE_EXAMPLES]) +] + +TWO_FEATURES_EXAMPLES = np.array([[1, 5],[3, 10],[-14, 0],[0.5, 0.5]], dtype=np.float32) Review Comment: Same as above -- This is an automated message from the Apache Git Service. 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