pranavm-nvidia commented on code in PR #22131: URL: https://github.com/apache/beam/pull/22131#discussion_r917945253
########## sdks/python/apache_beam/ml/inference/tensorrt_inference.py: ########## @@ -0,0 +1,213 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # pylint: disable=line-too-long +# 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 pycuda.driver as cuda +import sys +import tensorrt as trt +from typing import Any, Dict, Iterable, Optional, Sequence + +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import ModelHandler, PredictionResult + +LOGGER = trt.Logger(trt.Logger.INFO) + + +def _load_engine(engine_path): + file = FileSystems.open(engine_path, 'rb') + runtime = trt.Runtime(LOGGER) + engine = runtime.deserialize_cuda_engine(file.read()) + assert engine + return engine + + +def _load_onnx(onnx_path): + builder = trt.Builder(LOGGER) + network = builder.create_network( + flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + parser = trt.OnnxParser(network, LOGGER) + with FileSystems.open(onnx_path) as f: + if not parser.parse(f.read()): + print("Failed to load ONNX file: {}".format(onnx_path)) + for error in range(parser.num_errors): + print(parser.get_error(error)) + sys.exit(1) + builder.reset() + return network + + +def _build_engine(network): + builder = trt.Builder(LOGGER) + config = builder.create_builder_config() + runtime = trt.Runtime(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 TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray, + PredictionResult, + trt.ICudaEngine]): + 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(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) -> trt.ICudaEngine: + """Loads and initializes a TensorRT engine for processing.""" + return _load_engine(self.engine_path) + + def load_onnx(self) -> trt.INetworkDefinition: + """Loads and parses an onnx model for processing.""" + return _load_onnx(self.onnx_path) + + # + def build_engine(self, network: trt.INetworkDefinition) -> trt.ICudaEngine: + """Build an engine according to parsed/created network.""" + return _build_engine(network) + + def run_inference( + self, + batch: np.ndarray, + engine: trt.ICudaEngine, + inference_args: Optional[Dict[str, Any]] = None + ) -> Iterable[PredictionResult]: + """ + Runs inferences on a batch of Tensors and returns an Iterable of + TensorRT Predictions. + + Args: + batch: A np.ndarray or a np.ndarray that represents a concatenation + of multiple arrays as a batch. + engine: A TensorRT engine. + inference_args: Any additional arguments for an inference + that are not applicable to TensorRT. + + Returns: + An Iterable of type PredictionResult. + """ + _validate_inference_args(inference_args) + context = engine.create_execution_context() + assert context + # Setup I/O bindings + inputs = [] + outputs = [] + allocations = [] + for i in range(engine.num_bindings): + is_input = False + if engine.binding_is_input(i): + is_input = True + name = engine.get_binding_name(i) + dtype = engine.get_binding_dtype(i) + shape = 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 + allocation = cuda.mem_alloc(size) + binding = { + 'index': i, + 'name': name, + 'dtype': np.dtype(trt.nptype(dtype)), + 'shape': list(shape), + 'allocation': allocation, + } + allocations.append(allocation) + if engine.binding_is_input(i): + inputs.append(binding) + else: + outputs.append(binding) + + assert batch_size > 0 + assert len(inputs) > 0 + assert len(outputs) > 0 + assert len(allocations) > 0 + # Prepare the output data + predictions = [] + for output in outputs: + predictions.append(np.zeros(output['shape'], output['dtype'])) + # Process I/O and execute the network + cuda.memcpy_htod(inputs[0]['allocation'], np.ascontiguousarray(batch)) + context.execute_v2(allocations) + for output in range(len(predictions)): + cuda.memcpy_dtoh(predictions[output], outputs[output]['allocation']) Review Comment: You might see some perf benefit by overlapping execution/memcpy's by using `memcpy_htod_async`, `execute_async_v2`, and `memcpy_dtoh_async`. -- This is an automated message from the Apache Git Service. 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