mmdeploy.backend.tensorrt.utils 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os
import re
import sys
from typing import Any, Dict, Optional, Sequence, Union

import onnx
import tensorrt as trt
from packaging import version

from mmdeploy.utils import get_root_logger
from .init_plugins import load_tensorrt_plugin

[文档]def save(engine: Any, path: str) -> None: """Serialize TensorRT engine to disk. Args: engine (Any): TensorRT engine to be serialized. path (str): The absolute disk path to write the engine. """ with open(path, mode='wb') as f: if isinstance(engine, trt.ICudaEngine): engine = engine.serialize() f.write(bytearray(engine))
[文档]def load(path: str, allocator: Optional[Any] = None) -> trt.ICudaEngine: """Deserialize TensorRT engine from disk. Args: path (str): The disk path to read the engine. allocator (Any): gpu allocator Returns: tensorrt.ICudaEngine: The TensorRT engine loaded from disk. """ load_tensorrt_plugin() with trt.Logger() as logger, trt.Runtime(logger) as runtime: if allocator is not None: runtime.gpu_allocator = allocator with open(path, mode='rb') as f: engine_bytes = trt.init_libnvinfer_plugins(logger, namespace='') engine = runtime.deserialize_cuda_engine(engine_bytes) return engine
def search_cuda_version() -> str: """try cmd to get cuda version, then try `torch.cuda` Returns: str: cuda version, for example 10.2 """ version = None pattern = re.compile(r'[0-9]+\.[0-9]+') platform = sys.platform.lower() def cmd_result(txt: str): cmd = os.popen(txt) return if platform == 'linux' or platform == 'darwin' or platform == 'freebsd': # noqa E501 version = cmd_result( " nvcc --version | grep release | awk '{print $5}' | awk -F , '{print $1}' " # noqa E501 ) if version is None or pattern.match(version) is None: version = cmd_result( " nvidia-smi | grep CUDA | awk '{print $9}' ") elif platform == 'win32' or platform == 'cygwin': # nvcc_release = "Cuda compilation tools, release 10.2, V10.2.89" nvcc_release = cmd_result(' nvcc --version | find "release" ') if nvcc_release is not None: result = pattern.findall(nvcc_release) if len(result) > 0: version = result[0] if version is None or pattern.match(version) is None: # nvidia_smi = "| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |" # noqa E501 nvidia_smi = cmd_result(' nvidia-smi | find "CUDA Version" ') result = pattern.findall(nvidia_smi) if len(result) > 2: version = result[2] if version is None or pattern.match(version) is None: try: import torch version = torch.version.cuda except Exception: pass return version
[文档]def from_onnx(onnx_model: Union[str, onnx.ModelProto], output_file_prefix: str, input_shapes: Dict[str, Sequence[int]], max_workspace_size: int = 0, fp16_mode: bool = False, int8_mode: bool = False, int8_param: Optional[dict] = None, device_id: int = 0, log_level: trt.Logger.Severity = trt.Logger.ERROR, **kwargs) -> trt.ICudaEngine: """Create a tensorrt engine from ONNX. Args: onnx_model (str or onnx.ModelProto): Input onnx model to convert from. output_file_prefix (str): The path to save the output ncnn file. input_shapes (Dict[str, Sequence[int]]): The min/opt/max shape of each input. max_workspace_size (int): To set max workspace size of TensorRT engine. some tactics and layers need large workspace. Defaults to `0`. fp16_mode (bool): Specifying whether to enable fp16 mode. Defaults to `False`. int8_mode (bool): Specifying whether to enable int8 mode. Defaults to `False`. int8_param (dict): A dict of parameter int8 mode. Defaults to `None`. device_id (int): Choice the device to create engine. Defaults to `0`. log_level (trt.Logger.Severity): The log level of TensorRT. Defaults to `trt.Logger.ERROR`. Returns: tensorrt.ICudaEngine: The TensorRT engine created from onnx_model. Example: >>> from mmdeploy.apis.tensorrt import from_onnx >>> engine = from_onnx( >>> "onnx_model.onnx", >>> {'input': {"min_shape" : [1, 3, 160, 160], >>> "opt_shape" : [1, 3, 320, 320], >>> "max_shape" : [1, 3, 640, 640]}}, >>> log_level=trt.Logger.WARNING, >>> fp16_mode=True, >>> max_workspace_size=1 << 30, >>> device_id=0) >>> }) """ if int8_mode or device_id != 0: import pycuda.autoinit # noqa:F401 if device_id != 0: import os old_cuda_device = os.environ.get('CUDA_DEVICE', None) os.environ['CUDA_DEVICE'] = str(device_id) if old_cuda_device is not None: os.environ['CUDA_DEVICE'] = old_cuda_device else: os.environ.pop('CUDA_DEVICE') # build a mmdeploy logger logger = get_root_logger() load_tensorrt_plugin() # build a tensorrt logger trt_logger = trt.Logger(log_level) # create builder and network builder = trt.Builder(trt_logger) # TODO: use TorchAllocator as builder.gpu_allocator EXPLICIT_BATCH = 1 << (int)( trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) network = builder.create_network(EXPLICIT_BATCH) # parse onnx parser = trt.OnnxParser(network, trt_logger) if isinstance(onnx_model, str): parse_valid = parser.parse_from_file(onnx_model) elif isinstance(onnx_model, onnx.ModelProto): parse_valid = parser.parse(onnx_model.SerializeToString()) else: raise TypeError('Unsupported onnx model type!') if not parse_valid: error_msgs = '' for error in range(parser.num_errors): error_msgs += f'{parser.get_error(error)}\n' raise RuntimeError(f'Failed to parse onnx, {error_msgs}') # config builder if version.parse(trt.__version__) < version.parse('8'): builder.max_workspace_size = max_workspace_size config = builder.create_builder_config() if hasattr(config, 'set_memory_pool_limit'): config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, max_workspace_size) else: config.max_workspace_size = max_workspace_size cuda_version = search_cuda_version() if cuda_version is not None: version_major = int(cuda_version.split('.')[0]) if version_major < 11: # cu11 support cublasLt, so cudnn heuristic tactic should disable CUBLAS_LT # noqa E501 tactic_source = config.get_tactic_sources() - ( 1 << int(trt.TacticSource.CUBLAS_LT)) config.set_tactic_sources(tactic_source) profile = builder.create_optimization_profile() for input_name, param in input_shapes.items(): min_shape = param['min_shape'] opt_shape = param['opt_shape'] max_shape = param['max_shape'] profile.set_shape(input_name, min_shape, opt_shape, max_shape) if config.add_optimization_profile(profile) < 0: logger.warning(f'Invalid optimization profile {profile}.') if fp16_mode: if not getattr(builder, 'platform_has_fast_fp16', True): logger.warning('Platform does not has fast native fp16.') if version.parse(trt.__version__) < version.parse('8'): builder.fp16_mode = fp16_mode config.set_flag(trt.BuilderFlag.FP16) if int8_mode: if not getattr(builder, 'platform_has_fast_int8', True): logger.warning('Platform does not has fast native int8.') from .calib_utils import HDF5Calibrator config.set_flag(trt.BuilderFlag.INT8) assert int8_param is not None config.int8_calibrator = HDF5Calibrator( int8_param['calib_file'], input_shapes, model_type=int8_param['model_type'], device_id=device_id, algorithm=int8_param.get( 'algorithm', trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2)) if version.parse(trt.__version__) < version.parse('8'): builder.int8_mode = int8_mode builder.int8_calibrator = config.int8_calibrator # create engine if hasattr(builder, 'build_serialized_network'): engine = builder.build_serialized_network(network, config) else: engine = builder.build_engine(network, config) assert engine is not None, 'Failed to create TensorRT engine' save(engine, output_file_prefix + '.engine') return engine
def get_trt_log_level() -> trt.Logger.Severity: """Get tensorrt log level from root logger. Returns: level (tensorrt.Logger.Severity): Logging level of tensorrt.Logger. """ logger = get_root_logger() level = logger.level trt_log_level = trt.Logger.INFO if level == logging.ERROR: trt_log_level = trt.Logger.ERROR elif level == logging.WARNING: trt_log_level = trt.Logger.WARNING elif level == logging.DEBUG: trt_log_level = trt.Logger.VERBOSE return trt_log_level
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