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Source code for torch_tensorrt.logging

import logging
from typing import Any

import torch
from torch_tensorrt._features import ENABLED_FEATURES

import tensorrt as trt

logging.captureWarnings(True)
_LOGGER = logging.getLogger("torch_tensorrt [TensorRT Conversion Context]")


class _TRTLogger(trt.ILogger):  # type: ignore[misc]

    def __init__(self) -> None:
        trt.ILogger.__init__(self)

    def log(self, severity: trt.ILogger.Severity, msg: str) -> None:
        # TODO: Move to match once py39 reaches EoL
        if severity == trt.ILogger.Severity.INTERNAL_ERROR:
            _LOGGER.critical(msg)
            raise RuntimeError(msg)
        elif severity == trt.ILogger.Severity.ERROR:
            _LOGGER.error(msg)
        elif severity == trt.ILogger.Severity.WARNING:
            _LOGGER.warning(msg)
        elif severity == trt.ILogger.Severity.INFO:
            _LOGGER.info(msg)
        elif severity == trt.ILogger.Severity.VERBOSE:
            _LOGGER.debug(msg)


TRT_LOGGER = _TRTLogger()


[docs]class internal_errors: """Context-manager to limit displayed log messages to just internal errors Example:: with torch_tensorrt.logging.internal_errors(): outputs = model_torchtrt(inputs) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.CRITICAL) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.InternalError) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level( int(trt.ILogger.Severity.INTERNAL_ERROR) ) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)
[docs]class errors: """Context-manager to limit displayed log messages to just errors and above Example:: with torch_tensorrt.logging.errors(): outputs = model_torchtrt(inputs) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.ERROR) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.Error) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.ERROR)) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)
[docs]class warnings: """Context-manager to limit displayed log messages to just warnings and above Example:: with torch_tensorrt.logging.warnings(): model_trt = torch_tensorrt.compile(model, **spec) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.WARNING) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.Warning) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.WARNING)) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)
[docs]class info: """Context-manager to display all info and greater severity messages Example:: with torch_tensorrt.logging.info(): model_trt = torch_tensorrt.compile(model, **spec) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.INFO) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.Info) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.INFO)) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)
[docs]class debug: """Context-manager to display full debug information through the logger Example:: with torch_tensorrt.logging.debug(): model_trt = torch_tensorrt.compile(model, **spec) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.DEBUG) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.Debug) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.VERBOSE)) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)
[docs]class graphs: """Context-manager to display the results of intermediate lowering passes as well as full debug information through the logger Example:: with torch_tensorrt.logging.graphs(): model_trt = torch_tensorrt.compile(model, **spec) """ def __enter__(self) -> None: self.external_lvl = _LOGGER.getEffectiveLevel() _LOGGER.setLevel(logging.NOTSET) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging self.ts_level = ts_logging.get_reportable_log_level() ts_logging.set_reportable_log_level(ts_logging.Level.Graph) elif ENABLED_FEATURES.torch_tensorrt_runtime: self.rt_level = torch.ops.tensorrt.get_logging_level() torch.ops.tensorrt.set_logging_level(int(trt.ILogger.Severity.VERBOSE) + 1) def __exit__(self, exc_type: Any, exc_value: Any, exc_tb: Any) -> None: _LOGGER.setLevel(self.external_lvl) if ENABLED_FEATURES.torchscript_frontend: from torch_tensorrt.ts import logging as ts_logging ts_logging.set_reportable_log_level(self.ts_level) elif ENABLED_FEATURES.torch_tensorrt_runtime: torch.ops.tensorrt.set_logging_level(self.rt_level)

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