blakeblackshear.frigate/frigate/detectors/plugins/rocm.py

import ctypes
import logging
import os
import subprocess
import sys

import numpy as np
from pydantic import Field
from typing_extensions import Literal

from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.detectors.util import preprocess

logger = logging.getLogger(__name__)

DETECTOR_KEY = "rocm"


def detect_gfx_version():
    return subprocess.getoutput(
        "unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo | grep gfx |head -1|awk '{print $2}'"
    )


def auto_override_gfx_version():
    # If environment variable already in place, do not override
    gfx_version = detect_gfx_version()
    old_override = os.getenv("HSA_OVERRIDE_GFX_VERSION")
    if old_override not in (None, ""):
        logger.warning(
            f"AMD/ROCm: detected {gfx_version} but HSA_OVERRIDE_GFX_VERSION already present ({old_override}), not overriding!"
        )
        return old_override
    mapping = {
        "gfx90c": "9.0.0",
        "gfx1031": "10.3.0",
        "gfx1103": "11.0.0",
    }
    override = mapping.get(gfx_version)
    if override is not None:
        logger.warning(
            f"AMD/ROCm: detected {gfx_version}, overriding HSA_OVERRIDE_GFX_VERSION={override}"
        )
        os.putenv("HSA_OVERRIDE_GFX_VERSION", override)
        return override
    return ""


class ROCmDetectorConfig(BaseDetectorConfig):
    type: Literal[DETECTOR_KEY]
    conserve_cpu: bool = Field(
        default=True,
        title="Conserve CPU at the expense of latency (and reduced max throughput)",
    )
    auto_override_gfx: bool = Field(
        default=True, title="Automatically detect and override gfx version"
    )


class ROCmDetector(DetectionApi):
    type_key = DETECTOR_KEY

    def __init__(self, detector_config: ROCmDetectorConfig):
        if detector_config.auto_override_gfx:
            auto_override_gfx_version()

        try:
            sys.path.append("/opt/rocm/lib")
            import migraphx

            logger.info("AMD/ROCm: loaded migraphx module")
        except ModuleNotFoundError:
            logger.error("AMD/ROCm: module loading failed, missing ROCm environment?")
            raise

        path = detector_config.model.path
        mxr_path = os.path.splitext(path)[0] + ".mxr"
        if path.endswith(".mxr"):
            logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
            self.model = migraphx.load(mxr_path)
        elif os.path.exists(mxr_path):
            logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
            self.model = migraphx.load(mxr_path)
        else:
            logger.info(f"AMD/ROCm: loading model from {path}")
            if path.endswith(".onnx"):
                self.model = migraphx.parse_onnx(path)
            elif (
                path.endswith(".tf")
                or path.endswith(".tf2")
                or path.endswith(".tflite")
            ):
                # untested
                self.model = migraphx.parse_tf(path)
            else:
                raise Exception(f"AMD/ROCm: unknown model format {path}")
            logger.info("AMD/ROCm: compiling the model")
            self.model.compile(
                migraphx.get_target("gpu"), offload_copy=True, fast_math=True
            )
            logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}")
            os.makedirs("/config/model_cache/rocm", exist_ok=True)
            migraphx.save(self.model, mxr_path)
        logger.info("AMD/ROCm: model loaded")

    def detect_raw(self, tensor_input):
        model_input_name = self.model.get_parameter_names()[0]
        model_input_shape = tuple(
            self.model.get_parameter_shapes()[model_input_name].lens()
        )
        tensor_input = preprocess(tensor_input, model_input_shape, np.float32)

        detector_result = self.model.run({model_input_name: tensor_input})[0]

        addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
        # ruff: noqa: F841
        tensor_output = np.ctypeslib.as_array(
            addr, shape=detector_result.get_shape().lens()
        )

        raise Exception(
            "No models are currently supported for rocm. See the docs for more info."
        )