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Remove rocm detector (#16913)
* Remove rocm detector plugin * Update docs to recommend using onnx for rocm * Formatting
This commit is contained in:
Nicolas Mowen
@@ -1,170 +0,0 @@
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import ctypes
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import logging
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import os
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import subprocess
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import sys
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import cv2
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import numpy as np
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from pydantic import Field
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from typing_extensions import Literal
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from frigate.const import MODEL_CACHE_DIR
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import (
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BaseDetectorConfig,
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ModelTypeEnum,
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PixelFormatEnum,
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)
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "rocm"
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def detect_gfx_version():
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return subprocess.getoutput(
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"unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo | grep gfx |head -1|awk '{print $2}'"
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)
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def auto_override_gfx_version():
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# If environment variable already in place, do not override
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gfx_version = detect_gfx_version()
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old_override = os.getenv("HSA_OVERRIDE_GFX_VERSION")
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if old_override not in (None, ""):
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logger.warning(
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f"AMD/ROCm: detected {gfx_version} but HSA_OVERRIDE_GFX_VERSION already present ({old_override}), not overriding!"
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)
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return old_override
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mapping = {
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"gfx90c": "9.0.0",
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"gfx1031": "10.3.0",
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"gfx1103": "11.0.0",
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}
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override = mapping.get(gfx_version)
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if override is not None:
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logger.warning(
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f"AMD/ROCm: detected {gfx_version}, overriding HSA_OVERRIDE_GFX_VERSION={override}"
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)
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os.putenv("HSA_OVERRIDE_GFX_VERSION", override)
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return override
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return ""
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class ROCmDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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conserve_cpu: bool = Field(
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default=True,
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title="Conserve CPU at the expense of latency (and reduced max throughput)",
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)
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auto_override_gfx: bool = Field(
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default=True, title="Automatically detect and override gfx version"
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)
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class ROCmDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: ROCmDetectorConfig):
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if detector_config.auto_override_gfx:
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auto_override_gfx_version()
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try:
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sys.path.append("/opt/rocm/lib")
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import migraphx
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logger.info("AMD/ROCm: loaded migraphx module")
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except ModuleNotFoundError:
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logger.error("AMD/ROCm: module loading failed, missing ROCm environment?")
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raise
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if detector_config.conserve_cpu:
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logger.info("AMD/ROCm: switching HIP to blocking mode to conserve CPU")
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ctypes.CDLL("/opt/rocm/lib/libamdhip64.so").hipSetDeviceFlags(4)
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self.h = detector_config.model.height
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self.w = detector_config.model.width
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self.rocm_model_type = detector_config.model.model_type
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self.rocm_model_px = detector_config.model.input_pixel_format
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path = detector_config.model.path
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mxr_path = os.path.splitext(path)[0] + ".mxr"
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if path.endswith(".mxr"):
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logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
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self.model = migraphx.load(mxr_path)
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elif os.path.exists(mxr_path):
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logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
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self.model = migraphx.load(mxr_path)
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else:
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logger.info(f"AMD/ROCm: loading model from {path}")
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if (
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path.endswith(".tf")
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or path.endswith(".tf2")
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or path.endswith(".tflite")
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):
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# untested
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self.model = migraphx.parse_tf(path)
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else:
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self.model = migraphx.parse_onnx(path)
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logger.info("AMD/ROCm: compiling the model")
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self.model.compile(
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migraphx.get_target("gpu"), offload_copy=True, fast_math=True
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)
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logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}")
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os.makedirs(os.path.join(MODEL_CACHE_DIR, "rocm"), exist_ok=True)
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migraphx.save(self.model, mxr_path)
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logger.info("AMD/ROCm: model loaded")
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def detect_raw(self, tensor_input):
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model_input_name = self.model.get_parameter_names()[0]
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model_input_shape = tuple(
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self.model.get_parameter_shapes()[model_input_name].lens()
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)
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tensor_input = cv2.dnn.blobFromImage(
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tensor_input[0],
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1.0,
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(model_input_shape[3], model_input_shape[2]),
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None,
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swapRB=self.rocm_model_px == PixelFormatEnum.bgr,
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).astype(np.uint8)
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detector_result = self.model.run({model_input_name: tensor_input})[0]
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addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
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tensor_output = np.ctypeslib.as_array(
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addr, shape=detector_result.get_shape().lens()
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)
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if self.rocm_model_type == ModelTypeEnum.yolonas:
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predictions = tensor_output
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detections = np.zeros((20, 6), np.float32)
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for i, prediction in enumerate(predictions):
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if i == 20:
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break
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(_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
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# when running in GPU mode, empty predictions in the output have class_id of -1
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if class_id < 0:
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break
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detections[i] = [
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class_id,
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confidence,
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y_min / self.h,
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x_min / self.w,
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y_max / self.h,
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x_max / self.w,
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]
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return detections
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else:
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raise Exception(
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f"{self.rocm_model_type} is currently not supported for rocm. See the docs for more info on supported models."
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)
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