perf: RKNN half RAM model usage + no weight duplication in multi-core (#23958)
* perf(ml): share RKNN weights across NPU cores and halve model RAM * perf(ml): default rknn threads to npu cores and cache shape setup * perf(ml): serve rknn runs in arrival order (cherry picked from commit e659fb62f2bb3f6b1db08ed643d3d054a539c415)
This commit is contained in:
Horatiu George Todoran
committed by
Codex
Codex
parent
1f422ea4c4
commit
f418967d55
@@ -85,7 +85,9 @@ You do not need to redo any machine learning jobs after enabling hardware accele
|
||||
- RKNPU driver V0.9.8 or later must be available in the host server
|
||||
- You may confirm this by running `cat /sys/kernel/debug/rknpu/version` to check the version
|
||||
- Optional: Configure your `.env` file, see [environment variables](/install/environment-variables) for RKNN specific settings
|
||||
- In particular, setting `MACHINE_LEARNING_RKNN_THREADS` to 2 or 3 can _dramatically_ improve performance for RK3576 and RK3588 compared to the default of 1, at the expense of multiplying the amount of RAM each model uses by that amount.
|
||||
- By default each model gets one context per NPU core (1 on RK3566/RK3568, 2 on RK3576, 3 on RK3588), so concurrent requests run on all cores at once. The contexts of a model share its weights, but each one needs its own working memory. Extra contexts therefore cost little for models that are mostly weights, such as CLIP, but for OCR detection each one can add close to the model's own memory again, more so at higher OCR resolutions. Set `MACHINE_LEARNING_RKNN_THREADS=1` to trade throughput for memory.
|
||||
- Keep `MACHINE_LEARNING_WORKERS=1` unless you have a specific reason not to: each worker process still loads its own copy of the weights.
|
||||
- Raise Immich job concurrency (Smart Search, Face Detection, OCR) a bit above the number of NPU cores, e.g. `4`–`6`, so a prepared image is always waiting when a core frees up.
|
||||
|
||||
## Setup
|
||||
|
||||
@@ -100,7 +102,7 @@ You do not need to redo any machine learning jobs after enabling hardware accele
|
||||
|
||||
You can confirm the device is being recognized and used by checking its utilization. There are many tools to display this, such as `nvtop` for NVIDIA or Intel, `intel_gpu_top` for Intel, and `radeontop` for AMD.
|
||||
|
||||
You can also check the logs of the `immich-machine-learning` container. When a Smart Search or Face Detection job begins, or when you search with text in Immich, you should either see a log for `Available ORT providers` containing the relevant provider (e.g. `CUDAExecutionProvider` in the case of CUDA), or a `Loaded ANN model` log entry without errors in the case of ARM NN.
|
||||
You can also check the logs of the `immich-machine-learning` container. When a Smart Search or Face Detection job begins, or when you search with text in Immich, you should either see a log for `Available ORT providers` containing the relevant provider (e.g. `CUDAExecutionProvider` in the case of CUDA), a `Loaded ANN model` log entry without errors in the case of ARM NN, or a `Loaded RKNN model ... threads sharing its weights` log entry for RKNN.
|
||||
|
||||
#### Single Compose File
|
||||
|
||||
@@ -174,7 +176,7 @@ Note that you should increase job concurrencies to increase overall utilization
|
||||
- Less heat generation
|
||||
- Very slightly lower accuracy (RKNPU always uses FP16, while ARM NN by default uses higher precision FP32 unless `MACHINE_LEARNING_ANN_FP16_TURBO` is enabled)
|
||||
- Varying speed (tested on RK3588):
|
||||
- If `MACHINE_LEARNING_RKNN_THREADS` is at the default of 1, RKNPU will have substantially lower throughput for ML jobs than ARM NN in most cases, but similar latency (such as when searching)
|
||||
- If `MACHINE_LEARNING_RKNN_THREADS` is set to 3, it will be somewhat faster than ARM NN at FP32, but somewhat slower than ARM NN if `MACHINE_LEARNING_ANN_FP16_TURBO` is enabled
|
||||
- With a single context (`MACHINE_LEARNING_RKNN_THREADS=1`), RKNPU will have substantially lower throughput for ML jobs than ARM NN in most cases, but similar latency (such as when searching)
|
||||
- With three contexts (the default on RK3588), it will be somewhat faster than ARM NN at FP32, but somewhat slower than ARM NN if `MACHINE_LEARNING_ANN_FP16_TURBO` is enabled
|
||||
- When other tasks also use the GPU (like transcoding), RKNPU has a significant advantage over ARM NN as it uses the otherwise idle NPU instead of competing for GPU usage
|
||||
- Lower RAM usage if `MACHINE_LEARNING_RKNN_THREADS` is at the default of 1, but significantly higher if greater than 1 (which is necessary for it to fully utilize the NPU and hence be comparable in speed to ARM NN)
|
||||
- Lower RAM usage than earlier RKNN builds: the contexts of a model share its weights instead of each loading their own copy. Keep `MACHINE_LEARNING_WORKERS=1` and raise Immich ML job concurrency a bit above the core count so every NPU core stays busy.
|
||||
|
||||
@@ -179,7 +179,7 @@ Redis (Sentinel) URL example JSON before encoding:
|
||||
| `MACHINE_LEARNING_MAX_BATCH_SIZE__FACIAL_RECOGNITION` | Set the maximum number of faces that will be processed at once by the facial recognition model | `4` | machine learning |
|
||||
| `MACHINE_LEARNING_MAX_BATCH_SIZE__OCR` | Set the maximum number of boxes that will be processed at once by the OCR model | `6` | machine learning |
|
||||
| `MACHINE_LEARNING_RKNN` | Enable RKNN hardware acceleration if supported | `True` | machine learning |
|
||||
| `MACHINE_LEARNING_RKNN_THREADS` | How many threads of RKNN runtime should be spun up while inferencing. | `1` | machine learning |
|
||||
| `MACHINE_LEARNING_RKNN_THREADS` | Number of RKNN contexts per model, which share its weights | number of NPU cores | machine learning |
|
||||
| `MACHINE_LEARNING_MODEL_ARENA` | Pre-allocates CPU memory to avoid memory fragmentation | true | machine learning |
|
||||
| `MACHINE_LEARNING_MODEL_REVISION` | Model revision to download; `main` selects the original exports and `v2` selects the newer exports | `main` | machine learning |
|
||||
|
||||
|
||||
@@ -70,7 +70,7 @@ class Settings(BaseSettings):
|
||||
ann_fp16_turbo: bool = False
|
||||
ann_tuning_level: int = 2
|
||||
rknn: bool = True
|
||||
rknn_threads: int = 1
|
||||
rknn_threads: int | None = None
|
||||
preload: PreloadModelData | None = None
|
||||
max_batch_size: MaxBatchSize = MaxBatchSize()
|
||||
model_organization: ModelOrganization = ModelOrganization.APP
|
||||
|
||||
@@ -100,8 +100,7 @@ SUPPORTED_PROVIDERS = [
|
||||
"CPUExecutionProvider",
|
||||
]
|
||||
|
||||
RKNN_SUPPORTED_SOCS = ["rk3566", "rk3568", "rk3576", "rk3588"]
|
||||
RKNN_COREMASK_SUPPORTED_SOCS = ["rk3576", "rk3588"]
|
||||
RKNN_NPU_CORES = {"rk3566": 1, "rk3568": 1, "rk3576": 2, "rk3588": 3}
|
||||
|
||||
|
||||
WEBLATE_TO_FLORES200 = {
|
||||
|
||||
@@ -9,9 +9,10 @@ import orjson
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from immich_ml.config import log, settings
|
||||
from immich_ml.models.constants import RKNN_NPU_CORES
|
||||
from immich_ml.schemas import ModelInput, ModelTensor, SessionNode, Shape
|
||||
|
||||
from .rknnpool import RknnNode, RknnPoolExecutor, is_available, native_outputs, soc_name
|
||||
from .rknnpool import RknnNode, RknnPoolExecutor, is_available, soc_name
|
||||
|
||||
is_available = is_available and settings.rknn
|
||||
model_prefix = Path("rknpu") / soc_name if is_available and soc_name is not None else None
|
||||
@@ -21,12 +22,6 @@ def model_path(model_dir: Path, variant: str = "") -> Path:
|
||||
return (model_dir / model_prefix if model_prefix else model_dir) / variant / "model.rknn"
|
||||
|
||||
|
||||
def run_inference(rknn_lite: Any, inputs: list[ModelTensor], data_format: str | None) -> list[NDArray[np.float32]]:
|
||||
rknn_lite.rknn_runtime.set_inputs(inputs, None, data_format)
|
||||
rknn_lite.rknn_runtime.run(False)
|
||||
return native_outputs(rknn_lite)
|
||||
|
||||
|
||||
def input_layout(compiled: tuple[int, ...], array: ModelTensor) -> str | None:
|
||||
if array.ndim != 4:
|
||||
return None # token ids carry no layout
|
||||
@@ -39,11 +34,11 @@ def input_layout(compiled: tuple[int, ...], array: ModelTensor) -> str | None:
|
||||
|
||||
class RknnSession:
|
||||
def __init__(self, model_path: Path) -> None:
|
||||
self.tpe = settings.rknn_threads
|
||||
self.tpe = settings.rknn_threads or RKNN_NPU_CORES.get(soc_name or "", 1)
|
||||
|
||||
log.info(f"Loading RKNN model from {model_path} with {self.tpe} threads.")
|
||||
self.rknnpool = RknnPoolExecutor(model_path=model_path.as_posix(), tpes=self.tpe, func=run_inference)
|
||||
log.info(f"Loaded RKNN model from {model_path} with {self.tpe} threads.")
|
||||
self.rknnpool = RknnPoolExecutor(model_path=model_path.as_posix(), tpes=self.tpe)
|
||||
log.info(f"Loaded RKNN model from {model_path} with {self.tpe} threads sharing its weights.")
|
||||
# the shapes the binary was compiled for, among which the runtime routes itself
|
||||
batch = self.rknnpool.inputs[0].shape[0]
|
||||
declared = orjson.loads(self.rknnpool.custom_string) if self.rknnpool.custom_string else {}
|
||||
@@ -77,7 +72,7 @@ class RknnSession:
|
||||
input_feed: ModelInput,
|
||||
run_options: Any = None,
|
||||
) -> list[NDArray[np.float32]]:
|
||||
inputs = [array if array.flags.c_contiguous else array.copy() for array in input_feed.values()]
|
||||
inputs = list(input_feed.values())
|
||||
return self.rknnpool.run(inputs, input_layout(self.rknnpool.inputs[0].shape, inputs[0]))
|
||||
|
||||
|
||||
|
||||
@@ -1,18 +1,15 @@
|
||||
# This code is from leafqycc/rknn-multi-threaded
|
||||
# Following Apache License 2.0
|
||||
|
||||
import ctypes
|
||||
import logging
|
||||
import threading
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from ctypes import byref, sizeof
|
||||
from pathlib import Path
|
||||
from typing import Any, Callable, NamedTuple
|
||||
from queue import SimpleQueue
|
||||
from typing import Any, NamedTuple
|
||||
|
||||
import numpy as np
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from immich_ml.config import log
|
||||
from immich_ml.models.constants import RKNN_COREMASK_SUPPORTED_SOCS, RKNN_SUPPORTED_SOCS
|
||||
from immich_ml.models.constants import RKNN_NPU_CORES
|
||||
from immich_ml.schemas import ModelTensor
|
||||
|
||||
|
||||
@@ -20,7 +17,7 @@ def get_soc(device_tree_path: Path | str) -> str | None:
|
||||
try:
|
||||
with Path(device_tree_path).open() as f:
|
||||
device_compatible_str = f.read()
|
||||
for soc in RKNN_SUPPORTED_SOCS:
|
||||
for soc in RKNN_NPU_CORES:
|
||||
if soc in device_compatible_str:
|
||||
return soc
|
||||
log.warning("Device is not supported for RKNN")
|
||||
@@ -29,26 +26,34 @@ def get_soc(device_tree_path: Path | str) -> str | None:
|
||||
return None
|
||||
|
||||
|
||||
soc_name = None
|
||||
is_available = False
|
||||
try:
|
||||
from rknnlite.api import RKNNLite, rknn_runtime
|
||||
|
||||
soc_name = get_soc("/proc/device-tree/compatible")
|
||||
is_available = soc_name is not None
|
||||
except ImportError:
|
||||
log.debug("RKNN is not available")
|
||||
|
||||
|
||||
class RknnNode(NamedTuple):
|
||||
name: str
|
||||
shape: tuple[int, ...]
|
||||
|
||||
|
||||
_CUSTOM_STRING_QUERY = 7 # RKNN_QUERY_CUSTOM_STRING
|
||||
# RKNN_QUERY_OUTPUT_ATTR, and RKNN_QUERY_CURRENT_OUTPUT_ATTR for the shape a dynamic binary last produced
|
||||
_OUTPUT_QUERY, _CURRENT_OUTPUT_QUERY = 2, 15
|
||||
_DTYPES = {0: np.float32, 1: np.float16} # rknn_tensor_type
|
||||
# rknn_query_cmd
|
||||
_IN_OUT_NUM, _INPUT_ATTR, _OUTPUT_ATTR, _CUSTOM_STRING = 0, 1, 2, 7
|
||||
_INPUT_DYNAMIC_RANGE, _CURRENT_OUTPUT_ATTR = 13, 15
|
||||
# rknn_tensor_format
|
||||
_NCHW, _NHWC = 0, 1
|
||||
# rknn_tensor_type, in enum order
|
||||
_DTYPES: list[type[np.generic]] = [
|
||||
np.float32,
|
||||
np.float16,
|
||||
np.int8,
|
||||
np.uint8,
|
||||
np.int16,
|
||||
np.uint16,
|
||||
np.int32,
|
||||
np.uint32,
|
||||
np.int64,
|
||||
np.bool_,
|
||||
]
|
||||
_TYPES = {np.dtype(dtype): index for index, dtype in enumerate(_DTYPES)}
|
||||
|
||||
|
||||
class _IoNum(ctypes.Structure):
|
||||
_fields_ = [("n_input", ctypes.c_uint32), ("n_output", ctypes.c_uint32)]
|
||||
|
||||
|
||||
class _CustomString(ctypes.Structure):
|
||||
@@ -76,94 +81,194 @@ class _TensorAttr(ctypes.Structure): # rknn_tensor_attr
|
||||
]
|
||||
|
||||
|
||||
def native_outputs(rknn_lite: "RKNNLite") -> list[NDArray[Any]]:
|
||||
"""The outputs in the precision the NPU computed them in, which RKNNLite would widen to fp32 on the CPU."""
|
||||
runtime: Any = rknn_lite.rknn_runtime
|
||||
count = runtime.get_in_out_num()[1]
|
||||
buffers = (rknn_runtime.RKNNOutput * count)()
|
||||
for index, buffer in enumerate(buffers):
|
||||
buffer.index = index
|
||||
if runtime.lib.rknn_outputs_get(runtime.context, count, buffers, None) != 0:
|
||||
raise RuntimeError("RKNN inference failed!")
|
||||
query = _CURRENT_OUTPUT_QUERY if runtime.is_dynamic_shape else _OUTPUT_QUERY
|
||||
outputs = []
|
||||
for buffer in buffers:
|
||||
attr = _TensorAttr(index=buffer.index)
|
||||
runtime.lib.rknn_query(runtime.context, query, ctypes.byref(attr), ctypes.sizeof(attr))
|
||||
data = ctypes.cast(buffer.buf, ctypes.POINTER(ctypes.c_char * buffer.size)).contents
|
||||
outputs.append(np.frombuffer(data, _DTYPES[attr.type]).reshape(attr.dims[: attr.n_dims]).copy())
|
||||
runtime.lib.rknn_outputs_release(runtime.context, count, buffers)
|
||||
return outputs
|
||||
class _InputRange(ctypes.Structure):
|
||||
_fields_ = [
|
||||
("index", ctypes.c_uint32),
|
||||
("shape_number", ctypes.c_uint32),
|
||||
("fmt", ctypes.c_int),
|
||||
("name", ctypes.c_char * 256),
|
||||
("dyn_range", (ctypes.c_uint32 * 16) * 512),
|
||||
("n_dims", ctypes.c_uint32),
|
||||
]
|
||||
|
||||
|
||||
def tensor_nodes(rknn_lite: "RKNNLite") -> tuple[list[RknnNode], list[RknnNode]]:
|
||||
runtime = rknn_lite.rknn_runtime
|
||||
n_inputs, n_outputs = runtime.get_in_out_num()
|
||||
|
||||
def node(index: int, is_output: bool) -> RknnNode:
|
||||
attr = runtime.get_tensor_attr(index, is_output=is_output)
|
||||
return RknnNode(attr.name.decode(), tuple(attr.dims[: attr.n_dims]))
|
||||
|
||||
return [node(i, False) for i in range(n_inputs)], [node(i, True) for i in range(n_outputs)]
|
||||
class _Input(ctypes.Structure):
|
||||
_fields_ = [
|
||||
("index", ctypes.c_uint32),
|
||||
("buf", ctypes.c_void_p),
|
||||
("size", ctypes.c_uint32),
|
||||
("pass_through", ctypes.c_uint8),
|
||||
("type", ctypes.c_int),
|
||||
("fmt", ctypes.c_int),
|
||||
]
|
||||
|
||||
|
||||
def custom_string(rknn_lite: "RKNNLite") -> str:
|
||||
runtime: Any = rknn_lite.rknn_runtime
|
||||
buffer = _CustomString()
|
||||
# rknnlite declares argtypes on the shared handle, so pass plain values and let ctypes convert
|
||||
runtime.lib.rknn_query(runtime.context, _CUSTOM_STRING_QUERY, ctypes.byref(buffer), ctypes.sizeof(buffer))
|
||||
declared: str = buffer.string.decode()
|
||||
return declared
|
||||
class _Output(ctypes.Structure):
|
||||
_fields_ = [
|
||||
("want_float", ctypes.c_uint8),
|
||||
("is_prealloc", ctypes.c_uint8),
|
||||
("index", ctypes.c_uint32),
|
||||
("buf", ctypes.c_void_p),
|
||||
("size", ctypes.c_uint32),
|
||||
]
|
||||
|
||||
|
||||
def init_rknn(model_path: str) -> "RKNNLite":
|
||||
if not is_available:
|
||||
raise RuntimeError("rknn is not available!")
|
||||
rknn_lite = RKNNLite()
|
||||
rknn_lite.rknn_log.logger.setLevel(logging.ERROR)
|
||||
ret = rknn_lite.load_rknn(model_path)
|
||||
_Key = tuple[tuple[int, ...], ...]
|
||||
_Spec = tuple[tuple[int, ...], np.dtype[Any]]
|
||||
_context = ctypes.c_uint64 # rknn_context on 64-bit
|
||||
_ctx_p = ctypes.POINTER(_context)
|
||||
_u32 = ctypes.c_uint32
|
||||
|
||||
soc_name = None
|
||||
is_available = False
|
||||
try:
|
||||
lib = ctypes.CDLL("librknnrt.so") # CDLL releases the GIL for each call, so contexts run in parallel
|
||||
lib.rknn_init.argtypes = _ctx_p, ctypes.c_char_p, _u32, _u32, ctypes.c_void_p
|
||||
lib.rknn_dup_context.argtypes = _ctx_p, _ctx_p
|
||||
lib.rknn_destroy.argtypes = (_context,)
|
||||
lib.rknn_query.argtypes = _context, ctypes.c_int, ctypes.c_void_p, _u32
|
||||
lib.rknn_set_input_shapes.argtypes = _context, _u32, ctypes.POINTER(_TensorAttr)
|
||||
lib.rknn_inputs_set.argtypes = _context, _u32, ctypes.POINTER(_Input)
|
||||
lib.rknn_run.argtypes = _context, ctypes.c_void_p
|
||||
lib.rknn_outputs_get.argtypes = _context, _u32, ctypes.POINTER(_Output), ctypes.c_void_p
|
||||
lib.rknn_outputs_release.argtypes = _context, _u32, ctypes.POINTER(_Output)
|
||||
|
||||
soc_name = get_soc("/proc/device-tree/compatible")
|
||||
is_available = soc_name is not None
|
||||
except OSError:
|
||||
log.debug("RKNN is not available")
|
||||
|
||||
|
||||
def _check(ret: int, call: str) -> None:
|
||||
if ret != 0:
|
||||
raise RuntimeError("Failed to load RKNN model")
|
||||
raise RuntimeError(f"{call} failed with RKNN error {ret}")
|
||||
|
||||
if soc_name in RKNN_COREMASK_SUPPORTED_SOCS:
|
||||
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO)
|
||||
else:
|
||||
ret = rknn_lite.init_runtime() # Please do not set this parameter on other platforms.
|
||||
|
||||
if ret != 0:
|
||||
raise RuntimeError("Failed to initialize RKNN runtime environment")
|
||||
def _query(ctx: int, cmd: int, info: ctypes.Structure) -> None:
|
||||
_check(lib.rknn_query(ctx, cmd, byref(info), sizeof(info)), "rknn_query")
|
||||
|
||||
return rknn_lite
|
||||
|
||||
def _attr(ctx: int, cmd: int, index: int) -> _TensorAttr:
|
||||
attr = _TensorAttr(index=index)
|
||||
_query(ctx, cmd, attr)
|
||||
return attr
|
||||
|
||||
|
||||
def _specs(attrs: list[_TensorAttr]) -> list[_Spec]:
|
||||
return [(tuple(attr.dims[: attr.n_dims]), np.dtype(_DTYPES[attr.type])) for attr in attrs]
|
||||
|
||||
|
||||
class RknnPoolExecutor:
|
||||
def __init__(
|
||||
self,
|
||||
model_path: str,
|
||||
tpes: int,
|
||||
func: Callable[["RKNNLite", list[ModelTensor], str | None], list[NDArray[np.float32]]],
|
||||
) -> None:
|
||||
self.tpes = tpes
|
||||
self.rknn_pool = [init_rknn(model_path) for _ in range(tpes)]
|
||||
self.inputs, self.outputs = tensor_nodes(self.rknn_pool[0])
|
||||
self.custom_string = custom_string(self.rknn_pool[0])
|
||||
self.pool = ThreadPoolExecutor(max_workers=tpes)
|
||||
self.func = func
|
||||
self.num = 0
|
||||
self.lock = threading.Lock()
|
||||
"""One context per thread, all but the first duplicated from it so that they share its weights.
|
||||
|
||||
def run(self, inputs: list[ModelTensor], data_format: str | None) -> list[NDArray[np.float32]]:
|
||||
with self.lock:
|
||||
idx = self.num % self.tpes
|
||||
self.num += 1
|
||||
The model is loaded by path rather than from a buffer, so the runtime holds the only copy of it."""
|
||||
|
||||
fut = self.pool.submit(self.func, self.rknn_pool[idx], inputs, data_format)
|
||||
return fut.result()
|
||||
def __init__(self, model_path: str, tpes: int) -> None:
|
||||
self.contexts: list[int] = []
|
||||
self.executor = ThreadPoolExecutor(tpes, thread_name_prefix="rknn")
|
||||
master = _context()
|
||||
_check(lib.rknn_init(byref(master), model_path.encode(), 0, 0, None), "rknn_init")
|
||||
self.contexts.append(master.value)
|
||||
for _ in range(tpes - 1):
|
||||
child = _context()
|
||||
_check(lib.rknn_dup_context(byref(master), byref(child)), "rknn_dup_context")
|
||||
self.contexts.append(child.value)
|
||||
self.idle: SimpleQueue[int] = SimpleQueue()
|
||||
for ctx in self.contexts:
|
||||
self.idle.put(ctx)
|
||||
|
||||
io = _IoNum()
|
||||
_query(master.value, _IN_OUT_NUM, io)
|
||||
self.input_attrs = [_attr(master.value, _INPUT_ATTR, i) for i in range(io.n_input)]
|
||||
self.output_attrs = [_attr(master.value, _OUTPUT_ATTR, i) for i in range(io.n_output)]
|
||||
self.inputs = [RknnNode(a.name.decode(), tuple(a.dims[: a.n_dims])) for a in self.input_attrs]
|
||||
self.outputs = [RknnNode(a.name.decode(), tuple(a.dims[: a.n_dims])) for a in self.output_attrs]
|
||||
custom = _CustomString()
|
||||
lib.rknn_query(master.value, _CUSTOM_STRING, byref(custom), sizeof(custom))
|
||||
self.custom_string = custom.string.decode()
|
||||
|
||||
ranges = [_InputRange(index=index) for index in range(io.n_input)]
|
||||
for rng in ranges:
|
||||
lib.rknn_query(master.value, _INPUT_DYNAMIC_RANGE, byref(rng), sizeof(rng))
|
||||
self.formats = [rng.fmt for rng in ranges]
|
||||
self.compiled = [{tuple(rng.dyn_range[i][: rng.n_dims]) for i in range(rng.shape_number)} for rng in ranges]
|
||||
self.dynamic = any(self.compiled)
|
||||
self.shape_attrs: dict[_Key, ctypes.Array[_TensorAttr]] = {}
|
||||
self.output_specs: dict[_Key, list[_Spec]] = {(): _specs(self.output_attrs)}
|
||||
self.current: dict[int, _Key] = {}
|
||||
|
||||
def run(self, inputs: list[ModelTensor], data_format: str | None) -> list[NDArray[Any]]:
|
||||
return self.executor.submit(self._next, inputs, data_format).result()
|
||||
|
||||
def _next(self, inputs: list[ModelTensor], data_format: str | None) -> list[NDArray[Any]]:
|
||||
ctx = self.idle.get()
|
||||
try:
|
||||
return self._run(ctx, inputs, data_format)
|
||||
finally:
|
||||
self.idle.put(ctx)
|
||||
|
||||
def _run(self, ctx: int, inputs: list[ModelTensor], data_format: str | None) -> list[NDArray[Any]]:
|
||||
fed: list[NDArray[Any]] = [
|
||||
np.ascontiguousarray(array.transpose(0, 2, 3, 1) if data_format == "nchw" and array.ndim == 4 else array)
|
||||
for array in inputs
|
||||
]
|
||||
key = self._set_shapes(ctx, fed) if self.dynamic else ()
|
||||
feed = (_Input * len(fed))(
|
||||
*(
|
||||
_Input(index=i, buf=array.ctypes.data, size=array.nbytes, type=_TYPES[array.dtype], fmt=_NHWC)
|
||||
for i, array in enumerate(fed)
|
||||
)
|
||||
)
|
||||
_check(lib.rknn_inputs_set(ctx, len(fed), feed), "rknn_inputs_set")
|
||||
_check(lib.rknn_run(ctx, None), "rknn_run")
|
||||
|
||||
specs = self.output_specs.get(key)
|
||||
if specs is None:
|
||||
attrs = [_attr(ctx, _CURRENT_OUTPUT_ATTR, i) for i in range(len(self.output_attrs))]
|
||||
specs = self.output_specs[key] = _specs(attrs)
|
||||
outputs = [np.empty(dims, dtype) for dims, dtype in specs]
|
||||
buffers = (_Output * len(outputs))(
|
||||
*(
|
||||
_Output(is_prealloc=1, index=i, buf=array.ctypes.data, size=array.nbytes)
|
||||
for i, array in enumerate(outputs)
|
||||
)
|
||||
)
|
||||
_check(lib.rknn_outputs_get(ctx, len(outputs), buffers, None), "rknn_outputs_get")
|
||||
lib.rknn_outputs_release(ctx, len(outputs), buffers)
|
||||
return outputs
|
||||
|
||||
def _set_shapes(self, ctx: int, inputs: list[NDArray[Any]]) -> _Key:
|
||||
key = tuple(
|
||||
self._compiled_shape(fmt, compiled, array.shape)
|
||||
for fmt, compiled, array in zip(self.formats, self.compiled, inputs)
|
||||
)
|
||||
if self.current.get(ctx) == key:
|
||||
return key
|
||||
attrs = self.shape_attrs.get(key)
|
||||
if attrs is None:
|
||||
attrs = self.shape_attrs[key] = (_TensorAttr * len(key))()
|
||||
for i, (source, compiled, fmt, shape) in enumerate(zip(self.input_attrs, self.compiled, self.formats, key)):
|
||||
attrs[i] = _TensorAttr.from_buffer_copy(source)
|
||||
if compiled:
|
||||
attrs[i].n_dims = len(shape)
|
||||
attrs[i].dims[: len(shape)] = shape
|
||||
attrs[i].fmt = fmt
|
||||
_check(lib.rknn_set_input_shapes(ctx, len(key), attrs), "rknn_set_input_shapes")
|
||||
self.current[ctx] = key
|
||||
return key
|
||||
|
||||
@staticmethod
|
||||
def _compiled_shape(fmt: int, compiled: set[tuple[int, ...]], fed: tuple[int, ...]) -> tuple[int, ...]:
|
||||
shape = (fed[0], fed[3], fed[1], fed[2]) if len(fed) == 4 and fmt == _NCHW else fed
|
||||
if compiled and shape not in compiled:
|
||||
raise ValueError(f"binary was compiled for {sorted(compiled)}, fed {list(fed)}")
|
||||
return shape
|
||||
|
||||
def release(self) -> None:
|
||||
self.pool.shutdown()
|
||||
for rknn_lite in self.rknn_pool:
|
||||
rknn_lite.release()
|
||||
self.executor.shutdown()
|
||||
for ctx in reversed(self.contexts): # duplicates before the context whose weights they share
|
||||
lib.rknn_destroy(ctx)
|
||||
self.contexts = []
|
||||
|
||||
def __del__(self) -> None:
|
||||
self.release()
|
||||
|
||||
@@ -57,7 +57,7 @@ cuda = [
|
||||
]
|
||||
openvino = ["onnxruntime-openvino>=1.24.1,<2"]
|
||||
armnn = ["onnxruntime>=1.23.2,<2"]
|
||||
rknn = ["onnxruntime>=1.23.2,<2", "rknn-toolkit-lite2>=2.3.0,<3"]
|
||||
rknn = ["onnxruntime>=1.23.2,<2"]
|
||||
rocm = ["onnxruntime-migraphx>=1.23.2,<2"]
|
||||
|
||||
[tool.uv]
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import ctypes
|
||||
import json
|
||||
import os
|
||||
import pickle
|
||||
@@ -63,8 +64,9 @@ from immich_ml.schemas import (
|
||||
from immich_ml.sessions.ann import AnnSession
|
||||
from immich_ml.sessions.ort import Device, GraphSpec, OrtSession, flush_denormals, fresh, prepared
|
||||
from immich_ml.sessions.policy import ShapePolicy, batches, runs
|
||||
from immich_ml.sessions.rknn import RknnSession, run_inference
|
||||
from immich_ml.sessions.rknn import RknnSession
|
||||
from immich_ml.sessions.rknn import model_path as rknn_model_path
|
||||
from immich_ml.sessions.rknn.rknnpool import RknnPoolExecutor
|
||||
|
||||
|
||||
class TestBase:
|
||||
@@ -960,9 +962,11 @@ class TestRknnSession:
|
||||
mocker.patch("immich_ml.sessions.rknn.is_available", True)
|
||||
RknnSession(model_path)
|
||||
|
||||
rknn_session.assert_called_once_with(model_path=model_path.as_posix(), tpes=tpe, func=run_inference)
|
||||
rknn_session.assert_called_once_with(model_path=model_path.as_posix(), tpes=tpe)
|
||||
|
||||
info.assert_has_calls([mock.call(f"Loaded RKNN model from {model_path} with {tpe} threads.")])
|
||||
info.assert_has_calls(
|
||||
[mock.call(f"Loaded RKNN model from {model_path} with {tpe} threads sharing its weights.")]
|
||||
)
|
||||
|
||||
def test_run_rknn(self, rknn_session: mock.Mock, mocker: MockerFixture) -> None:
|
||||
rknn_session.return_value.load.return_value = 123
|
||||
@@ -974,7 +978,20 @@ class TestRknnSession:
|
||||
session.run(None, input_feed)
|
||||
|
||||
rknn_session.return_value.run.assert_called_once_with([input1, input2], "nchw")
|
||||
assert all(fed.flags.c_contiguous for fed in rknn_session.return_value.run.call_args.args[0])
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("soc", "threads", "expected"),
|
||||
[("rk3566", None, 1), ("rk3576", None, 2), ("rk3588", None, 3), ("rk3588", 2, 2)],
|
||||
)
|
||||
def test_uses_a_context_per_npu_core_unless_set(
|
||||
self, soc: str, threads: int | None, expected: int, rknn_session: mock.Mock, mocker: MockerFixture
|
||||
) -> None:
|
||||
mocker.patch("immich_ml.sessions.rknn.soc_name", soc)
|
||||
mocker.patch.object(settings, "rknn_threads", threads)
|
||||
|
||||
RknnSession(Path("ViT-B-32__openai"))
|
||||
|
||||
assert rknn_session.call_args.kwargs["tpes"] == expected
|
||||
|
||||
def test_run_rknn_rejects_a_shape_the_binary_was_not_compiled_for(
|
||||
self, rknn_session: mock.Mock, mocker: MockerFixture
|
||||
@@ -1011,6 +1028,237 @@ class TestRknnSession:
|
||||
assert RknnSession(Path("buffalo_l")).shapes == (Shape(batch=1),)
|
||||
|
||||
|
||||
class FakeRknn:
|
||||
"""librknnrt for one input and one output, writing through the pointers as the runtime does."""
|
||||
|
||||
def __init__(self, ranges: list[tuple[int, ...]] | None = None) -> None:
|
||||
self.ranges = ranges
|
||||
self.contexts = 0
|
||||
self.fed: list[tuple[int, int, int]] = [] # (context, type, fmt) per run
|
||||
self.data: list[bytes] = []
|
||||
self.buffers: list[int] = []
|
||||
self.output_queries = 0
|
||||
self.set_shapes: list[tuple[int, tuple[int, ...]]] = []
|
||||
self.rknn_destroy = mock.Mock(return_value=0)
|
||||
self.rknn_outputs_release = mock.Mock(return_value=0)
|
||||
self.rknn_run = mock.Mock(return_value=0)
|
||||
|
||||
def rknn_init(self, ctx: Any, model: bytes, size: int, flag: int, extend: None) -> int:
|
||||
self.contexts += 1
|
||||
ctx._obj.value = self.contexts
|
||||
return 0
|
||||
|
||||
def rknn_dup_context(self, ctx_in: Any, ctx_out: Any) -> int:
|
||||
self.contexts += 1
|
||||
ctx_out._obj.value = self.contexts
|
||||
return 0
|
||||
|
||||
def rknn_query(self, ctx: int, cmd: int, info: Any, size: int) -> int:
|
||||
obj = info._obj
|
||||
if cmd == 0:
|
||||
obj.n_input, obj.n_output = 1, 1
|
||||
elif cmd == 1:
|
||||
self._fill(obj, b"x", self.ranges[0] if self.ranges else (1, 48, 320, 3), 0)
|
||||
obj.fmt = 1
|
||||
elif cmd == 2:
|
||||
self._fill(obj, b"logits", (1, 40, 97), 1)
|
||||
elif cmd == 7:
|
||||
obj.string = b'{"dims":[{}]}'
|
||||
elif cmd == 13:
|
||||
if self.ranges is None:
|
||||
return -1
|
||||
obj.shape_number, obj.n_dims, obj.fmt = len(self.ranges), 4, 1
|
||||
for i, shape in enumerate(self.ranges):
|
||||
obj.dyn_range[i][:4] = shape
|
||||
elif cmd == 15:
|
||||
self.output_queries += 1
|
||||
width = dict(self.set_shapes)[ctx][2]
|
||||
self._fill(obj, b"logits", (1, width // 8, 97), 1)
|
||||
return 0
|
||||
|
||||
@staticmethod
|
||||
def _fill(attr: Any, name: bytes, dims: tuple[int, ...], dtype: int) -> None:
|
||||
attr.name, attr.n_dims, attr.type = name, len(dims), dtype
|
||||
attr.dims[: len(dims)] = dims
|
||||
|
||||
def rknn_set_input_shapes(self, ctx: int, n: int, attrs: Any) -> int:
|
||||
self.set_shapes.append((ctx, tuple(attrs[0].dims[: attrs[0].n_dims])))
|
||||
return 0
|
||||
|
||||
def rknn_inputs_set(self, ctx: int, n: int, inputs: Any) -> int:
|
||||
self.fed.append((ctx, inputs[0].type, inputs[0].fmt))
|
||||
self.data.append(ctypes.string_at(inputs[0].buf, inputs[0].size))
|
||||
self.buffers.append(inputs[0].buf)
|
||||
return 0
|
||||
|
||||
def rknn_outputs_get(self, ctx: int, n: int, outputs: Any, extend: None) -> int:
|
||||
assert outputs[0].is_prealloc and not outputs[0].want_float
|
||||
np.frombuffer((ctypes.c_char * outputs[0].size).from_address(outputs[0].buf), np.uint8)[:] = 0
|
||||
return 0
|
||||
|
||||
|
||||
class TestRknnPool:
|
||||
@pytest.fixture
|
||||
def lib(self, mocker: MockerFixture) -> FakeRknn:
|
||||
fake = FakeRknn()
|
||||
mocker.patch("immich_ml.sessions.rknn.rknnpool.lib", fake, create=True)
|
||||
return fake
|
||||
|
||||
@pytest.fixture
|
||||
def dynamic_lib(self, mocker: MockerFixture) -> FakeRknn:
|
||||
fake = FakeRknn(ranges=[(1, 48, 320, 3), (1, 48, 640, 3)])
|
||||
mocker.patch("immich_ml.sessions.rknn.rknnpool.lib", fake, create=True)
|
||||
return fake
|
||||
|
||||
def test_duplicates_one_context_per_thread(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 3)
|
||||
|
||||
assert pool.contexts == [1, 2, 3]
|
||||
assert pool.inputs[0] == ("x", (1, 48, 320, 3))
|
||||
assert pool.outputs[0] == ("logits", (1, 40, 97))
|
||||
assert pool.custom_string == '{"dims":[{}]}'
|
||||
assert not pool.dynamic
|
||||
|
||||
pool.release()
|
||||
|
||||
assert lib.rknn_destroy.call_args_list == [mock.call(3), mock.call(2), mock.call(1)]
|
||||
|
||||
def test_feeds_the_dtype_given_as_nhwc(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
image = np.arange(48 * 320 * 3, dtype=np.uint8).reshape(1, 48, 320, 3)
|
||||
|
||||
pool.run([image], "nhwc")
|
||||
pool.run([image.transpose(0, 3, 1, 2).astype(np.float32)], "nchw")
|
||||
|
||||
assert lib.fed == [(1, 3, 1), (1, 0, 1)] # (context, rknn_tensor_type, rknn_tensor_format)
|
||||
assert lib.data[0] == image.tobytes()
|
||||
assert lib.data[1] == image.astype(np.float32).tobytes() # transposed back to channels last
|
||||
|
||||
def test_feeds_a_channels_first_view_of_channels_last_data_without_a_copy(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
image = np.zeros((1, 48, 320, 3), dtype=np.float32)
|
||||
|
||||
pool.run([image.transpose(0, 3, 1, 2)], "nchw")
|
||||
|
||||
assert lib.buffers == [image.ctypes.data]
|
||||
|
||||
def test_makes_every_input_contiguous(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
image = np.arange(48 * 640 * 3, dtype=np.uint8).reshape(1, 48, 640, 3)[:, :, ::2]
|
||||
|
||||
pool.run([image], "nhwc")
|
||||
|
||||
assert lib.data == [np.ascontiguousarray(image).tobytes()]
|
||||
|
||||
def test_returns_outputs_in_the_precision_computed(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
|
||||
[output] = pool.run([np.zeros((1, 48, 320, 3), dtype=np.uint8)], "nhwc")
|
||||
|
||||
assert output.dtype == np.float16
|
||||
assert output.shape == (1, 40, 97)
|
||||
lib.rknn_outputs_release.assert_called_once()
|
||||
|
||||
def test_a_failed_init_destroys_nothing(self, lib: FakeRknn, mocker: MockerFixture) -> None:
|
||||
mocker.patch.object(lib, "rknn_init", return_value=-6)
|
||||
|
||||
with pytest.raises(RuntimeError, match="rknn_init failed"):
|
||||
RknnPoolExecutor("model.rknn", 3)
|
||||
|
||||
lib.rknn_destroy.assert_not_called()
|
||||
|
||||
def test_returns_the_context_when_a_run_fails(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 2)
|
||||
lib.rknn_run.return_value = -1
|
||||
|
||||
with pytest.raises(RuntimeError, match="rknn_run failed"):
|
||||
pool.run([np.zeros((1, 48, 320, 3), dtype=np.uint8)], "nhwc")
|
||||
|
||||
assert pool.idle.qsize() == 2
|
||||
|
||||
def test_runs_on_every_context_at_once(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 3)
|
||||
barrier = threading.Barrier(3, timeout=5) # breaks unless all three are inside rknn_run together
|
||||
lib.rknn_run.side_effect = lambda ctx, extend: barrier.wait() * 0
|
||||
|
||||
with ThreadPoolExecutor(3) as executor:
|
||||
futures = [executor.submit(pool.run, [np.zeros((1, 48, 320, 3), dtype=np.uint8)], "nhwc") for _ in range(3)]
|
||||
[future.result() for future in futures]
|
||||
|
||||
assert sorted(ctx for ctx, _, _ in lib.fed) == [1, 2, 3]
|
||||
|
||||
def test_a_waiting_run_is_not_overtaken_by_the_caller_that_just_ran(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
|
||||
def run(ctx: int, extend: None) -> int:
|
||||
time.sleep(0.01)
|
||||
return 0
|
||||
|
||||
lib.rknn_run.side_effect = run
|
||||
busy, waiting = np.zeros((1, 48, 320, 3), np.uint8), np.ones((1, 48, 320, 3), np.uint8)
|
||||
caller = threading.Thread(target=lambda: [pool.run([busy], "nhwc") for _ in range(20)])
|
||||
|
||||
caller.start()
|
||||
time.sleep(0.005)
|
||||
pool.run([waiting], "nhwc")
|
||||
caller.join(5)
|
||||
|
||||
assert [data[0] for data in lib.data].index(1) <= 2
|
||||
|
||||
def test_does_not_set_shapes_for_a_static_binary(self, lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
|
||||
pool.run([np.zeros((1, 48, 320, 3), dtype=np.uint8)], "nhwc")
|
||||
|
||||
assert lib.set_shapes == []
|
||||
|
||||
def test_routes_a_dynamic_binary_to_the_shape_fed(self, dynamic_lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
|
||||
[wide] = pool.run([np.zeros((1, 3, 48, 640), dtype=np.float32)], "nchw")
|
||||
[narrow] = pool.run([np.zeros((1, 48, 320, 3), dtype=np.uint8)], "nhwc")
|
||||
|
||||
assert pool.dynamic
|
||||
assert dynamic_lib.set_shapes == [(1, (1, 48, 640, 3)), (1, (1, 48, 320, 3))]
|
||||
assert wide.shape == (1, 80, 97)
|
||||
assert narrow.shape == (1, 40, 97)
|
||||
|
||||
def test_sets_the_shape_on_the_context_that_runs(self, dynamic_lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 2)
|
||||
image = np.zeros((1, 48, 640, 3), dtype=np.uint8)
|
||||
|
||||
pool.run([image], "nhwc")
|
||||
pool.run([image], "nhwc")
|
||||
|
||||
assert dynamic_lib.set_shapes == [(1, (1, 48, 640, 3)), (2, (1, 48, 640, 3))]
|
||||
|
||||
def test_sets_a_shape_again_only_when_it_changes(self, dynamic_lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
wide, narrow = np.zeros((1, 48, 640, 3), dtype=np.uint8), np.zeros((1, 48, 320, 3), dtype=np.uint8)
|
||||
|
||||
for image in (wide, wide, narrow, narrow, wide):
|
||||
pool.run([image], "nhwc")
|
||||
|
||||
assert dynamic_lib.set_shapes == [(1, (1, 48, 640, 3)), (1, (1, 48, 320, 3)), (1, (1, 48, 640, 3))]
|
||||
|
||||
def test_queries_output_shapes_once_per_input_shape(self, dynamic_lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 2)
|
||||
image = np.zeros((1, 48, 640, 3), dtype=np.uint8)
|
||||
|
||||
outputs = [pool.run([image], "nhwc")[0] for _ in range(4)]
|
||||
|
||||
assert dynamic_lib.output_queries == 1
|
||||
assert all(output.shape == (1, 80, 97) for output in outputs)
|
||||
|
||||
def test_rejects_a_shape_the_binary_was_not_compiled_for(self, dynamic_lib: FakeRknn) -> None:
|
||||
pool = RknnPoolExecutor("model.rknn", 1)
|
||||
|
||||
with pytest.raises(ValueError, match="compiled for"):
|
||||
pool.run([np.zeros((1, 48, 480, 3), dtype=np.uint8)], "nhwc")
|
||||
|
||||
assert pool.idle.qsize() == 1
|
||||
|
||||
|
||||
class TestCLIP:
|
||||
embedding = np.random.rand(512).astype(np.float32)
|
||||
cache_dir = Path("test_cache")
|
||||
|
||||
Generated
-53
@@ -868,7 +868,6 @@ openvino = [
|
||||
]
|
||||
rknn = [
|
||||
{ name = "onnxruntime" },
|
||||
{ name = "rknn-toolkit-lite2" },
|
||||
]
|
||||
rocm = [
|
||||
{ name = "onnxruntime-migraphx" },
|
||||
@@ -943,7 +942,6 @@ requires-dist = [
|
||||
{ name = "python-multipart", specifier = ">=0.0.6,<1.0" },
|
||||
{ name = "rapidocr", specifier = ">=3.1.0" },
|
||||
{ name = "rich", specifier = ">=13.4.2" },
|
||||
{ name = "rknn-toolkit-lite2", marker = "extra == 'rknn'", specifier = ">=2.3.0,<3" },
|
||||
{ name = "tokenizers", specifier = ">=0.15.0,<1.0" },
|
||||
{ name = "uvicorn", extras = ["standard"], specifier = ">=0.22.0,<1.0" },
|
||||
]
|
||||
@@ -2303,57 +2301,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/82/3b/64d4899d73f91ba49a8c18a8ff3f0ea8f1c1d75481760df8c68ef5235bf5/rich-15.0.0-py3-none-any.whl", hash = "sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb", size = 310654, upload-time = "2026-04-12T08:24:02.83Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rknn-toolkit-lite2"
|
||||
version = "2.3.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
{ name = "psutil" },
|
||||
{ name = "ruamel-yaml" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/db/64c756f3f06b219e92ff4f0fd4e000870ee49f214d505ff01c8b0275e26d/rknn_toolkit_lite2-2.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1e4ec691fed900c0e6fde5e7d8eeba17f806aa45092b63b361ee775e2c1b50e", size = 527458, upload-time = "2025-04-09T09:39:58.881Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ruamel-yaml"
|
||||
version = "0.18.10"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "ruamel-yaml-clib", marker = "python_full_version < '3.13' and platform_python_implementation == 'CPython'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ea/46/f44d8be06b85bc7c4d8c95d658be2b68f27711f279bf9dd0612a5e4794f5/ruamel.yaml-0.18.10.tar.gz", hash = "sha256:20c86ab29ac2153f80a428e1254a8adf686d3383df04490514ca3b79a362db58", size = 143447, upload-time = "2025-01-06T14:08:51.334Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c2/36/dfc1ebc0081e6d39924a2cc53654497f967a084a436bb64402dfce4254d9/ruamel.yaml-0.18.10-py3-none-any.whl", hash = "sha256:30f22513ab2301b3d2b577adc121c6471f28734d3d9728581245f1e76468b4f1", size = 117729, upload-time = "2025-01-06T14:08:47.471Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ruamel-yaml-clib"
|
||||
version = "0.2.12"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/20/84/80203abff8ea4993a87d823a5f632e4d92831ef75d404c9fc78d0176d2b5/ruamel.yaml.clib-0.2.12.tar.gz", hash = "sha256:6c8fbb13ec503f99a91901ab46e0b07ae7941cd527393187039aec586fdfd36f", size = 225315, upload-time = "2024-10-20T10:10:56.22Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/48/41/e7a405afbdc26af961678474a55373e1b323605a4f5e2ddd4a80ea80f628/ruamel.yaml.clib-0.2.12-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:20b0f8dc160ba83b6dcc0e256846e1a02d044e13f7ea74a3d1d56ede4e48c632", size = 133433, upload-time = "2024-10-20T10:12:55.657Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/b0/b850385604334c2ce90e3ee1013bd911aedf058a934905863a6ea95e9eb4/ruamel.yaml.clib-0.2.12-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:943f32bc9dedb3abff9879edc134901df92cfce2c3d5c9348f172f62eb2d771d", size = 647362, upload-time = "2024-10-20T10:12:57.155Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/44/d0/3f68a86e006448fb6c005aee66565b9eb89014a70c491d70c08de597f8e4/ruamel.yaml.clib-0.2.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95c3829bb364fdb8e0332c9931ecf57d9be3519241323c5274bd82f709cebc0c", size = 754118, upload-time = "2024-10-20T10:12:58.501Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/52/a9/d39f3c5ada0a3bb2870d7db41901125dbe2434fa4f12ca8c5b83a42d7c53/ruamel.yaml.clib-0.2.12-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:749c16fcc4a2b09f28843cda5a193e0283e47454b63ec4b81eaa2242f50e4ccd", size = 706497, upload-time = "2024-10-20T10:13:00.211Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b0/fa/097e38135dadd9ac25aecf2a54be17ddf6e4c23e43d538492a90ab3d71c6/ruamel.yaml.clib-0.2.12-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bf165fef1f223beae7333275156ab2022cffe255dcc51c27f066b4370da81e31", size = 698042, upload-time = "2024-10-21T11:26:46.038Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/d5/a659ca6f503b9379b930f13bc6b130c9f176469b73b9834296822a83a132/ruamel.yaml.clib-0.2.12-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:32621c177bbf782ca5a18ba4d7af0f1082a3f6e517ac2a18b3974d4edf349680", size = 745831, upload-time = "2024-10-21T11:26:47.487Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/db/5d/36619b61ffa2429eeaefaab4f3374666adf36ad8ac6330d855848d7d36fd/ruamel.yaml.clib-0.2.12-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:b82a7c94a498853aa0b272fd5bc67f29008da798d4f93a2f9f289feb8426a58d", size = 715692, upload-time = "2024-12-11T19:58:17.252Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b1/82/85cb92f15a4231c89b95dfe08b09eb6adca929ef7df7e17ab59902b6f589/ruamel.yaml.clib-0.2.12-cp312-cp312-win32.whl", hash = "sha256:e8c4ebfcfd57177b572e2040777b8abc537cdef58a2120e830124946aa9b42c5", size = 98777, upload-time = "2024-10-20T10:13:01.395Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d7/8f/c3654f6f1ddb75daf3922c3d8fc6005b1ab56671ad56ffb874d908bfa668/ruamel.yaml.clib-0.2.12-cp312-cp312-win_amd64.whl", hash = "sha256:0467c5965282c62203273b838ae77c0d29d7638c8a4e3a1c8bdd3602c10904e4", size = 115523, upload-time = "2024-10-20T10:13:02.768Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/29/00/4864119668d71a5fa45678f380b5923ff410701565821925c69780356ffa/ruamel.yaml.clib-0.2.12-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4c8c5d82f50bb53986a5e02d1b3092b03622c02c2eb78e29bec33fd9593bae1a", size = 132011, upload-time = "2024-10-20T10:13:04.377Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/5e/212f473a93ae78c669ffa0cb051e3fee1139cb2d385d2ae1653d64281507/ruamel.yaml.clib-0.2.12-cp313-cp313-manylinux2014_aarch64.whl", hash = "sha256:e7e3736715fbf53e9be2a79eb4db68e4ed857017344d697e8b9749444ae57475", size = 642488, upload-time = "2024-10-20T10:13:05.906Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/1f/8f/ecfbe2123ade605c49ef769788f79c38ddb1c8fa81e01f4dbf5cf1a44b16/ruamel.yaml.clib-0.2.12-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b7e75b4965e1d4690e93021adfcecccbca7d61c7bddd8e22406ef2ff20d74ef", size = 745066, upload-time = "2024-10-20T10:13:07.26Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e2/a9/28f60726d29dfc01b8decdb385de4ced2ced9faeb37a847bd5cf26836815/ruamel.yaml.clib-0.2.12-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:96777d473c05ee3e5e3c3e999f5d23c6f4ec5b0c38c098b3a5229085f74236c6", size = 701785, upload-time = "2024-10-20T10:13:08.504Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/84/7e/8e7ec45920daa7f76046578e4f677a3215fe8f18ee30a9cb7627a19d9b4c/ruamel.yaml.clib-0.2.12-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:3bc2a80e6420ca8b7d3590791e2dfc709c88ab9152c00eeb511c9875ce5778bf", size = 693017, upload-time = "2024-10-21T11:26:48.866Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c5/b3/d650eaade4ca225f02a648321e1ab835b9d361c60d51150bac49063b83fa/ruamel.yaml.clib-0.2.12-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:e188d2699864c11c36cdfdada94d781fd5d6b0071cd9c427bceb08ad3d7c70e1", size = 741270, upload-time = "2024-10-21T11:26:50.213Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/87/b8/01c29b924dcbbed75cc45b30c30d565d763b9c4d540545a0eeecffb8f09c/ruamel.yaml.clib-0.2.12-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4f6f3eac23941b32afccc23081e1f50612bdbe4e982012ef4f5797986828cd01", size = 709059, upload-time = "2024-12-11T19:58:18.846Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/30/8c/ed73f047a73638257aa9377ad356bea4d96125b305c34a28766f4445cc0f/ruamel.yaml.clib-0.2.12-cp313-cp313-win32.whl", hash = "sha256:6442cb36270b3afb1b4951f060eccca1ce49f3d087ca1ca4563a6eb479cb3de6", size = 98583, upload-time = "2024-10-20T10:13:09.658Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b0/85/e8e751d8791564dd333d5d9a4eab0a7a115f7e349595417fd50ecae3395c/ruamel.yaml.clib-0.2.12-cp313-cp313-win_amd64.whl", hash = "sha256:e5b8daf27af0b90da7bb903a876477a9e6d7270be6146906b276605997c7e9a3", size = 115190, upload-time = "2024-10-20T10:13:10.66Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.17.1"
|
||||
|
||||
Reference in New Issue
Block a user