enhance armnn conversion
This commit is contained in:
mertalev
@@ -0,0 +1,297 @@
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import os
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import platform
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import subprocess
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from tempfile import TemporaryDirectory
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from typing import Callable, ClassVar
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import onnx
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import torch
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from onnx2torch import convert
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from onnx2torch.node_converters.registry import add_converter
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from onnxruntime.tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
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from tinynn.converter import TFLiteConverter
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from huggingface_hub import snapshot_download
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from onnx2torch.onnx_graph import OnnxGraph
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from onnx2torch.onnx_node import OnnxNode
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from onnx2torch.utils.common import OperationConverterResult, onnx_mapping_from_node
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from onnx.shape_inference import infer_shapes_path
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from huggingface_hub import login, upload_file
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# egregious hacks:
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# changed `Clip`'s min/max logic to skip empty strings
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# changed OnnxSqueezeDynamicAxes to use `sorted` instead of `torch.sort``
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# commented out shape inference in `fix_output_shapes``
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class ArgMax(torch.nn.Module):
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def __init__(self, dim: int = -1, keepdim: bool = False):
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super().__init__()
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self.dim = dim
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self.keepdim = keepdim
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return torch.argmax(input, dim=self.dim, keepdim=self.keepdim)
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class Erf(torch.nn.Module):
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return torch.erf(input)
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@add_converter(operation_type="ArgMax", version=13)
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def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:
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return OperationConverterResult(
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torch_module=ArgMax(),
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onnx_mapping=onnx_mapping_from_node(node=node),
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)
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class ExportBase(torch.nn.Module):
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task: ClassVar[str]
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def __init__(
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self,
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name: str,
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input_shape: tuple[int, ...],
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pretrained: str | None = None,
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optimization_level: int = 5,
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):
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super().__init__()
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self.name = name
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self.optimize = optimization_level
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self.nchw_transpose = False
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self.input_shape = input_shape
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self.pretrained = pretrained
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self.dummy_param = torch.nn.Parameter(torch.empty(0))
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self.model = self.load().eval()
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for param in self.parameters():
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param.requires_grad_(False)
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self.eval()
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def load(self) -> torch.nn.Module:
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cache_dir = os.path.join(os.environ["CACHE_DIR"], self.model_name)
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task_path = os.path.join(cache_dir, self.task)
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model_path = os.path.join(task_path, "model.onnx")
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if not os.path.isfile(model_path):
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snapshot_download(self.repo_name, cache_dir=cache_dir, local_dir=cache_dir)
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infer_shapes_path(model_path, check_type=True, strict_mode=True, data_prop=True)
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onnx_model = onnx.load_model(model_path)
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make_input_shape_fixed(onnx_model.graph, onnx_model.graph.input[0].name, self.input_shape)
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fix_output_shapes(onnx_model)
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# try:
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# onnx.save(onnx_model, model_path)
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# except:
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# onnx.save(onnx_model, model_path, save_as_external_data=True, all_tensors_to_one_file=False)
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# infer_shapes_path(model_path, check_type=True, strict_mode=True, data_prop=True)
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# onnx_model = onnx.load_model(model_path)
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# onnx_model = infer_shapes(onnx_model, check_type=True, strict_mode=True, data_prop=True)
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return convert(onnx_model)
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def forward(self, *inputs: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor]:
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if self.precision == "fp16":
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inputs = tuple(i.half() for i in inputs)
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out = self._forward(*inputs)
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if self.precision == "fp16":
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if isinstance(out, tuple):
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return tuple(o.float() for o in out)
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return out.float()
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return out
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def _forward(self, *inputs: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor]:
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return self.model(*inputs)
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def to_armnn(self, output_path: str) -> None:
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output_dir = os.path.dirname(output_path)
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os.makedirs(output_dir, exist_ok=True)
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self(*self.dummy_inputs)
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print(f"Exporting {self.model_name} ({self.task}) with {self.precision} precision")
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jit = torch.jit.trace(self, self.dummy_inputs).eval()
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with TemporaryDirectory() as tmpdir:
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tflite_model_path = os.path.join(tmpdir, "model.tflite")
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converter = TFLiteConverter(
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jit,
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self.dummy_inputs,
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tflite_model_path,
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optimize=self.optimize,
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nchw_transpose=self.nchw_transpose,
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)
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# segfaults on ARM, must run on x86_64 / AMD64
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converter.convert()
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subprocess.run(
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[
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"./armnnconverter",
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"-f",
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"tflite-binary",
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"-m",
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tflite_model_path,
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"-i",
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"input_tensor",
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"-o",
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"output_tensor",
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"-p",
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output_path,
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],
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capture_output=True,
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)
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print(f"Finished exporting {self.name} ({self.task}) with {self.precision} precision")
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@property
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def dummy_inputs(self) -> tuple[torch.FloatTensor]:
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return (torch.rand(self.input_shape, device=self.device, dtype=self.dtype),)
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@property
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def model_name(self) -> str:
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return f"{self.name}__{self.pretrained}" if self.pretrained else self.name
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@property
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def repo_name(self) -> str:
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return f"immich-app/{self.model_name}"
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@property
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def device(self) -> torch.device:
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return self.dummy_param.device
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@property
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def dtype(self) -> torch.dtype:
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return self.dummy_param.dtype
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@property
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def precision(self) -> str:
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match self.dtype:
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case torch.float32:
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return "fp32"
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case torch.float16:
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return "fp16"
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case _:
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raise ValueError(f"Unsupported dtype {self.dtype}")
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class ArcFace(ExportBase):
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task = "recognition"
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class RetinaFace(ExportBase):
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task = "detection"
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class OpenClipVisual(ExportBase):
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task = "visual"
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class OpenClipTextual(ExportBase):
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task = "textual"
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@property
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def dummy_inputs(self) -> tuple[torch.LongTensor]:
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return (torch.randint(0, 5000, self.input_shape, device=self.device, dtype=torch.int32),)
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class MClipTextual(ExportBase):
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task = "textual"
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@property
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def dummy_inputs(self) -> tuple[torch.LongTensor]:
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return (
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torch.randint(0, 5000, self.input_shape, device=self.device, dtype=torch.int32),
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torch.randint(0, 1, self.input_shape, device=self.device, dtype=torch.int32),
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)
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def main() -> None:
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if platform.machine() not in ("x86_64", "AMD64"):
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raise RuntimeError(f"Can only run on x86_64 / AMD64, not {platform.machine()}")
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login(token=os.environ["HF_AUTH_TOKEN"])
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os.environ["LD_LIBRARY_PATH"] = "armnn"
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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failed: list[Callable[[], ExportBase]] = [
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lambda: OpenClipVisual("ViT-H-14-378-quickgelu", (1, 3, 378, 378), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
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lambda: OpenClipVisual("ViT-H-14-quickgelu", (1, 3, 224, 224), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
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lambda: OpenClipTextual("nllb-clip-base-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
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lambda: OpenClipTextual("nllb-clip-large-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b_e16"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b_e16"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b-s34b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b-s34b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16-plus-240", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16-plus-240", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b82k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion2b-s32b-b82k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-H-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-H-14", (1, 77), pretrained="laion2b-s32b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-g-14", (1, 3, 224, 224), pretrained="laion2b-s12b-b42k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-g-14", (1, 77), pretrained="laion2b-s12b-b42k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-16Plus", (1, 3, 240, 240)), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("XLM-Roberta-Large-ViT-H-14", (1, 3, 224, 224), pretrained="frozen_laion5b_s13b_b90k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("nllb-clip-base-siglip", (1, 3, 384, 384), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("nllb-clip-large-siglip", (1, 3, 384, 384), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
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lambda: OpenClipTextual("RN50", (1, 77), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
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lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="cc12m"), # BatchNorm operation with mean/var output is not implemented
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lambda: OpenClipTextual("RN50", (1, 77), pretrained="cc12m"), # BatchNorm operation with mean/var output is not implemented
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lambda: MClipTextual("XLM-Roberta-Large-Vit-L-14", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
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lambda: MClipTextual("XLM-Roberta-Large-Vit-B-16Plus", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
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lambda: MClipTextual("LABSE-Vit-L-14", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
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lambda: OpenClipTextual("XLM-Roberta-Large-ViT-H-14", (1, 77), pretrained="frozen_laion5b_s13b_b90k"), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
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]
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succeeded: list[Callable[[], ExportBase]] = [
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="openai"),
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="openai"),
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="openai"),
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lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="openai"),
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="openai"),
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="openai"),
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lambda: OpenClipVisual("ViT-L-14-336", (1, 3, 336, 336), pretrained="openai"),
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lambda: OpenClipTextual("ViT-L-14-336", (1, 77), pretrained="openai"),
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lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="openai"),
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lambda: OpenClipTextual("RN50", (1, 77), pretrained="openai"),
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lambda: OpenClipTextual("ViT-H-14-quickgelu", (1, 77), pretrained="dfn5b"),
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lambda: OpenClipTextual("ViT-H-14-378-quickgelu", (1, 77), pretrained="dfn5b"),
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lambda: OpenClipVisual("XLM-Roberta-Large-Vit-L-14", (1, 3, 224, 224)),
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lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-32", (1, 3, 224, 224)),
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lambda: ArcFace("buffalo_s", (1, 3, 112, 112), optimization_level=3),
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lambda: RetinaFace("buffalo_s", (1, 3, 640, 640), optimization_level=3),
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lambda: ArcFace("buffalo_m", (1, 3, 112, 112), optimization_level=3),
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lambda: RetinaFace("buffalo_m", (1, 3, 640, 640), optimization_level=3),
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lambda: ArcFace("buffalo_l", (1, 3, 112, 112), optimization_level=3),
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lambda: RetinaFace("buffalo_l", (1, 3, 640, 640), optimization_level=3),
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lambda: ArcFace("antelopev2", (1, 3, 112, 112), optimization_level=3),
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lambda: RetinaFace("antelopev2", (1, 3, 640, 640), optimization_level=3),
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]
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models: list[Callable[[], ExportBase]] = [*failed, *succeeded]
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for _model in succeeded:
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model = _model().to(device)
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try:
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relative_path = os.path.join(model.task, "model.armnn")
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output_path = os.path.join("output", model.model_name, relative_path)
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model.to_armnn(output_path)
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upload_file(path_or_fileobj=output_path, path_in_repo=relative_path, repo_id=model.repo_name)
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if device == torch.device("cuda"):
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model.half()
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relative_path = os.path.join(model.task, "fp16", "model.armnn")
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output_path = os.path.join("output", model.model_name, relative_path)
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model.to_armnn(output_path)
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upload_file(path_or_fileobj=output_path, path_in_repo=relative_path, repo_id=model.repo_name)
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except Exception as exc:
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print(f"Failed to export {model.model_name} ({model.task}): {exc}")
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if __name__ == "__main__":
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with torch.no_grad():
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main()
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