wehub-resource-sync
989 lines
39 KiB
Python
989 lines
39 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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import os
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import sys
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import tempfile
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from polygraphy import constants, mod, util
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from polygraphy.backend.base import BaseLoader
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from polygraphy.backend.onnx import util as onnx_util
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from polygraphy.datatype import DataType
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from polygraphy.logger import G_LOGGER, LogMode
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np = mod.lazy_import("numpy")
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onnx = mod.lazy_import("onnx>=1.8.1")
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onnxrt = mod.lazy_import("onnxruntime>=1.10.0")
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onnxmltools = mod.lazy_import(
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"onnxmltools==1.11.1", requires=["onnxconverter_common>=1.12.2"]
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)
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tf = mod.lazy_import("tensorflow<2.0")
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tf2onnx = mod.lazy_import("tf2onnx")
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tf_util = mod.lazy_import("polygraphy.backend.tf.util", log=False)
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gs = mod.lazy_import("onnx_graphsurgeon>=0.3.27")
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# ONNX-RT's shape inference also requires "sympy", but it is not reported as a dependency,
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# so we work around it by checking for it manually.
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onnxrt_symbolic_shape_inference = mod.lazy_import(
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"onnxruntime.tools.symbolic_shape_infer>=1.10.0", requires=["sympy"]
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)
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LARGE_MODEL_THRESHOLD = 512 << 20 # 512 MiB
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PROTOBUF_THRESHOLD = 2e9
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class BaseLoadOnnxCopy(BaseLoader):
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"""
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Abstract base class for loaders that require loading an ONNX model and potentially
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making a copy.
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"""
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def __init__(self, model, copy=None):
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"""
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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copy (bool): Whether to create a copy of the model first. Defaults to False.
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"""
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self._model = model
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self.copy = util.default(copy, False)
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def load(self):
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model, _ = util.invoke_if_callable(self._model)
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if self.copy:
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model = copy.copy(model)
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return model
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class _GSGraphManager:
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"""
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Imports an ONNX-GraphSurgeon graph.
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If the provided model is already a graph, the graph is not
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exported to ONNX.
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"""
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def __init__(self, model):
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self._model = model
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def __enter__(self):
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model, _ = util.invoke_if_callable(self._model)
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self.USE_GS_GRAPH = isinstance(model, gs.Graph)
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if self.USE_GS_GRAPH:
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self.graph = model.copy()
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else:
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self.graph = gs_from_onnx(model)
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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if self.USE_GS_GRAPH:
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self.retval = self.graph
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else:
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self.retval = gs.export_onnx(self.graph, do_type_check=False)
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@mod.export(funcify=True)
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class GsFromOnnx(BaseLoader):
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"""
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Functor that creates an ONNX-GraphSurgeon graph from an ONNX ModelProto.
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"""
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def __init__(self, model):
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"""
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Creates an ONNX-GraphSurgeon graph from an ONNX ModelProto.
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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"""
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self._model = model
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx_graphsurgeon.Graph: The ONNX-GraphSurgeon representation of the ONNX model
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"""
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model, _ = util.invoke_if_callable(self._model)
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return gs.import_onnx(model)
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@mod.export(funcify=True)
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class OnnxFromPath(BaseLoader):
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"""
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Functor that loads an ONNX model from a file.
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"""
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def __init__(self, path, external_data_dir=None, ignore_external_data=None):
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"""
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Loads an ONNX model from a file.
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Args:
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path (str): The path from which to load the model.
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external_data_dir (str): The directory where external data for the model is stored.
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ignore_external_data (bool):
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Whether to ignore any external data and just load the model structure without any weights.
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The model will be usable only for purposes that don't require weights, such as extracting
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subgraphs or inspecting model structure.
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This can be useful in cases where external data is not available.
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Defaults to False.
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"""
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self.path = path
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self.external_data_dir = external_data_dir
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self.ignore_external_data = util.default(ignore_external_data, False)
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The ONNX model
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"""
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G_LOGGER.info(f"Loading model: {self.path}")
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# If external_data_dir is not None, we'll load external data ourselves
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auto_load_ext_data = (
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self.external_data_dir is None and not self.ignore_external_data
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)
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try:
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model = onnx.load(self.path, load_external_data=auto_load_ext_data)
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except FileNotFoundError:
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if auto_load_ext_data:
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G_LOGGER.warning(
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"Failed to load model. This could be because external data could not be loaded.\n"
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"Hint: If you don't need the model weights, try ignoring external data by setting `ignore_external_data=True` "
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"or using the `--ignore-external-data` command-line option."
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)
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raise
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if self.external_data_dir is not None:
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G_LOGGER.verbose(f"Loading external data from: {self.external_data_dir}")
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onnx.external_data_helper.load_external_data_for_model(
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model, self.external_data_dir
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)
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return model
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@mod.export(funcify=True)
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class OnnxFromTfGraph(BaseLoader):
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"""
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Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter.
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"""
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def __init__(self, graph, opset=None, optimize=None):
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"""
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Converts a TensorFlow model into ONNX.
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Args:
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graph (Union[Tuple[tf.Graph, Sequence[str]], Callable() -> Tuple[tf.Graph, Sequence[str]]]):
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A tuple containing a TensorFlow graph and output names or a callable that returns one.
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opset (int): The ONNX opset to use during conversion.
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optimize (bool): Whether to use tf2onnx's graph optimization pass.
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"""
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self._graph = graph
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self.opset = util.default(opset, 11)
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self.optimize = util.default(optimize, True)
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The ONNX model.
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"""
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(graph, output_names), _ = util.invoke_if_callable(self._graph)
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input_names = list(tf_util.get_input_metadata(graph).keys())
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graphdef = graph.as_graph_def()
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if self.optimize:
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graphdef = tf2onnx.tfonnx.tf_optimize(
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input_names, output_names, graph.as_graph_def()
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)
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with tf.Graph().as_default() as graph, tf.compat.v1.Session(
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graph=graph
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) as sess:
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tf.import_graph_def(graphdef, name="")
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onnx_graph = tf2onnx.tfonnx.process_tf_graph(
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graph,
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input_names=input_names,
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output_names=output_names,
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opset=self.opset,
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)
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if self.optimize:
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onnx_graph = tf2onnx.optimizer.optimize_graph(onnx_graph)
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return onnx_graph.make_model("model")
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@mod.export(funcify=True)
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class ModifyOutputs(BaseLoadOnnxCopy):
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"""
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Functor that modifies the outputs of an ONNX model.
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"""
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def __init__(self, model, outputs=None, exclude_outputs=None, copy=None):
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"""
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Modifies outputs of an ONNX model.
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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outputs (Sequence[str]):
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Names of tensors to mark as outputs. If provided, this will override the
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existing model outputs.
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If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked.
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exclude_outputs (Sequence[str]):
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Names of tensors to exclude as outputs. This can be useful in conjunction with
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``outputs=constants.MARK_ALL`` to omit outputs.
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copy (bool): Whether to create a copy of the model first. Defaults to False.
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"""
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super().__init__(model, copy)
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self.outputs = outputs
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self.exclude_outputs = exclude_outputs
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The ONNX model with modified outputs.
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"""
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model = self.load()
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if self.outputs == constants.MARK_ALL:
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G_LOGGER.verbose("Marking all ONNX tensors as outputs")
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model = onnx_util.mark_layerwise(model)
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elif self.outputs is not None:
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model = onnx_util.mark_outputs(model, self.outputs)
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if self.exclude_outputs is not None:
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model = onnx_util.unmark_outputs(model, self.exclude_outputs)
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return model
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@mod.export(funcify=True)
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class ConvertToFp16(BaseLoadOnnxCopy):
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"""
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Functor that converts all floating point tensors in the model to 16-bit precision.
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This is *not* needed in order to use TensorRT's fp16 precision, but may be useful for other backends.
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"""
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def __init__(self, model, copy=None):
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"""
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Converts all floating point tensors in the model to 16-bit precision.
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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copy (bool): Whether to create a copy of the model first. Defaults to False.
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"""
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super().__init__(model, copy)
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The modified ONNX model.
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"""
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model = self.load()
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G_LOGGER.info("Converting float tensors to float16")
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model = onnxmltools.utils.float16_converter.convert_float_to_float16(
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model, keep_io_types=True, disable_shape_infer=True
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)
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return model
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@mod.export(funcify=True)
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class FoldConstants(BaseLoadOnnxCopy):
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"""
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Functor that folds constants in an ONNX model.
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"""
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def __init__(
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self,
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model,
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num_passes=None,
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do_shape_inference=None,
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partitioning=None,
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fold_shapes=None,
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copy=None,
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error_ok=None,
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size_threshold=None,
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allow_onnxruntime_shape_inference=None,
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):
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"""
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Fold constants in an ONNX model.
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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num_passes (int):
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The number of constant folding passes to run.
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Sometimes, subgraphs that compute tensor shapes may not be foldable in a single pass.
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By default, Polygraphy will automatically determine the number of passes required.
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do_shape_inference (bool):
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Whether to run shape inference in the model between passes.
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This enables the loader to fold `Shape` nodes.
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Only effective if `fold_shapes` is True.
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Defaults to True.
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partitioning (Union[str, None]):
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Whether/How to partition the graph so that errors in folding one
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part of a model do not affect other parts. Available modes are:
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- None: Do not partition the graph. If inference fails, no constants are folded.
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- 'basic': Partition the graph. If inference fails in one partition, other partitions will remain unaffected.
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- 'recursive': Parition the graph recursively. If inference fails in a partition, the partition will be further partitioned.
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Defaults to None.
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fold_shapes (bool):
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Whether to fold `Shape` nodes in the graph.
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This requires shapes to be inferred in the graph, and can only fold
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static shapes.
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Defaults to True.
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copy (bool):
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Whether to create a copy of the model first.
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Defaults to False.
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error_ok (bool):
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Whether to suppress errors during constant folding.
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If this is set to ``False``, errors will be re-raised.
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Defaults to True.
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size_threshold (int):
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The maximum size threshold, in bytes, for which to fold constants.
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Any tensors larger than this value will not be folded.
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Set to ``None`` to disable the size threshold and always fold constants.
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For example, some models may apply ops like `Tile` or `Expand` to constants, which can
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result in very large tensors. Rather than pre-computing those constants and bloating
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the model size, it may be desirable to skip folding them and allow them to be computed
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at runtime.
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Defaults to None.
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allow_onnxruntime_shape_inference (bool):
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Allow ONNX-Runtime's shape inference to be used if available instead of ONNX's
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shape inference utilities. The former may provide performance or memory usage benefits.
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Has no effect if ``do_shape_inference`` is False.
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Defaults to True.
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"""
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super().__init__(model, copy)
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self.num_passes = num_passes
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self.do_shape_inference = util.default(do_shape_inference, True)
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self.partitioning = partitioning
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self.fold_shapes = util.default(fold_shapes, True)
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self.error_ok = util.default(error_ok, True)
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self.size_threshold = size_threshold
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self.allow_onnxruntime_shape_inference = allow_onnxruntime_shape_inference
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@util.check_called_by("__call__")
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The new ONNX model with constants folded.
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"""
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def run_const_fold_pass(model):
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graph = gs_from_onnx(model)
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del model
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graph.fold_constants(
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fold_shapes=self.fold_shapes,
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partitioning=self.partitioning,
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size_threshold=self.size_threshold,
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)
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model = gs.export_onnx(graph.cleanup(), do_type_check=False)
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del graph
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if self.fold_shapes and self.do_shape_inference:
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model = infer_shapes(
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model, allow_onnxruntime=self.allow_onnxruntime_shape_inference
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)
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return model
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# Need to manually trigger the autoinstall this since it's used by ONNX-GS, which does not have an autoinstall mechanism.
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mod.autoinstall(onnxrt)
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if not onnxrt.is_installed() or not onnxrt.is_importable():
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G_LOGGER.error(
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f"ONNX-Runtime is not installed, so constant folding may be suboptimal or not work at all.\n"
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f"Consider installing ONNX-Runtime: {sys.executable} -m pip install onnxruntime"
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)
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model = self.load()
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prefold_num_nodes = len(model.graph.node)
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postfold_num_nodes = -1
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index = 0
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while (prefold_num_nodes != postfold_num_nodes) and (
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self.num_passes is None or index < self.num_passes
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):
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prefold_num_nodes = onnx_util.get_num_nodes(model)
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G_LOGGER.start(f"Folding Constants | Pass {index + 1}")
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try:
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model = run_const_fold_pass(model)
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except Exception as err:
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if not self.error_ok:
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raise
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G_LOGGER.warning(
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f"Constant folding pass failed. Skipping subsequent passes.\nNote: Error was:\n{err}"
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)
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break
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else:
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postfold_num_nodes = onnx_util.get_num_nodes(model)
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index += 1
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G_LOGGER.finish(
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f"{constants.TAB}Total Nodes | Original: {prefold_num_nodes:5}, "
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f"After Folding: {postfold_num_nodes:5} | {prefold_num_nodes - postfold_num_nodes:5} Nodes Folded"
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)
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return model
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@mod.export(funcify=True)
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class SetUpperBound(BaseLoadOnnxCopy):
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"""
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Functor that sets upper bounds for tensors with unbounded DDS in an ONNX model.
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Requires that the model has been constant folded and has shapes inferred.
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"""
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def __init__(
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self,
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model,
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upper_bounds,
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copy=None,
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):
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"""
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Set upper bounds for tensors with unbounded DDS in an ONNX model.
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Args:
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model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
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An ONNX model or a callable that returns one.
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upper_bounds (Union[int, Dict[str, int]]):
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The upper bounds for tensors with unbounded DDS.
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If a single integer is provided, it will be used as the default upper bound for all tensors with unbounded DDS.
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This can also be provided on a per-tensor basis using a dictionary. In that case, use an empty string ("") as the
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key to specify default upper bound for tensors not explicitly listed.
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copy (bool):
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Whether to create a copy of the model first.
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Defaults to False.
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"""
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super().__init__(model, copy)
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self.upper_bounds = upper_bounds
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def call_impl(self):
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"""
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Returns:
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onnx.ModelProto: The new ONNX model.
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"""
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# Set upper bounds for tensors with unbounded DDS in the onnx model.
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def set_upper_bound(graph, target_tensor_list):
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applied_bounds = {}
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for tensor in target_tensor_list:
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upper_bound = util.value_or_from_dict(self.upper_bounds, tensor.name)
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if upper_bound is None:
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|
continue
|
|
# Insert a min operator to set the upper bound for the target tensor.
|
|
# A target tensor should always be produced from a single node.
|
|
assert len(tensor.inputs) == 1
|
|
producer = tensor.inputs[0]
|
|
producer_idx = producer.outputs.index(tensor)
|
|
tensor_copy = gs.Variable(
|
|
tensor.name + "_copy", dtype=tensor.dtype, shape=tensor.shape
|
|
)
|
|
upper_bound_values = np.array(upper_bound)
|
|
if tensor.shape is not None and len(tensor.shape) > 0:
|
|
upper_bound_values = np.array([upper_bound] * len(tensor.shape))
|
|
tensor_upper_bound = gs.Constant(
|
|
tensor.name + "_upper_bound", values=upper_bound_values
|
|
)
|
|
min_node = gs.Node(
|
|
op="Min", inputs=[tensor_copy, tensor_upper_bound], outputs=[tensor]
|
|
)
|
|
producer.outputs[producer_idx] = tensor_copy
|
|
tensor.inputs = [min_node]
|
|
graph.nodes.append(min_node)
|
|
applied_bounds[tensor.name] = upper_bound
|
|
G_LOGGER.info(f"Set tensor upper bounds: {applied_bounds}")
|
|
return graph
|
|
|
|
model = self.load()
|
|
graph = gs_from_onnx(model)
|
|
|
|
target_tensor_list = onnx_util.get_unbounded_dds_tensors(graph)
|
|
|
|
tensor_map = graph.tensors()
|
|
target_names = {tensor.name for tensor in target_tensor_list}
|
|
if isinstance(self.upper_bounds, dict):
|
|
input_names = set(self.upper_bounds.keys()) - {""}
|
|
# Report error when input tensor name is not in the graph.
|
|
util.check_sequence_contains(
|
|
set(tensor_map.keys()),
|
|
input_names,
|
|
name="the upper bounds dictionary",
|
|
items_name="tensors",
|
|
check_extra=False,
|
|
)
|
|
# Report warning when input tensor is not a unbounded DDS tensor.
|
|
util.check_sequence_contains(
|
|
set(target_names),
|
|
input_names,
|
|
name="the upper bounds dictionary",
|
|
items_name="tensors",
|
|
log_func=G_LOGGER.warning,
|
|
check_extra=False,
|
|
)
|
|
# Still set upper bound for input tensors with bounded shapes.
|
|
target_names.update(input_names)
|
|
graph = set_upper_bound(graph, [tensor_map[name] for name in target_names])
|
|
model = gs.export_onnx(graph.cleanup(), do_type_check=False)
|
|
|
|
return model
|
|
|
|
|
|
@mod.export(funcify=True)
|
|
class InferShapes(BaseLoader):
|
|
"""
|
|
Functor that runs shape inference on an ONNX model.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model,
|
|
error_ok=None,
|
|
external_data_dir=None,
|
|
save_to_disk_threshold_bytes=None,
|
|
allow_onnxruntime=None,
|
|
):
|
|
"""
|
|
Run shape inference on an ONNX model.
|
|
|
|
Args:
|
|
model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto, str, Callable() -> str]):
|
|
An ONNX model or a callable that returns one, or a path to a model.
|
|
Supports models larger than the 2 GB protobuf limit.
|
|
|
|
error_ok (bool):
|
|
Whether errors during shape inference should be suppressed.
|
|
Defaults to True.
|
|
external_data_dir (str):
|
|
The directory where external data for the model is stored.
|
|
Only used if the model is provided via a path rather than a loader.
|
|
save_to_disk_threshold_bytes (int):
|
|
The size in bytes above which a ModelProto will be serialized to the disk
|
|
before running shape inference.
|
|
This can be used to work around the 2 GB protobuf limitation.
|
|
Defaults to 2 GB.
|
|
allow_onnxruntime (bool):
|
|
Allow ONNX-Runtime's shape inference to be used if available instead of ONNX's
|
|
shape inference utilities. The former may provide performance or memory usage benefits.
|
|
Defaults to True.
|
|
"""
|
|
self._model = model
|
|
self.error_ok = util.default(error_ok, True)
|
|
self.external_data_dir = external_data_dir
|
|
# Subtract a little so we're below the real threshold
|
|
self.save_to_disk_threshold_bytes = util.default(
|
|
save_to_disk_threshold_bytes, PROTOBUF_THRESHOLD
|
|
)
|
|
self.allow_onnxruntime = util.default(allow_onnxruntime, True)
|
|
|
|
def _run_onnx_shape_inference(self, model, external_data_dir):
|
|
if isinstance(model, onnx.ModelProto):
|
|
MODEL_SIZE = model.ByteSize()
|
|
if MODEL_SIZE > LARGE_MODEL_THRESHOLD:
|
|
G_LOGGER.warning(
|
|
f"Attempting to run shape inference on a large model ({MODEL_SIZE // 1024.0 ** 2} MiB). "
|
|
"This may require a large amount of memory.\nIf memory consumption becomes too high, "
|
|
"the process may be killed. You may want to try disabling shape inference in that case. ",
|
|
mode=LogMode.ONCE,
|
|
)
|
|
|
|
if MODEL_SIZE >= self.save_to_disk_threshold_bytes:
|
|
G_LOGGER.warning(
|
|
f"Model size ({MODEL_SIZE / 1024.0 ** 2} MiB) exceeds the in-memory size threshold: "
|
|
f"{self.save_to_disk_threshold_bytes / 1024.0 ** 2} MiB.\n"
|
|
f"The model will be saved to a temporary file before shape inference is run.",
|
|
mode=LogMode.ONCE,
|
|
)
|
|
outdir = tempfile.TemporaryDirectory()
|
|
outpath = os.path.join(outdir.name, "tmp_model.onnx")
|
|
save_onnx(model, outpath, external_data_path="ext.data")
|
|
model = outpath
|
|
external_data_dir = outdir.name
|
|
|
|
if isinstance(model, onnx.ModelProto):
|
|
model = onnx.shape_inference.infer_shapes(model)
|
|
else:
|
|
tmp_path = util.NamedTemporaryFile(
|
|
prefix="tmp_polygraphy_", suffix=".onnx"
|
|
).name
|
|
G_LOGGER.verbose(f"Writing shape-inferred model to: {tmp_path}")
|
|
onnx.shape_inference.infer_shapes_path(model, tmp_path)
|
|
# In cases where the original model had external data stored in the same directory,
|
|
# the external data directory may not be explicitly specified.
|
|
# In such cases, we need to use the model's directory as the external data path
|
|
# for the newly generated model.
|
|
model = onnx_from_path(
|
|
tmp_path,
|
|
external_data_dir=util.default(
|
|
external_data_dir, os.path.dirname(model) or None
|
|
),
|
|
)
|
|
return model
|
|
|
|
def _run_onnxruntime_shape_inference(self, model, external_data_dir):
|
|
if not isinstance(model, onnx.ModelProto):
|
|
model = onnx_from_path(model, external_data_dir=external_data_dir)
|
|
return onnxrt_symbolic_shape_inference.SymbolicShapeInference.infer_shapes(
|
|
model, auto_merge=True
|
|
)
|
|
|
|
@util.check_called_by("__call__")
|
|
def call_impl(self):
|
|
"""
|
|
Returns:
|
|
onnx.ModelProto: The new ONNX model with shapes inferred.
|
|
"""
|
|
model, _ = util.invoke_if_callable(self._model)
|
|
external_data_dir = self.external_data_dir
|
|
|
|
G_LOGGER.verbose("Starting shape inference")
|
|
|
|
try:
|
|
use_onnx_shape_inference = not self.allow_onnxruntime
|
|
if self.allow_onnxruntime:
|
|
try:
|
|
model = self._run_onnxruntime_shape_inference(
|
|
model, external_data_dir
|
|
)
|
|
G_LOGGER.verbose(
|
|
"Inferred shapes in the model with `onnxruntime.tools.symbolic_shape_infer`.\n"
|
|
"Note: To force Polygraphy to use `onnx.shape_inference` instead, set `allow_onnxruntime=False` or "
|
|
"use the `--no-onnxruntime-shape-inference` command-line option.",
|
|
mode=LogMode.ONCE,
|
|
)
|
|
except Exception as err:
|
|
use_onnx_shape_inference = True
|
|
G_LOGGER.extra_verbose(
|
|
f"Error while running `onnxruntime.tools.symbolic_shape_infer`:\n{err}"
|
|
)
|
|
G_LOGGER.warning(
|
|
"Falling back to `onnx.shape_inference` because `onnxruntime.tools.symbolic_shape_infer` either could not be loaded "
|
|
"or did not run successfully.\n"
|
|
"Note that using ONNX-Runtime for shape inference may be faster and require less memory.\n"
|
|
"Consider installing ONNX-Runtime or setting POLYGRAPHY_AUTOINSTALL_DEPS=1 in your environment "
|
|
"variables to allow Polygraphy to do so automatically.",
|
|
mode=LogMode.ONCE,
|
|
)
|
|
|
|
if use_onnx_shape_inference:
|
|
model = self._run_onnx_shape_inference(model, external_data_dir)
|
|
except Exception as err:
|
|
if not self.error_ok:
|
|
raise
|
|
G_LOGGER.warning(f"ONNX shape inference exited with an error:\n{err}")
|
|
G_LOGGER.internal_error(
|
|
f"ONNX shape inference exited with an error:\n{err}"
|
|
)
|
|
|
|
if not isinstance(model, onnx.ModelProto):
|
|
model = onnx_from_path(model, external_data_dir=external_data_dir)
|
|
else:
|
|
G_LOGGER.verbose("Shape inference completed successfully")
|
|
|
|
return model
|
|
|
|
|
|
@mod.export(funcify=True)
|
|
class ExtractSubgraph(BaseLoader):
|
|
"""
|
|
Functor that extracts a subgraph from an ONNX model.
|
|
"""
|
|
|
|
def __init__(
|
|
self, model, input_metadata=None, output_metadata=None, check_meta=None
|
|
):
|
|
"""
|
|
Extracts a subgraph from an ONNX model.
|
|
|
|
Args:
|
|
model (Union[Union[onnx.ModelProto, onnx_graphsurgeon.Graph], Callable() -> Union[onnx.ModelProto, onnx_graphsurgeon.Graph]]):
|
|
An ONNX model or ONNX-GraphSurgeon Graph or a callable that returns one.
|
|
|
|
input_metadata (TensorMetadata):
|
|
Metadata for the inputs of the subgraph.
|
|
Name, shape, and data type are required.
|
|
If not provided, the graph outputs are not modified.
|
|
output_metadata (TensorMetadata):
|
|
Metadata for the outputs of the subgraph.
|
|
Name and data type are required.
|
|
If not provided, the graph outputs are not modified.
|
|
check_meta (bool):
|
|
Whether to check that the provided input and output metadata include
|
|
all the expected fields.
|
|
Defaults to True.
|
|
"""
|
|
self._model = model
|
|
self.input_metadata = input_metadata
|
|
self.output_metadata = output_metadata
|
|
self.check_meta = util.default(check_meta, True)
|
|
|
|
@util.check_called_by("__call__")
|
|
def call_impl(self):
|
|
"""
|
|
Returns:
|
|
Union[onnx.ModelProto, onnx_graphsurgeon.Graph]:
|
|
The new ONNX model or ONNX-GraphSurgeon Graph.
|
|
"""
|
|
with _GSGraphManager(self._model) as manager:
|
|
graph = manager.graph
|
|
TENSOR_MAP = graph.tensors()
|
|
|
|
def get_tensor(name):
|
|
if name not in TENSOR_MAP:
|
|
G_LOGGER.critical(f"Tensor: {name} does not exist in the model.")
|
|
return TENSOR_MAP[name]
|
|
|
|
def update_tensor(name, dtype, shape):
|
|
tensor = get_tensor(name)
|
|
# No need to update constants
|
|
if isinstance(tensor, gs.Variable):
|
|
tensor.dtype, tensor.shape = (
|
|
DataType.to_dtype(DataType.from_dtype(dtype), "onnx")
|
|
if dtype is not None
|
|
else None
|
|
) or tensor.dtype, shape or tensor.shape
|
|
return tensor
|
|
|
|
def check_meta(name, dtype, shape, meta_type, needs_shape=True):
|
|
if not self.check_meta:
|
|
return
|
|
if needs_shape and shape is None:
|
|
G_LOGGER.warning(
|
|
f"{meta_type} metadata should include shape, but no shape was provided for tensor: {name}"
|
|
)
|
|
if dtype is None:
|
|
G_LOGGER.warning(
|
|
f"{meta_type} metadata should include data type, but no data type was provided for tensor: {name}"
|
|
)
|
|
|
|
if self.input_metadata is not None:
|
|
graph.inputs.clear()
|
|
for name, (dtype, shape) in self.input_metadata.items():
|
|
tensor = update_tensor(name, dtype, shape)
|
|
check_meta(name, tensor.dtype, tensor.shape, "Input")
|
|
tensor.inputs.clear()
|
|
graph.inputs.append(tensor)
|
|
|
|
if self.output_metadata is not None:
|
|
graph.outputs.clear()
|
|
for name, (dtype, shape) in self.output_metadata.items():
|
|
tensor = update_tensor(name, dtype, shape)
|
|
check_meta(
|
|
name, tensor.dtype, tensor.shape, "Output", needs_shape=False
|
|
)
|
|
graph.outputs.append(tensor)
|
|
|
|
graph.cleanup()
|
|
|
|
tensor_map = graph.tensors()
|
|
for tensor in tensor_map.values():
|
|
if (
|
|
isinstance(tensor, gs.Variable)
|
|
and not tensor.inputs
|
|
and tensor not in graph.inputs
|
|
):
|
|
consumer_nodes = [
|
|
f"Node: '{node.name}' (Op: {node.op})"
|
|
for node in tensor.outputs
|
|
]
|
|
G_LOGGER.error(
|
|
f"Tensor: '{tensor.name}' is a variable tensor consumed by: {consumer_nodes}, "
|
|
"but is not produced by a node or marked as a graph input."
|
|
f"\nDid you forget to mark a tensor as a graph input? Hint: Try inspecting the resulting model. "
|
|
f"\nNote: The resulting model will not be valid!"
|
|
)
|
|
|
|
return manager.retval
|
|
|
|
|
|
@mod.export(funcify=True)
|
|
class SaveOnnx(BaseLoader):
|
|
"""
|
|
Functor that saves an ONNX model to the specified path.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model,
|
|
path,
|
|
external_data_path=None,
|
|
size_threshold=None,
|
|
all_tensors_to_one_file=None,
|
|
):
|
|
"""
|
|
Saves an ONNX model to the specified path.
|
|
|
|
Args:
|
|
model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
|
|
An ONNX model or a callable that returns one.
|
|
path (str): Path at which to write the ONNX model.
|
|
external_data_path (str):
|
|
Path to save external data.
|
|
This is always a relative path; external data is always written to the same
|
|
directory as the model.
|
|
Set to an empty string to use the default path.
|
|
Set to None to disable.
|
|
Defaults to None if the model is within the protobuf size threshold and an empty string otherwise.
|
|
size_threshold (int):
|
|
Tensor size threshold, in bytes, above which tensor data will be
|
|
stored in the external file.
|
|
Tensors smaller that this threshold will remain in the ONNX file.
|
|
Has no effect if external_data_path is not set.
|
|
Defaults to 1024.
|
|
all_tensors_to_one_file (bool):
|
|
Whether to write all tensors to one file when saving external data.
|
|
Has no effect if external_data_path is not set.
|
|
Defaults to True.
|
|
"""
|
|
self._model = model
|
|
self.path = path
|
|
self.external_data_path = external_data_path
|
|
self.size_threshold = size_threshold
|
|
self.all_tensors_to_one_file = all_tensors_to_one_file
|
|
|
|
@util.check_called_by("__call__")
|
|
def call_impl(self):
|
|
"""
|
|
Returns:
|
|
onnx.ModelProto: The model, after saving it.
|
|
"""
|
|
model, _ = util.invoke_if_callable(self._model)
|
|
G_LOGGER.info(f"Saving ONNX model to: {self.path}")
|
|
|
|
model_size = model.ByteSize()
|
|
if self.external_data_path is None and model_size >= PROTOBUF_THRESHOLD:
|
|
external_data_path = ""
|
|
G_LOGGER.warning(
|
|
f"Model size ({model_size // 1024.0 ** 2} MiB) exceeds protobuf size threshold ({PROTOBUF_THRESHOLD // 1024 ** 2} MiB). "
|
|
f"Will save weight data to an external file.\n"
|
|
f"To control the location of this file, use the `external_data_path` parameter or the `--external-data-path` command-line option. "
|
|
)
|
|
else:
|
|
external_data_path = self.external_data_path
|
|
|
|
if external_data_path is not None:
|
|
G_LOGGER.verbose(
|
|
f"Saving external data for ONNX model to: {external_data_path}"
|
|
)
|
|
try:
|
|
onnx.external_data_helper.convert_model_to_external_data(
|
|
model,
|
|
location=external_data_path,
|
|
all_tensors_to_one_file=util.default(
|
|
self.all_tensors_to_one_file, True
|
|
),
|
|
size_threshold=util.default(self.size_threshold, 1024),
|
|
)
|
|
except TypeError:
|
|
if self.size_threshold is not None:
|
|
G_LOGGER.warning(
|
|
"This version of onnx does not support size_threshold in convert_model_to_external_data"
|
|
)
|
|
onnx.external_data_helper.convert_model_to_external_data(
|
|
model,
|
|
location=external_data_path,
|
|
all_tensors_to_one_file=util.default(
|
|
self.all_tensors_to_one_file, True
|
|
),
|
|
)
|
|
else:
|
|
if self.size_threshold is not None:
|
|
G_LOGGER.warning(
|
|
"size_threshold is set, but external data path has not been set. "
|
|
"No external data will be written."
|
|
)
|
|
if self.all_tensors_to_one_file is not None:
|
|
G_LOGGER.warning(
|
|
"all_tensors_to_one_file is set, but external data path has not been set. "
|
|
"No external data will be written."
|
|
)
|
|
|
|
util.makedirs(self.path)
|
|
onnx.save(model, self.path)
|
|
return model
|
|
|
|
|
|
@mod.export(funcify=True)
|
|
class BytesFromOnnx(BaseLoader):
|
|
"""
|
|
Functor that serializes an ONNX model.
|
|
"""
|
|
|
|
def __init__(self, model):
|
|
"""
|
|
Serializes an ONNX model.
|
|
|
|
Args:
|
|
model (Union[onnx.ModelProto, Callable() -> onnx.ModelProto]):
|
|
An ONNX model or a callable that returns one.
|
|
"""
|
|
self._model = model
|
|
|
|
@util.check_called_by("__call__")
|
|
def call_impl(self):
|
|
"""
|
|
Returns:
|
|
bytes: The serialized model.
|
|
"""
|
|
model, _ = util.invoke_if_callable(self._model)
|
|
return model.SerializeToString()
|
|
|
|
|
|
@mod.export(funcify=True)
|
|
class OnnxFromBytes(BaseLoader):
|
|
"""
|
|
Functor that deserializes an ONNX model.
|
|
"""
|
|
|
|
def __init__(self, serialized_onnx):
|
|
"""
|
|
Deserializes an ONNX model.
|
|
|
|
Args:
|
|
serialized_onnx (Union[bytes, Callable() -> bytes]):
|
|
A serialized ONNX model or a callable that returns one.
|
|
"""
|
|
self._serialized_onnx = serialized_onnx
|
|
|
|
@util.check_called_by("__call__")
|
|
def call_impl(self):
|
|
"""
|
|
Returns:
|
|
onnx.ModelProto: The ONNX model.
|
|
"""
|
|
serialized_onnx, _ = util.invoke_if_callable(self._serialized_onnx)
|
|
model = onnx.ModelProto()
|
|
model.ParseFromString(serialized_onnx)
|
|
return model
|