From 1ad4953a662b56cc5855c7a4406bfbd4aab407af Mon Sep 17 00:00:00 2001 From: iguazio-cicd Date: Mon, 2 Oct 2023 09:13:09 +0000 Subject: [PATCH] Automatically generated by github-worflow[bot] for commit: 05e5f5b --- README.md | 60 ++ catalog.json | 2 +- functions/development/catalog.json | 2 +- .../onnx_utils/1.2.0/src/function.yaml | 194 ++++++ .../onnx_utils/1.2.0/src/item.yaml | 35 ++ .../onnx_utils/1.2.0/src/onnx_utils.ipynb | 396 +++++++++++++ .../onnx_utils/1.2.0/src/onnx_utils.py | 263 +++++++++ .../onnx_utils/1.2.0/src/requirements.txt | 12 + .../onnx_utils/1.2.0/src/test_onnx_utils.py | 378 ++++++++++++ .../1.2.0/static/documentation.html | 259 ++++++++ .../onnx_utils/1.2.0/static/example.html | 554 ++++++++++++++++++ .../onnx_utils/1.2.0/static/function.html | 216 +++++++ .../onnx_utils/1.2.0/static/item.html | 57 ++ .../onnx_utils/1.2.0/static/onnx_utils.html | 403 +++++++++++++ .../onnx_utils/1.2.0/static/source.html | 285 +++++++++ .../onnx_utils/latest/src/function.yaml | 24 +- .../onnx_utils/latest/src/item.yaml | 4 +- .../onnx_utils/latest/static/function.html | 24 +- .../onnx_utils/latest/static/item.html | 4 +- functions/development/tags.json | 2 +- .../transcribe/0.0.1/src/test_transcribe.py | 2 +- .../transcribe/latest/src/test_transcribe.py | 2 +- 22 files changed, 3151 insertions(+), 27 deletions(-) create mode 100644 functions/development/onnx_utils/1.2.0/src/function.yaml create mode 100644 functions/development/onnx_utils/1.2.0/src/item.yaml create mode 100644 functions/development/onnx_utils/1.2.0/src/onnx_utils.ipynb create mode 100644 functions/development/onnx_utils/1.2.0/src/onnx_utils.py create mode 100644 functions/development/onnx_utils/1.2.0/src/requirements.txt create mode 100644 functions/development/onnx_utils/1.2.0/src/test_onnx_utils.py create mode 100644 functions/development/onnx_utils/1.2.0/static/documentation.html create mode 100644 functions/development/onnx_utils/1.2.0/static/example.html create mode 100644 functions/development/onnx_utils/1.2.0/static/function.html create mode 100644 functions/development/onnx_utils/1.2.0/static/item.html create mode 100644 functions/development/onnx_utils/1.2.0/static/onnx_utils.html create mode 100644 functions/development/onnx_utils/1.2.0/static/source.html diff --git a/README.md b/README.md index f3428729..84614b86 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,63 @@ +### Change log [2023-10-02 09:13:07] +1. Item Updated: `feature_selection` (from version: `1.3.0` to `1.3.0`) +2. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`) +3. Item Updated: `open_archive` (from version: `1.1.0` to `1.1.0`) +4. Item Updated: `slack_notify` (from version: `1.1.0` to `1.1.0`) +5. Item Updated: `hugging_face_classifier_trainer` (from version: `0.1.0` to `0.1.0`) +6. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`) +7. Item Updated: `model_server` (from version: `1.1.0` to `1.1.0`) +8. Item Updated: `get_offline_features` (from version: `1.2.0` to `1.2.0`) +9. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`) +10. Item Updated: `sentiment_analysis_serving` (from version: `1.1.0` to `1.1.0`) +11. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`) +12. Item Updated: `ingest` (from version: `1.1.0` to `1.1.0`) +13. Item Updated: `tf2_serving_v2` (from version: `1.1.0` to `1.1.0`) +14. Item Updated: `snowflake_dask` (from version: `1.1.0` to `1.1.0`) +15. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`) +16. Item Updated: `auto_trainer` (from version: `1.3.0` to `1.3.0`) +17. Item Updated: `pandas_profiling_report` (from version: `1.1.0` to `1.1.0`) +18. Item Updated: `hugging_face_serving` (from version: `1.0.0` to `1.0.0`) +19. Item Updated: `xgb_trainer` (from version: `1.1.1` to `1.1.1`) +20. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`) +21. Item Updated: `huggingface_auto_trainer` (from version: `1.0.0` to `1.0.0`) +22. Item Updated: `aggregate` (from version: `1.3.0` to `1.3.0`) +23. Item Updated: `model_monitoring_stream` (from version: `1.1.0` to `1.1.0`) +24. Item Updated: `transcribe` (from version: `0.0.1` to `0.0.1`) +25. Item Updated: `model_monitoring_batch` (from version: `1.1.0` to `1.1.0`) +26. Item Updated: `rnn_serving` (from version: `1.1.0` to `1.1.0`) +27. Item Updated: `bert_embeddings` (from version: `1.2.0` to `1.2.0`) +28. Item Updated: `xgb_test` (from version: `1.1.1` to `1.1.1`) +29. Item Updated: `sql_to_file` (from version: `1.1.0` to `1.1.0`) +30. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`) +31. Item Updated: `feature_perms` (from version: `1.1.0` to `1.1.0`) +32. Item Updated: `batch_inference_v2` (from version: `1.8.0` to `1.8.0`) +33. Item Updated: `tf1_serving` (from version: `1.1.0` to `1.1.0`) +34. Item Updated: `validate_great_expectations` (from version: `1.1.0` to `1.1.0`) +35. Item Updated: `coxph_trainer` (from version: `1.1.0` to `1.1.0`) +36. Item Updated: `xgb_serving` (from version: `1.1.2` to `1.1.2`) +37. Item Updated: `concept_drift` (from version: `1.1.0` to `1.1.0`) +38. Item Updated: `question_answering` (from version: `0.2.0` to `0.2.0`) +39. Item Updated: `load_dataset` (from version: `1.1.0` to `1.1.0`) +40. Item Updated: `concept_drift_streaming` (from version: `1.1.0` to `1.1.0`) +41. Item Updated: `virtual_drift` (from version: `1.1.0` to `1.1.0`) +42. Item Updated: `batch_inference` (from version: `1.6.0` to `1.6.0`) +43. Item Updated: `coxph_test` (from version: `1.1.0` to `1.1.0`) +44. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`) +45. Item Updated: `churn_server` (from version: `1.1.0` to `1.1.0`) +46. Item Updated: `stream_to_parquet` (from version: `1.1.0` to `1.1.0`) +47. Item Updated: `pii_recognizer` (from version: `0.1.0` to `0.1.0`) +48. Item Updated: `load_dask` (from version: `1.1.0` to `1.1.0`) +49. Item Updated: `onnx_utils` (from version: `1.2.0` to `1.2.0`) +50. Item Updated: `xgb_custom` (from version: `1.1.0` to `1.1.0`) +51. Item Updated: `arc_to_parquet` (from version: `1.4.1` to `1.4.1`) +52. Item Updated: `gen_class_data` (from version: `1.2.0` to `1.2.0`) +53. Item Updated: `describe` (from version: `1.2.0` to `1.2.0`) +54. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`) +55. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`) +56. Item Updated: `describe_dask` (from version: `1.1.0` to `1.1.0`) +57. Item Updated: `v2_model_server` (from version: `1.1.0` to `1.1.0`) +58. Item Updated: `azureml_utils` (from version: `1.2.0` to `1.2.0`) + ### Change log [2023-10-01 10:02:30] 1. Item Updated: `feature_selection` (from version: `1.3.0` to `1.3.0`) 2. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`) diff --git a/catalog.json b/catalog.json index 34d0b227..ac44a175 100644 --- a/catalog.json +++ b/catalog.json @@ -1 +1 @@ -{"functions": {"development": {"tf2_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": 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"maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.0-rc13", "name": "batch_inference_v2", "platformVersion": "3.5.3", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference_v2.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.7.0", "assets": {"example": "src/batch_inference_v2.ipynb", "source": "src/batch_inference_v2.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}}} \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/src/function.yaml b/functions/development/onnx_utils/1.2.0/src/function.yaml new file mode 100644 index 00000000..7a0054c4 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/function.yaml @@ -0,0 +1,194 @@ +kind: job +metadata: + name: onnx-utils + tag: '' + hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2 + project: '' + labels: + author: guyl + categories: + - utils +spec: + command: '' + args: [] + image: '' + build: + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 + base_image: mlrun/mlrun + commands: [] + code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + with_mlrun: false + auto_build: true + requirements: + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 + entry_points: + tf_keras_to_onnx: + name: tf_keras_to_onnx + doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a + new model object. + parameters: + - name: model_handler + doc: An initialized TFKerasModelHandler with a loaded model to convert to + ONNX. + default: '' + - name: onnx_model_name + type: str + doc: The name to use to log the converted ONNX model. If not given, the given + `model_name` will be used with an additional suffix `_onnx`. Defaulted to + None. + default: null + - name: optimize_model + type: bool + doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before + saving the model. Defaulted to True. + default: true + - name: input_signature + type: List[Tuple[Tuple[int], str]] + doc: 'A list of the input layers shape and data type properties. Expected + to receive a list where each element is an input layer tuple. An input layer + tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s + data type, a mlrun.data_types.ValueType string. If None, the input signature + will be tried to be read from the model artifact. Defaulted to None.' + default: null + outputs: + - default: '' + lineno: 26 + pytorch_to_onnx: + name: pytorch_to_onnx + doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a + new model object. + parameters: + - name: model_handler + doc: An initialized PyTorchModelHandler with a loaded model to convert to + ONNX. + default: '' + - name: onnx_model_name + type: str + doc: The name to use to log the converted ONNX model. If not given, the given + `model_name` will be used with an additional suffix `_onnx`. Defaulted to + None. + default: null + - name: optimize_model + type: bool + doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before + saving the model. Defaulted to True. + default: true + - name: input_signature + type: List[Tuple[Tuple[int, ], str]] + doc: 'A list of the input layers shape and data type properties. Expected + to receive a list where each element is an input layer tuple. An input layer + tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s + data type, a mlrun.data_types.ValueType string. If None, the input signature + will be tried to be read from the model artifact. Defaulted to None.' + default: null + - name: input_layers_names + type: List[str] + doc: 'List of names to assign to the input nodes of the graph in order. All + of the other parameters (inner layers) can be set as well by passing additional + names in the list. The order is by the order of the parameters in the model. + If None, the inputs will be read from the handler''s inputs. If its also + None, it is defaulted to: "input_0", "input_1", ...' + default: null + - name: output_layers_names + type: List[str] + doc: 'List of names to assign to the output nodes of the graph in order. If + None, the outputs will be read from the handler''s outputs. If its also + None, it is defaulted to: "output_0" (for multiple outputs, this parameter + must be provided).' + default: null + - name: dynamic_axes + type: Dict[str, Dict[int, str]] + doc: 'If part of the input / output shape is dynamic, like (batch_size, 3, + 32, 32) you can specify it by giving a dynamic axis to the input / output + layer by its name as follows: { "input layer name": {0: "batch_size"}, "output + layer name": {0: "batch_size"}, } If provided, the ''is_batched'' flag will + be ignored. Defaulted to None.' + default: null + - name: is_batched + type: bool + doc: Whether to include a batch size as the first axis in every input and + output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided. + default: true + outputs: + - default: '' + lineno: 81 + to_onnx: + name: to_onnx + doc: Convert the given model to an ONNX model. + parameters: + - name: context + type: MLClientCtx + doc: The MLRun function execution context + default: '' + - name: model_path + type: str + doc: The model path store object. + default: '' + - name: onnx_model_name + type: str + doc: The name to use to log the converted ONNX model. If not given, the given + `model_name` will be used with an additional suffix `_onnx`. Defaulted to + None. + default: null + - name: optimize_model + type: bool + doc: Whether to optimize the ONNX model using 'onnxoptimizer' before saving + the model. Defaulted to True. + default: true + - name: framework_kwargs + type: Dict[str, Any] + doc: Additional arguments each framework may require in order to convert to + ONNX. To get the doc string of the desired framework onnx conversion function, + pass "help". + default: null + outputs: + - default: '' + lineno: 160 + optimize: + name: optimize + doc: Optimize the given ONNX model. + parameters: + - name: context + type: MLClientCtx + doc: The MLRun function execution context. + default: '' + - name: model_path + type: str + doc: Path to the ONNX model object. + default: '' + - name: optimizations + type: List[str] + doc: List of possible optimizations. To see what optimizations are available, + pass "help". If None, all of the optimizations will be used. Defaulted to + None. + default: null + - name: fixed_point + type: bool + doc: Optimize the weights using fixed point. Defaulted to False. + default: false + - name: optimized_model_name + type: str + doc: The name of the optimized model. If None, the original model will be + overridden. Defaulted to None. + default: null + outputs: + - default: '' + lineno: 219 + description: ONNX intigration in MLRun, some utils functions for the ONNX framework, + optimizing and converting models from different framework to ONNX using MLRun. + default_handler: to_onnx + disable_auto_mount: false + allow_empty_resources: true + clone_target_dir: '' + env: [] + priority_class_name: '' + preemption_mode: prevent + affinity: null + tolerations: null + security_context: {} +verbose: false diff --git a/functions/development/onnx_utils/1.2.0/src/item.yaml b/functions/development/onnx_utils/1.2.0/src/item.yaml new file mode 100644 index 00000000..36335837 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/item.yaml @@ -0,0 +1,35 @@ +apiVersion: v1 +categories: +- utils +description: ONNX intigration in MLRun, some utils functions for the ONNX framework, + optimizing and converting models from different framework to ONNX using MLRun. +doc: '' +example: onnx_utils.ipynb +generationDate: 2022-08-28:17-25 +hidden: false +icon: '' +labels: + author: guyl +maintainers: [] +marketplaceType: '' +mlrunVersion: 1.1.0 +name: onnx_utils +platformVersion: 3.5.0 +spec: + extra_spec: + allow_empty_resources: true + build: + auto_build: true + with_mlrun: false + filename: onnx_utils.py + handler: to_onnx + image: mlrun/mlrun + kind: job + requirements: + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 +url: '' +version: 1.2.0 diff --git a/functions/development/onnx_utils/1.2.0/src/onnx_utils.ipynb b/functions/development/onnx_utils/1.2.0/src/onnx_utils.ipynb new file mode 100644 index 00000000..e44f1524 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/onnx_utils.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ONNX Utils\n", + "\n", + "A collection of ONNX utils in one MLRun function. The function includes the following handlers:\n", + "\n", + "1. [to_onnx](#handler1) - Convert your model into `onnx` format.\n", + "2. [optimize](#handler2) - Perform ONNX optimizations using `onnxmodeloptimizer` on a given ONNX model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 1. to_onnx\n", + "\n", + "### 1.1. Docs\n", + "Convert the given model to an ONNX model.\n", + "\n", + "#### Parameters:\n", + "* **`context`**: `mlrun.MLClientCtx` - The MLRun function execution context\n", + "* **`model_path`**: `str` - The model path store object.\n", + "* **`onnx_model_name`**: `str = None` - The name to use to log the converted ONNX model. If not given, the given `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.\n", + "* **`optimize_model`**: `bool = True` - Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted to True.\n", + "* **`framework`**: `str = None` - The model's framework. If None, it will be read from the 'framework' label of the model artifact provided. Defaulted to None.\n", + "* **`framework_kwargs`**: `Dict[str, Any] = None` - Additional arguments each framework may require in order to convert to ONNX. *To get the doc string of the desired framework onnx conversion function, **pass \"help\"**.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### Supported keyword arguments (`framework_kwargs`) per framework:\n", + "`tensorflow.keras`:\n", + "* **`input_signature`**: `List[Tuple[Tuple[int], str]] = None` - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:\n", + " * [0] = Layer's shape, a tuple of integers.\n", + " * [1] = Layer's data type, a mlrun.data_types.ValueType string.\n", + "\n", + " If None, the input signature will be tried to be read automatically before converting to ONNX or from the model artifact if available. Defaulted to None.\n", + "\n", + "`torch`:\n", + "* **`input_signature`**: `List[Tuple[Tuple[int], str]] = None` - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:\n", + " * [0] = Layer's shape, a tuple of integers.\n", + " * [1] = Layer's data type, a mlrun.data_types.ValueType string.\n", + "\n", + " If None, the input signature will be read from the model artifact if available. Defaulted to None.\n", + "* **`input_layers_names`**: `List[str] = None` - List of names to assign to the input nodes of the graph in order. All of the other parameters (inner layers) can be set as well by passing additional names in the list. The order is by the order of the parameters in the model. If None, the inputs will be read from the handler's inputs. If its also None, it is defaulted to: \"input_0\", \"input_1\", ...\n", + "* **`output_layers_names`**: `List[str] = None` - List of names to assign to the output nodes of the graph in order. If None, the outputs will be read from the handler's outputs. If its also None, it is defaulted to: \"output_0\" (for multiple outputs, this parameter must be provided).\n", + "* **`param dynamic_axes`**: `Dict[str, Dict[int, str]] = None` - If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can specify it by giving a dynamic axis to the input / output layer by its name as follows:\n", + "```python\n", + "{\n", + " \"input layer name\": {0: \"batch_size\"},\n", + " \"output layer name\": {0: \"batch_size\"},\n", + "}\n", + "```\n", + "If provided, the 'is_batched' flag will be ignored. Defaulted to None.\n", + "* **`is_batched`**: `bool = True` - Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 1.2. Demo\n", + "\n", + "We will use the `TF.Keras` framework, a `MobileNetV2` as our model and we will convert it to ONNX using the `to_onnx` handler.\n", + "\n", + "1.2.1. First we will set a temporary artifact path for our model to be saved in and choose the models names:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os\n", + "from tempfile import TemporaryDirectory\n", + "\n", + "# Create a temporary directory for the model artifact:\n", + "ARTIFACT_PATH = TemporaryDirectory().name\n", + "os.makedirs(ARTIFACT_PATH)\n", + "\n", + "# Choose our model's name:\n", + "MODEL_NAME = \"mobilenetv2\"\n", + "\n", + "# Choose our ONNX version model's name:\n", + "ONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n", + "\n", + "# Choose our optimized ONNX version model's name:\n", + "OPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "1.2.2. Download the model from `keras.applications` and log it with MLRun's `TFKerasModelHandler`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# mlrun: start-code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow import keras\n", + "\n", + "import mlrun\n", + "import mlrun.frameworks.tf_keras as mlrun_tf_keras\n", + "\n", + "\n", + "def get_model(context: mlrun.MLClientCtx, model_name: str):\n", + " # Download the MobileNetV2 model:\n", + " model = keras.applications.mobilenet_v2.MobileNetV2()\n", + "\n", + " # Initialize a model handler for logging the model:\n", + " model_handler = mlrun_tf_keras.TFKerasModelHandler(\n", + " model_name=model_name,\n", + " model=model,\n", + " context=context\n", + " )\n", + "\n", + " # Log the model:\n", + " model_handler.log()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# mlrun: end-code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "1.2.3. Create the function using MLRun's `code_to_function` and run it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import mlrun\n", + "\n", + "\n", + "# Create the function parsing this notebook's code using 'code_to_function':\n", + "get_model_function = mlrun.code_to_function(\n", + " name=\"get_mobilenetv2\",\n", + " kind=\"job\",\n", + " image=\"mlrun/ml-models\"\n", + ")\n", + "\n", + "# Run the function to log the model:\n", + "get_model_run = get_model_function.run(\n", + " handler=\"get_model\",\n", + " artifact_path=ARTIFACT_PATH,\n", + " params={\n", + " \"model_name\": MODEL_NAME\n", + " },\n", + " local=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.2.4. Import the `onnx_utils` MLRun function and run it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Import the ONNX function from the marketplace:\n", + "onnx_utils_function = mlrun.import_function(\"hub://onnx_utils\")\n", + "\n", + "# Run the function to convert our model to ONNX:\n", + "to_onnx_run = onnx_utils_function.run(\n", + " handler=\"to_onnx\",\n", + " artifact_path=ARTIFACT_PATH,\n", + " params={\n", + " \"model_name\": MODEL_NAME,\n", + " \"model_path\": get_model_run.outputs[MODEL_NAME], # <- Take the logged model from the previous function.\n", + " \"onnx_model_name\": ONNX_MODEL_NAME,\n", + " \"optimize_model\": False # <- For optimizing it later in the demo, we mark the flag as False\n", + " },\n", + " local=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.2.5. Now, listing the artifact directory we will see both our `tf.keras` model and the `onnx` model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "\n", + "print(os.listdir(ARTIFACT_PATH))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 2. optimize\n", + "\n", + "### 2.1. Docs\n", + "Optimize the given ONNX model.\n", + "\n", + "#### Parameters:\n", + "* **`context`**: `mlrun.MLClientCtx` - The MLRun function execution context\n", + "* **`model_path`**: `str` - The model path store object.\n", + "* **`optimizations`**: `List[str] = None` - List of possible optimizations. *To see what optimizations are available, **pass \"help\"**.* If None, all of the optimizations will be used. Defaulted to None.\n", + "* **`fixed_point`**: `bool = False` - Optimize the weights using fixed point. Defaulted to False.\n", + "* **`optimized_model_name`**: `str = None` - The name of the optimized model. If None, the original model will be overridden. Defaulted to None." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Demo\n", + "\n", + "We will use our converted model from the last example and optimize it.\n", + "\n", + "2.2.1. We will call now the `optimize` handler:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "onnx_utils_function.run(\n", + " handler=\"optimize\",\n", + " artifact_path=ARTIFACT_PATH,\n", + " params={\n", + " \"model_name\": ONNX_MODEL_NAME,\n", + " \"model_path\": to_onnx_run.output(ONNX_MODEL_NAME), # <- Take the logged model from the previous function.\n", + " \"optimized_model_name\": OPTIMIZED_ONNX_MODEL_NAME,\n", + " },\n", + " local=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.2.2. And now our model was optimized and can be seen under the `ARTIFACT_PATH`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "print(os.listdir(ARTIFACT_PATH))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Lastly, run this code to clean up the models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "\n", + "shutil.rmtree(ARTIFACT_PATH)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/src/onnx_utils.py b/functions/development/onnx_utils/1.2.0/src/onnx_utils.py new file mode 100644 index 00000000..2a7c1f5b --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/onnx_utils.py @@ -0,0 +1,263 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Any, Callable, Dict, List, Tuple + +import mlrun + + +class _ToONNXConversions: + """ + An ONNX conversion functions library class. + """ + + @staticmethod + def tf_keras_to_onnx( + model_handler, + onnx_model_name: str = None, + optimize_model: bool = True, + input_signature: List[Tuple[Tuple[int], str]] = None, + ): + """ + Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object. + + :param model_handler: An initialized TFKerasModelHandler with a loaded model to convert to ONNX. + :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name` + will be used with an additional suffix `_onnx`. Defaulted to None. + :param optimize_model: Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model. + Defaulted to True. + :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list + where each element is an input layer tuple. An input layer tuple is a tuple of: + [0] = Layer's shape, a tuple of integers. + [1] = Layer's data type, a mlrun.data_types.ValueType string. + If None, the input signature will be tried to be read from the model artifact. Defaulted + to None. + """ + # Import the framework and handler: + import tensorflow as tf + from mlrun.frameworks.tf_keras import TFKerasUtils + + # Check the given 'input_signature' parameter: + if input_signature is None: + # Read the inputs from the model: + try: + model_handler.read_inputs_from_model() + except Exception as error: + raise mlrun.errors.MLRunRuntimeError( + f"Please provide the 'input_signature' parameter. The function tried reading the input layers " + f"information automatically but failed with the following error: {error}" + ) + else: + # Parse the 'input_signature' parameter: + input_signature = [ + tf.TensorSpec( + shape=shape, + dtype=TFKerasUtils.convert_value_type_to_tf_dtype( + value_type=value_type + ), + ) + for (shape, value_type) in input_signature + ] + + # Convert to ONNX: + model_handler.to_onnx( + model_name=onnx_model_name, + input_signature=input_signature, + optimize=optimize_model, + ) + + @staticmethod + def pytorch_to_onnx( + model_handler, + onnx_model_name: str = None, + optimize_model: bool = True, + input_signature: List[Tuple[Tuple[int, ...], str]] = None, + input_layers_names: List[str] = None, + output_layers_names: List[str] = None, + dynamic_axes: Dict[str, Dict[int, str]] = None, + is_batched: bool = True, + ): + """ + Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object. + + :param model_handler: An initialized PyTorchModelHandler with a loaded model to convert to ONNX. + :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given + `model_name` will be used with an additional suffix `_onnx`. Defaulted to None. + :param optimize_model: Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the + model. Defaulted to True. + :param input_signature: A list of the input layers shape and data type properties. Expected to receive a + list where each element is an input layer tuple. An input layer tuple is a tuple of: + [0] = Layer's shape, a tuple of integers. + [1] = Layer's data type, a mlrun.data_types.ValueType string. + If None, the input signature will be tried to be read from the model artifact. + Defaulted to None. + :param input_layers_names: List of names to assign to the input nodes of the graph in order. All of the other + parameters (inner layers) can be set as well by passing additional names in the + list. The order is by the order of the parameters in the model. If None, the inputs + will be read from the handler's inputs. If its also None, it is defaulted to: + "input_0", "input_1", ... + :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the + outputs will be read from the handler's outputs. If its also None, it is defaulted + to: "output_0" (for multiple outputs, this parameter must be provided). + :param dynamic_axes: If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can + specify it by giving a dynamic axis to the input / output layer by its name as + follows: { + "input layer name": {0: "batch_size"}, + "output layer name": {0: "batch_size"}, + } + If provided, the 'is_batched' flag will be ignored. Defaulted to None. + :param is_batched: Whether to include a batch size as the first axis in every input and output layer. + Defaulted to True. Will be ignored if 'dynamic_axes' is provided. + """ + # Import the framework and handler: + import torch + from mlrun.frameworks.pytorch import PyTorchUtils + + # Parse the 'input_signature' parameter: + if input_signature is not None: + input_signature = tuple( + [ + torch.zeros( + size=shape, + dtype=PyTorchUtils.convert_value_type_to_torch_dtype( + value_type=value_type + ), + ) + for (shape, value_type) in input_signature + ] + ) + + # Convert to ONNX: + model_handler.to_onnx( + model_name=onnx_model_name, + input_sample=input_signature, + optimize=optimize_model, + input_layers_names=input_layers_names, + output_layers_names=output_layers_names, + dynamic_axes=dynamic_axes, + is_batched=is_batched + ) + + +# Map for getting the conversion function according to the provided framework: +_CONVERSION_MAP = { + "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx, + "torch": _ToONNXConversions.pytorch_to_onnx, +} # type: Dict[str, Callable] + + +def to_onnx( + context: mlrun.MLClientCtx, + model_path: str, + onnx_model_name: str = None, + optimize_model: bool = True, + framework_kwargs: Dict[str, Any] = None, +): + """ + Convert the given model to an ONNX model. + + :param context: The MLRun function execution context + :param model_path: The model path store object. + :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name` will + be used with an additional suffix `_onnx`. Defaulted to None. + :param optimize_model: Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted + to True. + :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc + string of the desired framework onnx conversion function, pass "help". + """ + from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun + + # Get a model handler of the required framework: + model_handler = AutoMLRun.load_model(model_path=model_path, context=context) + + # Get the model's framework: + framework = model_handler.FRAMEWORK_NAME + + # Use the conversion map to get the specific framework to onnx conversion: + if framework not in _CONVERSION_MAP: + raise mlrun.errors.MLRunInvalidArgumentError( + f"The following framework: '{framework}', has no ONNX conversion." + ) + conversion_function = _CONVERSION_MAP[framework] + + # Check if needed to print the function's doc string ("help" is passed): + if framework_kwargs == "help": + print(conversion_function.__doc__) + return + + # Set the default empty framework kwargs if needed: + if framework_kwargs is None: + framework_kwargs = {} + + # Run the conversion: + try: + conversion_function( + model_handler=model_handler, + onnx_model_name=onnx_model_name, + optimize_model=optimize_model, + **framework_kwargs, + ) + except TypeError as exception: + raise mlrun.errors.MLRunInvalidArgumentError( + f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. " + f"Please read the {framework} framework conversion function doc string by passing 'help' in the " + f"'framework_kwargs' dictionary parameter." + ) + + +def optimize( + context: mlrun.MLClientCtx, + model_path: str, + optimizations: List[str] = None, + fixed_point: bool = False, + optimized_model_name: str = None, +): + """ + Optimize the given ONNX model. + + :param context: The MLRun function execution context. + :param model_path: Path to the ONNX model object. + :param optimizations: List of possible optimizations. To see what optimizations are available, pass "help". + If None, all of the optimizations will be used. Defaulted to None. + :param fixed_point: Optimize the weights using fixed point. Defaulted to False. + :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden. + Defaulted to None. + """ + # Import the model handler: + import onnxoptimizer + from mlrun.frameworks.onnx import ONNXModelHandler + + # Check if needed to print the available optimizations ("help" is passed): + if optimizations == "help": + available_passes = "\n* ".join(onnxoptimizer.get_available_passes()) + print(f"The available optimizations are:\n* {available_passes}") + return + + # Create the model handler: + model_handler = ONNXModelHandler( + model_path=model_path, context=context + ) + + # Load the ONNX model: + model_handler.load() + + # Optimize the model using the given configurations: + model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point) + + # Rename if needed: + if optimized_model_name is not None: + model_handler.set_model_name(model_name=optimized_model_name) + + # Log the optimized model: + model_handler.log() diff --git a/functions/development/onnx_utils/1.2.0/src/requirements.txt b/functions/development/onnx_utils/1.2.0/src/requirements.txt new file mode 100644 index 00000000..dcc80321 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/requirements.txt @@ -0,0 +1,12 @@ +tqdm~=4.62.3 +tensorflow~=2.7.0 +torch~=1.10.0 +torchvision~=0.11.1 +onnx~=1.10.1 +onnxruntime~=1.8.1 +onnxoptimizer~=0.2.0 +onnxmltools~=1.9.0 +tf2onnx~=1.9.0 +mlrun +plotly~=5.4.0 +wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/src/test_onnx_utils.py b/functions/development/onnx_utils/1.2.0/src/test_onnx_utils.py new file mode 100644 index 00000000..35b224c4 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/src/test_onnx_utils.py @@ -0,0 +1,378 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import shutil +import tempfile + +import mlrun + +# Choose our model's name: +MODEL_NAME = "model" + +# Choose our ONNX version model's name: +ONNX_MODEL_NAME = f"onnx_{MODEL_NAME}" + +# Choose our optimized ONNX version model's name: +OPTIMIZED_ONNX_MODEL_NAME = f"optimized_{ONNX_MODEL_NAME}" + + +def _setup_environment() -> str: + """ + Setup the test environment, creating the artifacts path of the test. + + :returns: The temporary directory created for the test artifacts path. + """ + artifact_path = tempfile.TemporaryDirectory().name + os.makedirs(artifact_path) + return artifact_path + + +def _cleanup_environment(artifact_path: str): + """ + Cleanup the test environment, deleting files and artifacts created during the test. + + :param artifact_path: The artifact path to delete. + """ + # Clean the local directory: + for test_output in [ + *os.listdir(artifact_path), + "schedules", + "runs", + "artifacts", + "functions", + ]: + test_output_path = os.path.abspath(f"./{test_output}") + if os.path.exists(test_output_path): + if os.path.isdir(test_output_path): + shutil.rmtree(test_output_path) + else: + os.remove(test_output_path) + + # Clean the artifacts directory: + shutil.rmtree(artifact_path) + + +def _log_tf_keras_model(context: mlrun.MLClientCtx, model_name: str): + """ + Create and log a tf.keras model - MobileNetV2. + + :param context: The context to log to. + :param model_name: The model name to use. + """ + from mlrun.frameworks.tf_keras import TFKerasModelHandler + from tensorflow import keras + + # Download the MobileNetV2 model: + model = keras.applications.mobilenet_v2.MobileNetV2() + + # Initialize a model handler for logging the model: + model_handler = TFKerasModelHandler( + model_name=model_name, model=model, context=context + ) + + # Log the model: + model_handler.log() + + +def _log_pytorch_model(context: mlrun.MLClientCtx, model_name: str): + """ + Create and log a pytorch model - MobileNetV2. + + :param context: The context to log to. + :param model_name: The model name to use. + """ + from mlrun.frameworks.pytorch import PyTorchModelHandler + import torchvision + + # Download the MobileNetV2 model: + model = torchvision.models.mobilenet_v2() + + # Initialize a model handler for logging the model: + model_handler = PyTorchModelHandler( + model_name=model_name, + model=model, + model_class="mobilenet_v2", + modules_map={"torchvision.models": "mobilenet_v2"}, + context=context, + ) + + # Log the model: + model_handler.log() + + +def _log_onnx_model(context: mlrun.MLClientCtx, model_name: str): + """ + Create and log an ONNX model - MNIST. + + :param context: The context to log to. + :param model_name: The model name to use. + """ + import mlrun.frameworks.onnx as mlrun_onnx + import requests + + # Download the MNIST model: + mnist_model_name = "mnist-12" + requested_model = requests.get( + f"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/{mnist_model_name}.onnx?raw=true" + ) + with open( + os.path.join(context.artifact_path, f"{model_name}.onnx"), "bw" + ) as onnx_file: + onnx_file.write(requested_model.content) + + # Initialize a model handler for logging the model: + model_handler = mlrun_onnx.ONNXModelHandler( + model_name=model_name, + model_path=context.artifact_path, + context=context, + ) + model_handler.load() + + # Log the model: + model_handler.log() + + +def test_to_onnx_help(): + """ + Test the 'to_onnx' handler, passing "help" in the 'framework_kwargs'. + """ + # Setup the tests environment: + artifact_path = _setup_environment() + + # Create the function parsing this notebook's code using 'code_to_function': + log_model_function = mlrun.code_to_function( + filename="test_onnx_utils.py", + name="log_model", + kind="job", + image="mlrun/ml-models", + ) + + # Run the function to log the model: + log_model_run = log_model_function.run( + handler="_log_tf_keras_model", + artifact_path=artifact_path, + params={"model_name": MODEL_NAME}, + local=True, + ) + + # Import the ONNX Utils function: + onnx_function = mlrun.import_function("function.yaml") + + # Run the function, passing "help" in 'framework_kwargs' and see that no exception was raised: + is_test_passed = True + try: + onnx_function.run( + handler="to_onnx", + artifact_path=artifact_path, + params={ + "model_path": log_model_run.outputs[ + "model" + ], # <- Take the logged model from the previous function. + "framework_kwargs": "help", + }, + local=True, + ) + except TypeError as exception: + print( + f"The test failed, the help was not handled properly and raised the following error: {exception}" + ) + is_test_passed = False + + # Cleanup the tests environment: + _cleanup_environment(artifact_path=artifact_path) + + assert is_test_passed + + +def test_tf_keras_to_onnx(): + """ + Test the 'to_onnx' handler, giving it a tf.keras model. + """ + # Setup the tests environment: + artifact_path = _setup_environment() + + # Create the function parsing this notebook's code using 'code_to_function': + log_model_function = mlrun.code_to_function( + filename="test_onnx_utils.py", + name="log_model", + kind="job", + image="mlrun/ml-models", + ) + + # Run the function to log the model: + log_model_run = log_model_function.run( + handler="_log_tf_keras_model", + artifact_path=artifact_path, + params={"model_name": MODEL_NAME}, + local=True, + ) + + # Import the ONNX Utils function: + onnx_function = mlrun.import_function("function.yaml") + + # Run the function to convert our model to ONNX: + onnx_function_run = onnx_function.run( + handler="to_onnx", + artifact_path=artifact_path, + params={ + "model_path": log_model_run.outputs[ + "model" + ], # <- Take the logged model from the previous function. + "onnx_model_name": ONNX_MODEL_NAME, + }, + local=True, + ) + + # Cleanup the tests environment: + _cleanup_environment(artifact_path=artifact_path) + + # Print the outputs list: + print(f"Produced outputs: {onnx_function_run.outputs}") + + # Verify the '.onnx' model was created: + assert "model" in onnx_function_run.outputs + + +def test_pytorch_to_onnx(): + """ + Test the 'to_onnx' handler, giving it a pytorch model. + """ + # Setup the tests environment: + artifact_path = _setup_environment() + + # Create the function parsing this notebook's code using 'code_to_function': + log_model_function = mlrun.code_to_function( + filename="test_onnx_utils.py", + name="log_model", + kind="job", + image="mlrun/ml-models", + ) + + # Run the function to log the model: + log_model_run = log_model_function.run( + handler="_log_pytorch_model", + artifact_path=artifact_path, + params={"model_name": MODEL_NAME}, + local=True, + ) + + # Import the ONNX Utils function: + onnx_function = mlrun.import_function("function.yaml") + + # Run the function to convert our model to ONNX: + onnx_function_run = onnx_function.run( + handler="to_onnx", + artifact_path=artifact_path, + params={ + "model_path": log_model_run.outputs[ + "model" + ], # <- Take the logged model from the previous function. + "onnx_model_name": ONNX_MODEL_NAME, + "framework_kwargs": {"input_signature": [((32, 3, 224, 224), "float32")]}, + }, + local=True, + ) + + # Cleanup the tests environment: + _cleanup_environment(artifact_path=artifact_path) + + # Print the outputs list: + print(f"Produced outputs: {onnx_function_run.outputs}") + + # Verify the '.onnx' model was created: + assert "model" in onnx_function_run.outputs + + +def test_optimize_help(): + """ + Test the 'optimize' handler, passing "help" in the 'optimizations'. + """ + # Setup the tests environment: + artifact_path = _setup_environment() + + # Import the ONNX Utils function: + onnx_function = mlrun.import_function("function.yaml") + + # Run the function, passing "help" in 'optimizations' and see that no exception was raised: + is_test_passed = True + try: + onnx_function.run( + handler="optimize", + artifact_path=artifact_path, + params={ + "model_path": "", + "optimizations": "help", + }, + local=True, + ) + except TypeError as exception: + print( + f"The test failed, the help was not handled properly and raised the following error: {exception}" + ) + is_test_passed = False + + # Cleanup the tests environment: + _cleanup_environment(artifact_path=artifact_path) + + assert is_test_passed + + +def test_optimize(): + """ + Test the 'optimize' handler, giving it a model from the ONNX zoo git repository. + """ + # Setup the tests environment: + artifact_path = _setup_environment() + + # Create the function parsing this notebook's code using 'code_to_function': + log_model_function = mlrun.code_to_function( + filename="test_onnx_utils.py", + name="log_model", + kind="job", + image="mlrun/ml-models", + ) + + # Run the function to log the model: + log_model_run = log_model_function.run( + handler="_log_onnx_model", + artifact_path=artifact_path, + params={"model_name": MODEL_NAME}, + local=True, + ) + + # Import the ONNX Utils function: + onnx_function = mlrun.import_function("function.yaml") + + # Run the function to optimize our model: + onnx_function_run = onnx_function.run( + handler="optimize", + artifact_path=artifact_path, + params={ + "model_path": log_model_run.outputs[ + "model" + ], # <- Take the logged model from the previous function. + "optimized_model_name": OPTIMIZED_ONNX_MODEL_NAME, + }, + local=True, + ) + + # Cleanup the tests environment: + _cleanup_environment(artifact_path=artifact_path) + + # Print the outputs list: + print(f"Produced outputs: {onnx_function_run.outputs}") + + # Verify the '.onnx' model was created: + assert "model" in onnx_function_run.outputs diff --git a/functions/development/onnx_utils/1.2.0/static/documentation.html b/functions/development/onnx_utils/1.2.0/static/documentation.html new file mode 100644 index 00000000..0d0f67d9 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/documentation.html @@ -0,0 +1,259 @@ + + + + + + + +onnx_utils package + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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onnx_utils package

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onnx_utils package#

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Submodules#

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onnx_utils.onnx_utils module#

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+onnx_utils.onnx_utils.optimize(context: mlrun.execution.MLClientCtx, model_path: str, optimizations: Optional[List[str]] = None, fixed_point: bool = False, optimized_model_name: Optional[str] = None)[source]#
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Optimize the given ONNX model.

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  • context – The MLRun function execution context.

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  • model_path – Path to the ONNX model object.

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  • optimizations – List of possible optimizations. To see what optimizations are available, pass “help”. +If None, all of the optimizations will be used. Defaulted to None.

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  • fixed_point – Optimize the weights using fixed point. Defaulted to False.

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  • optimized_model_name – The name of the optimized model. If None, the original model will be overridden. +Defaulted to None.

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+onnx_utils.onnx_utils.to_onnx(context: mlrun.execution.MLClientCtx, model_path: str, onnx_model_name: Optional[str] = None, optimize_model: bool = True, framework_kwargs: Optional[Dict[str, Any]] = None)[source]#
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Convert the given model to an ONNX model.

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  • context – The MLRun function execution context

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  • model_path – The model path store object.

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  • onnx_model_name – The name to use to log the converted ONNX model. If not given, the given model_name will +be used with an additional suffix _onnx. Defaulted to None.

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  • optimize_model – Whether to optimize the ONNX model using ‘onnxoptimizer’ before saving the model. Defaulted +to True.

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  • framework_kwargs – Additional arguments each framework may require in order to convert to ONNX. To get the doc +string of the desired framework onnx conversion function, pass “help”.

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Module contents#

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+ + + + \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/static/example.html b/functions/development/onnx_utils/1.2.0/static/example.html new file mode 100644 index 00000000..c0a76600 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/example.html @@ -0,0 +1,554 @@ + + + + + + + +ONNX Utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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+ Contents +
+ +
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+ +
+

ONNX Utils

+ +
+ +
+
+
+
+
+

ONNX Utils#

+

A collection of ONNX utils in one MLRun function. The function includes the following handlers:

+
    +

  1. to_onnx - Convert your model into onnx format.

    2. +

  2. optimize - Perform ONNX optimizations using onnxmodeloptimizer on a given ONNX model.

    3. +
+

+
+

1. to_onnx#

+
+

1.1. Docs#

+

Convert the given model to an ONNX model.

+
+

Parameters:#

+
    +

  • context: mlrun.MLClientCtx - The MLRun function execution context

    • +

  • model_path: str - The model path store object.

    • +

  • onnx_model_name: str = None - The name to use to log the converted ONNX model. If not given, the given model_name will be used with an additional suffix _onnx. Defaulted to None.

    • +

  • optimize_model: bool = True - Whether to optimize the ONNX model using ‘onnxoptimizer’ before saving the model. Defaulted to True.

    • +

  • framework: str = None - The model’s framework. If None, it will be read from the ‘framework’ label of the model artifact provided. Defaulted to None.

    • +

  • framework_kwargs: Dict[str, Any] = None - Additional arguments each framework may require in order to convert to ONNX. To get the doc string of the desired framework onnx conversion function, pass “help”.

    • +
+
+
+

Supported keyword arguments (framework_kwargs) per framework:#

+

tensorflow.keras:

+
    +

  • input_signature: List[Tuple[Tuple[int], str]] = None - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:

    +
      +

    • [0] = Layer’s shape, a tuple of integers.

      • +

    • [1] = Layer’s data type, a mlrun.data_types.ValueType string.

      • +
    +

    If None, the input signature will be tried to be read automatically before converting to ONNX or from the model artifact if available. Defaulted to None.

    +
    • +
+

torch:

+
    +

  • input_signature: List[Tuple[Tuple[int], str]] = None - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:

    +
      +

    • [0] = Layer’s shape, a tuple of integers.

      • +

    • [1] = Layer’s data type, a mlrun.data_types.ValueType string.

      • +
    +

    If None, the input signature will be read from the model artifact if available. Defaulted to None.

    +
    • +

  • input_layers_names: List[str] = None - List of names to assign to the input nodes of the graph in order. All of the other parameters (inner layers) can be set as well by passing additional names in the list. The order is by the order of the parameters in the model. If None, the inputs will be read from the handler’s inputs. If its also None, it is defaulted to: “input_0”, “input_1”, …

    • +

  • output_layers_names: List[str] = None - List of names to assign to the output nodes of the graph in order. If None, the outputs will be read from the handler’s outputs. If its also None, it is defaulted to: “output_0” (for multiple outputs, this parameter must be provided).

    • +

  • param dynamic_axes: Dict[str, Dict[int, str]] = None - If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can specify it by giving a dynamic axis to the input / output layer by its name as follows:

    • +
+
{
+    "input layer name": {0: "batch_size"},
+    "output layer name": {0: "batch_size"},
+}
+
+
+

If provided, the ‘is_batched’ flag will be ignored. Defaulted to None.

+
    +

  • is_batched: bool = True - Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if ‘dynamic_axes’ is provided.

    • +
+
+
+
+

1.2. Demo#

+

We will use the TF.Keras framework, a MobileNetV2 as our model and we will convert it to ONNX using the to_onnx handler.

+

1.2.1. First we will set a temporary artifact path for our model to be saved in and choose the models names:

+
+
+
import os
+from tempfile import TemporaryDirectory
+
+# Create a temporary directory for the model artifact:
+ARTIFACT_PATH = TemporaryDirectory().name
+os.makedirs(ARTIFACT_PATH)
+
+# Choose our model's name:
+MODEL_NAME = "mobilenetv2"
+
+# Choose our ONNX version model's name:
+ONNX_MODEL_NAME = "onnx_mobilenetv2"
+
+# Choose our optimized ONNX version model's name:
+OPTIMIZED_ONNX_MODEL_NAME = "optimized_onnx_mobilenetv2"
+
+
+
+
+

1.2.2. Download the model from keras.applications and log it with MLRun’s TFKerasModelHandler:

+
+
+
# mlrun: start-code
+
+
+
+
+
+
+
from tensorflow import keras
+
+import mlrun
+import mlrun.frameworks.tf_keras as mlrun_tf_keras
+
+
+def get_model(context: mlrun.MLClientCtx, model_name: str):
+    # Download the MobileNetV2 model:
+    model = keras.applications.mobilenet_v2.MobileNetV2()
+
+    # Initialize a model handler for logging the model:
+    model_handler = mlrun_tf_keras.TFKerasModelHandler(
+        model_name=model_name,
+        model=model,
+        context=context
+    )
+
+    # Log the model:
+    model_handler.log()
+
+
+
+
+
+
+
# mlrun: end-code
+
+
+
+
+

1.2.3. Create the function using MLRun’s code_to_function and run it:

+
+
+
import mlrun
+
+
+# Create the function parsing this notebook's code using 'code_to_function':
+get_model_function = mlrun.code_to_function(
+    name="get_mobilenetv2",
+    kind="job",
+    image="mlrun/ml-models"
+)
+
+# Run the function to log the model:
+get_model_run = get_model_function.run(
+    handler="get_model",
+    artifact_path=ARTIFACT_PATH,
+    params={
+        "model_name": MODEL_NAME
+    },
+    local=True
+)
+
+
+
+
+

1.2.4. Import the onnx_utils MLRun function and run it:

+
+
+
# Import the ONNX function from the marketplace:
+onnx_utils_function = mlrun.import_function("hub://onnx_utils")
+
+# Run the function to convert our model to ONNX:
+to_onnx_run = onnx_utils_function.run(
+    handler="to_onnx",
+    artifact_path=ARTIFACT_PATH,
+    params={
+        "model_name": MODEL_NAME,
+        "model_path": get_model_run.outputs[MODEL_NAME],  # <- Take the logged model from the previous function.
+        "onnx_model_name": ONNX_MODEL_NAME,
+        "optimize_model": False  # <- For optimizing it later in the demo, we mark the flag as False
+    },
+    local=True
+)
+
+
+
+
+

1.2.5. Now, listing the artifact directory we will see both our tf.keras model and the onnx model:

+
+
+
import os
+
+
+print(os.listdir(ARTIFACT_PATH))
+
+
+
+
+

+
+
+
+

2. optimize#

+
+

2.1. Docs#

+

Optimize the given ONNX model.

+
+

Parameters:#

+
    +

  • context: mlrun.MLClientCtx - The MLRun function execution context

    • +

  • model_path: str - The model path store object.

    • +

  • optimizations: List[str] = None - List of possible optimizations. To see what optimizations are available, pass “help”. If None, all of the optimizations will be used. Defaulted to None.

    • +

  • fixed_point: bool = False - Optimize the weights using fixed point. Defaulted to False.

    • +

  • optimized_model_name: str = None - The name of the optimized model. If None, the original model will be overridden. Defaulted to None.

    • +
+
+
+
+

2.2. Demo#

+

We will use our converted model from the last example and optimize it.

+

2.2.1. We will call now the optimize handler:

+
+
+
onnx_utils_function.run(
+    handler="optimize",
+    artifact_path=ARTIFACT_PATH,
+    params={
+        "model_name": ONNX_MODEL_NAME,
+        "model_path": to_onnx_run.output(ONNX_MODEL_NAME),  # <- Take the logged model from the previous function.
+        "optimized_model_name": OPTIMIZED_ONNX_MODEL_NAME,
+    },
+    local=True
+)
+
+
+
+
+

2.2.2. And now our model was optimized and can be seen under the ARTIFACT_PATH:

+
+
+
print(os.listdir(ARTIFACT_PATH))
+
+
+
+
+

Lastly, run this code to clean up the models:

+
+
+
import shutil
+
+
+shutil.rmtree(ARTIFACT_PATH)
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/static/function.html b/functions/development/onnx_utils/1.2.0/static/function.html new file mode 100644 index 00000000..feceb076 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/function.html @@ -0,0 +1,216 @@ + + + + + + + + + + + Source + + + + + 
+        
+kind: job
+metadata:
+  name: onnx-utils
+  tag: ''
+  hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2
+  project: ''
+  labels:
+    author: guyl
+  categories:
+  - utils
+spec:
+  command: ''
+  args: []
+  image: ''
+  build:
+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()

+    base_image: mlrun/mlrun
+    commands: []
+    code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    with_mlrun: false
+    auto_build: true
+    requirements:
+    - onnx~=1.13.0
+    - onnxruntime~=1.14.0
+    - onnxoptimizer~=0.3.0
+    - onnxmltools~=1.11.0
+    - tf2onnx~=1.13.0
+  entry_points:
+    tf_keras_to_onnx:
+      name: tf_keras_to_onnx
+      doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized TFKerasModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      outputs:
+      - default: ''
+      lineno: 26
+    pytorch_to_onnx:
+      name: pytorch_to_onnx
+      doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized PyTorchModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int, ], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      - name: input_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the input nodes of the graph in order. All
+          of the other parameters (inner layers) can be set as well by passing additional
+          names in the list. The order is by the order of the parameters in the model.
+          If None, the inputs will be read from the handler''s inputs. If its also
+          None, it is defaulted to: "input_0", "input_1", ...'
+        default: null
+      - name: output_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the output nodes of the graph in order. If
+          None, the outputs will be read from the handler''s outputs. If its also
+          None, it is defaulted to: "output_0" (for multiple outputs, this parameter
+          must be provided).'
+        default: null
+      - name: dynamic_axes
+        type: Dict[str, Dict[int, str]]
+        doc: 'If part of the input / output shape is dynamic, like (batch_size, 3,
+          32, 32) you can specify it by giving a dynamic axis to the input / output
+          layer by its name as follows: { "input layer name": {0: "batch_size"}, "output
+          layer name": {0: "batch_size"}, } If provided, the ''is_batched'' flag will
+          be ignored. Defaulted to None.'
+        default: null
+      - name: is_batched
+        type: bool
+        doc: Whether to include a batch size as the first axis in every input and
+          output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        default: true
+      outputs:
+      - default: ''
+      lineno: 81
+    to_onnx:
+      name: to_onnx
+      doc: Convert the given model to an ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context
+        default: ''
+      - name: model_path
+        type: str
+        doc: The model path store object.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether to optimize the ONNX model using 'onnxoptimizer' before saving
+          the model. Defaulted to True.
+        default: true
+      - name: framework_kwargs
+        type: Dict[str, Any]
+        doc: Additional arguments each framework may require in order to convert to
+          ONNX. To get the doc string of the desired framework onnx conversion function,
+          pass "help".
+        default: null
+      outputs:
+      - default: ''
+      lineno: 160
+    optimize:
+      name: optimize
+      doc: Optimize the given ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context.
+        default: ''
+      - name: model_path
+        type: str
+        doc: Path to the ONNX model object.
+        default: ''
+      - name: optimizations
+        type: List[str]
+        doc: List of possible optimizations. To see what optimizations are available,
+          pass "help". If None, all of the optimizations will be used. Defaulted to
+          None.
+        default: null
+      - name: fixed_point
+        type: bool
+        doc: Optimize the weights using fixed point. Defaulted to False.
+        default: false
+      - name: optimized_model_name
+        type: str
+        doc: The name of the optimized model. If None, the original model will be
+          overridden. Defaulted to None.
+        default: null
+      outputs:
+      - default: ''
+      lineno: 219
+  description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+    optimizing and converting models from different framework to ONNX using MLRun.
+  default_handler: to_onnx
+  disable_auto_mount: false
+  allow_empty_resources: true
+  clone_target_dir: ''
+  env: []
+  priority_class_name: ''
+  preemption_mode: prevent
+  affinity: null
+  tolerations: null
+  security_context: {}
+verbose: false
+
+        
+
+ + \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/static/item.html b/functions/development/onnx_utils/1.2.0/static/item.html new file mode 100644 index 00000000..07129b01 --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/item.html @@ -0,0 +1,57 @@ + + + + + + + + + + + Source + + + + + 
+        
+apiVersion: v1
+categories:
+- utils
+description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+  optimizing and converting models from different framework to ONNX using MLRun.
+doc: ''
+example: onnx_utils.ipynb
+generationDate: 2022-08-28:17-25
+hidden: false
+icon: ''
+labels:
+  author: guyl
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.1.0
+name: onnx_utils
+platformVersion: 3.5.0
+spec:
+  extra_spec:
+    allow_empty_resources: true
+    build:
+      auto_build: true
+      with_mlrun: false
+  filename: onnx_utils.py
+  handler: to_onnx
+  image: mlrun/mlrun
+  kind: job
+  requirements:
+  - onnx~=1.13.0
+  - onnxruntime~=1.14.0
+  - onnxoptimizer~=0.3.0
+  - onnxmltools~=1.11.0
+  - tf2onnx~=1.13.0
+url: ''
+version: 1.2.0
+
+        
+
+ + \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/static/onnx_utils.html b/functions/development/onnx_utils/1.2.0/static/onnx_utils.html new file mode 100644 index 00000000..52c337bc --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/onnx_utils.html @@ -0,0 +1,403 @@ + + + + + + + +onnx_utils.onnx_utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+
+ +
+
+ +
+ +
+
+
+
+
+
+ +
+ +
+ +
+
+
+
+ +
+
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+ +
+

+ +
+
+
+
+
+
+
+

Source code for onnx_utils.onnx_utils

+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import Any, Callable, Dict, List, Tuple
+
+import mlrun
+
+
+class _ToONNXConversions:
+    """
+    An ONNX conversion functions library class.
+    """
+
+    @staticmethod
+    def tf_keras_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int], str]] = None,
+    ):
+        """
+        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
+                                will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
+                                Defaulted to True.
+        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
+                                where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                [0] = Layer's shape, a tuple of integers.
+                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                If None, the input signature will be tried to be read from the model artifact. Defaulted
+                                to None.
+        """
+        # Import the framework and handler:
+        import tensorflow as tf
+        from mlrun.frameworks.tf_keras import TFKerasUtils
+
+        # Check the given 'input_signature' parameter:
+        if input_signature is None:
+            # Read the inputs from the model:
+            try:
+                model_handler.read_inputs_from_model()
+            except Exception as error:
+                raise mlrun.errors.MLRunRuntimeError(
+                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
+                    f"information automatically but failed with the following error: {error}"
+                )
+        else:
+            # Parse the 'input_signature' parameter:
+            input_signature = [
+                tf.TensorSpec(
+                    shape=shape,
+                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
+                        value_type=value_type
+                    ),
+                )
+                for (shape, value_type) in input_signature
+            ]
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_signature=input_signature,
+            optimize=optimize_model,
+        )
+
+    @staticmethod
+    def pytorch_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
+        input_layers_names: List[str] = None,
+        output_layers_names: List[str] = None,
+        dynamic_axes: Dict[str, Dict[int, str]] = None,
+        is_batched: bool = True,
+    ):
+        """
+        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
+                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
+                                    model. Defaulted to True.
+        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
+                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                    [0] = Layer's shape, a tuple of integers.
+                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                    If None, the input signature will be tried to be read from the model artifact.
+                                    Defaulted to None.
+        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
+                                    parameters (inner layers) can be set as well by passing additional names in the
+                                    list. The order is by the order of the parameters in the model. If None, the inputs
+                                    will be read from the handler's inputs. If its also None, it is defaulted to:
+                                    "input_0", "input_1", ...
+        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
+                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
+                                    to: "output_0" (for multiple outputs, this parameter must be provided).
+        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
+                                    specify it by giving a dynamic axis to the input / output layer by its name as
+                                    follows: {
+                                        "input layer name": {0: "batch_size"},
+                                        "output layer name": {0: "batch_size"},
+                                    }
+                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
+        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
+                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        """
+        # Import the framework and handler:
+        import torch
+        from mlrun.frameworks.pytorch import PyTorchUtils
+
+        # Parse the 'input_signature' parameter:
+        if input_signature is not None:
+            input_signature = tuple(
+                [
+                    torch.zeros(
+                        size=shape,
+                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
+                            value_type=value_type
+                        ),
+                    )
+                    for (shape, value_type) in input_signature
+                ]
+            )
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_sample=input_signature,
+            optimize=optimize_model,
+            input_layers_names=input_layers_names,
+            output_layers_names=output_layers_names,
+            dynamic_axes=dynamic_axes,
+            is_batched=is_batched
+        )
+
+
+# Map for getting the conversion function according to the provided framework:
+_CONVERSION_MAP = {
+    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
+    "torch": _ToONNXConversions.pytorch_to_onnx,
+}  # type: Dict[str, Callable]
+
+
+
[docs]def to_onnx(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    onnx_model_name: str = None,
+    optimize_model: bool = True,
+    framework_kwargs: Dict[str, Any] = None,
+):
+    """
+    Convert the given model to an ONNX model.
+
+    :param context:          The MLRun function execution context
+    :param model_path:       The model path store object.
+    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
+                             be used with an additional suffix `_onnx`. Defaulted to None.
+    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
+                             to True.
+    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
+                             string of the desired framework onnx conversion function, pass "help".
+    """
+    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun
+
+    # Get a model handler of the required framework:
+    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+    # Get the model's framework:
+    framework = model_handler.FRAMEWORK_NAME
+
+    # Use the conversion map to get the specific framework to onnx conversion:
+    if framework not in _CONVERSION_MAP:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"The following framework: '{framework}', has no ONNX conversion."
+        )
+    conversion_function = _CONVERSION_MAP[framework]
+
+    # Check if needed to print the function's doc string ("help" is passed):
+    if framework_kwargs == "help":
+        print(conversion_function.__doc__)
+        return
+
+    # Set the default empty framework kwargs if needed:
+    if framework_kwargs is None:
+        framework_kwargs = {}
+
+    # Run the conversion:
+    try:
+        conversion_function(
+            model_handler=model_handler,
+            onnx_model_name=onnx_model_name,
+            optimize_model=optimize_model,
+            **framework_kwargs,
+        )
+    except TypeError as exception:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
+            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
+            f"'framework_kwargs' dictionary parameter."
+        )

+
+
+
[docs]def optimize(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    optimizations: List[str] = None,
+    fixed_point: bool = False,
+    optimized_model_name: str = None,
+):
+    """
+    Optimize the given ONNX model.
+
+    :param context:              The MLRun function execution context.
+    :param model_path:           Path to the ONNX model object.
+    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
+                                 If None, all of the optimizations will be used. Defaulted to None.
+    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
+    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
+                                 Defaulted to None.
+    """
+    # Import the model handler:
+    import onnxoptimizer
+    from mlrun.frameworks.onnx import ONNXModelHandler
+
+    # Check if needed to print the available optimizations ("help" is passed):
+    if optimizations == "help":
+        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
+        print(f"The available optimizations are:\n* {available_passes}")
+        return
+
+    # Create the model handler:
+    model_handler = ONNXModelHandler(
+        model_path=model_path, context=context
+    )
+
+    # Load the ONNX model:
+    model_handler.load()
+
+    # Optimize the model using the given configurations:
+    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)
+
+    # Rename if needed:
+    if optimized_model_name is not None:
+        model_handler.set_model_name(model_name=optimized_model_name)
+
+    # Log the optimized model:
+    model_handler.log()

+
+
+
+
+ +
+
+
+
+
+
+

+ + © Copyright .
+

+
+
+
+
+
+ + + + \ No newline at end of file diff --git a/functions/development/onnx_utils/1.2.0/static/source.html b/functions/development/onnx_utils/1.2.0/static/source.html new file mode 100644 index 00000000..f10ac64f --- /dev/null +++ b/functions/development/onnx_utils/1.2.0/static/source.html @@ -0,0 +1,285 @@ + + + + + + + + + + + Source + + + + + 
+        
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import Any, Callable, Dict, List, Tuple
+
+import mlrun
+
+
+class _ToONNXConversions:
+    """
+    An ONNX conversion functions library class.
+    """
+
+    @staticmethod
+    def tf_keras_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int], str]] = None,
+    ):
+        """
+        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
+                                will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
+                                Defaulted to True.
+        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
+                                where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                [0] = Layer's shape, a tuple of integers.
+                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                If None, the input signature will be tried to be read from the model artifact. Defaulted
+                                to None.
+        """
+        # Import the framework and handler:
+        import tensorflow as tf
+        from mlrun.frameworks.tf_keras import TFKerasUtils
+
+        # Check the given 'input_signature' parameter:
+        if input_signature is None:
+            # Read the inputs from the model:
+            try:
+                model_handler.read_inputs_from_model()
+            except Exception as error:
+                raise mlrun.errors.MLRunRuntimeError(
+                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
+                    f"information automatically but failed with the following error: {error}"
+                )
+        else:
+            # Parse the 'input_signature' parameter:
+            input_signature = [
+                tf.TensorSpec(
+                    shape=shape,
+                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
+                        value_type=value_type
+                    ),
+                )
+                for (shape, value_type) in input_signature
+            ]
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_signature=input_signature,
+            optimize=optimize_model,
+        )
+
+    @staticmethod
+    def pytorch_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
+        input_layers_names: List[str] = None,
+        output_layers_names: List[str] = None,
+        dynamic_axes: Dict[str, Dict[int, str]] = None,
+        is_batched: bool = True,
+    ):
+        """
+        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
+                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
+                                    model. Defaulted to True.
+        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
+                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                    [0] = Layer's shape, a tuple of integers.
+                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                    If None, the input signature will be tried to be read from the model artifact.
+                                    Defaulted to None.
+        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
+                                    parameters (inner layers) can be set as well by passing additional names in the
+                                    list. The order is by the order of the parameters in the model. If None, the inputs
+                                    will be read from the handler's inputs. If its also None, it is defaulted to:
+                                    "input_0", "input_1", ...
+        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
+                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
+                                    to: "output_0" (for multiple outputs, this parameter must be provided).
+        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
+                                    specify it by giving a dynamic axis to the input / output layer by its name as
+                                    follows: {
+                                        "input layer name": {0: "batch_size"},
+                                        "output layer name": {0: "batch_size"},
+                                    }
+                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
+        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
+                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        """
+        # Import the framework and handler:
+        import torch
+        from mlrun.frameworks.pytorch import PyTorchUtils
+
+        # Parse the 'input_signature' parameter:
+        if input_signature is not None:
+            input_signature = tuple(
+                [
+                    torch.zeros(
+                        size=shape,
+                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
+                            value_type=value_type
+                        ),
+                    )
+                    for (shape, value_type) in input_signature
+                ]
+            )
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_sample=input_signature,
+            optimize=optimize_model,
+            input_layers_names=input_layers_names,
+            output_layers_names=output_layers_names,
+            dynamic_axes=dynamic_axes,
+            is_batched=is_batched
+        )
+
+
+# Map for getting the conversion function according to the provided framework:
+_CONVERSION_MAP = {
+    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
+    "torch": _ToONNXConversions.pytorch_to_onnx,
+}  # type: Dict[str, Callable]
+
+
+def to_onnx(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    onnx_model_name: str = None,
+    optimize_model: bool = True,
+    framework_kwargs: Dict[str, Any] = None,
+):
+    """
+    Convert the given model to an ONNX model.
+
+    :param context:          The MLRun function execution context
+    :param model_path:       The model path store object.
+    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
+                             be used with an additional suffix `_onnx`. Defaulted to None.
+    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
+                             to True.
+    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
+                             string of the desired framework onnx conversion function, pass "help".
+    """
+    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun
+
+    # Get a model handler of the required framework:
+    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+    # Get the model's framework:
+    framework = model_handler.FRAMEWORK_NAME
+
+    # Use the conversion map to get the specific framework to onnx conversion:
+    if framework not in _CONVERSION_MAP:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"The following framework: '{framework}', has no ONNX conversion."
+        )
+    conversion_function = _CONVERSION_MAP[framework]
+
+    # Check if needed to print the function's doc string ("help" is passed):
+    if framework_kwargs == "help":
+        print(conversion_function.__doc__)
+        return
+
+    # Set the default empty framework kwargs if needed:
+    if framework_kwargs is None:
+        framework_kwargs = {}
+
+    # Run the conversion:
+    try:
+        conversion_function(
+            model_handler=model_handler,
+            onnx_model_name=onnx_model_name,
+            optimize_model=optimize_model,
+            **framework_kwargs,
+        )
+    except TypeError as exception:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
+            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
+            f"'framework_kwargs' dictionary parameter."
+        )
+
+
+def optimize(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    optimizations: List[str] = None,
+    fixed_point: bool = False,
+    optimized_model_name: str = None,
+):
+    """
+    Optimize the given ONNX model.
+
+    :param context:              The MLRun function execution context.
+    :param model_path:           Path to the ONNX model object.
+    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
+                                 If None, all of the optimizations will be used. Defaulted to None.
+    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
+    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
+                                 Defaulted to None.
+    """
+    # Import the model handler:
+    import onnxoptimizer
+    from mlrun.frameworks.onnx import ONNXModelHandler
+
+    # Check if needed to print the available optimizations ("help" is passed):
+    if optimizations == "help":
+        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
+        print(f"The available optimizations are:\n* {available_passes}")
+        return
+
+    # Create the model handler:
+    model_handler = ONNXModelHandler(
+        model_path=model_path, context=context
+    )
+
+    # Load the ONNX model:
+    model_handler.load()
+
+    # Optimize the model using the given configurations:
+    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)
+
+    # Rename if needed:
+    if optimized_model_name is not None:
+        model_handler.set_model_name(model_name=optimized_model_name)
+
+    # Log the optimized model:
+    model_handler.log()
+
+        
+
+ + \ No newline at end of file diff --git a/functions/development/onnx_utils/latest/src/function.yaml b/functions/development/onnx_utils/latest/src/function.yaml index d59fa2dd..7a0054c4 100644 --- a/functions/development/onnx_utils/latest/src/function.yaml +++ b/functions/development/onnx_utils/latest/src/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: onnx-utils tag: '' - hash: d81c2cec3242be94fc9c207bb75fc6495f2a34ae + hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2 project: '' labels: author: guyl @@ -11,16 +11,21 @@ metadata: spec: command: '' args: [] - image: mlrun/ml-models + image: '' build: functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 - commands: - - python -m pip install onnx~=1.13.0 onnxruntime~=1.14.0 onnxoptimizer~=0.3.0 - onnxmltools~=1.11.0 tf2onnx~=1.13.0 - code_origin: https://github.com/mlrun/functions.git#b1365179513d1457e28a797e7d04f63a91238f02:/Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py - origin_filename: /Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py + base_image: mlrun/mlrun + commands: [] + code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py with_mlrun: false auto_build: true + requirements: + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 entry_points: tf_keras_to_onnx: name: tf_keras_to_onnx @@ -43,7 +48,7 @@ spec: saving the model. Defaulted to True. default: true - name: input_signature - type: List[Tuple[int], str] + type: List[Tuple[Tuple[int], str]] doc: 'A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s @@ -74,7 +79,7 @@ spec: saving the model. Defaulted to True. default: true - name: input_signature - type: List[Tuple[int, ], str] + type: List[Tuple[Tuple[int, ], str]] doc: 'A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s @@ -179,6 +184,7 @@ spec: default_handler: to_onnx disable_auto_mount: false allow_empty_resources: true + clone_target_dir: '' env: [] priority_class_name: '' preemption_mode: prevent diff --git a/functions/development/onnx_utils/latest/src/item.yaml b/functions/development/onnx_utils/latest/src/item.yaml index b7ca891d..36335837 100644 --- a/functions/development/onnx_utils/latest/src/item.yaml +++ b/functions/development/onnx_utils/latest/src/item.yaml @@ -23,7 +23,7 @@ spec: with_mlrun: false filename: onnx_utils.py handler: to_onnx - image: mlrun/ml-models + image: mlrun/mlrun kind: job requirements: - onnx~=1.13.0 @@ -32,4 +32,4 @@ spec: - onnxmltools~=1.11.0 - tf2onnx~=1.13.0 url: '' -version: 1.1.1 +version: 1.2.0 diff --git a/functions/development/onnx_utils/latest/static/function.html b/functions/development/onnx_utils/latest/static/function.html index 6dfede79..feceb076 100644 --- a/functions/development/onnx_utils/latest/static/function.html +++ b/functions/development/onnx_utils/latest/static/function.html @@ -19,7 +19,7 @@ metadata: name: onnx-utils tag: '' - hash: d81c2cec3242be94fc9c207bb75fc6495f2a34ae + hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2 project: '' labels: author: guyl @@ -28,16 +28,21 @@ spec: command: '' args: [] - image: mlrun/ml-models + image: '' build: functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 - commands: - - python -m pip install onnx~=1.13.0 onnxruntime~=1.14.0 onnxoptimizer~=0.3.0 - onnxmltools~=1.11.0 tf2onnx~=1.13.0 - code_origin: https://github.com/mlrun/functions.git#b1365179513d1457e28a797e7d04f63a91238f02:/Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py - origin_filename: /Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py + base_image: mlrun/mlrun + commands: [] + code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py with_mlrun: false auto_build: true + requirements: + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 entry_points: tf_keras_to_onnx: name: tf_keras_to_onnx @@ -60,7 +65,7 @@ saving the model. Defaulted to True. default: true - name: input_signature - type: List[Tuple[int], str] + type: List[Tuple[Tuple[int], str]] doc: 'A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s @@ -91,7 +96,7 @@ saving the model. Defaulted to True. default: true - name: input_signature - type: List[Tuple[int, ], str] + type: List[Tuple[Tuple[int, ], str]] doc: 'A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s @@ -196,6 +201,7 @@ default_handler: to_onnx disable_auto_mount: false allow_empty_resources: true + clone_target_dir: '' env: [] priority_class_name: '' preemption_mode: prevent diff --git a/functions/development/onnx_utils/latest/static/item.html b/functions/development/onnx_utils/latest/static/item.html index 526607d9..07129b01 100644 --- a/functions/development/onnx_utils/latest/static/item.html +++ b/functions/development/onnx_utils/latest/static/item.html @@ -40,7 +40,7 @@ with_mlrun: false filename: onnx_utils.py handler: to_onnx - image: mlrun/ml-models + image: mlrun/mlrun kind: job requirements: - onnx~=1.13.0 @@ -49,7 +49,7 @@ - onnxmltools~=1.11.0 - tf2onnx~=1.13.0 url: '' -version: 1.1.1 +version: 1.2.0 diff --git a/functions/development/tags.json b/functions/development/tags.json index 829f5f6b..cfa2eb83 100644 --- a/functions/development/tags.json +++ b/functions/development/tags.json @@ -1 +1 @@ -{"categories": ["etl", "data-analysis", "feature-store", "utils", "model-training", "data-validation", "machine-learning", "data-preparation", "model-testing", "model-serving", "monitoring"], "kind": ["dask", "nuclio:serving", "serving", "nuclio", "job"]} \ No newline at end of file +{"categories": ["etl", "machine-learning", "model-training", "feature-store", "utils", "data-analysis", "model-serving", "monitoring", "data-validation", "data-preparation", "model-testing"], "kind": ["serving", "dask", "job", "nuclio:serving", "nuclio"]} \ No newline at end of file diff --git a/functions/development/transcribe/0.0.1/src/test_transcribe.py b/functions/development/transcribe/0.0.1/src/test_transcribe.py index cab2b663..d8448004 100644 --- a/functions/development/transcribe/0.0.1/src/test_transcribe.py +++ b/functions/development/transcribe/0.0.1/src/test_transcribe.py @@ -29,7 +29,7 @@ "The crowd roars, a symphony of passion, " "as the game writes its unpredictable story on the field of destiny.", ] -whisper_models = ["tiny_en", "tiny", "base_en", "base_en"] +whisper_models = ["tiny.en", "tiny", "base.en", "base"] @pytest.mark.skipif( condition=sys.version_info[:2] < (3, 8), diff --git a/functions/development/transcribe/latest/src/test_transcribe.py b/functions/development/transcribe/latest/src/test_transcribe.py index cab2b663..d8448004 100644 --- a/functions/development/transcribe/latest/src/test_transcribe.py +++ b/functions/development/transcribe/latest/src/test_transcribe.py @@ -29,7 +29,7 @@ "The crowd roars, a symphony of passion, " "as the game writes its unpredictable story on the field of destiny.", ] -whisper_models = ["tiny_en", "tiny", "base_en", "base_en"] +whisper_models = ["tiny.en", "tiny", "base.en", "base"] @pytest.mark.skipif( condition=sys.version_info[:2] < (3, 8),