Module hummingbird.ml.convert
Hummingbird main (converters) API.
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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
Hummingbird main (converters) API.
"""
from copy import deepcopy
import numpy as np
from .operator_converters import constants
from ._parse import parse_sklearn_api_model, parse_onnx_api_model
from ._topology import convert as topology_converter
from ._utils import torch_installed, lightgbm_installed, xgboost_installed
from .exceptions import MissingConverter, MissingBackend
from .supported import backends
# Invoke the registration of all our converters.
from . import operator_converters # noqa
# Set up the converter dispatcher.
from .supported import xgb_operator_list # noqa
from .supported import lgbm_operator_list # noqa
def _is_onnx_model(model):
"""
Function returning whether the input model is an ONNX model or not.
"""
return type(model).__name__ == "ModelProto"
def _supported_backend_check(backend):
"""
Function used to check whether the specified backend is supported or not.
"""
if backend is None:
raise MissingBackend("Backend: {}".format(backend))
def _supported_backend_check_config(model, backend, extra_config):
"""
Function used to check whether the specified backend and configuration pair is supported or not.
"""
assert torch_installed(), "To use Hummingbird you need to install torch."
import onnx
import torch
if backend is torch.jit.__name__ and constants.TEST_INPUT not in extra_config:
raise RuntimeError("Backend {} requires test inputs. Please pass some test input to the convert.".format(backend))
if backend is onnx.__name__:
if constants.ONNX_INITIAL_TYPES not in extra_config and constants.TEST_INPUT not in extra_config:
raise RuntimeError("Cannot generate test input data for ONNX. Either pass some input data or the initial_types")
if _is_onnx_model(model) and constants.ONNX_INITIAL_TYPES not in extra_config and constants.TEST_INPUT not in extra_config:
raise RuntimeError(
"Cannot extract number of input features from the ONNX. Either pass some input data or the initial_types"
)
def _convert_sklearn(model, backend, test_input, device, extra_config={}):
"""
This function converts the specified *scikit-learn* (API) model into its *backend* counterpart.
The supported operators and backends can be found at `hummingbird.ml.supported`.
"""
assert model is not None
assert torch_installed(), "To use Hummingbird you need to install torch."
import torch
# Parse scikit-learn model as our internal data structure (i.e., Topology)
# We modify the scikit learn model during translation.
model = deepcopy(model)
topology = parse_sklearn_api_model(model)
# Convert the Topology object into a PyTorch model.
hb_model = topology_converter(topology, backend, device, extra_config=extra_config)
return hb_model
def _convert_lightgbm(model, backend, test_input, device, extra_config={}):
"""
This function is used to generate a *backend* model from a given input [LightGBM] model.
[LightGBM]: https://lightgbm.readthedocs.io/
"""
assert (
lightgbm_installed()
), "To convert LightGBM models you need to install LightGBM (or `pip install hummingbird-ml[extra]`)."
return _convert_sklearn(model, backend, test_input, device, extra_config)
def _convert_xgboost(model, backend, test_input, device, extra_config={}):
"""
This function is used to generate a *backend* model from a given input [XGBoost] model.
[XGBoost]: https://xgboost.readthedocs.io/
"""
assert (
xgboost_installed()
), "To convert XGboost models you need to instal XGBoost (or `pip install hummingbird-ml[extra]`)."
# XGBoostRegressor and Classifier have different APIs for extracting the number of features.
# In the former case we need to infer them from the test_input.
if constants.N_FEATURES not in extra_config:
if "_features_count" in dir(model):
extra_config[constants.N_FEATURES] = model._features_count
elif test_input is not None:
if type(test_input) is np.ndarray and len(test_input.shape) == 2:
extra_config[constants.N_FEATURES] = test_input.shape[1]
else:
raise RuntimeError(
"XGBoost converter is not able to infer the number of input features.\
Apparently test_input is not an ndarray. \
Please fill an issue at https://github.com/microsoft/hummingbird/."
)
else:
raise RuntimeError(
"XGBoost converter is not able to infer the number of input features.\
Please pass some test_input to the converter."
)
return _convert_sklearn(model, backend, test_input, device, extra_config)
def _convert_onnxml(model, backend, test_input, device, extra_config={}):
"""
This function converts the specified [ONNX-ML] model into its *backend* counterpart.
The supported operators can be found at `hummingbird.ml.supported`.
"""
assert model is not None
assert torch_installed(), "To use Hummingbird you need to install torch."
import onnx
# The conversion requires some test input for tracing.
# Test inputs can be either provided or generate from the inital types.
# Get the initial types if any.
initial_types = None
if constants.ONNX_INITIAL_TYPES in extra_config:
initial_types = extra_config[constants.ONNX_INITIAL_TYPES]
# Generate some test input if necessary.
if test_input is None:
if backend == onnx.__name__:
assert (
initial_types is not None and not initial_types[0][1].shape is None
), "Cannot generate test input data. Initial_types do not contain shape information."
assert len(initial_types[0][1].shape) == 2, "Hummingbird currently support only inputs with len(shape) == 2."
from onnxconverter_common.data_types import FloatTensorType, Int32TensorType
test_input = np.random.rand(initial_types[0][1].shape[0], initial_types[0][1].shape[1])
extra_config[constants.N_FEATURES] = initial_types[0][1].shape[1]
if type(initial_types[0][1]) is FloatTensorType:
test_input = np.array(test_input, dtype=np.float32)
elif type(initial_types[0][1]) is Int32TensorType:
test_input = np.array(test_input, dtype=np.int32)
else:
raise RuntimeError(
"Type {} not supported. Please fill an issue on https://github.com/microsoft/hummingbird/.".format(
type(initial_types[0][1])
)
)
else:
extra_config[constants.N_FEATURES] = np.array(test_input).shape[1]
extra_config[constants.TEST_INPUT] = test_input
# Set the initializers. Some converter requires the access to initializers.
initializers = {} if model.graph.initializer is None else {in_.name: in_ for in_ in model.graph.initializer}
extra_config[constants.ONNX_INITIALIZERS] = initializers
# Parse ONNX model as our internal data structure (i.e., Topology).
topology = parse_onnx_api_model(model)
# Convert the Topology object into a PyTorch model.
hb_model = topology_converter(topology, backend, device, extra_config=extra_config)
return hb_model
def convert(model, backend, test_input=None, device="cpu", extra_config={}):
"""
This function converts the specified input *model* into an implementation targeting *backend*.
*Convert* supports [Sklearn], [LightGBM], [XGBoost] and [ONNX] models.
For *LightGBM* and *XGBoost* currently only the Sklearn API is supported.
The detailed list of models and backends can be found at `hummingbird.ml.supported`.
The *onnx* backend requires either a test_input of a the initial types set through the exta_config parameter.
The *torch.jit* backend requires a test_input.
[Sklearn]: https://scikit-learn.org/
[LightGBM]: https://lightgbm.readthedocs.io/
[XGBoost]: https://xgboost.readthedocs.io/
[ONNX]: https://onnx.ai/
[ONNX-ML]: https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md
[ONNX operators]: https://github.com/onnx/onnx/blob/master/docs/Operators.md
Args:
model: An input model
backend: The target for the conversion
test_input: Some input data used to trace the model execution.
For the ONNX backend the test_input size is supposed to be as large as the expected batch size.
device: The target device the model should be run. This parameter is only used by the *torch** backends, and
the devices supported are the one supported by PyTorch, i.e., 'cpu' or 'cuda'.
extra_config: Extra configurations to be used by the individual operator converters.
The set of supported extra configurations can be found at `hummingbird.ml.supported`
Examples:
>>> pytorch_model = convert(sklearn_model,`torch`)
Returns:
A model implemented in *backend*, which is equivalent to the input model
"""
assert model is not None
# We destroy extra_config during conversion, we create a copy here.
extra_config = deepcopy(extra_config)
# Add test input as extra configuration for conversion.
if test_input is not None and constants.TEST_INPUT not in extra_config:
extra_config[constants.TEST_INPUT] = test_input
# We do some normalization on backends.
backend = backend.lower()
backend = backends[backend]
# Check whether we actually support the backend.
_supported_backend_check(backend)
_supported_backend_check_config(model, backend, extra_config)
if type(model) in xgb_operator_list:
return _convert_xgboost(model, backend, test_input, device, extra_config)
if type(model) in lgbm_operator_list:
return _convert_lightgbm(model, backend, test_input, device, extra_config)
if _is_onnx_model(model):
return _convert_onnxml(model, backend, test_input, device, extra_config)
return _convert_sklearn(model, backend, test_input, device, extra_config)
Functions
def convert(model, backend, test_input=None, device='cpu', extra_config={})
-
This function converts the specified input model into an implementation targeting backend. Convert supports Sklearn, LightGBM, XGBoost and ONNX models. For LightGBM and XGBoost currently only the Sklearn API is supported. The detailed list of models and backends can be found at
hummingbird.ml.supported
. The onnx backend requires either a test_input of a the initial types set through the exta_config parameter. The torch.jit backend requires a test_input.Args
model
- An input model
backend
- The target for the conversion
test_input
- Some input data used to trace the model execution. For the ONNX backend the test_input size is supposed to be as large as the expected batch size.
device
- The target device the model should be run. This parameter is only used by the torch* backends, and the devices supported are the one supported by PyTorch, i.e., 'cpu' or 'cuda'.
extra_config
- Extra configurations to be used by the individual operator converters.
The set of supported extra configurations can be found at
hummingbird.ml.supported
Examples
>>> pytorch_model = convert(sklearn_model,<code>torch</code>)
Returns
A model implemented in backend, which is equivalent to the input model
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def convert(model, backend, test_input=None, device="cpu", extra_config={}): """ This function converts the specified input *model* into an implementation targeting *backend*. *Convert* supports [Sklearn], [LightGBM], [XGBoost] and [ONNX] models. For *LightGBM* and *XGBoost* currently only the Sklearn API is supported. The detailed list of models and backends can be found at `hummingbird.ml.supported`. The *onnx* backend requires either a test_input of a the initial types set through the exta_config parameter. The *torch.jit* backend requires a test_input. [Sklearn]: https://scikit-learn.org/ [LightGBM]: https://lightgbm.readthedocs.io/ [XGBoost]: https://xgboost.readthedocs.io/ [ONNX]: https://onnx.ai/ [ONNX-ML]: https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md [ONNX operators]: https://github.com/onnx/onnx/blob/master/docs/Operators.md Args: model: An input model backend: The target for the conversion test_input: Some input data used to trace the model execution. For the ONNX backend the test_input size is supposed to be as large as the expected batch size. device: The target device the model should be run. This parameter is only used by the *torch** backends, and the devices supported are the one supported by PyTorch, i.e., 'cpu' or 'cuda'. extra_config: Extra configurations to be used by the individual operator converters. The set of supported extra configurations can be found at `hummingbird.ml.supported` Examples: >>> pytorch_model = convert(sklearn_model,`torch`) Returns: A model implemented in *backend*, which is equivalent to the input model """ assert model is not None # We destroy extra_config during conversion, we create a copy here. extra_config = deepcopy(extra_config) # Add test input as extra configuration for conversion. if test_input is not None and constants.TEST_INPUT not in extra_config: extra_config[constants.TEST_INPUT] = test_input # We do some normalization on backends. backend = backend.lower() backend = backends[backend] # Check whether we actually support the backend. _supported_backend_check(backend) _supported_backend_check_config(model, backend, extra_config) if type(model) in xgb_operator_list: return _convert_xgboost(model, backend, test_input, device, extra_config) if type(model) in lgbm_operator_list: return _convert_lightgbm(model, backend, test_input, device, extra_config) if _is_onnx_model(model): return _convert_onnxml(model, backend, test_input, device, extra_config) return _convert_sklearn(model, backend, test_input, device, extra_config)