diff --git a/README.md b/README.md index 73ff40347..133f1e248 100644 --- a/README.md +++ b/README.md @@ -193,7 +193,6 @@ To cite Concrete ML, notably in academic papers, please use the following entry, - ## License. This software is distributed under the BSD-3-Clause-Clear license. If you have any questions, please contact us at hello@zama.ai. diff --git a/docs/conf.py b/docs/conf.py index 4a59c28e5..ddcd4fb27 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ root_url = root_url if root_url.endswith("/") else root_url + "/" # The full version, including alpha/beta/rc tags -release = "1.4.0" +release = "1.4.1" # -- General configuration --------------------------------------------------- diff --git a/docs/developer-guide/api/concrete.ml.onnx.ops_impl.md b/docs/developer-guide/api/concrete.ml.onnx.ops_impl.md index e12e5bece..f71c2a3ec 100644 --- a/docs/developer-guide/api/concrete.ml.onnx.ops_impl.md +++ b/docs/developer-guide/api/concrete.ml.onnx.ops_impl.md @@ -1333,10 +1333,12 @@ ______________________________________________________________________ ```python numpy_avgpool( x: ndarray, - ceil_mode: int, kernel_shape: Tuple[int, ], - pads: Tuple[int, ] = None, - strides: Tuple[int, ] = None + auto_pad: str = 'NOTSET', + ceil_mode: int = 0, + count_include_pad: int = 1, + pads: Optional[Tuple[int, ]] = None, + strides: Optional[Tuple[int, ]] = None ) → Tuple[ndarray] ``` @@ -1348,24 +1350,22 @@ See: https://github.com/onnx/onnx/blob/main/docs/Operators.md#AveragePool **Args:** -- `x` (numpy.ndarray): input data (many dtypes are supported). Shape is N x C x H x W for 2d -- `ceil_mode` (int): ONNX rounding parameter, expected 0 (torch style dimension computation) -- `kernel_shape` (Tuple\[int, ...\]): shape of the kernel. Should have 2 elements for 2d conv -- `pads` (Tuple\[int, ...\]): padding in ONNX format (begin, end) on each axis -- `strides` (Tuple\[int, ...\]): stride of the convolution on each axis +- `x` (numpy.ndarray): Input data of shape (N, C, H, W), as only 2D inputs are currently supported. +- `kernel_shape` (Tuple\[int, ...\]): The size of the kernel along each axis. Currently, only 2D kernels are supported. +- `auto_pad` (str): Only the default "NOTSET" value is currently supported, which means explicit padding is used. +- `ceil_mode` (int): Whether to use ONNX's ceil (1) or floor (0, the default) to compute the output shape. +- `count_include_pad` (int): Whether include pad pixels when calculating values for the edges. Currently, setting this parameter to 0 is not supported in Concrete ML. +- `pads` (Tuple\[int, ...\]): Padding for the beginning and ending along each spatial axis. Expected format is \[x1_begin, x2_begin...x1_end, x2_end, ...\] where xi_begin (resp. xi_end) is the number of pixels added at the beginning (resp. end) of axis `i`. +- `strides` (Tuple\[int, ...\]): Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis. **Returns:** - `res` (numpy.ndarray): a tensor of size (N x InChannels x OutHeight x OutWidth). - `See https`: //pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html -**Raises:** - -- `AssertionError`: if the pooling arguments are wrong - ______________________________________________________________________ - + ## function `numpy_maxpool` @@ -1406,7 +1406,7 @@ See: https://github.com/onnx/onnx/blob/main/docs/Operators.md#MaxPool ______________________________________________________________________ - + ## function `numpy_cast` @@ -1431,7 +1431,7 @@ See: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Cast ______________________________________________________________________ - + ## function `numpy_batchnorm` @@ -1473,7 +1473,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#BatchNormalization- ______________________________________________________________________ - + ## function `numpy_flatten` @@ -1496,7 +1496,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Flatten-13. ______________________________________________________________________ - + ## function `numpy_or` @@ -1519,7 +1519,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Or-7 ______________________________________________________________________ - + ## function `numpy_or_float` @@ -1542,7 +1542,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Or-7 ______________________________________________________________________ - + ## function `numpy_round` @@ -1564,7 +1564,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Round-11 Remark tha ______________________________________________________________________ - + ## function `numpy_pow` @@ -1587,7 +1587,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Pow-13 ______________________________________________________________________ - + ## function `numpy_floor` @@ -1609,7 +1609,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Floor-1 ______________________________________________________________________ - + ## function `numpy_max` @@ -1634,7 +1634,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Max-1 ______________________________________________________________________ - + ## function `numpy_min` @@ -1659,7 +1659,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Max-1 ______________________________________________________________________ - + ## function `numpy_sign` @@ -1681,7 +1681,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sign-9 ______________________________________________________________________ - + ## function `numpy_neg` @@ -1703,7 +1703,7 @@ See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sign-9 ______________________________________________________________________ - + ## function `numpy_concatenate` diff --git a/docs/developer-guide/api/concrete.ml.quantization.base_quantized_op.md b/docs/developer-guide/api/concrete.ml.quantization.base_quantized_op.md index b82818135..faecefe79 100644 --- a/docs/developer-guide/api/concrete.ml.quantization.base_quantized_op.md +++ b/docs/developer-guide/api/concrete.ml.quantization.base_quantized_op.md @@ -15,7 +15,7 @@ Base Quantized Op class that implements quantization for a float numpy op. ______________________________________________________________________ - + ## class `QuantizedOp` @@ -29,7 +29,7 @@ Base class for quantized ONNX ops implemented in numpy. - `constant_inputs` (Optional\[Union\[Dict\[str, Any\], Dict\[int, Any\]\]\]): The constant tensors that are inputs to this op - `input_quant_opts` (QuantizationOptions): Input quantizer options, determine the quantization that is applied to input tensors (that are not constants) - + ### method `__init__` @@ -56,7 +56,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `calibrate` @@ -76,7 +76,7 @@ Create corresponding QuantizedArray for the output of the activation function. ______________________________________________________________________ - + ### method `call_impl` @@ -97,7 +97,7 @@ Call self.impl to centralize mypy bug workaround. ______________________________________________________________________ - + ### method `can_fuse` @@ -115,7 +115,7 @@ This function shall be overloaded by inheriting classes to test self.\_int_input ______________________________________________________________________ - + ### method `dump` @@ -131,7 +131,7 @@ Dump itself to a file. ______________________________________________________________________ - + ### method `dump_dict` @@ -147,7 +147,7 @@ Dump itself to a dict. ______________________________________________________________________ - + ### method `dumps` @@ -163,7 +163,7 @@ Dump itself to a string. ______________________________________________________________________ - + ### method `load_dict` @@ -183,7 +183,7 @@ Load itself from a string. ______________________________________________________________________ - + ### classmethod `must_quantize_input` @@ -205,7 +205,7 @@ Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be eithe ______________________________________________________________________ - + ### classmethod `op_type` @@ -221,7 +221,7 @@ Get the type of this operation. ______________________________________________________________________ - + ### method `prepare_output` @@ -243,15 +243,15 @@ The calibrate method needs to be called with sample data before using this funct ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Execute the quantized forward. @@ -267,7 +267,7 @@ Execute the quantized forward. ______________________________________________________________________ - + ## class `QuantizedOpUnivariateOfEncrypted` @@ -275,7 +275,7 @@ An univariate operator of an encrypted value. This operation is not really operating as a quantized operation. It is useful when the computations get fused into a TLU, as in e.g., Act(x) = x || (x + 42)). - + ### method `__init__` @@ -302,7 +302,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `calibrate` @@ -322,7 +322,7 @@ Create corresponding QuantizedArray for the output of the activation function. ______________________________________________________________________ - + ### method `call_impl` @@ -343,7 +343,7 @@ Call self.impl to centralize mypy bug workaround. ______________________________________________________________________ - + ### method `can_fuse` @@ -361,7 +361,7 @@ This operation can be fused and computed in float when a single integer tensor g ______________________________________________________________________ - + ### method `dump` @@ -377,7 +377,7 @@ Dump itself to a file. ______________________________________________________________________ - + ### method `dump_dict` @@ -393,7 +393,7 @@ Dump itself to a dict. ______________________________________________________________________ - + ### method `dumps` @@ -409,7 +409,7 @@ Dump itself to a string. ______________________________________________________________________ - + ### method `load_dict` @@ -429,7 +429,7 @@ Load itself from a string. ______________________________________________________________________ - + ### classmethod `must_quantize_input` @@ -451,7 +451,7 @@ Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be eithe ______________________________________________________________________ - + ### classmethod `op_type` @@ -467,7 +467,7 @@ Get the type of this operation. ______________________________________________________________________ - + ### method `prepare_output` @@ -489,15 +489,15 @@ The calibrate method needs to be called with sample data before using this funct ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Execute the quantized forward. @@ -513,7 +513,7 @@ Execute the quantized forward. ______________________________________________________________________ - + ## class `QuantizedMixingOp` @@ -521,7 +521,7 @@ An operator that mixes (adds or multiplies) together encrypted inputs. Mixing operators cannot be fused to TLUs. - + ### method `__init__` @@ -549,7 +549,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `calibrate` @@ -569,7 +569,7 @@ Create corresponding QuantizedArray for the output of the activation function. ______________________________________________________________________ - + ### method `call_impl` @@ -590,7 +590,7 @@ Call self.impl to centralize mypy bug workaround. ______________________________________________________________________ - + ### method `can_fuse` @@ -608,7 +608,7 @@ Mixing operations cannot be fused since it must be performed over integer tensor ______________________________________________________________________ - + ### method `cnp_round` @@ -634,7 +634,7 @@ Round the input array to the specified number of bits. ______________________________________________________________________ - + ### method `dump` @@ -650,7 +650,7 @@ Dump itself to a file. ______________________________________________________________________ - + ### method `dump_dict` @@ -666,7 +666,7 @@ Dump itself to a dict. ______________________________________________________________________ - + ### method `dumps` @@ -682,7 +682,7 @@ Dump itself to a string. ______________________________________________________________________ - + ### method `load_dict` @@ -702,7 +702,7 @@ Load itself from a string. ______________________________________________________________________ - + ### method `make_output_quant_parameters` @@ -728,7 +728,7 @@ Build a quantized array from quantized integer results of the op and quantizatio ______________________________________________________________________ - + ### classmethod `must_quantize_input` @@ -750,7 +750,7 @@ Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be eithe ______________________________________________________________________ - + ### classmethod `op_type` @@ -766,7 +766,7 @@ Get the type of this operation. ______________________________________________________________________ - + ### method `prepare_output` @@ -788,15 +788,15 @@ The calibrate method needs to be called with sample data before using this funct ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Execute the quantized forward. diff --git a/docs/developer-guide/api/concrete.ml.quantization.post_training.md b/docs/developer-guide/api/concrete.ml.quantization.post_training.md index 12d91cb17..6f4de1017 100644 --- a/docs/developer-guide/api/concrete.ml.quantization.post_training.md +++ b/docs/developer-guide/api/concrete.ml.quantization.post_training.md @@ -14,7 +14,7 @@ Post Training Quantization methods. ______________________________________________________________________ - + ## function `get_n_bits_dict` @@ -36,7 +36,7 @@ Convert the n_bits parameter into a proper dictionary. ______________________________________________________________________ - + ## class `ONNXConverter` @@ -54,7 +54,7 @@ This class should be sub-classed to provide specific calibration and quantizatio - `numpy_model` (NumpyModule): Model in numpy. - `rounding_threshold_bits` (int): if not None, every accumulators in the model are rounded down to the given bits of precision - + ### method `__init__` @@ -108,7 +108,7 @@ Get the number of bits to use for the quantization of any constants (usually wei ______________________________________________________________________ - + ### method `quantize_module` @@ -130,7 +130,7 @@ Following https://arxiv.org/abs/1712.05877 guidelines. ______________________________________________________________________ - + ## class `PostTrainingAffineQuantization` @@ -153,7 +153,7 @@ Create the quantized version of the passed numpy module. - `QuantizedModule`: A quantized version of the numpy model. - + ### method `__init__` @@ -207,7 +207,7 @@ Get the number of bits to use for the quantization of any constants (usually wei ______________________________________________________________________ - + ### method `quantize_module` @@ -229,7 +229,7 @@ Following https://arxiv.org/abs/1712.05877 guidelines. ______________________________________________________________________ - + ## class `PostTrainingQATImporter` @@ -237,7 +237,7 @@ Converter of Quantization Aware Training networks. This class provides specific configuration for QAT networks during ONNX network conversion to Concrete ML computation graphs. - + ### method `__init__` @@ -291,7 +291,7 @@ Get the number of bits to use for the quantization of any constants (usually wei ______________________________________________________________________ - + ### method `quantize_module` diff --git a/docs/developer-guide/api/concrete.ml.quantization.quantized_module.md b/docs/developer-guide/api/concrete.ml.quantization.quantized_module.md index b5ddeb09a..e7643331b 100644 --- a/docs/developer-guide/api/concrete.ml.quantization.quantized_module.md +++ b/docs/developer-guide/api/concrete.ml.quantization.quantized_module.md @@ -216,7 +216,7 @@ forward( *x: ndarray, fhe: Union[FheMode, str] = , debug: bool = False -) → Union[ndarray, Tuple[ndarray, ], Tuple[Union[Tuple[ndarray, ], ndarray], Dict[str, Dict[Union[int, str], Union[ndarray, QuantizedArray, NoneType]]]]] +) → Union[ndarray, Tuple[ndarray, ], Tuple[Union[Tuple[ndarray, ], ndarray], Dict[str, Dict[Union[int, str], Union[ndarray, QuantizedArray, NoneType, bool, int, float]]]]] ``` Forward pass with numpy function only on floating points. diff --git a/docs/developer-guide/api/concrete.ml.quantization.quantized_ops.md b/docs/developer-guide/api/concrete.ml.quantization.quantized_ops.md index 5ef04d265..406f7b4ab 100644 --- a/docs/developer-guide/api/concrete.ml.quantization.quantized_ops.md +++ b/docs/developer-guide/api/concrete.ml.quantization.quantized_ops.md @@ -265,10 +265,10 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ @@ -312,10 +312,10 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ @@ -364,9 +364,9 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ @@ -485,9 +485,9 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ @@ -534,9 +534,9 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Reshape the input integer encrypted tensor. @@ -607,10 +607,10 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Compute the quantized convolution between two quantized tensors. @@ -665,27 +665,27 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `QuantizedMaxPool` Quantized Max Pooling op. - + ### method `__init__` @@ -712,7 +712,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -730,26 +730,26 @@ Max Pooling operation can not be fused since it must be performed over integer t ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `QuantizedPad` Quantized Padding op. - + ### method `__init__` @@ -776,7 +776,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -794,21 +794,21 @@ Pad operation cannot be fused since it must be performed over integer tensors. ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `QuantizedWhere` @@ -816,7 +816,7 @@ Where operator on quantized arrays. Supports only constants for the results produced on the True/False branches. - + ### method `__init__` @@ -843,7 +843,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedCast` @@ -863,7 +863,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedGreater` @@ -871,7 +871,7 @@ Comparison operator >. Only supports comparison with a constant. - + ### method `__init__` @@ -898,7 +898,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedGreaterOrEqual` @@ -906,7 +906,7 @@ Comparison operator >=. Only supports comparison with a constant. - + ### method `__init__` @@ -933,7 +933,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedLess` @@ -941,7 +941,7 @@ Comparison operator \<. Only supports comparison with a constant. - + ### method `__init__` @@ -968,7 +968,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedLessOrEqual` @@ -976,7 +976,7 @@ Comparison operator \<=. Only supports comparison with a constant. - + ### method `__init__` @@ -1003,7 +1003,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedOr` @@ -1023,7 +1023,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedDiv` @@ -1043,7 +1043,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedMul` @@ -1063,7 +1063,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedSub` @@ -1107,14 +1107,14 @@ ______________________________________________________________________ ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `QuantizedBatchNormalization` @@ -1132,7 +1132,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedFlatten` @@ -1150,7 +1150,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1168,15 +1168,15 @@ Flatten operation cannot be fused since it must be performed over integer tensor ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Flatten the input integer encrypted tensor. @@ -1192,13 +1192,13 @@ Flatten the input integer encrypted tensor. ______________________________________________________________________ - + ## class `QuantizedReduceSum` ReduceSum with encrypted input. - + ### method `__init__` @@ -1239,7 +1239,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `calibrate` @@ -1259,16 +1259,16 @@ Create corresponding QuantizedArray for the output of the activation function. ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], calibrate_rounding: bool = False, **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Sum the encrypted tensor's values along the given axes. @@ -1285,7 +1285,7 @@ Sum the encrypted tensor's values along the given axes. ______________________________________________________________________ - + ## class `QuantizedErf` @@ -1303,7 +1303,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedNot` @@ -1321,13 +1321,13 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedBrevitasQuant` Brevitas uniform quantization with encrypted input. - + ### method `__init__` @@ -1370,7 +1370,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `calibrate` @@ -1390,15 +1390,15 @@ Create corresponding QuantizedArray for the output of Quantization function. ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Quantize values. @@ -1414,7 +1414,7 @@ Quantize values. ______________________________________________________________________ - + ## class `QuantizedTranspose` @@ -1434,7 +1434,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1452,15 +1452,15 @@ Transpose can not be fused since it must be performed over integer tensors as it ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Transpose the input integer encrypted tensor. @@ -1476,7 +1476,7 @@ Transpose the input integer encrypted tensor. ______________________________________________________________________ - + ## class `QuantizedFloor` @@ -1494,7 +1494,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedMax` @@ -1512,7 +1512,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedMin` @@ -1530,7 +1530,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedNeg` @@ -1548,7 +1548,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedSign` @@ -1566,7 +1566,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ## class `QuantizedUnsqueeze` @@ -1584,7 +1584,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1602,15 +1602,15 @@ Unsqueeze can not be fused since it must be performed over integer tensors as it ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Unsqueeze the input tensors on a given axis. @@ -1626,7 +1626,7 @@ Unsqueeze the input tensors on a given axis. ______________________________________________________________________ - + ## class `QuantizedConcat` @@ -1644,7 +1644,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1662,15 +1662,15 @@ Concatenation can not be fused since it must be performed over integer tensors a ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Concatenate the input tensors on a given axis. @@ -1686,7 +1686,7 @@ Concatenate the input tensors on a given axis. ______________________________________________________________________ - + ## class `QuantizedSqueeze` @@ -1704,7 +1704,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1722,15 +1722,15 @@ Squeeze can not be fused since it must be performed over integer tensors as it r ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Squeeze the input tensors on a given axis. @@ -1746,7 +1746,7 @@ Squeeze the input tensors on a given axis. ______________________________________________________________________ - + ## class `ONNXShape` @@ -1764,7 +1764,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1782,20 +1782,20 @@ This operation returns the shape of the tensor and thus can not be fused into a ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `ONNXConstantOfShape` @@ -1813,7 +1813,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1831,7 +1831,7 @@ This operation returns a new encrypted tensor and thus can not be fused. ______________________________________________________________________ - + ## class `ONNXGather` @@ -1851,7 +1851,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1869,20 +1869,20 @@ This operation returns values from a tensor and thus can not be fused into a uni ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `ONNXSlice` @@ -1900,7 +1900,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1918,20 +1918,20 @@ This operation returns values from a tensor and thus can not be fused into a uni ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` ______________________________________________________________________ - + ## class `QuantizedExpand` @@ -1949,7 +1949,7 @@ Get the names of encrypted integer tensors that are used by this op. ______________________________________________________________________ - + ### method `can_fuse` @@ -1967,15 +1967,15 @@ Unsqueeze can not be fused since it must be performed over integer tensors as it ______________________________________________________________________ - + ### method `q_impl` ```python q_impl( - *q_inputs: Optional[ndarray, QuantizedArray], + *q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float], **attrs -) → Union[ndarray, QuantizedArray, NoneType] +) → Union[ndarray, QuantizedArray, NoneType, bool, int, float] ``` Expand the input tensor to a specified shape. @@ -1991,7 +1991,7 @@ Expand the input tensor to a specified shape. ______________________________________________________________________ - + ## class `QuantizedEqual` @@ -1999,7 +1999,7 @@ Comparison operator ==. Only supports comparison with a constant. - + ### method `__init__` diff --git a/docs/developer-guide/api/concrete.ml.quantization.quantizers.md b/docs/developer-guide/api/concrete.ml.quantization.quantizers.md index 367af67b7..c4ed34e4e 100644 --- a/docs/developer-guide/api/concrete.ml.quantization.quantizers.md +++ b/docs/developer-guide/api/concrete.ml.quantization.quantizers.md @@ -789,7 +789,7 @@ See https://arxiv.org/abs/1712.05877. ```python __init__( n_bits, - values: 'Optional[ndarray]', + values: 'Union[None, float, int, ndarray]', value_is_float: 'bool' = True, options: 'Optional[QuantizationOptions]' = None, stats: 'Optional[MinMaxQuantizationStats]' = None, @@ -800,23 +800,23 @@ __init__( ______________________________________________________________________ - + ### method `dequant` ```python -dequant() → ndarray +dequant() → Union[ndarray, Tracer] ``` De-quantize self.qvalues. **Returns:** -- `numpy.ndarray`: De-quantized values. +- `Union[numpy.ndarray, Tracer]`: De-quantized values. ______________________________________________________________________ - + ### method `dump` @@ -832,7 +832,7 @@ Dump itself to a file. ______________________________________________________________________ - + ### method `dump_dict` @@ -848,7 +848,7 @@ Dump itself to a dict. ______________________________________________________________________ - + ### method `dumps` @@ -864,7 +864,7 @@ Dump itself to a string. ______________________________________________________________________ - + ### method `load_dict` @@ -884,56 +884,58 @@ Load itself from a string. ______________________________________________________________________ - + ### method `quant` ```python -quant() → Optional[ndarray] +quant() → Union[ndarray, Tracer] ``` Quantize self.values. **Returns:** -- `numpy.ndarray`: Quantized values. +- `Union[numpy.ndarray, Tracer]`: Quantized values. ______________________________________________________________________ - + ### method `update_quantized_values` ```python -update_quantized_values(qvalues: 'ndarray') → ndarray +update_quantized_values( + qvalues: 'Union[ndarray, Tracer]' +) → Union[ndarray, Tracer] ``` Update qvalues to get their corresponding values using the related quantized parameters. **Args:** -- `qvalues` (numpy.ndarray): Values to replace self.qvalues +- `qvalues` (Union\[numpy.ndarray, Tracer\]): Values to replace self.qvalues **Returns:** -- `values` (numpy.ndarray): Corresponding values +- `values` (Union\[numpy.ndarray, Tracer\]): Corresponding values ______________________________________________________________________ - + ### method `update_values` ```python -update_values(values: 'ndarray') → ndarray +update_values(values: 'Union[ndarray, Tracer]') → Union[ndarray, Tracer] ``` Update values to get their corresponding qvalues using the related quantized parameters. **Args:** -- `values` (numpy.ndarray): Values to replace self.values +- `values` (Union\[numpy.ndarray, Tracer\]): Values to replace self.values **Returns:** -- `qvalues` (numpy.ndarray): Corresponding qvalues +- `qvalues` (Union\[numpy.ndarray, Tracer\]): Corresponding qvalues diff --git a/docs/developer-guide/api/concrete.ml.sklearn.linear_model.md b/docs/developer-guide/api/concrete.ml.sklearn.linear_model.md index 68fa38e31..6ead761ff 100644 --- a/docs/developer-guide/api/concrete.ml.sklearn.linear_model.md +++ b/docs/developer-guide/api/concrete.ml.sklearn.linear_model.md @@ -223,7 +223,7 @@ Using this attribute is deprecated. ______________________________________________________________________ - + ### method `dump_dict` @@ -233,7 +233,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### method `fit` @@ -284,7 +284,7 @@ get_sklearn_params(deep: bool = True) → dict ______________________________________________________________________ - + ### classmethod `load_dict` @@ -294,7 +294,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ### method `partial_fit` @@ -328,7 +328,7 @@ post_processing(y_preds: ndarray) → ndarray ______________________________________________________________________ - + ### method `predict_proba` @@ -368,7 +368,7 @@ The justification for the formula in the loss="modified_huber" case is in the ap ______________________________________________________________________ - + ## class `SGDRegressor` @@ -382,7 +382,7 @@ An FHE linear regression model fitted with stochastic gradient descent. For more details on SGDRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html - + ### method `__init__` @@ -457,7 +457,7 @@ Is None if the model is not fitted. ______________________________________________________________________ - + ### method `dump_dict` @@ -467,7 +467,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### classmethod `load_dict` @@ -477,7 +477,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ## class `ElasticNet` @@ -491,7 +491,7 @@ An ElasticNet regression model with FHE. For more details on ElasticNet please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html - + ### method `__init__` @@ -559,7 +559,7 @@ Is None if the model is not fitted. ______________________________________________________________________ - + ### method `dump_dict` @@ -569,7 +569,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### classmethod `load_dict` @@ -579,7 +579,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ## class `Lasso` @@ -593,7 +593,7 @@ A Lasso regression model with FHE. For more details on Lasso please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html - + ### method `__init__` @@ -660,7 +660,7 @@ Is None if the model is not fitted. ______________________________________________________________________ - + ### method `dump_dict` @@ -670,7 +670,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### classmethod `load_dict` @@ -680,7 +680,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ## class `Ridge` @@ -694,7 +694,7 @@ A Ridge regression model with FHE. For more details on Ridge please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html - + ### method `__init__` @@ -759,7 +759,7 @@ Is None if the model is not fitted. ______________________________________________________________________ - + ### method `dump_dict` @@ -769,7 +769,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### classmethod `load_dict` @@ -779,7 +779,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ## class `LogisticRegression` @@ -793,7 +793,7 @@ A logistic regression model with FHE. For more details on LogisticRegression please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html - + ### method `__init__` @@ -888,7 +888,7 @@ Using this attribute is deprecated. ______________________________________________________________________ - + ### method `dump_dict` @@ -898,7 +898,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### classmethod `load_dict` diff --git a/docs/developer-guide/api/concrete.ml.sklearn.qnn.md b/docs/developer-guide/api/concrete.ml.sklearn.qnn.md index 312790534..74de5a9c9 100644 --- a/docs/developer-guide/api/concrete.ml.sklearn.qnn.md +++ b/docs/developer-guide/api/concrete.ml.sklearn.qnn.md @@ -120,7 +120,7 @@ Get the output quantizers. ______________________________________________________________________ - + ### method `dump_dict` @@ -130,7 +130,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### method `fit` @@ -145,7 +145,7 @@ fit( ______________________________________________________________________ - + ### method `fit_benchmark` @@ -160,7 +160,7 @@ fit_benchmark( ______________________________________________________________________ - + ### classmethod `load_dict` @@ -170,7 +170,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ### method `predict` @@ -183,7 +183,7 @@ predict( ______________________________________________________________________ - + ### method `predict_proba` @@ -196,7 +196,7 @@ predict_proba( ______________________________________________________________________ - + ## class `NeuralNetClassifier` @@ -206,7 +206,7 @@ This class wraps a quantized neural network implemented using Torch tools as a s Inputs that are float64 will be casted to float32 before training as Torch does not handle float64 types properly. Thus should not have a significant impact on the model's performances. If the targets are integers of lower bit-width, they will be safely casted to int64. Else, an error is raised. - + ### method `__init__` @@ -331,7 +331,7 @@ Using this attribute is deprecated. ______________________________________________________________________ - + ### method `dump_dict` @@ -341,7 +341,7 @@ dump_dict() → Dict[str, Any] ______________________________________________________________________ - + ### method `fit` @@ -356,7 +356,7 @@ fit( ______________________________________________________________________ - + ### method `fit_benchmark` @@ -371,7 +371,7 @@ fit_benchmark( ______________________________________________________________________ - + ### classmethod `load_dict` @@ -381,7 +381,7 @@ load_dict(metadata: Dict) ______________________________________________________________________ - + ### method `predict` @@ -394,7 +394,7 @@ predict( ______________________________________________________________________ - + ### method `predict_proba` diff --git a/docs/developer-guide/api/concrete.ml.torch.compile.md b/docs/developer-guide/api/concrete.ml.torch.compile.md index 92875e204..64883b1a5 100644 --- a/docs/developer-guide/api/concrete.ml.torch.compile.md +++ b/docs/developer-guide/api/concrete.ml.torch.compile.md @@ -91,7 +91,7 @@ Take a model in torch or ONNX, turn it to numpy, quantize its inputs / weights / ______________________________________________________________________ - + ## function `compile_torch_model` @@ -125,7 +125,9 @@ Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs - `configuration` (Configuration): Configuration object to use during compilation - `artifacts` (DebugArtifacts): Artifacts object to fill during compilation - `show_mlir` (bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo -- `n_bits`: the number of bits for the quantization +- `n_bits` (Union\[int, Dict\[str, int\]\]): number of bits for quantization, can be a single value or a dictionary with the following keys : + \- "op_inputs" and "op_weights" (mandatory) + \- "model_inputs" and "model_outputs" (optional, default to 5 bits). When using a single integer for n_bits, its value is assigned to "op_inputs" and "op_weights" bits. Default is 8 bits. - `rounding_threshold_bits` (int): if not None, every accumulators in the model are rounded down to the given bits of precision - `p_error` (Optional\[float\]): probability of error of a single PBS - `global_p_error` (Optional\[float\]): probability of error of the full circuit. In FHE simulation `global_p_error` is set to 0 @@ -139,7 +141,7 @@ Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs ______________________________________________________________________ - + ## function `compile_onnx_model` @@ -173,7 +175,9 @@ Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs - `configuration` (Configuration): Configuration object to use during compilation - `artifacts` (DebugArtifacts): Artifacts object to fill during compilation - `show_mlir` (bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo -- `n_bits`: the number of bits for the quantization +- `n_bits` (Union\[int, Dict\[str, int\]\]): number of bits for quantization, can be a single value or a dictionary with the following keys : + \- "op_inputs" and "op_weights" (mandatory) + \- "model_inputs" and "model_outputs" (optional, default to 5 bits). When using a single integer for n_bits, its value is assigned to "op_inputs" and "op_weights" bits. Default is 8 bits. - `rounding_threshold_bits` (int): if not None, every accumulators in the model are rounded down to the given bits of precision - `p_error` (Optional\[float\]): probability of error of a single PBS - `global_p_error` (Optional\[float\]): probability of error of the full circuit. In FHE simulation `global_p_error` is set to 0 @@ -187,7 +191,7 @@ Take a model in torch, turn it to numpy, quantize its inputs / weights / outputs ______________________________________________________________________ - + ## function `compile_brevitas_qat_model` diff --git a/pyproject.toml b/pyproject.toml index 770db77f4..57111c87e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "concrete-ml" -version = "1.4.0" +version = "1.4.1" description = "Concrete ML is an open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists." license = "BSD-3-Clause-Clear" authors = [ diff --git a/src/concrete/ml/version.py b/src/concrete/ml/version.py index d598872a5..3fc8d961b 100644 --- a/src/concrete/ml/version.py +++ b/src/concrete/ml/version.py @@ -1,3 +1,3 @@ """File to manage the version of the package.""" # Auto-generated by "make set_version" do not modify -__version__ = "1.4.0" +__version__ = "1.4.1"