chore: updating the CP version

zama-ai · Sep 28, 2023 · 274f0e4 · 274f0e4
1 parent da85d43
commit 274f0e4
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Showing 6 changed files with 57 additions and 107 deletions.
diff --git a/Makefile b/Makefile
@@ -19,7 +19,7 @@ OPEN_PR="true"
 # If one wants to force the installation of a given rc version
 # /!\ WARNING /!\: This version should NEVER be a wildcard as it might create some
 # issues when trying to run it in the future.
-CP_VERSION_SPEC_FOR_RC="concrete-python==2.2.0"
+CP_VERSION_SPEC_FOR_RC="concrete-python==2.4.0rc1"
 
 # If one wants to use the last RC version
 # CP_VERSION_SPEC_FOR_RC="$$(poetry run python \

diff --git a/deps_licenses/licenses_linux_user.txt b/deps_licenses/licenses_linux_user.txt
@@ -9,7 +9,7 @@ certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.2.0, MIT License
 click, 8.1.7, BSD License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.2.0, BSD-3-Clause
+concrete-python, 2.4.0rc1, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.7, BSD License
 exceptiongroup, 1.1.3, MIT License

diff --git a/deps_licenses/licenses_linux_user.txt.md5 b/deps_licenses/licenses_linux_user.txt.md5
@@ -1 +1 @@
-88fe2fa1ad3c3d2c74d44e85dd4d9662
+60ba8097c49614bc52a447b05062107f
diff --git a/docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb b/docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb
@@ -525,9 +525,6 @@
  "metadata": {
   "execution": {
    "timeout": 10800
-  },
-  "language_info": {
-   "name": "python"
   }
  },
  "nbformat": 4,

diff --git a/src/concrete/ml/common/utils.py b/src/concrete/ml/common/utils.py
@@ -9,8 +9,6 @@
 import numpy
 import onnx
 import torch
-from concrete.fhe import ParameterSelectionStrategy
-from concrete.fhe.compilation.configuration import Configuration
 from concrete.fhe.dtypes import Integer
 from sklearn.base import is_classifier, is_regressor
 
@@ -584,35 +582,3 @@ def all_values_are_of_dtype(*values: Any, dtypes: Union[str, List[str]]) -> bool
         supported_dtypes[dtype] = supported_dtype
 
     return all(_is_of_dtype(value, supported_dtypes) for value in values)
-
-
-# Remove this function once Concrete Python fixes the multi-parameter bug with KNN
-# circuits
-# FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
-def force_mono_parameter_in_configuration(configuration: Optional[Configuration], **kwargs):
-    """Force configuration to mono-parameter strategy.
-
-    If the given Configuration instance is None, build a new instance with mono-parameter and the
-    additional keyword arguments.
-
-    Args:
-        configuration (Optional[Configuration]): The configuration to consider.
-        **kwargs: Additional parameters to use for instantiating a new Configuration instance, if
-            configuration is None.
-
-    Returns:
-        configuration (Configuration): A configuration with mono-parameter strategy.
-    """
-    assert (
-        "parameter_selection_strategy" not in kwargs
-    ), "Please do not provide a parameter_selection_strategy parameter as it will be set to MONO."
-
-    if configuration is None:
-        configuration = Configuration(
-            parameter_selection_strategy=ParameterSelectionStrategy.MONO, **kwargs
-        )
-
-    else:
-        configuration.parameter_selection_strategy = ParameterSelectionStrategy.MONO
-
-    return configuration
diff --git a/src/concrete/ml/sklearn/base.py b/src/concrete/ml/sklearn/base.py
@@ -14,6 +14,7 @@
 from typing import Any, Callable, Dict, List, Optional, Set, TextIO, Type, Union
 
 import brevitas.nn as qnn
+import concrete.fhe as cnp
 import numpy
 import onnx
 import sklearn
@@ -37,7 +38,6 @@
     USE_OLD_VL,
     FheMode,
     check_there_is_no_p_error_options_in_configuration,
-    force_mono_parameter_in_configuration,
     generate_proxy_function,
     manage_parameters_for_pbs_errors,
 )
@@ -1999,56 +1999,59 @@ def scatter1d(x, v, indices):
                 # d: Length of the bitonic sequence
                 d = p
 
-                for bq in range(ln2n - 1, t - 1, -1):
-                    q = 2**bq
-                    # Determine the range of indexes to be compared
-                    range_i = numpy.array(
-                        [i for i in range(0, n - d) if i & p == r and comparisons[i] < k]
-                    )
-                    if len(range_i) == 0:
-                        # Edge case, for k=1
-                        continue
-
-                    # Select 2 bitonic sequences `a` and `b` of length `d`
-                    # a = x[range_i]: first bitonic sequence
-                    # a_i = idx[range_i]: Indexes of a_i elements in the original x
-                    a = gather1d(x, range_i)
-                    # a_i = gather1d(idx, range_i)
-                    # b = x[range_i + d]: Second bitonic sequence
-                    # b_i = idx[range_i + d]: Indexes of b_i elements in the original x
-                    b = gather1d(x, range_i + d)
-                    # b_i = gather1d(idx, range_i + d)
-
-                    labels_a = gather1d(labels, range_i)  #
-                    labels_b = gather1d(labels, range_i + d)  # idx[range_i + d]
-
-                    # Select max(a, b)
-                    diff = a - b
-                    max_x = a + numpy.maximum(0, b - a)
-
-                    # Swap if a > b
-                    # x[range_i] = max_x(a, b): First bitonic sequence gets min(a, b)
-                    x = scatter1d(x, a + b - max_x, range_i)
-                    # x[range_i + d] = min(a, b): Second bitonic sequence gets max(a, b)
-                    x = scatter1d(x, max_x, range_i + d)
-
-                    # Max index selection
-                    is_a_greater_than_b = diff <= 0
-
-                    # Update labels array according to the max items
-                    max_labels = labels_a + (labels_b - labels_a) * is_a_greater_than_b
-                    labels = scatter1d(labels, labels_a + labels_b - max_labels, range_i)
-                    labels = scatter1d(labels, max_labels, range_i + d)
-
-                    # Update
-                    comparisons[range_i + d] = comparisons[range_i + d] + 1
-                    d = q - p
-                    r = p
-
-            # Return only the topk indexes
-            topk_labels = fhe_array(labels[:k])
-
-            return topk_labels
+                with cnp.tag(f"top_k_t_{t}"):
+                    for bq in range(ln2n - 1, t - 1, -1):
+                        q = 2**bq
+                        # Determine the range of indexes to be compared
+                        range_i = numpy.array(
+                            [i for i in range(0, n - d) if i & p == r and comparisons[i] < k]
+                        )
+                        if len(range_i) == 0:
+                            # Edge case, for k=1
+                            continue
+
+                        # Select 2 bitonic sequences `a` and `b` of length `d`
+                        # a = x[range_i]: first bitonic sequence
+                        # a_i = idx[range_i]: Indexes of a_i elements in the original x
+                        a = gather1d(x, range_i)
+                        # a_i = gather1d(idx, range_i)
+                        # b = x[range_i + d]: Second bitonic sequence
+                        # b_i = idx[range_i + d]: Indexes of b_i elements in the original x
+                        b = gather1d(x, range_i + d)
+                        # b_i = gather1d(idx, range_i + d)
+
+                        labels_a = gather1d(labels, range_i)  #
+                        labels_b = gather1d(labels, range_i + d)  # idx[range_i + d]
+
+                        with cnp.tag("max"):
+                            # Select max(a, b)
+                            diff = a - b
+                            max_x = a + numpy.maximum(0, b - a)
+
+                        # Swap if a > b
+                        # x[range_i] = max_x(a, b): First bitonic sequence gets min(a, b)
+                        x = scatter1d(x, a + b - max_x, range_i)
+                        # x[range_i + d] = min(a, b): Second bitonic sequence gets max(a, b)
+                        x = scatter1d(x, max_x, range_i + d)
+
+                        with cnp.tag("sign"):
+                            # Max index selection
+                            is_a_greater_than_b = diff <= 0
+
+                        # Update labels array according to the max items
+                        with cnp.tag("label_swap"):
+                            max_labels = labels_a + (labels_b - labels_a) * is_a_greater_than_b
+
+                        with cnp.tag("label_set"):
+                            labels = scatter1d(labels, labels_a + labels_b - max_labels, range_i)
+                            labels = scatter1d(labels, max_labels, range_i + d)
+
+                        # Update
+                        comparisons[range_i + d] = comparisons[range_i + d] + 1
+                        d = q - p
+                        r = p
+
+            return labels[0 : self.n_neighbors]
 
         # 1. Pairwise_euclidiean distance
         distance_matrix = pairwise_euclidean_distance(q_X)
@@ -2062,23 +2065,7 @@ def scatter1d(x, v, indices):
 
         return numpy.expand_dims(topk_labels, axis=0)
 
-    # KNN works only for MONO in the latest concrete Python version
-    # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3978
     def compile(self, *args, **kwargs) -> Circuit:
-        # If a configuration instance is given as a positional parameter, set the strategy to
-        # multi-parameter
-        if len(args) >= 2:
-            configuration = force_mono_parameter_in_configuration(args[1])
-            args_list = list(args)
-            args_list[1] = configuration
-            args = tuple(args_list)
-
-        # Else, retrieve the configuration in kwargs if it exists, or create a new one, and set the
-        # strategy to multi-parameter
-        else:
-            configuration = kwargs.get("configuration", None)
-            kwargs["configuration"] = force_mono_parameter_in_configuration(configuration)
-
         return BaseEstimator.compile(self, *args, **kwargs)
 
     def post_processing(self, y_preds: numpy.ndarray) -> numpy.ndarray:
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		88fe2fa1ad3c3d2c74d44e85dd4d9662
		60ba8097c49614bc52a447b05062107f