chore: update Concrete Python to 2.5.1

- update Concrete Python to 2.5.1
- update mypy and solve some type hint issues

Makefile

@@ -19,7 +19,7 @@ OPEN_PR="true"
 # Force the installation of a Concrete Python version, which is very useful with nightly versions
 # /!\ WARNING /!\: This version should NEVER be a wildcard as it might create some
 # issues when trying to run it in the future.
-CONCRETE_PYTHON_VERSION="concrete-python==2.5.0"
+CONCRETE_PYTHON_VERSION="concrete-python==2.5.1"
 
 # Force the installation of Concrete Python's latest version, release-candidates included
 # CONCRETE_PYTHON_VERSION="$$(poetry run python \

deps_licenses/licenses_linux_user.txt

@@ -1,19 +1,19 @@
 Name, Version, License
 GitPython, 3.1.41, BSD License
 PyYAML, 6.0.1, MIT License
-anyio, 4.2.0, MIT License
-boto3, 1.34.37, Apache Software License
-botocore, 1.34.37, Apache Software License
+anyio, 3.7.1, MIT License
+boto3, 1.34.38, Apache Software License
+botocore, 1.34.38, Apache Software License
 brevitas, 0.8.0, UNKNOWN
 certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.3.2, MIT License
 click, 8.1.7, BSD License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.5, BSD-3-Clause
+concrete-python, 2.5.1, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.8, BSD License
 exceptiongroup, 1.2.0, MIT License
-fastapi, 0.102.0, MIT License
+fastapi, 0.103.2, MIT License
 filelock, 3.13.1, The Unlicense (Unlicense)
 flatbuffers, 23.5.26, Apache Software License
 fsspec, 2024.2.0, BSD License

deps_licenses/licenses_linux_user.txt.md5

@@ -1 +1 @@
-f709427468f5be4ee6836603495fea72
+a923947bfb17b658ab8efe61d5cafe96

deps_licenses/licenses_mac_intel_user.txt

@@ -1,6 +1,7 @@
 Name, Version, License
 GitPython, 3.1.41, BSD License
 PyYAML, 6.0.1, MIT License
+annotated-types, 0.6.0, MIT License
 anyio, 4.2.0, MIT License
 boto3, 1.34.37, Apache Software License
 botocore, 1.34.37, Apache Software License
@@ -9,11 +10,11 @@ certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.3.2, MIT License
 click, 8.1.7, BSD License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.5, BSD-3-Clause
+concrete-python, 2.5.1, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.8, BSD License
 exceptiongroup, 1.2.0, MIT License
-fastapi, 0.102.0, MIT License
+fastapi, 0.109.2, MIT License
 filelock, 3.13.1, The Unlicense (Unlicense)
 flatbuffers, 23.5.26, Apache Software License
 fsspec, 2024.2.0, BSD License
@@ -39,7 +40,8 @@ packaging, 23.2, Apache Software License; BSD License
 pluggy, 1.4.0, MIT License
 protobuf, 3.20.3, BSD-3-Clause
 psutil, 5.9.8, BSD License
-pydantic, 1.10.14, MIT License
+pydantic, 2.6.1, MIT License
+pydantic_core, 2.16.2, MIT License
 pytest, 7.4.1, MIT License
 pytest-json-report, 1.5.0, MIT
 pytest-metadata, 3.1.0, Mozilla Public License 2.0 (MPL 2.0)
@@ -56,7 +58,7 @@ skops, 0.5.0, MIT
 skorch, 0.11.0, new BSD 3-Clause
 smmap, 5.0.1, BSD License
 sniffio, 1.3.0, Apache Software License; MIT License
-starlette, 0.27.0, BSD License
+starlette, 0.36.3, BSD License
 sympy, 1.12, BSD License
 tabulate, 0.8.10, MIT License
 threadpoolctl, 3.2.0, BSD License

deps_licenses/licenses_mac_silicon_user.txt

@@ -1,19 +1,19 @@
 Name, Version, License
 GitPython, 3.1.41, BSD License
 PyYAML, 6.0.1, MIT License
-anyio, 4.2.0, MIT License
-boto3, 1.34.37, Apache Software License
-botocore, 1.34.37, Apache Software License
+anyio, 3.7.1, MIT License
+boto3, 1.34.38, Apache Software License
+botocore, 1.34.38, Apache Software License
 brevitas, 0.8.0, UNKNOWN
 certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.3.2, MIT License
 click, 8.1.7, BSD License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.5, BSD-3-Clause
+concrete-python, 2.5.1, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.8, BSD License
 exceptiongroup, 1.2.0, MIT License
-fastapi, 0.102.0, MIT License
+fastapi, 0.103.2, MIT License
 filelock, 3.13.1, The Unlicense (Unlicense)
 flatbuffers, 23.5.26, Apache Software License
 fsspec, 2024.2.0, BSD License
@@ -66,7 +66,8 @@ torch, 1.13.1, BSD License
 tqdm, 4.66.1, MIT License; Mozilla Public License 2.0 (MPL 2.0)
 transformers, 4.37.2, Apache Software License
 typing_extensions, 4.5.0, Python Software Foundation License
-urllib3, 2.0.7, MIT License
+urllib3, 1.26.18, MIT License
 uvicorn, 0.21.1, BSD License
 xgboost, 1.6.2, Apache Software License
 z3-solver, 4.12.5.0, MIT License
+zipp, 3.17.0, MIT License

deps_licenses/licenses_mac_silicon_user.txt.md5

@@ -1 +1 @@
-f709427468f5be4ee6836603495fea72
+a923947bfb17b658ab8efe61d5cafe96

pyproject.toml

@@ -34,7 +34,7 @@ readme = "README.md"
 # Investigate if it is better to fix specific versions or use lower and upper bounds  
 # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/2665
 python = ">=3.8.1,<3.11"
-concrete-python = "2.5.0"
+concrete-python = "2.5.1"
 setuptools = "65.6.3"
 skops = {version = "0.5.0"}
 xgboost = "1.6.2"
@@ -53,7 +53,7 @@ protobuf = "3.20.3"
 
 # Deployment
 boto3 = "^1.23.5"
-fastapi = "^0.102.0"
+fastapi = "^0.103.2"
 uvicorn = "^0.21.0"
 tqdm = "^4.64.1"
 transformers = "^4.36.0"
@@ -70,7 +70,7 @@ pylint = "^2.13.0"
 pytest = "7.4.1"
 pytest-cov = "^4.1.0"
 pytest_codeblocks = "^0.14.0"
-mypy = "^0.991"
+mypy = "^1.8.0"
 pydocstyle = "^6.1.1"
 python-semantic-release = "^7.27.0"
 semver = "^2.13.0"
@@ -113,9 +113,9 @@ flake8-bugbear = "23.2.13"
 flake8 = "6.0.0"
 sphinx = "6.1.3"
 # The following was inspired from https://github.com/python-poetry/poetry/issues/8271
-tensorflow = {version = "2.13.0"}
+tensorflow = {version = "2.13.1"}
 tensorflow-macos = {version = "2.13.0", platform = "darwin", markers = "platform_machine=='arm64'" }
-tensorflow-metal = {version = "0.8.0", markers="sys_platform == 'darwin' and platform_machine == 'arm'"}
+tensorflow-metal = {version = "1.1.0", markers="sys_platform == 'darwin' and platform_machine == 'arm'"}
 tensorflow-io-gcs-filesystem = [
     {version = "0.34.0", markers = "platform_machine!='arm64' or platform_system!='Darwin'" },
 ]

src/concrete/ml/quantization/base_quantized_op.py

@@ -20,7 +20,14 @@
     UniformQuantizationParameters,
 )
 
-ONNXOpInputOutputType = Union[numpy.ndarray, QuantizedArray, None]
+ONNXOpInputOutputType = Union[
+    numpy.ndarray,
+    QuantizedArray,
+    None,
+    bool,
+    int,
+    float,
+]
 
 ALL_QUANTIZED_OPS: Set[Type] = set()
 
@@ -969,7 +976,8 @@ def cnp_round(
 
             # Update the lsbs_to_remove value in the dictionary
             self.lsbs_to_remove[rounding_operation_id] = max(
-                self.lsbs_to_remove.get(rounding_operation_id, 0), computed_lsbs_to_remove
+                self.lsbs_to_remove.get(rounding_operation_id, 0),
+                computed_lsbs_to_remove,
             )
 
         # Rounding logic

src/concrete/ml/quantization/post_training.py

@@ -5,6 +5,7 @@
 
 import numpy
 import onnx
+from concrete.fhe.tracing import Tracer
 from onnx import numpy_helper
 
 from ..common.debugging import assert_true
@@ -400,7 +401,9 @@ def _calibrate_layers_activation(
         )
 
     @abstractmethod
-    def _process_initializer(self, n_bits: int, values: numpy.ndarray) -> QuantizedArray:
+    def _process_initializer(
+        self, n_bits: int, values: Union[numpy.ndarray, float, int, bool]
+    ) -> Union[QuantizedArray, RawOpOutput]:
         """Transform a constant tensor according to the model conversion mode.
 
         The values supplied are floating point values that will be quantized.
@@ -410,7 +413,8 @@ def _process_initializer(self, n_bits: int, values: numpy.ndarray) -> QuantizedA
             values (numpy.ndarray): Float values that initialize this tensor
 
         Returns:
-            QuantizedArray: a quantized tensor with integer values on n_bits bits
+            Union[QuantizedArray, RawOpOutput]: a quantized tensor with integer
+                values on n_bits bits
         """
 
     @abstractmethod
@@ -535,14 +539,13 @@ def _quantize_layers(self, *input_calibration_data: numpy.ndarray):
                         curr_cst_inputs[input_idx] = value
                     else:
                         # Initializers are ndarray or scalar
-                        assert value is not None
-                        assert isinstance(value, numpy.ndarray) or numpy.isscalar(value)
+                        assert isinstance(value, (numpy.ndarray, float, int, bool))
                         curr_cst_inputs[input_idx] = self._process_initializer(
                             self.n_bits_op_weights, value
                         )
                 else:
                     # Initializers are ndarray or scalar
-                    assert isinstance(value, numpy.ndarray) or numpy.isscalar(value)
+                    assert isinstance(value, (numpy.ndarray, float, int, bool))
                     curr_cst_inputs[input_idx] = value
 
             has_variable_inputs = (len(curr_inputs) - len(curr_cst_inputs)) > 0
@@ -865,7 +868,9 @@ def _process_layer(
             True, quantized_op, *calibration_data, quantizers=quantizers
         )
 
-    def _process_initializer(self, n_bits: int, values: numpy.ndarray):
+    def _process_initializer(
+        self, n_bits: int, values: Union[numpy.ndarray, float, int, bool]
+    ) -> Union[QuantizedArray, RawOpOutput]:
         """Quantize a network constant tensor (a weights tensor).
 
         The values supplied are floating point values that will be quantized.
@@ -875,13 +880,14 @@ def _process_initializer(self, n_bits: int, values: numpy.ndarray):
             values (numpy.ndarray): Float values that initialize this tensor
 
         Returns:
-            QuantizedArray: a quantized tensor with integer values on n_bits bits
+            Union[QuantizedArray, RawOpOutput]: a quantized tensor with integer
+                values on n_bits bits
         """
 
         if isinstance(values, numpy.ndarray) and numpy.issubdtype(values.dtype, numpy.integer):
             return values.view(RawOpOutput)
-
-        assert isinstance(values, (numpy.ndarray, float))
+        if not isinstance(values, (numpy.ndarray, Tracer)):
+            values = numpy.array(values)
         is_signed = is_symmetric = self._check_distribution_is_symmetric_around_zero(values)
 
         return QuantizedArray(
@@ -990,7 +996,9 @@ def _process_layer(
             False, quantized_op, *calibration_data, quantizers=quantizers
         )
 
-    def _process_initializer(self, n_bits: int, values: numpy.ndarray):
+    def _process_initializer(
+        self, n_bits: int, values: Union[numpy.ndarray, float, int, bool]
+    ) -> Union[QuantizedArray, RawOpOutput]:
         """Process an already quantized weight tensor.
 
         The values supplied are in floating point, but are discrete, in the sense
@@ -1003,8 +1011,8 @@ def _process_initializer(self, n_bits: int, values: numpy.ndarray):
             values (numpy.ndarray): Discrete float values that initialize this tensor
 
         Returns:
-            QuantizedArray: a quantized tensor with integer values on n_bits bits and with alpha as
-                the scaling factor.
+            Union[QuantizedArray, RawOpOutput]: a quantized tensor with integer values on
+                n_bits bits and with alpha as the scaling factor.
         """
 
         # Assume that integer initializer op inputs are raw values that should not be quantized