diff --git a/deps_licenses/licenses_linux_user.txt b/deps_licenses/licenses_linux_user.txt index f94218522..d15531268 100644 --- a/deps_licenses/licenses_linux_user.txt +++ b/deps_licenses/licenses_linux_user.txt @@ -4,15 +4,16 @@ MarkupSafe, 2.1.5, BSD License PyYAML, 6.0.2, MIT License brevitas, 0.10.2, UNKNOWN certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0) -charset-normalizer, 3.3.2, MIT License +charset-normalizer, 3.4.0, MIT License coloredlogs, 15.0.1, MIT License +concrete-ml-extensions, 0.1.2, BSD-3-Clause-Clear concrete-python, 2.8.1, BSD-3-Clause dependencies, 2.0.1, BSD License -dill, 0.3.8, BSD License +dill, 0.3.9, BSD License filelock, 3.16.1, The Unlicense (Unlicense) flatbuffers, 2.0.7, Apache Software License fsspec, 2024.9.0, BSD License -huggingface-hub, 0.25.1, Apache Software License +huggingface-hub, 0.25.2, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.11, MIT License idna, 3.10, BSD License @@ -32,7 +33,7 @@ nvidia-curand-cu12, 10.3.2.106, Other/Proprietary License nvidia-cusolver-cu12, 11.4.5.107, Other/Proprietary License nvidia-cusparse-cu12, 12.1.0.106, Other/Proprietary License nvidia-nccl-cu12, 2.20.5, Other/Proprietary License -nvidia-nvjitlink-cu12, 12.6.68, Other/Proprietary License +nvidia-nvjitlink-cu12, 12.6.77, Other/Proprietary License nvidia-nvtx-cu12, 12.1.105, Other/Proprietary License onnx, 1.16.1, Apache License v2.0 onnxconverter-common, 1.13.0, MIT License diff --git a/deps_licenses/licenses_linux_user.txt.md5 b/deps_licenses/licenses_linux_user.txt.md5 index 69aa4887b..c5f2bcbd8 100644 --- a/deps_licenses/licenses_linux_user.txt.md5 +++ b/deps_licenses/licenses_linux_user.txt.md5 @@ -1 +1 @@ -ac76836858506534a0dc01cae9341f7d +8ea8aec4f5aac03565c2dcb9f3f8a1da diff --git a/deps_licenses/licenses_mac_intel_user.txt b/deps_licenses/licenses_mac_intel_user.txt index 13f108a93..f3917dab5 100644 --- a/deps_licenses/licenses_mac_intel_user.txt +++ b/deps_licenses/licenses_mac_intel_user.txt @@ -4,15 +4,15 @@ MarkupSafe, 2.1.5, BSD License PyYAML, 6.0.2, MIT License brevitas, 0.10.2, UNKNOWN certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0) -charset-normalizer, 3.3.2, MIT License +charset-normalizer, 3.4.0, MIT License coloredlogs, 15.0.1, MIT License concrete-python, 2.8.1, BSD-3-Clause dependencies, 2.0.1, BSD License -dill, 0.3.8, BSD License +dill, 0.3.9, BSD License filelock, 3.16.1, The Unlicense (Unlicense) flatbuffers, 2.0.7, Apache Software License fsspec, 2024.9.0, BSD License -huggingface-hub, 0.25.1, Apache Software License +huggingface-hub, 0.25.2, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.11, MIT License idna, 3.10, BSD License diff --git a/deps_licenses/licenses_mac_intel_user.txt.md5 b/deps_licenses/licenses_mac_intel_user.txt.md5 index 69aa4887b..c5f2bcbd8 100644 --- a/deps_licenses/licenses_mac_intel_user.txt.md5 +++ b/deps_licenses/licenses_mac_intel_user.txt.md5 @@ -1 +1 @@ -ac76836858506534a0dc01cae9341f7d +8ea8aec4f5aac03565c2dcb9f3f8a1da diff --git a/deps_licenses/licenses_mac_silicon_user.txt b/deps_licenses/licenses_mac_silicon_user.txt index 58d45eb08..36e4d389a 100644 --- a/deps_licenses/licenses_mac_silicon_user.txt +++ b/deps_licenses/licenses_mac_silicon_user.txt @@ -4,15 +4,15 @@ MarkupSafe, 2.1.5, BSD License PyYAML, 6.0.2, MIT License brevitas, 0.10.2, UNKNOWN certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0) -charset-normalizer, 3.3.2, MIT License +charset-normalizer, 3.4.0, MIT License coloredlogs, 15.0.1, MIT License concrete-python, 2.8.1, BSD-3-Clause dependencies, 2.0.1, BSD License -dill, 0.3.8, BSD License +dill, 0.3.9, BSD License filelock, 3.16.1, The Unlicense (Unlicense) flatbuffers, 2.0.7, Apache Software License fsspec, 2024.9.0, BSD License -huggingface-hub, 0.25.1, Apache Software License +huggingface-hub, 0.25.2, Apache Software License humanfriendly, 10.0, MIT License hummingbird-ml, 0.4.11, MIT License idna, 3.10, BSD License diff --git a/deps_licenses/licenses_mac_silicon_user.txt.md5 b/deps_licenses/licenses_mac_silicon_user.txt.md5 index 69aa4887b..c5f2bcbd8 100644 --- a/deps_licenses/licenses_mac_silicon_user.txt.md5 +++ b/deps_licenses/licenses_mac_silicon_user.txt.md5 @@ -1 +1 @@ -ac76836858506534a0dc01cae9341f7d +8ea8aec4f5aac03565c2dcb9f3f8a1da diff --git a/docs/deep-learning/fhe_assistant.md b/docs/deep-learning/fhe_assistant.md index aac5d4383..89eba0e42 100644 --- a/docs/deep-learning/fhe_assistant.md +++ b/docs/deep-learning/fhe_assistant.md @@ -77,7 +77,7 @@ concrete_clf.compile(X, debug_config) #### 3. Quantization import failed -**Error message**: `Error occurred during quantization aware training (QAT) import [...] Could not determine a unique scale for the quantization!`. +**Error message**: `Error occurred during quantization aware training (QAT) import [...] Are you missing a QuantIdentity layer in your Brevitas model?`. **Cause**: This error occurs when the model imported as a quantized-aware training model lacks quantization operators. See [this guide](../deep-learning/fhe_friendly_models.md) on how to use Brevitas layers. This error message indicates that some layers do not take inputs quantized through `QuantIdentity` layers. diff --git a/docs/guides/prediction_with_fhe.md b/docs/guides/prediction_with_fhe.md index df85a5b08..e201a3b7f 100644 --- a/docs/guides/prediction_with_fhe.md +++ b/docs/guides/prediction_with_fhe.md @@ -112,11 +112,12 @@ class FCSmall(nn.Module): super().__init__() self.quant_input = qnn.QuantIdentity(bit_width=3) self.fc1 = qnn.QuantLinear(in_features=input_output, out_features=input_output, weight_bit_width=3, bias=True) + self.quant_2 = qnn.QuantIdentity(bit_width=3) self.act_f = nn.ReLU() self.fc2 = qnn.QuantLinear(in_features=input_output, out_features=input_output, weight_bit_width=3, bias=True) def forward(self, x): - return self.fc2(self.act_f(self.fc1(self.quant_input(x)))) + return self.fc2(self.quant_2(self.act_f(self.fc1(self.quant_input(x))))) torch_model = FCSmall(3) diff --git a/poetry.lock b/poetry.lock index ad21937e3..86cbeb4cb 100644 --- a/poetry.lock +++ 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+concrete-ml-extensions = [ + {version = "0.1.2", platform = "linux" } +] setuptools = "65.6.3" skops = {version = "0.5.0"} xgboost = "1.6.2" @@ -152,6 +155,7 @@ filterwarnings = [ "ignore:You are using `torch.load`*", "ignore:open_text is deprecated.*:DeprecationWarning", "ignore:read_text is deprecated.*:DeprecationWarning", + "ignore:open_binary is deprecated.*:DeprecationWarning", ] [tool.semantic_release] diff --git a/script/make_utils/licenses.sh b/script/make_utils/licenses.sh index a334cbe8a..6c75717c8 100755 --- a/script/make_utils/licenses.sh +++ b/script/make_utils/licenses.sh @@ -161,6 +161,7 @@ then # And check with a white-list # Brevitas has an "UNKNOWN" license, but is actually a BSD, so it is ignored in this test # pkg-resources reports UNKNOWN due to a Ubuntu bug, but is Apache - ignore + # concrete-ml-extensions has the same license as Concrete ML, so skip checking LICENSES_WHITELIST="new BSD 3-Clause" LICENSES_WHITELIST="${LICENSES_WHITELIST};3-Clause BSD License" LICENSES_WHITELIST="${LICENSES_WHITELIST};new BSD" @@ -181,7 +182,7 @@ then LICENSES_WHITELIST="${LICENSES_WHITELIST};ISC License (ISCL)" LICENSES_WHITELIST="${LICENSES_WHITELIST};The Unlicense (Unlicense)" - pip-licenses --allow-only="${LICENSES_WHITELIST}" --ignore-packages brevitas pkg-resources pkg_resources concrete-ml-extensions-brevitas + pip-licenses --allow-only="${LICENSES_WHITELIST}" --ignore-packages brevitas pkg-resources pkg_resources concrete-ml-extensions deactivate diff --git a/src/concrete/ml/common/utils.py b/src/concrete/ml/common/utils.py index d8b38537b..1189fa597 100644 --- a/src/concrete/ml/common/utils.py +++ b/src/concrete/ml/common/utils.py @@ -105,6 +105,17 @@ def is_valid(fhe: Union["FheMode", str]) -> bool: return fhe in FheMode.__members__.values() +class HybridFHEMode(enum.Enum): + """Simple enum for different modes of execution of HybridModel.""" + + DISABLE = "disable" # Use torch weights + REMOTE = "remote" # Use remote FHE server + SIMULATE = "simulate" # Use FHE simulation + CALIBRATE = "calibrate" # Use calibration (to run before FHE compilation) + EXECUTE = "execute" # Use FHE execution + TORCH = "torch" # Use torch layers + + def replace_invalid_arg_name_chars(arg_name: str) -> str: """Sanitize arg_name, replacing invalid chars by _. diff --git a/src/concrete/ml/pytest/torch_models.py b/src/concrete/ml/pytest/torch_models.py index 2ffcbea31..48a522fe4 100644 --- a/src/concrete/ml/pytest/torch_models.py +++ b/src/concrete/ml/pytest/torch_models.py @@ -63,11 +63,13 @@ def forward(self, inputs): class FCSmall(nn.Module): """Torch model for the tests.""" - def __init__(self, input_output, activation_function): + def __init__(self, input_output, activation_function, hidden=None): super().__init__() - self.fc1 = nn.Linear(in_features=input_output, out_features=input_output) + + hidden_size = input_output if hidden is None else hidden + self.fc1 = nn.Linear(in_features=input_output, out_features=hidden_size) self.act_f = activation_function() - self.fc2 = nn.Linear(in_features=input_output, out_features=input_output) + self.fc2 = nn.Linear(in_features=hidden_size, out_features=input_output) def forward(self, x): """Forward pass. @@ -850,7 +852,7 @@ def forward(self, x): return x -class SimpleQAT(nn.Module): +class StepFunctionPTQ(nn.Module): """Torch model implements a step function that needs Greater, Cast and Where.""" def __init__(self, input_output, activation_function, n_bits=2, disable_bit_check=False): @@ -1354,17 +1356,17 @@ def __init__( super().__init__() self.n_blocks = n_blocks - self.quant_1 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=True) + self.quant_1 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=False) self.fc1 = qnn.QuantLinear(input_shape, hidden_shape, bias=False, weight_bit_width=n_bits) - self.quant_concat = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=True) + self.quant_concat = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=False) - self.quant_2 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=True) + self.quant_2 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=False) self.fc2 = qnn.QuantLinear( hidden_shape * self.n_blocks, hidden_shape, bias=True, weight_bit_width=n_bits ) - self.quant_3 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=True) + self.quant_3 = qnn.QuantIdentity(bit_width=n_bits, return_quant_tensor=False) self.fc4 = qnn.QuantLinear(hidden_shape, output_shape, bias=True, weight_bit_width=n_bits) def forward(self, x): @@ -1379,9 +1381,9 @@ def forward(self, x): x_pre = [] for i in range(self.n_blocks): - x_block = x[:, i, :] - q1_out = self.quant_1(x_block) - fc1_out = self.fc1(q1_out) + q_x = self.quant_1(x) + q_x_block = q_x[:, i, :] + fc1_out = self.fc1(q_x_block) q_concat_out = self.quant_concat(fc1_out) x_pre.append(q_concat_out) diff --git a/src/concrete/ml/quantization/base_quantized_op.py b/src/concrete/ml/quantization/base_quantized_op.py index ff509fb16..70b7d9102 100644 --- a/src/concrete/ml/quantization/base_quantized_op.py +++ b/src/concrete/ml/quantization/base_quantized_op.py @@ -18,6 +18,7 @@ QuantizationOptions, QuantizedArray, UniformQuantizationParameters, + UniformQuantizer, ) # pylint: disable=too-many-lines @@ -559,7 +560,10 @@ def _prepare_quantized_input(self, input_: QuantizedArray) -> QuantizedArray: # but when parsing the ONNX graph, some options can be overwritten. Thus # when evaluating QAT layers we ignore one of these options to allow the # override. - if quant_opts.is_equal(input_.quantizer.quant_options, ignore_sign_qat=True): + if ( + quant_opts.is_equal(input_.quantizer.quant_options, ignore_sign_qat=True) + or input_.quantizer.quant_options.is_precomputed_qat + ): # Pass-through the input quantizer when the input is already quantized in # the manner that this op requires: this makes the op use the qvalues directly, # in q_impl and will avoid a TLU to re-quantize. @@ -661,7 +665,9 @@ def _prepare_inputs_with_constants( elif calibrate or is_clear_value: # This is used during calibration with numpy.ndarrays # or then the input is raw (not quantized) - prepared_inputs[curr_input_fill_idx] = input_ + prepared_inputs[curr_input_fill_idx] = ( + input_.values if isinstance(input_, QuantizedArray) else input_ + ) elif quantize_actual_values: # This is used by mixing (conv/gemm) or value re-arranging ops (reshape) input_ = cast(QuantizedArray, input_) @@ -674,9 +680,6 @@ def _prepare_inputs_with_constants( new_input.quantizer.is_qat and not input_.quantizer.is_precomputed_qat and self.error_tracker is not None - and not new_input.quantizer.check_is_uniform_quantized( - new_input.quantizer.quant_options - ) ): self.error_tracker.append(input_idx) @@ -700,7 +703,7 @@ def _prepare_inputs_with_constants( return prepared_inputs - def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: + def calibrate(self, *inputs: Union[QuantizedArray, numpy.ndarray]) -> numpy.ndarray: """Create corresponding QuantizedArray for the output of the activation function. Args: @@ -712,6 +715,8 @@ def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: # Here we need the actual values of the constants, we need to pass through # the numpy.ndarrays in the computation graph + # Mixing ops may be calibrated using QuantizedArray inputs, in order + # to pre-compute anlytical output quantization prepared_inputs = self._prepare_inputs_with_constants( *inputs, calibrate=True, quantize_actual_values=False ) @@ -720,12 +725,48 @@ def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: if isinstance(raw_result, RawOpOutput): return raw_result - quantized_samples = QuantizedArray(self.n_bits, raw_result) + # If the caller passes only QuantizedArray it means + # that they are asking to quantized using analytical + # formulas + requested_analytical_quant = all( + isinstance(qv, QuantizedArray) for qv in inputs + ) and isinstance(self, QuantizedMixingOp) + if requested_analytical_quant: + assert_true( + self.supported_by_linear_backend(), + "Calibration using QuantizedArray is only possible" + " for operations that can calibrate analytically", + ) + q_prepared_inputs = self._prepare_inputs_with_constants( + *inputs, calibrate=False, quantize_actual_values=True + ) + quantizer = self.calibrate_analytical_output(*q_prepared_inputs) + self.output_quant_params = quantizer.quant_params + self.output_quant_stats = quantizer.quant_stats + else: + # These output quantization parameters are only used + # for operations that produce graph output operation + # and are a non-linear + quantized_samples = QuantizedArray(self.n_bits, raw_result) + + self.output_quant_params = quantized_samples.quantizer.quant_params + self.output_quant_stats = quantized_samples.quantizer.quant_stats + + return raw_result - self.output_quant_params = quantized_samples.quantizer.quant_params - self.output_quant_stats = quantized_samples.quantizer.quant_stats + def calibrate_analytical_output(self, *inputs: QuantizedArray) -> UniformQuantizer: + """Calibrate output quantization based on analytical formulas. - return quantized_samples.values + Args: + *inputs (QuantizedArray): quantized operation inputs. Quantized weights + are storea in the op instance + + Raises: + AssertionError: if the operation does not support analytical calibration + """ + raise AssertionError( + f"calibrate_analytical_output: not implemented for {self._impl_for_op_named} op" + ) def prepare_output(self, qoutput_activation: numpy.ndarray) -> QuantizedArray: """Quantize the output of the activation function. @@ -817,6 +858,15 @@ def _get_output_quant_opts(self): output_quant_opts.is_qat = False return output_quant_opts + @classmethod + def supported_by_linear_backend(cls) -> bool: + """Indicate if this op can be executed on the GLWE linear backend. + + Returns: + bool: True if the op can be executed with GLWE. + """ + return False + class QuantizedOpUnivariateOfEncrypted(QuantizedOp, is_utility=True): """An univariate operator of an encrypted value. @@ -931,11 +981,6 @@ def make_output_quant_parameters( Returns: QuantizedArray: the quantized array that will be passed to the QuantizedModule output. """ - - out_opts = self._get_output_quant_opts() - out_opts.is_signed = False - out_opts.is_symmetric = False - # Since we don't know the real bit-width of these quantized values, # return a quantizer that has zero offset out_params = UniformQuantizationParameters( diff --git a/src/concrete/ml/quantization/linear_op_glwe_backend.py b/src/concrete/ml/quantization/linear_op_glwe_backend.py new file mode 100644 index 000000000..2407b9089 --- /dev/null +++ b/src/concrete/ml/quantization/linear_op_glwe_backend.py @@ -0,0 +1,155 @@ +"""GLWE backend for some supported layers.""" + +import json + +import numpy + +from ..common.utils import HybridFHEMode, to_tuple +from .quantized_module import QuantizedModule + +try: + import concrete_ml_extensions as fhext + + _HAS_GLWE_BACKEND = True +except ImportError: # pragma: no cover + fhext = None + _HAS_GLWE_BACKEND = False + + +class GLWELinearLayerExecutor: + """GLWE execution helper for pure linear layers.""" + + def __init__( + self, + private_key=None, + compression_key=None, + ): + self.compression_key = compression_key + self.private_key = private_key + + default_crypto_params_glwe = json.loads(fhext.default_params()) # pylint: disable=no-member + self.glwe_crypto_params = ( + fhext.MatmulCryptoParameters.deserialize( # pylint: disable=no-member + json.dumps(default_crypto_params_glwe) + ) + ) + self.poly_size = default_crypto_params_glwe["packing_ks_polynomial_size"] + self.calibrated_max_bits = default_crypto_params_glwe["bits_reserved_for_computation"] + + def keygen(self): + """Generate private and compression key.""" + # pylint: disable-next=no-member + self.private_key, self.compression_key = fhext.create_private_key(self.glwe_crypto_params) + + def forward( + self, x: numpy.ndarray, q_module: QuantizedModule, fhe: HybridFHEMode + ) -> numpy.ndarray: + """Perform the inference of this linear layer. + + Args: + x (numpy.ndarray): inputs or previous layer activations + q_module (QuantizedModule): quantized module that contains the layer which + is executed through this helper. Stores quantized weights and input + and output quantizers + fhe (HybridFHEMode): execution mode, can be 'disable' or 'execute' + + Returns: + numpy.ndarray: result of applying the linear layer + """ + + # Extract all the layers in this quantized module + # and check that there is only one, as only a single linear layer QM + # can be optimized + layers_in_module = list(q_module.quant_layers_dict.values()) + assert len(layers_in_module) == 1 + + # Get the single linear op in this module + quantized_linear_op = layers_in_module[0][1] + assert quantized_linear_op.supported_by_linear_backend() + + # Use default false so we also support MatMul impl, MatMul does not have these flags + transpose_inputs1 = quantized_linear_op.attrs.get("transA", False) + transpose_inputs2 = quantized_linear_op.attrs.get("transB", False) + + # Extract the weights and bias in this single linear layer + weight_bias = list(quantized_linear_op.constant_inputs.values()) + + # Make sure the weights used symmetric quantization + assert weight_bias[0].quantizer.quant_params.zero_point == 0 + + # Retrieve quantized weights + q_weight = weight_bias[0].qvalues + + q_weight = numpy.transpose(q_weight) if transpose_inputs2 else q_weight + + q_x = q_module.quantize_input(x) + assert q_x is not None + assert isinstance(q_x, numpy.ndarray) + + q_x = numpy.transpose(q_x) if transpose_inputs1 else q_x + + if fhe == HybridFHEMode.DISABLE: + # There is no need to add the bias to the de-quantized values + # as the bias is already included in the output quantizer + # zero-point, in the analytical calibration + q_x = q_x.astype(numpy.float32) + q_weight = q_weight.astype(numpy.float32) + y = q_module.dequantize_output(*to_tuple(numpy.matmul(q_x, q_weight))) + else: + # Need to slice the last GLWE (this will be improved in later cml-extensions) + num_valid_glwe_values_in_last_ciphertext = ( + q_weight.shape[1] % self.poly_size or self.poly_size + ) + + # The GLWE backend needs uint64 encoding for both neg/pos values + q_weight = q_weight.astype(numpy.uint64) + + # Some models have (B, C, H)-size activations, + # for example LLMs: B=batch size, C=context length, H=hidden dime + # while other models only have (B, H)-size activations. + # Add a B=1 dimension if needed + return_2d = False + if q_x.ndim == 2: + return_2d = True + q_x = numpy.expand_dims(q_x, 0) + + # The GLWE backend needs contiguous memory uint64 encoding for both neg/pos values + q_x = numpy.ascontiguousarray(q_x.astype(numpy.uint64)) + + assert q_weight.ndim == 2 + result_buffer = numpy.zeros( + (q_x.shape[0], q_x.shape[1], q_weight.shape[1]), dtype=numpy.int64 + ) + + for idx, q_x_sample in enumerate(q_x): + + ciphertext = fhext.encrypt_matrix( # pylint: disable=no-member + pkey=self.private_key, crypto_params=self.glwe_crypto_params, data=q_x_sample + ) + encrypted_result = fhext.matrix_multiplication( # pylint: disable=no-member + encrypted_matrix=ciphertext, + data=q_weight.astype(numpy.uint64), + compression_key=self.compression_key, + ) + q_result = fhext.decrypt_matrix( # pylint: disable=no-member + encrypted_result, + self.private_key, + self.glwe_crypto_params, + num_valid_glwe_values_in_last_ciphertext, + ) + q_result = q_result.astype(numpy.int64) + + result_buffer[idx, :] = q_result + + # There is no need to add the bias to the de-quantized values + # as the bias is already included in the output quantizer + # zero-point, in the analytical calibration + y = q_module.dequantize_output(*to_tuple(result_buffer)) + + if return_2d: + y = numpy.squeeze(y) + + # Only single outputs are supported + assert isinstance(y, numpy.ndarray) + + return y diff --git a/src/concrete/ml/quantization/post_training.py b/src/concrete/ml/quantization/post_training.py index 2d3f54f9f..0e5387565 100644 --- a/src/concrete/ml/quantization/post_training.py +++ b/src/concrete/ml/quantization/post_training.py @@ -1,5 +1,6 @@ """Post Training Quantization methods.""" +import enum from abc import abstractmethod from typing import Dict, List, Optional, Set, Tuple, Type, Union, cast @@ -25,9 +26,8 @@ from .quantized_ops import QuantizedBrevitasQuant from .quantizers import QuantizationOptions, QuantizedArray, UniformQuantizer -# pylint: disable=too-many-lines - +# pylint: disable=too-many-lines def _inspect_tree_n_bits(n_bits): """Validate the 'n_bits' parameter for tree-based models. @@ -187,6 +187,14 @@ def get_n_bits_dict(n_bits: Union[int, Dict[str, int]]) -> Dict[str, int]: return n_bits_dict +class CalibrationMode(enum.Enum): + """Simple enum for different modes of execution of HybridModel.""" + + RAW = "raw" # Output the raw float values, process rounding + QUANTIZED = "quantized" # Output the de-quantized values, process rounding + FAST_RAW = "fast" # Output raw float values, don't process rounding + + class ONNXConverter: """Base ONNX to Concrete ML computation graph conversion class. @@ -280,6 +288,7 @@ def _process_layer( quantized_op: QuantizedOp, *calibration_data: numpy.ndarray, quantizers: List[Optional[UniformQuantizer]], + fast_calibration: bool = False, ) -> Tuple[numpy.ndarray, Optional[UniformQuantizer]]: """Configure a graph operation according to model conversion mode. @@ -291,6 +300,10 @@ def _process_layer( should produce the quantized values used in calibration. If none are given, the calibration will generate the quantized values with the layer's input calibration options. + fast_calibration (bool): whether to perform calibration without + computing the result of the operation on calibration data, but + rather by using analytical formulas to determine the output + quantization parameters Returns: numpy.ndarray: calibration data for the following operators @@ -298,7 +311,7 @@ def _process_layer( def _calibrate_layers_activation( self, - calibrate_quantized: bool, + calibrate_mode: CalibrationMode, quantized_op: QuantizedOp, *calibration_data: numpy.ndarray, quantizers: List[Optional[UniformQuantizer]], @@ -306,8 +319,9 @@ def _calibrate_layers_activation( """Calibrate the QuantizedOp with the previous layer's output calibration data. Args: - calibrate_quantized (bool): determines if we use de-quantized values (True) or - raw values (False) during calibration. + calibrate_mode (CalibrationMode): whether to use data-based quantization for + output de-quantized values or raw values during calibration, + or analytically determine output quantization parameters (for linear layers) quantized_op (QuantizedOp): the quantized operator for the current layer. *calibration_data: numpy.ndarray: the previous layer's calibration data. quantizers (List[Optional[UniformQuantizer]]): a list of quantizers that @@ -318,9 +332,6 @@ def _calibrate_layers_activation( Returns: numpy.ndarray: the output of the newly calibrated layer. """ - # Calibrate the output of the layer - raw_result = quantized_op.calibrate(*calibration_data) - # Some operators need to quantize their inputs using model_outputs instead of op_inputs in # order to reduce the impact of quantization. if quantized_op.quantize_inputs_with_model_outputs_precision: @@ -330,34 +341,55 @@ def _calibrate_layers_activation( # Create new calibration data (output of the previous layer) # Use the op's input options (thus behavior in calibration is the same as in compilation) - q_calibration_data: List[ONNXOpInputOutputType] = [] - for data in calibration_data: + q_calibration_data: List[Union[QuantizedArray, numpy.ndarray]] = [] + for idx, data in enumerate(calibration_data): is_clear_value = isinstance(data, RawOpOutput) if is_clear_value or data is None: q_calibration_data.append(data) else: - q_calibration_data.append( - QuantizedArray(n_bits, data, True, options=quantized_op.input_quant_opts) - ) + quantizer = quantizers[idx] + if quantizer is None: + q_calibration_data.append( + QuantizedArray(n_bits, data, True, options=quantized_op.input_quant_opts) + ) + else: + # Override, when necessary, the calibration data with data that is quantized + # with layer quantizers that are overridden by the QAT graph quantizers + q_calibration_data.append( + QuantizedArray( + quantizer.n_bits, + data, + True, + options=quantizer.quant_options, + stats=quantizer.quant_stats, + params=quantizer.quant_params, + ) + ) - # Override, when necessary, the calibration data with data that is quantized with - # layer quantizers that are overridden by the QAT graph quantizers - for idx, data in enumerate(calibration_data): - if quantizers[idx] is None: - continue + # Fast quantization relies on an analytical computation + # of the output quantization parameters + if calibrate_mode == CalibrationMode.FAST_RAW: + assert isinstance(quantized_op, QuantizedMixingOp) + assert all(isinstance(inp, QuantizedArray) for inp in q_calibration_data) - quantizer = quantizers[idx] - assert quantizer is not None + # Calibrate the output of the layer using + # analytical formuals to avoid computation on quantized values + raw_result = quantized_op.calibrate(*q_calibration_data) - q_calibration_data[idx] = QuantizedArray( - quantizer.n_bits, - data, - True, - options=quantizer.quant_options, - stats=quantizer.quant_stats, - params=quantizer.quant_params, + output_quant_opts = QuantizationOptions(quantized_op.input_quant_opts.n_bits) + output_quant_opts.copy_opts(quantized_op.input_quant_opts) + return ( + raw_result, + UniformQuantizer( + output_quant_opts, + quantized_op.output_quant_stats, + quantized_op.output_quant_params, + ), ) + # Calibrate the output of the layer + raw_result = quantized_op.calibrate(*calibration_data) + # Enable rounding calibration if used has set a rounding_threshold_bits calibrate_attr = ( {"calibrate_rounding": True} if isinstance(quantized_op, QuantizedMixingOp) else {} @@ -389,7 +421,9 @@ def _calibrate_layers_activation( ) # For PTQ, the calibration is performed on quantized data. But # raw operation output (RawOpOutput) data should not be quantized - if calibrate_quantized and not isinstance(quant_result, RawOpOutput): + if calibrate_mode == CalibrationMode.QUANTIZED and not isinstance( + quant_result, RawOpOutput + ): assert isinstance(quant_result, QuantizedArray) return ( quant_result.dequant(), @@ -478,6 +512,18 @@ def _quantize_layers(self, *input_calibration_data: numpy.ndarray): constants: Set[str] = set(self.quant_params.keys()) + # Check if the model has only GLWE supported linear layers. + # In this case, use analytical calibration which is much faster + fast_calibration = True + for node in graph.node: + op_type = get_op_type(node) + if op_type == "Constant": + continue + quantized_op_class = ONNX_OPS_TO_QUANTIZED_IMPL[op_type] + if not quantized_op_class.supported_by_linear_backend(): + fast_calibration = False + break + # We need to determine, for each op, whether it only performs univariate computations. # A univariate computation is one which depends on a single scalar integer encrypted input # which is only multiplied or added to constants or to itself, or a nonlinear function is @@ -625,7 +671,10 @@ def _quantize_layers(self, *input_calibration_data: numpy.ndarray): for input_name in variable_input_names ) output_calibration_data, layer_quantizer = self._process_layer( - quantized_op_instance, *curr_calibration_data, quantizers=layer_quant + quantized_op_instance, + *curr_calibration_data, + quantizers=layer_quant, + fast_calibration=fast_calibration, ) node_results[output_name] = output_calibration_data node_override_quantizer[output_name] = layer_quantizer @@ -859,6 +908,7 @@ def _process_layer( quantized_op: QuantizedOp, *calibration_data: numpy.ndarray, quantizers: List[Optional[UniformQuantizer]], + fast_calibration: bool = False, ) -> Tuple[numpy.ndarray, Optional[UniformQuantizer]]: """Configure a graph operation by performing calibration for uniform quantization. @@ -870,13 +920,29 @@ def _process_layer( should produce the quantized values used in calibration. If none are given, the calibration will generate the quantized values with the layer's input calibration options. + fast_calibration (bool): whether to perform calibration without + computing the result of the operation on calibration data, but + rather by using analytical formulas to determine the output + quantization parameters Returns: numpy.ndarray: calibration data for the following operators """ + # Fast calibration can only be enabled in special cases such as a module with + # only a single Gemm layer + calibrate_mode = CalibrationMode.FAST_RAW if fast_calibration else CalibrationMode.QUANTIZED + + # Only mixing ops (e.g., gemm/add) can use fast calibrate (which doesn't call the q_impl) + assert calibrate_mode != CalibrationMode.FAST_RAW or isinstance( + quantized_op, QuantizedMixingOp + ) + return self._calibrate_layers_activation( - True, quantized_op, *calibration_data, quantizers=quantizers + calibrate_mode, + quantized_op, + *calibration_data, + quantizers=quantizers, ) def _process_initializer( @@ -987,6 +1053,7 @@ def _process_layer( quantized_op: QuantizedOp, *calibration_data: numpy.ndarray, quantizers: List[Optional[UniformQuantizer]], + fast_calibration: bool = False, ) -> Tuple[numpy.ndarray, Optional[UniformQuantizer]]: """Configure a graph operation by calibrating it for Quantization Aware Training. @@ -998,13 +1065,17 @@ def _process_layer( should produce the quantized values used in calibration. If none are given, the calibration will generate the quantized values with the layer's input calibration options. + fast_calibration (bool): whether to perform calibration without + computing the result of the operation on calibration data, but + rather by using analytical formulas to determine the output + quantization parameters Returns: numpy.ndarray: calibration data for the following operators """ return self._calibrate_layers_activation( - False, quantized_op, *calibration_data, quantizers=quantizers + CalibrationMode.RAW, quantized_op, *calibration_data, quantizers=quantizers ) def _process_initializer( diff --git a/src/concrete/ml/quantization/quantized_module.py b/src/concrete/ml/quantization/quantized_module.py index 2e4d24232..7761d7bbe 100644 --- a/src/concrete/ml/quantization/quantized_module.py +++ b/src/concrete/ml/quantization/quantized_module.py @@ -55,7 +55,7 @@ def _raise_qat_import_error(bad_qat_ops: List[Tuple[str, str]]): bad_qat_ops, ) ) - + "\n\nCould not determine a unique scale for the quantization! " + + "\n\nAre you missing a QuantIdentity layer in your Brevitas model? " "Please check the ONNX graph of this model." ) diff --git a/src/concrete/ml/quantization/quantized_ops.py b/src/concrete/ml/quantization/quantized_ops.py index 73cd18193..eae19f22d 100644 --- a/src/concrete/ml/quantization/quantized_ops.py +++ b/src/concrete/ml/quantization/quantized_ops.py @@ -162,6 +162,10 @@ def __init__( f"Got alpha == {alpha} and beta == {beta}.", ) + @classmethod + def supported_by_linear_backend(cls) -> bool: + return True + # pylint: disable-next=too-many-statements,too-many-locals def q_impl( self, @@ -201,12 +205,12 @@ def q_impl( # Using snake case here to please the Python format, the original attrs don't have the '_' # Use default false so we also support MatMul impl, MatMul does not have these flags transpose_inputs1 = attrs.get("transA", False) - transpose_inputs2 = attrs.get("transB", False) - with tag(self.op_instance_name + ".input"): input1_q_values = ( numpy.transpose(q_input1.qvalues) if transpose_inputs1 else q_input1.qvalues ) + + transpose_inputs2 = attrs.get("transB", False) input2_q_values = ( numpy.transpose(q_input2.qvalues) if transpose_inputs2 else q_input2.qvalues ) @@ -491,6 +495,68 @@ def copy_function(x): params=self.output_quant_params, ) + def calibrate_analytical_output(self, *inputs: QuantizedArray) -> UniformQuantizer: + """Calibrate output quantization based on analytical formulas. + + Args: + *inputs (QuantizedArray): quantized operation inputs. Quantized weights + are stored in the op instance + + Returns: + res (UniformQuantizer): the quantizer of the operation's output values + that can be used to de-quantize these values. + """ + + q_input1 = inputs[0] + assert isinstance(q_input1, QuantizedArray) + q_input2 = inputs[1] + assert isinstance(q_input2, QuantizedArray) + + # In the operation Y = alpha * A' * B' + beta * C, q_bias is used for + # generalised matrix multiplication. q_bias is set to None for standard + # matrix multiplication (beta == 0 or only two inputs) + q_bias = None if len(inputs) == 2 or self.attrs["beta"] == 0 else inputs[2] + assert isinstance(q_bias, (type(None), QuantizedArray)) + + # Using snake case here to please the Python format, the original attrs don't have the '_' + # Use default false so we also support MatMul impl, MatMul does not have these flags + transpose_inputs2 = self.attrs.get("transB", False) + + p = q_input2.qvalues.shape[-2] + + assert q_input1.quantizer.scale is not None + assert q_input2.quantizer.scale is not None + m_matmul = q_input1.quantizer.scale * q_input2.quantizer.scale + + input2_q_values = ( + numpy.transpose(q_input2.qvalues) if transpose_inputs2 else q_input2.qvalues + ) + + # Compute the third term, the sum of the weights which is a constant + sum_weights = q_input1.quantizer.zero_point * numpy.sum( + input2_q_values, axis=-2, keepdims=True + ) + + assert q_input1.quantizer.zero_point is not None + assert q_input2.quantizer.zero_point is not None + final_term = p * q_input1.quantizer.zero_point * q_input2.quantizer.zero_point + + out_zp: Union[int, numpy.ndarray] = sum_weights - final_term + if q_bias is not None: + # Make mypy happy + assert q_bias is not None + # Reshape the biases to broadcast them to each neuron + bias_out = q_bias.values if isinstance(q_bias, QuantizedArray) else q_bias + out_zp = out_zp + bias_out / (-m_matmul) + + out_params = UniformQuantizationParameters( + scale=m_matmul, + zero_point=out_zp, + offset=0, + ) + + return UniformQuantizer(self._get_output_quant_opts(), self.output_quant_stats, out_params) + class QuantizedMatMul(QuantizedGemm): """Quantized MatMul op.""" @@ -1595,11 +1661,11 @@ def __init__( self.divider_quantizer: Optional[UniformQuantizer] = None self.min_non_zero_value: Optional[numpy.float64] = None - def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: + def calibrate(self, *inputs: Union[QuantizedArray, numpy.ndarray]) -> numpy.ndarray: """Create corresponding QuantizedArray for the output of the activation function. Args: - *inputs (numpy.ndarray): Calibration sample inputs. + *inputs (Union[QuantizedArray, numpy.ndarray]): Calibration sample inputs. Returns: numpy.ndarray: the output values for the provided calibration samples. @@ -1609,6 +1675,10 @@ def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: # we need to compute the quantizer of the divider since we are doing # an encrypted division where both numerator and denominator are encrypted if not self.can_fuse() and len(inputs) == 2: + assert isinstance( + inputs[0], numpy.ndarray + ), "Div calibrate does not support analytical calibration for now" + assert isinstance(inputs[1], numpy.ndarray) # FIXME https://github.com/zama-ai/concrete-ml-internal/issues/4556 min_non_zero_index = numpy.abs(inputs[1]).argmin(axis=None) @@ -1864,7 +1934,7 @@ class QuantizedBatchNormalization(QuantizedOp): _impl_for_op_named: str = "BatchNormalization" - def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: + def calibrate(self, *inputs: Union[QuantizedArray, numpy.ndarray]) -> numpy.ndarray: """Create corresponding QuantizedArray for the output of the activation function. Args: @@ -1874,6 +1944,10 @@ def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: numpy.ndarray: the output values for the provided calibration samples. """ + assert all( + isinstance(inp, numpy.ndarray) for inp in inputs + ), "Batch Normalization calibrate does not support analytical calibration" + # Here we need the actual values of the constants, we need to pass through # the numpy.ndarrays in the computation graph prepared_inputs = self._prepare_inputs_with_constants( @@ -2023,7 +2097,7 @@ def __init__( self.noop_with_empty_axes = attrs.get("noop_with_empty_axes", 0) self.copy_inputs = False - def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: + def calibrate(self, *inputs: Union[QuantizedArray, numpy.ndarray]) -> numpy.ndarray: """Create corresponding QuantizedArray for the output of the activation function. Args: @@ -2270,7 +2344,7 @@ def check_float(v, err_msg): self.output_quant_opts.is_narrow = self.is_narrow self.output_quant_opts.is_signed = self.is_signed - def calibrate(self, *inputs: numpy.ndarray) -> numpy.ndarray: + def calibrate(self, *inputs: Union[QuantizedArray, numpy.ndarray]) -> numpy.ndarray: """Create corresponding QuantizedArray for the output of Quantization function. Args: diff --git a/src/concrete/ml/quantization/quantizers.py b/src/concrete/ml/quantization/quantizers.py index c1bd058a0..8e65b54d3 100644 --- a/src/concrete/ml/quantization/quantizers.py +++ b/src/concrete/ml/quantization/quantizers.py @@ -11,18 +11,19 @@ from ..common.debugging import assert_true from ..common.serialization.dumpers import dump, dumps -from ..common.utils import QUANT_ROUND_LIKE_ROUND_PBS, array_allclose_and_same_shape +from ..common.utils import QUANT_ROUND_LIKE_ROUND_PBS STABILITY_CONST = 10**-6 -def fill_from_kwargs(obj, klass, **kwargs): +def fill_from_kwargs(obj, klass, accept_missing, **kwargs): """Fill a parameter set structure from kwargs parameters. Args: obj: an object of type klass, if None the object is created if any of the type's members appear in the kwargs klass: the type of object to fill + accept_missing: don't assert if the fields are None in the kwargs kwargs: parameter names and values to fill into an instance of the klass type Returns: @@ -62,9 +63,16 @@ def fill_from_kwargs(obj, klass, **kwargs): # If the structure was created or modified by a call to this function, check # that it is completely filled if obj is not None: - for name in hints: - if getattr(obj, name) is None: - raise TypeError(f"Missing quantizer parameter {name}") + all_members_missing = all(getattr(obj, name) is None for name in hints) + + if not accept_missing or (accept_missing and not all_members_missing): + missing_params_str = ",".join([name for name in hints if getattr(obj, name) is None]) + given_params_str = ",".join([name for name in hints if getattr(obj, name) is not None]) + if len(missing_params_str) > 0: + raise TypeError( + f"Missing quantizer parameter {missing_params_str}, " + f"but {given_params_str} were given" + ) # Return the parameter structure and the kwargs with the used parameters removed return obj, kwargs @@ -238,25 +246,18 @@ class MinMaxQuantizationStats: rmax: Optional[float] = None rmin: Optional[float] = None - uvalues: Optional[numpy.ndarray] = None def __init__( self, rmax: Optional[float] = None, rmin: Optional[float] = None, - uvalues: Optional[numpy.ndarray] = None, ): self.rmax = rmax self.rmin = rmin - self.uvalues = uvalues def __eq__(self, other) -> bool: # Disable mypy as numpy.array_equal properly handles None types - return ( - other.rmax == self.rmax - and other.rmin == self.rmin - and numpy.array_equal(other.uvalues, self.uvalues) # type: ignore[arg-type] - ) + return other.rmax == self.rmax and other.rmin == self.rmin def dump_dict(self) -> Dict: """Dump itself to a dict. @@ -268,7 +269,6 @@ def dump_dict(self) -> Dict: metadata["rmax"] = self.rmax metadata["rmin"] = self.rmin - metadata["uvalues"] = self.uvalues return metadata @staticmethod @@ -284,7 +284,6 @@ def load_dict(metadata: Dict): to_return = MinMaxQuantizationStats( rmax=metadata["rmax"], rmin=metadata["rmin"], - uvalues=metadata["uvalues"], ) return to_return @@ -315,16 +314,6 @@ def compute_quantization_stats(self, values: numpy.ndarray) -> None: self.rmin = numpy.min(values) self.rmax = numpy.max(values) - # To find unique float values we need to round. We round to 2 decimal figures. - # Floating point inaccuracies in computation can lead to differences in the last - # decimal figures. We want to ignore such differences but also avoid - # coalescing float values that should be distinct - rvalues = numpy.round(values, decimals=2) - - # Unique values from the distribution sample. These values are sorted - # in order to extract the quantization scale in the case of QAT - self.uvalues = numpy.unique(rvalues) - @property def quant_stats(self): """Get a copy of the calibration set statistics. @@ -347,35 +336,6 @@ def copy_stats(self, stats) -> None: self.rmax = stats.rmax self.rmin = stats.rmin - self.uvalues = stats.uvalues - - def check_is_uniform_quantized(self, options: QuantizationOptions) -> bool: - """Check if these statistics correspond to uniformly quantized values. - - Determines whether the values represented by this QuantizedArray show - a quantized structure that allows to infer the scale of quantization. - - Args: - options (QuantizationOptions): used to quantize the values in the QuantizedArray - - Returns: - bool: check result. - """ - - assert self.uvalues is not None - - if self.uvalues.size > 2**options.n_bits: - return False - - if self.uvalues.size == 1: - return False - - unique_scales = numpy.unique(numpy.diff(self.uvalues)) - min_scale = unique_scales[0] - - re_quant_scales = numpy.rint(unique_scales / min_scale) * min_scale - - return array_allclose_and_same_shape(unique_scales, re_quant_scales, atol=0.02) class UniformQuantizationParameters: @@ -532,9 +492,6 @@ def compute_quantization_parameters( / ((2**options.n_bits - 1 - self.offset)) ).astype(numpy.float64) else: - # Infer the QAT parameters if this is a custom QAT network - # which does not store scale/zero-point in the ONNX directly. - # Do not infer the parameters if the network was trained with Brevitas # they are stored in the ONNX file and are the true quantization parameters # used in training - no need to infer them. @@ -542,22 +499,6 @@ def compute_quantization_parameters( # If the parameters do not appear quantized, use PTQ for quantization. # The QuantizedModule will perform error checking of quantized tensors # and will issue an error if the network is not well quantized during training - if ( - options.is_qat - and not options.is_precomputed_qat - and stats.uvalues is not None - and stats.check_is_uniform_quantized(options) - ): - assert_true( - len(stats.uvalues) > 1, - "A single unique value was detected in a tensor of " - "quantized values in a QAT import.\n" - "Please check the stability thresholds.\n" - "This can occur with a badly trained model.", - ) - unique_scales = numpy.unique(numpy.diff(stats.uvalues)) - self.scale = numpy.float64(unique_scales[0]) - if self.scale is None: self.scale = numpy.float64( (stats.rmax - stats.rmin) / (2**options.n_bits - 1) @@ -633,7 +574,6 @@ def __eq__(self, other) -> bool: "is_precomputed_qat", "rmax", "rmin", - "uvalues", "scale", "zero_point", "offset", @@ -672,7 +612,6 @@ def dump_dict(self) -> Dict: "is_precomputed_qat", "rmax", "rmin", - "uvalues", "scale", "zero_point", "offset", @@ -706,7 +645,6 @@ def load_dict(metadata: Dict) -> UniformQuantizer: "is_precomputed_qat", "rmax", "rmin", - "uvalues", "scale", "zero_point", "offset", @@ -715,10 +653,6 @@ def load_dict(metadata: Dict) -> UniformQuantizer: if attribute in metadata: setattr(obj, attribute, metadata[attribute]) - # The `uvalues` attribute needs to be put back to a numpy.array object - if "uvalues" in metadata: - obj.uvalues = metadata["uvalues"] - return obj def dumps(self) -> str: @@ -757,13 +691,11 @@ def quant(self, values: numpy.ndarray) -> numpy.ndarray: else: qvalues = numpy.rint(values / self.scale + self.zero_point) - # Clipping can be performed for PTQ and for precomputed (for now only Brevitas) QAT + # Clipping must be performed for PTQ and for precomputed (for now only Brevitas) QAT # (where quantizer parameters are available in ONNX layers). - # For Custom QAT, with inferred parameters the type of quantization (signed/narrow) - # can not be inferred and thus clipping can not be performed reliably # It is possible to disable this clipping step for specific cases such as quantizing values # within fully-leveled circuits (where not bounds are needed) - if (not self.is_qat or self.is_precomputed_qat) and not self.no_clipping: + if not self.no_clipping: # Offset is either 2^(n-1) or 0, but for narrow range # the values should be clipped to [2^(n-1)+1, .. 2^(n-1)-1], so we add # one to the minimum value for narrow range @@ -852,9 +784,13 @@ def __init__( options.n_bits = n_bits self.n_bits = n_bits - options, kwargs = fill_from_kwargs(options, QuantizationOptions, **kwargs) - stats, kwargs = fill_from_kwargs(stats, MinMaxQuantizationStats, **kwargs) - params, kwargs = fill_from_kwargs(params, UniformQuantizationParameters, **kwargs) + # Options are alawys needed + options, kwargs = fill_from_kwargs(options, QuantizationOptions, False, **kwargs) + # Stats are only necessary for quantization but not needed for dequantiztion + # thus they can be considered optional + stats, kwargs = fill_from_kwargs(stats, MinMaxQuantizationStats, True, **kwargs) + # Params are needed for both quant / dequant + params, kwargs = fill_from_kwargs(params, UniformQuantizationParameters, False, **kwargs) # All kwargs should belong to one of the parameter sets, anything else is unsupported if len(kwargs) > 0: diff --git a/src/concrete/ml/sklearn/tree_to_numpy.py b/src/concrete/ml/sklearn/tree_to_numpy.py index 769363c5a..4fabd2fac 100644 --- a/src/concrete/ml/sklearn/tree_to_numpy.py +++ b/src/concrete/ml/sklearn/tree_to_numpy.py @@ -201,7 +201,6 @@ def preprocess_tree_predictions( is_signed = is_symmetric = False quant_args["rmax"] = numpy.max(init_tensor) quant_args["rmin"] = 0 - quant_args["uvalues"] = [] q_y = QuantizedArray( n_bits=output_n_bits, diff --git a/src/concrete/ml/torch/hybrid_model.py b/src/concrete/ml/torch/hybrid_model.py index a8d4b27e6..ae2b885e8 100644 --- a/src/concrete/ml/torch/hybrid_model.py +++ b/src/concrete/ml/torch/hybrid_model.py @@ -1,7 +1,8 @@ """Implement the conversion of a torch model to a hybrid fhe/torch inference.""" +# pylint: disable=too-many-lines import ast -import enum +import contextvars import io import sys import time @@ -19,25 +20,17 @@ from concrete.fhe import Configuration from torch import nn -from ..common.utils import MAX_BITWIDTH_BACKWARD_COMPATIBLE +from ..common.utils import MAX_BITWIDTH_BACKWARD_COMPATIBLE, HybridFHEMode from ..deployment.fhe_client_server import FHEModelClient, FHEModelDev, FHEModelServer +from ..quantization.linear_op_glwe_backend import _HAS_GLWE_BACKEND, GLWELinearLayerExecutor from .compile import ( QuantizedModule, + build_quantized_module, compile_brevitas_qat_model, compile_torch_model, has_any_qnn_layers, ) - - -class HybridFHEMode(enum.Enum): - """Simple enum for different modes of execution of HybridModel.""" - - DISABLE = "disable" # Use torch weights - REMOTE = "remote" # Use remote FHE server - SIMULATE = "simulate" # Use FHE simulation - CALIBRATE = "calibrate" # Use calibration (to run before FHE compilation) - EXECUTE = "execute" # Use FHE execution - TORCH = "torch" # Use torch layers +from .lora import BackwardModuleLinear, ForwardModuleLinear def tuple_to_underscore_str(tup: Tuple) -> str: @@ -109,6 +102,13 @@ def convert_conv1d_to_linear(layer_or_module): return layer_or_module +# This module member is instantiated by the Hybrid FHE model +# when hybrid FHE forward is called and the GLWE backend is available +_optimized_linear_executor: contextvars.ContextVar[Optional[GLWELinearLayerExecutor]] = ( + contextvars.ContextVar("optimized_linear_executor") +) + + # pylint: disable-next=too-many-instance-attributes class RemoteModule(nn.Module): """A wrapper class for the modules to be evaluated remotely with FHE.""" @@ -120,6 +120,7 @@ def __init__( module_name: Optional[str] = None, model_name: Optional[str] = None, verbose: int = 0, + optimized_linear_execution: bool = False, ): super().__init__() self.private_module: Optional[nn.Module] = module @@ -134,6 +135,7 @@ def __init__( self.module_name: Optional[str] = module_name self.model_name: Optional[str] = model_name self.verbose = verbose + self.optimized_linear_execution = optimized_linear_execution def init_fhe_client( self, path_to_client: Optional[Path] = None, path_to_keys: Optional[Path] = None @@ -248,12 +250,25 @@ def forward(self, x: torch.Tensor) -> Union[torch.Tensor, QuantTensor]: HybridFHEMode.TORCH, None, }: - # Using quantized module assert self.private_q_module is not None - y = torch.Tensor( - self.private_q_module.forward(x.detach().numpy(), fhe=self.fhe_local_mode.value) - ) + try: + optimized_linear_layer_executor = _optimized_linear_executor.get() + except LookupError: + optimized_linear_layer_executor = None + + if optimized_linear_layer_executor: + # Delegate to the optimized GLWE executor + y = torch.Tensor( + optimized_linear_layer_executor.forward( + x.detach().numpy(), self.private_q_module, self.fhe_local_mode + ) + ) + else: + # Delegate to the quantized module for all fhe modes + y = torch.Tensor( + self.private_q_module.forward(x.detach().numpy(), fhe=self.fhe_local_mode.value) + ) elif self.fhe_local_mode == HybridFHEMode.CALIBRATE: # Calling torch + gathering calibration data assert self.private_module is not None @@ -263,7 +278,16 @@ def forward(self, x: torch.Tensor) -> Union[torch.Tensor, QuantTensor]: elif self.fhe_local_mode == HybridFHEMode.REMOTE: # pragma:no cover # Remote call + try: + optimized_linear_layer_executor = _optimized_linear_executor.get() + except LookupError: + optimized_linear_layer_executor = None + + assert optimized_linear_layer_executor is None, ( + "Remote optimized linear layers " "are not yet implemented" + ) y = self.remote_call(x) + elif self.fhe_local_mode == HybridFHEMode.TORCH: # Using torch layers assert self.private_module is not None @@ -336,6 +360,7 @@ def remote_call(self, x: torch.Tensor) -> torch.Tensor: # pragma:no cover return torch.Tensor(numpy.array(inferences)).to(device=base_device) +# pylint: disable-next=too-many-instance-attributes class HybridFHEModel: """Convert a model to a hybrid model. @@ -375,25 +400,39 @@ def __init__( self.configuration: Optional[Configuration] = None self.model_name = model_name self.verbose = verbose + self._replace_modules() def _replace_modules(self): """Replace the private modules in the model with remote layers.""" + self._all_layers_are_pure_linear = True for module_name in self.module_names: - # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3858 # Conv1d introduce reshaping operations which adds more TLU self.private_modules[module_name] = convert_conv1d_to_linear( self.private_modules[module_name] ) + # Determine if this remote module is a pure linear one + # that is supported for compressed encrypted matmul + # Conv1D will have been converted to Linear by the line above + is_pure_linear_layer = isinstance( + self.private_modules[module_name], + (nn.Linear, ForwardModuleLinear, BackwardModuleLinear), + ) + if not is_pure_linear_layer: + self._all_layers_are_pure_linear = False + + for module_name in self.module_names: + # Create the optimized glwe linear layer executor if needed remote_module = RemoteModule( module=self.private_modules[module_name], server_remote_address=self.server_remote_address, module_name=module_name, model_name=self.model_name, verbose=self.verbose, + optimized_linear_execution=self._all_layers_are_pure_linear, ) self.remote_modules[module_name] = remote_module @@ -414,9 +453,41 @@ def forward(self, x: torch.Tensor, fhe: str = "disable") -> torch.Tensor: Returns: torch.Tensor: The output tensor. + + Raises: + AssertionError: if the execution mode is not supported """ self.set_fhe_mode(fhe) - return self.model(x) + + # Validate the FHE mode + fhe_mode = HybridFHEMode(fhe) + + if _HAS_GLWE_BACKEND and self._all_layers_are_pure_linear: + if fhe_mode == HybridFHEMode.SIMULATE: + raise AssertionError( + "When the HybridFHEModel is instantiated with only " + "linear remote layers, fhe=simulate is not supported for now.", + ) + + if fhe_mode in (HybridFHEMode.EXECUTE, HybridFHEMode.REMOTE, HybridFHEMode.DISABLE): + # If all layers are pure linear, enable the GLWE optimization for all layers + # and generate an encryption and compression key for all layers + # as they share crypto-parameters + + # Loading keys from a file could be done here, and the + # keys could be passed as arguments to the Executor + executor = GLWELinearLayerExecutor() + + if fhe_mode != HybridFHEMode.DISABLE: + executor.keygen() + + _optimized_linear_executor.set(executor) + + result = self.model(x) + + _optimized_linear_executor.set(None) + + return result def __call__(self, x: torch.Tensor, fhe: str = "disable") -> torch.Tensor: """Call method to run the model locally with a fhe mode. @@ -515,15 +586,25 @@ def compile_model( device=device, ) else: - self.private_q_modules[name] = compile_torch_model( - self.private_modules[name], - calibration_data_tensor, - n_bits=n_bits, - rounding_threshold_bits=rounding_threshold_bits, - configuration=configuration, - p_error=p_error, - device=device, - ) + # If all layers are linear and the GLWE backend is available + # then simply quantize the model without compiling with + # Concrete Python. + if self._all_layers_are_pure_linear and _HAS_GLWE_BACKEND: + self.private_q_modules[name] = build_quantized_module( + self.private_modules[name], + calibration_data_tensor, + n_bits=n_bits, + rounding_threshold_bits=rounding_threshold_bits, + ) + else: + self.private_q_modules[name] = compile_torch_model( + self.private_modules[name], + calibration_data_tensor, + n_bits=n_bits, + rounding_threshold_bits=rounding_threshold_bits, + configuration=configuration, + p_error=p_error, + ) self.remote_modules[name].private_q_module = self.private_q_modules[name] @@ -573,7 +654,14 @@ def save_and_clear_private_info(self, path: Path, via_mlir=True): torch.save(self.model.state_dict(), complete_model_path.resolve()) def clear_private_info(module): - for attr in ["private_module", "calibration_data", "private_q_module"]: + # Remove private information + for attr in [ + "private_module", + "calibration_data", + "private_q_module", + "private_key", + "compression_key", + ]: if hasattr(module, attr): setattr(module, attr, None) @@ -585,10 +673,7 @@ def clear_private_info(module): # Save the model with a specific filename model_path = path / "model.pth" - - # Save the model state dict due to a Brevitas issue - # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4572 - torch.save(self.model.state_dict(), model_path.resolve()) + torch.save(self.model, model_path.resolve()) # Save the FHE circuit in the same directory self._save_fhe_circuit(path, via_mlir=via_mlir) diff --git a/tests/quantization/test_quantized_ops.py b/tests/quantization/test_quantized_ops.py index d3ce2fafd..96b9404f9 100644 --- a/tests/quantization/test_quantized_ops.py +++ b/tests/quantization/test_quantized_ops.py @@ -58,6 +58,7 @@ QuantizedMax, QuantizedMaxPool, QuantizedMin, + QuantizedMixingOp, QuantizedMul, QuantizedNeg, QuantizedNot, @@ -122,6 +123,21 @@ def quantized_op_results_are_equal( return values_are_equal(value_1, value_2) +def quantized_op_implements_analytical_calibration(op_instance): + """Check that the op instance overloads the analytical calibration function.""" + # Can only be called on mixing ops + assert isinstance(op_instance, QuantizedMixingOp) + + # Can only be called on ops that do not overload the calibrate_analytical_output function + assert ( + type(op_instance.calibrate_analytical_output) # pylint: disable=unidiomatic-typecheck + != QuantizedMixingOp.calibrate_analytical_output + ) + + with pytest.raises(AssertionError, match=".*calibrate_analytical_output.*"): + op_instance.calibrate_analytical_output() + + @pytest.mark.parametrize( "n_bits", [pytest.param(n_bits) for n_bits in N_BITS_LIST], @@ -435,6 +451,9 @@ def test_all_arith_ops( q_op, operator, equal_method=partial(quantized_op_results_are_equal, q_input=q_inputs_1) ) + if isinstance(q_op, QuantizedMixingOp): + quantized_op_implements_analytical_calibration(q_op) + @pytest.mark.parametrize("n_bits", N_BITS_LIST) @pytest.mark.parametrize("batch_size", [None, 10, 100]) diff --git a/tests/quantization/test_quantizers.py b/tests/quantization/test_quantizers.py index abafc4437..1e5faac3e 100644 --- a/tests/quantization/test_quantizers.py +++ b/tests/quantization/test_quantizers.py @@ -115,8 +115,11 @@ def test_quantized_array_constructor(): value_shape = (10,) values = numpy.random.uniform(0, 1, size=value_shape) + # Missing rmin/rmax, should be recomputed + QuantizedArray(2, values, stats=None) + # Create an array with precomputed statistics - qarr = QuantizedArray(2, values, stats=None, rmax=2, rmin=-1, uvalues=[0, 1, 2]) + qarr = QuantizedArray(2, values, stats=None, rmax=2, rmin=-1) # Verify that the statistics were not recomputed assert qarr.quantizer.rmax == 2 @@ -127,7 +130,7 @@ def test_quantized_array_constructor(): QuantizedArray(2, values, stats=None, __InvalidParam=2) # Test an incomplete stats structure, should throw an error - with pytest.raises(TypeError): + with pytest.raises(TypeError, match="Missing quantizer parameter rmin, but rmax were given"): QuantizedArray(2, values, stats=None, rmax=2) diff --git a/tests/torch/test_compile_torch.py b/tests/torch/test_compile_torch.py index 80041144f..9ae14e237 100644 --- a/tests/torch/test_compile_torch.py +++ b/tests/torch/test_compile_torch.py @@ -47,9 +47,9 @@ PaddingNet, ShapeOperationsNet, SimpleNet, - SimpleQAT, SingleMixNet, StepActivationModule, + StepFunctionPTQ, TinyQATCNN, TorchDivide, TorchMultiply, @@ -603,7 +603,7 @@ def test_compile_torch_or_onnx_activations( @pytest.mark.parametrize( "model", [ - pytest.param(SimpleQAT), + pytest.param(StepFunctionPTQ), ], ) @pytest.mark.parametrize( @@ -629,7 +629,7 @@ def test_compile_torch_qat( # Import these networks from torch directly is_onnx = False - qat_bits = n_bits + qat_bits = 0 compile_and_test_torch_or_onnx( input_output_feature, @@ -955,7 +955,7 @@ def test_qat_import_check(default_configuration, check_is_good_execution_for_cml with pytest.raises(ValueError, match=error_message_pattern): compile_and_test_torch_or_onnx( 10, - partial(SimpleQAT, n_bits=6, disable_bit_check=True), + partial(StepFunctionPTQ, n_bits=6, disable_bit_check=True), nn.ReLU, qat_bits, default_configuration, diff --git a/tests/torch/test_hybrid_converter.py b/tests/torch/test_hybrid_converter.py index 50b2868a8..799020351 100644 --- a/tests/torch/test_hybrid_converter.py +++ b/tests/torch/test_hybrid_converter.py @@ -5,12 +5,16 @@ from pathlib import Path from typing import List, Union +import numpy import pytest import torch from concrete.fhe import Configuration +from sklearn.datasets import make_moons +from sklearn.model_selection import train_test_split from transformers import GPT2LMHeadModel, GPT2Tokenizer -from concrete.ml.pytest.torch_models import PartialQATModel +import concrete.ml.torch.hybrid_model +from concrete.ml.pytest.torch_models import FCSmall, PartialQATModel from concrete.ml.torch.hybrid_model import ( HybridFHEModel, tuple_to_underscore_str, @@ -33,7 +37,7 @@ def test_tuple_serialization(tup): assert tup == underscore_str_to_tuple(tuple_to_underscore_str(tup)) -# pylint: disable=too-many-locals +# pylint: disable=too-many-locals, too-many-branches, too-many-statements def run_hybrid_llm_test( model: torch.nn.Module, inputs: torch.Tensor, @@ -43,6 +47,7 @@ def run_hybrid_llm_test( has_pbs_reshape: bool, monkeypatch, transformers_installed, + glwe_backend_installed, ): """Run the test for any model with its private module names.""" @@ -52,11 +57,17 @@ def run_hybrid_llm_test( compress_input_ciphertexts=True, ) + logits_simulate = None + with monkeypatch.context() as m: if not transformers_installed: m.setitem(sys.modules, "transformers", None) if has_pbs_reshape: has_pbs = True + if not glwe_backend_installed: + m.setattr(concrete.ml.quantization.linear_op_glwe_backend, "_HAS_GLWE_BACKEND", False) + m.setattr(concrete.ml.torch.hybrid_model, "_HAS_GLWE_BACKEND", False) + # Create a hybrid model hybrid_model = HybridFHEModel(model, module_names) try: @@ -75,6 +86,13 @@ def run_hybrid_llm_test( assert "NoParametersFound" in error.args[0] pytest.skip(error.args[0]) + # Check we can run the simulate locally + if has_pbs or not glwe_backend_installed: + logits_simulate = hybrid_model(inputs, fhe="simulate").logits + else: + with pytest.raises(AssertionError, match=".*fhe=simulate is not supported.*"): + hybrid_model(inputs, fhe="simulate") + if has_pbs: # Check for non-zero programmable bootstrapping for module in hybrid_model.private_q_modules.values(): @@ -85,19 +103,19 @@ def run_hybrid_llm_test( else: # Check for zero programmable bootstrapping for module in hybrid_model.private_q_modules.values(): - assert module.fhe_circuit.statistics["programmable_bootstrap_count"] == 0, ( + # The RemoteModule does not have a circuit if it was optimized + # (in the case of pure linear remote modules) + assert ( + not module.fhe_circuit + or module.fhe_circuit.statistics["programmable_bootstrap_count"] == 0 + ), ( "Programmable bootstrap count should be 0, " f"but found {module.fhe_circuit.statistics['programmable_bootstrap_count']}" ) - # Check we can run the simulate locally - logits_simulate = hybrid_model(inputs, fhe="simulate").logits logits_disable = hybrid_model(inputs, fhe="disable").logits logits_original = hybrid_model(inputs, fhe="torch").logits - # Ensure logits_disable and logits_original return the same output for the logits - assert torch.allclose(logits_disable, logits_simulate, atol=1e-7), "Outputs do not match!" - # Compare the topk accuracy of the FHE simulate circuit vs. the original. k = 5 @@ -106,40 +124,52 @@ def run_hybrid_llm_test( # Get the topk indices for logits_disable and logits_simulate topk_disable = logits_disable.topk(k, dim=-1).indices - topk_simulate = logits_simulate.topk(k, dim=-1).indices topk_original = logits_original.topk(k, dim=-1).indices # Compute accuracy of disable and simulate by checking # how many labels correspond with the topk_original accuracy_disable = (topk_disable == topk_original).float().mean().item() - accuracy_simulate = (topk_simulate == topk_original).float().mean().item() - + # Ensure logits_disable and logits_original return the same output for the logits # Assert that both accuracy values are above the expected threshold assert ( accuracy_disable >= expected_accuracy ), f"Disable accuracy {accuracy_disable:.4f} is below the expected {expected_accuracy:.4f}" - assert ( - accuracy_simulate >= expected_accuracy - ), f"Simulate accuracy {accuracy_simulate:.4f} is below the expected {expected_accuracy:.4f}" + + if logits_simulate is not None: + assert torch.allclose(logits_disable, logits_simulate, atol=1e-7), "Outputs do not match!" + topk_simulate = logits_simulate.topk(k, dim=-1).indices + accuracy_simulate = (topk_simulate == topk_original).float().mean().item() + assert accuracy_simulate >= expected_accuracy, ( + f"Simulate accuracy {accuracy_simulate:.4f} is below " + f"the expected {expected_accuracy:.4f}" + ) with tempfile.TemporaryDirectory() as temp_dir: temp_dir_path = Path(temp_dir) # Get the temp directory path - hybrid_model.save_and_clear_private_info(temp_dir_path) - hybrid_model.set_fhe_mode("remote") - # At this point, the hybrid model does not have - # the parameters necessaryto run the module_names - module_names = module_names if isinstance(module_names, list) else [module_names] + if not has_pbs and glwe_backend_installed: + # Deployment of GLWE backend hybrid models is not yet supported + with pytest.raises(AttributeError, match="The quantized module is not compiled.*"): + hybrid_model.save_and_clear_private_info(temp_dir_path) - # Check that files are there - assert (temp_dir_path / "model.pth").exists() - for module_name in module_names: - module_dir_path = temp_dir_path / module_name - module_dir_files = set(str(elt.name) for elt in module_dir_path.glob("**/*")) - for file_name in ["client.zip", "server.zip"]: - assert file_name in module_dir_files + else: + hybrid_model.save_and_clear_private_info(temp_dir_path) + + hybrid_model.set_fhe_mode("remote") + + # At this point, the hybrid model does not have + # the parameters necessaryto run the module_names + module_names = module_names if isinstance(module_names, list) else [module_names] + + # Check that files are there + assert (temp_dir_path / "model.pth").exists() + for module_name in module_names: + module_dir_path = temp_dir_path / module_name + module_dir_files = set(str(elt.name) for elt in module_dir_path.glob("**/*")) + for file_name in ["client.zip", "server.zip"]: + assert file_name in module_dir_files # Dependency 'huggingface-hub' raises a 'FutureWarning' from version 0.23.0 when calling the @@ -154,12 +184,14 @@ def run_hybrid_llm_test( ], ) @pytest.mark.parametrize("transformers_installed", [True, False]) +@pytest.mark.parametrize("glwe_backend_installed", [True, False]) def test_gpt2_hybrid_mlp( list_or_str_private_modules_names, expected_accuracy, has_pbs, has_pbs_reshape, transformers_installed, + glwe_backend_installed, monkeypatch, ): """Test GPT2 hybrid.""" @@ -182,6 +214,7 @@ def test_gpt2_hybrid_mlp( has_pbs_reshape, monkeypatch, transformers_installed, + glwe_backend_installed, ) @@ -240,3 +273,86 @@ def test_invalid_model(): # Attempt to create a HybridFHEModel with an invalid model type and expect a TypeError with pytest.raises(TypeError, match="The model must be a PyTorch or Brevitas model."): HybridFHEModel(invalid_model, module_names="sub_module") + + +@pytest.mark.parametrize("n_hidden", [512, 2048]) +def test_hybrid_glwe_correctness(n_hidden): + """Tests that the GLWE backend produces correct results for the hybrid model.""" + + num_samples = 200 + + def prepare_data(x, y, test_size=0.1, random_state=42): + x_train, x_test, y_train, y_test = train_test_split( + x, y, test_size=test_size, random_state=random_state + ) + x_train = torch.tensor(x_train, dtype=torch.float32) + x_test = torch.tensor(x_test, dtype=torch.float32) + y_train = torch.tensor(y_train, dtype=torch.long) + y_test = torch.tensor(y_test, dtype=torch.long) + return x_train, x_test, y_train, y_test + + # Generate synthetic 2D data + x1_data, y1_data = make_moons(n_samples=num_samples, noise=0.2, random_state=42) + + # Prepare data + x1_train, x1_test, y1_train, y1_test = prepare_data(x1_data, y1_data) + + model = FCSmall(2, torch.nn.ReLU, hidden=n_hidden) + optimizer = torch.optim.Adam(model.parameters()) + + num_epochs = 100 + model.train() + for _ in range(num_epochs): + optimizer.zero_grad() + outputs = model(x1_train) + loss = torch.nn.functional.cross_entropy(outputs, y1_train) + loss.backward() + optimizer.step() + + model.eval() + + param_names = [] + for k, p in model.named_modules(): + if isinstance(p, torch.nn.Linear): + param_names.append(k) + + y_torch = model(x1_test).detach().numpy() + hybrid_local = HybridFHEModel(model, param_names) + + # This internal flag tells us whether all the layers + # were linear and were replaced with the GLWE backend + assert hybrid_local._all_layers_are_pure_linear # pylint: disable=protected-access + + hybrid_local.compile_model(x1_train, n_bits=10) + + y_qm = hybrid_local(x1_test, fhe="disable").numpy() + y_hybrid_torch = hybrid_local(x1_test, fhe="torch").detach().numpy() + y_glwe = hybrid_local(x1_test, fhe="execute").numpy() + + y1_test = y1_test.numpy() + n_correct_fp32 = numpy.sum(numpy.argmax(y_torch, axis=1) == y1_test) + n_correct_qm = numpy.sum(numpy.argmax(y_qm, axis=1) == y1_test) + n_correct_glwe = numpy.sum(numpy.argmax(y_glwe, axis=1) == y1_test) + + # These two should be exactly the same + assert numpy.all(numpy.allclose(y_torch, y_hybrid_torch, rtol=1, atol=0.001)) + + # The clear quantization vs fp32 test has more tolerance + threshold_fhe = 0.01 + + diff = numpy.abs(y_torch - y_glwe) > threshold_fhe + if numpy.any(diff): + print(f"Value discrepancy detected for GLWE backend, with epsilon={threshold_fhe}") + print("Model output (torch fp32)", y_torch[diff]) + print("Model output (glwe)", y_glwe[diff]) + print("Model output (quantized clear)", y_qm[diff]) + + assert numpy.all(numpy.allclose(y_qm, y_glwe, rtol=1, atol=threshold_fhe)) + assert numpy.all(numpy.allclose(y_torch, y_glwe, rtol=1, atol=threshold_fhe)) + + n_correct_delta_threshold_fhe = 1 + # Check accuracy between fp32 and glwe + assert numpy.abs(n_correct_fp32 - n_correct_glwe) <= n_correct_delta_threshold_fhe + + # Check accuracy between quantized and glwe + assert numpy.abs(n_correct_qm - n_correct_glwe) <= n_correct_delta_threshold_fhe