From f8162cfc8c4630e554073c9146fda8be576c07cb Mon Sep 17 00:00:00 2001 From: Yuval Avrahami <133960019+yuvalavr24@users.noreply.github.com> Date: Sun, 10 Nov 2024 16:57:35 +0200 Subject: [PATCH 1/8] Scan model-compression-kit in Black Duck (#1258) * Scan model-compression-kit in Black Duck * Fix branch name * Fix trigger --- .github/workflows/black_duck_scan.yml | 116 ++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 .github/workflows/black_duck_scan.yml diff --git a/.github/workflows/black_duck_scan.yml b/.github/workflows/black_duck_scan.yml new file mode 100644 index 000000000..57a57a476 --- /dev/null +++ b/.github/workflows/black_duck_scan.yml @@ -0,0 +1,116 @@ +name: Black Duck Scan + +on: + push: + branches: [ main ] + + +env: + BLACKDUCK_PROJECT_NAME: ${{ github.event.repository.name }} + BLACKDUCK_VERSION_NAME: "latest" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + blackduck: + name: Black-Duck action + runs-on: ubuntu-latest + continue-on-error: true + steps: + - name: Checkout Source + uses: actions/checkout@v4 + - name: Install Python 🔧 + uses: actions/setup-python@v4 + with: + python-version: '3.8' + - name: Build Wheel + id: get_version + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install twine + python setup.py bdist_wheel + unzip -o ./dist/model_compression_toolkit-2.2.0-py3-none-any.whl -d ./dist + version=$(python -c 'import model_compression_toolkit; print(model_compression_toolkit.__version__)') + echo "model_compression_version=$version" >> $GITHUB_OUTPUT + - name: Black Duck Full Scan + uses: synopsys-sig/synopsys-action@v1.12.0 + env: + DETECT_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} + DETECT_PROJECT_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} + DETECT_PIP_REQUIREMENTS_PATH: requirements.txt + DETECT_BLACKDUCK_SIGNATURE_SCANNER_PATHS: dist/model_compression_toolkit + DETECT_BLACKDUCK_SIGNATURE_SCANNER_SNIPPET_MATCHING: SNIPPET_MATCHING + DETECT_RISK_REPORT_PDF: true + DETECT_TIMEOUT: 360000 + with: + blackduck_url: ${{ secrets.BLACKDUCK_URL }} + blackduck_token: ${{ secrets.BLACKDUCK_API_TOKEN }} + - name: Handle Black Duck scan failure + if: ${{ failure() }} + shell: bash + run: | + echo "No report was created due to a failure in black duck scan" > model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json + cat model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json + mkdir model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + cp -r ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + ls model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + text="The Black-Duck scan failed; therefore, the JSON report created is a dump report.There are no PDF/ver_src reports" + echo "::warning::$text" + - name: Verify Black Duck Sources Scan + if: ${{ success() }} + uses: ssi-dnn/sdsp-converter-actions/black-duck-verify-sources@main + with: + BLACKDUCK_API_TOKEN: ${{ secrets.BLACKDUCK_API_TOKEN }} + BLACKDUCK_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} + BLACKDUCK_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} + BLACKDUCK_SUB_PROJECTS_NAME: ${{ env.BLACKDUCK_SUB_PROJECTS_NAME }} + BLACKDUCK_VERIFY_SOURCES_JSON: model_compression_ver_src_${{ steps.get_version.outputs.model_compression_version }}.json + - name: Get Black Duck Scan Result + if: ${{ success() }} + uses: ssi-dnn/sdsp-converter-actions/black-duck-tool@main + with: + BLACKDUCK_API_TOKEN: ${{ secrets.BLACKDUCK_API_TOKEN }} + BLACKDUCK_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} + BLACKDUCK_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} + BLACKDUCK_REPORT_FILE_NAME: model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json + - name: Create black duck dir + if: ${{ success() }} + run: | + ls + mkdir model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + pdf_project_name=$(echo "${{ env.BLACKDUCK_PROJECT_NAME }}" | tr '-' '_') + echo $pdf_project_name + cp -r ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + cp -r ./model_compression_ver_src_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + cp -r ./${pdf_project_name}_${{ env.BLACKDUCK_VERSION_NAME }}_BlackDuck_RiskReport.pdf model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + ls model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + - name: Upload scan result + if: ${{ always() }} + uses: actions/upload-artifact@v4 + env: + file_path: ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} + with: + name: bd-scan-result + path: ${{ env.file_path }} + retention-days: 1 + outputs: + model_compression_version: ${{ steps.get_version.outputs.model_compression_version }} + + + save_blackduck_scan_result: + name: Save scan result - Black-Duck + needs: [ blackduck ] + env: + PATH_TO_SAVE: /home/sdsprobot/blackduck_scans/model_compression_scans/model_compression_scan_${{ needs.blackduck.outputs.model_compression_version }}/ + runs-on: + group: DNN01 + labels: self-hosted + steps: + - name: Download artifact + uses: actions/download-artifact@v4 + with: + name: bd-scan-result + path: ${{ env.PATH_TO_SAVE }} From 19ea45256405549ea3c647866ac3106a84ee1cbb Mon Sep 17 00:00:00 2001 From: Yuval Avrahami <133960019+yuvalavr24@users.noreply.github.com> Date: Mon, 11 Nov 2024 11:45:00 +0200 Subject: [PATCH 2/8] Move workflow to different repository (#1262) * Scan model-compression-kit in Black Duck * Move workflow to different repository From f34972e3732a8f721a68a553a5c6bcc59caf0808 Mon Sep 17 00:00:00 2001 From: Yuval Avrahami <133960019+yuvalavr24@users.noreply.github.com> Date: Mon, 11 Nov 2024 12:35:23 +0200 Subject: [PATCH 3/8] Remove Black-Duck action (#1263) --- .github/workflows/black_duck_scan.yml | 116 -------------------------- 1 file changed, 116 deletions(-) delete mode 100644 .github/workflows/black_duck_scan.yml diff --git a/.github/workflows/black_duck_scan.yml b/.github/workflows/black_duck_scan.yml deleted file mode 100644 index 57a57a476..000000000 --- a/.github/workflows/black_duck_scan.yml +++ /dev/null @@ -1,116 +0,0 @@ -name: Black Duck Scan - -on: - push: - branches: [ main ] - - -env: - BLACKDUCK_PROJECT_NAME: ${{ github.event.repository.name }} - BLACKDUCK_VERSION_NAME: "latest" - -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} - -jobs: - blackduck: - name: Black-Duck action - runs-on: ubuntu-latest - continue-on-error: true - steps: - - name: Checkout Source - uses: actions/checkout@v4 - - name: Install Python 🔧 - uses: actions/setup-python@v4 - with: - python-version: '3.8' - - name: Build Wheel - id: get_version - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install twine - python setup.py bdist_wheel - unzip -o ./dist/model_compression_toolkit-2.2.0-py3-none-any.whl -d ./dist - version=$(python -c 'import model_compression_toolkit; print(model_compression_toolkit.__version__)') - echo "model_compression_version=$version" >> $GITHUB_OUTPUT - - name: Black Duck Full Scan - uses: synopsys-sig/synopsys-action@v1.12.0 - env: - DETECT_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} - DETECT_PROJECT_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} - DETECT_PIP_REQUIREMENTS_PATH: requirements.txt - DETECT_BLACKDUCK_SIGNATURE_SCANNER_PATHS: dist/model_compression_toolkit - DETECT_BLACKDUCK_SIGNATURE_SCANNER_SNIPPET_MATCHING: SNIPPET_MATCHING - DETECT_RISK_REPORT_PDF: true - DETECT_TIMEOUT: 360000 - with: - blackduck_url: ${{ secrets.BLACKDUCK_URL }} - blackduck_token: ${{ secrets.BLACKDUCK_API_TOKEN }} - - name: Handle Black Duck scan failure - if: ${{ failure() }} - shell: bash - run: | - echo "No report was created due to a failure in black duck scan" > model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json - cat model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json - mkdir model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - cp -r ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - ls model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - text="The Black-Duck scan failed; therefore, the JSON report created is a dump report.There are no PDF/ver_src reports" - echo "::warning::$text" - - name: Verify Black Duck Sources Scan - if: ${{ success() }} - uses: ssi-dnn/sdsp-converter-actions/black-duck-verify-sources@main - with: - BLACKDUCK_API_TOKEN: ${{ secrets.BLACKDUCK_API_TOKEN }} - BLACKDUCK_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} - BLACKDUCK_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} - BLACKDUCK_SUB_PROJECTS_NAME: ${{ env.BLACKDUCK_SUB_PROJECTS_NAME }} - BLACKDUCK_VERIFY_SOURCES_JSON: model_compression_ver_src_${{ steps.get_version.outputs.model_compression_version }}.json - - name: Get Black Duck Scan Result - if: ${{ success() }} - uses: ssi-dnn/sdsp-converter-actions/black-duck-tool@main - with: - BLACKDUCK_API_TOKEN: ${{ secrets.BLACKDUCK_API_TOKEN }} - BLACKDUCK_PROJECT_NAME: ${{ env.BLACKDUCK_PROJECT_NAME }} - BLACKDUCK_VERSION_NAME: ${{ env.BLACKDUCK_VERSION_NAME }} - BLACKDUCK_REPORT_FILE_NAME: model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json - - name: Create black duck dir - if: ${{ success() }} - run: | - ls - mkdir model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - pdf_project_name=$(echo "${{ env.BLACKDUCK_PROJECT_NAME }}" | tr '-' '_') - echo $pdf_project_name - cp -r ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - cp -r ./model_compression_ver_src_${{ steps.get_version.outputs.model_compression_version }}.json model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - cp -r ./${pdf_project_name}_${{ env.BLACKDUCK_VERSION_NAME }}_BlackDuck_RiskReport.pdf model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - ls model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - - name: Upload scan result - if: ${{ always() }} - uses: actions/upload-artifact@v4 - env: - file_path: ./model_compression_scan_${{ steps.get_version.outputs.model_compression_version }} - with: - name: bd-scan-result - path: ${{ env.file_path }} - retention-days: 1 - outputs: - model_compression_version: ${{ steps.get_version.outputs.model_compression_version }} - - - save_blackduck_scan_result: - name: Save scan result - Black-Duck - needs: [ blackduck ] - env: - PATH_TO_SAVE: /home/sdsprobot/blackduck_scans/model_compression_scans/model_compression_scan_${{ needs.blackduck.outputs.model_compression_version }}/ - runs-on: - group: DNN01 - labels: self-hosted - steps: - - name: Download artifact - uses: actions/download-artifact@v4 - with: - name: bd-scan-result - path: ${{ env.PATH_TO_SAVE }} From 9ac9f54ac485c026372e89280974cde1e1be2b30 Mon Sep 17 00:00:00 2001 From: Irena Byzalov <102301507+irenaby@users.noreply.github.com> Date: Tue, 12 Nov 2024 18:53:56 +0200 Subject: [PATCH 4/8] Enable SLA + gradual activation quantization by default for PyTorch (#1260) * update default reg factor and bias lr * update gptq config, set sla + gradual activation as default * fix tests * update hessian config * fix bug in hessian service --- .../common/hessian/hessian_info_service.py | 2 +- .../gptq/common/gptq_config.py | 49 ++++++----- .../gptq/common/gptq_constants.py | 4 +- .../gptq/common/gptq_training.py | 2 +- .../gptq/keras/gptq_training.py | 2 +- .../gptq/keras/quantization_facade.py | 14 ++-- .../gptq/pytorch/gptq_training.py | 4 +- .../gptq/pytorch/quantization_facade.py | 47 ++++++----- .../feature_networks/gptq/gptq_test.py | 83 ++++++++++++------- .../test_features_runner.py | 2 +- .../function_tests/test_get_gptq_config.py | 40 ++++++--- ...est_activation_quantization_holder_gptq.py | 5 +- .../model_tests/feature_models/gptq_test.py | 46 +++++----- .../gptq/test_gradual_act_quantization.py | 3 +- .../gptq/test_gradual_act_quantization.py | 3 +- 15 files changed, 186 insertions(+), 120 deletions(-) diff --git a/model_compression_toolkit/core/common/hessian/hessian_info_service.py b/model_compression_toolkit/core/common/hessian/hessian_info_service.py index 41bd896df..6d93dc126 100644 --- a/model_compression_toolkit/core/common/hessian/hessian_info_service.py +++ b/model_compression_toolkit/core/common/hessian/hessian_info_service.py @@ -204,7 +204,7 @@ def _fetch_hessians_with_compute(self, request: HessianScoresRequest, n_iteratio target_nodes = [n for n in orig_request.target_nodes if n.name in missing] request = request.clone(target_nodes=target_nodes) self._compute_hessians(request, n_iterations, count_by_cache=True) - res, missing = self.cache.fetch_hessian(request) + res, missing = self.cache.fetch_hessian(orig_request) assert not missing return res diff --git a/model_compression_toolkit/gptq/common/gptq_config.py b/model_compression_toolkit/gptq/common/gptq_config.py index 3a96fd86b..d7ef753a2 100644 --- a/model_compression_toolkit/gptq/common/gptq_config.py +++ b/model_compression_toolkit/gptq/common/gptq_config.py @@ -16,8 +16,7 @@ from enum import Enum from typing import Callable, Any, Dict, Optional -from model_compression_toolkit.constants import GPTQ_HESSIAN_NUM_SAMPLES, ACT_HESSIAN_DEFAULT_BATCH_SIZE -from model_compression_toolkit.gptq.common.gptq_constants import REG_DEFAULT +from model_compression_toolkit.constants import ACT_HESSIAN_DEFAULT_BATCH_SIZE class RoundingType(Enum): @@ -39,20 +38,26 @@ class GPTQHessianScoresConfig: Configuration to use for computing the Hessian-based scores for GPTQ loss metric. Args: + per_sample (bool): Whether to use per sample attention score. hessians_num_samples (int|None): Number of samples to use for computing the Hessian-based scores. If None, compute Hessian for all images. norm_scores (bool): Whether to normalize the returned scores of the weighted loss function (to get values between 0 and 1). log_norm (bool): Whether to use log normalization for the GPTQ Hessian-based scores. scale_log_norm (bool): Whether to scale the final vector of the Hessian-based scores. hessian_batch_size (int): The Hessian computation batch size. used only if using GPTQ with Hessian-based objective. - per_sample (bool): Whether to use per sample attention score. """ - hessians_num_samples: Optional[int] = GPTQ_HESSIAN_NUM_SAMPLES - norm_scores: bool = True - log_norm: bool = True + per_sample: bool + hessians_num_samples: Optional[int] + norm_scores: bool = None + log_norm: bool = None scale_log_norm: bool = False hessian_batch_size: int = ACT_HESSIAN_DEFAULT_BATCH_SIZE - per_sample: bool = False + + def __post_init__(self): + if self.norm_scores is None: + self.norm_scores = not self.per_sample + if self.log_norm is None: + self.log_norm = not self.per_sample @dataclass @@ -107,32 +112,30 @@ class GradientPTQConfig: Args: n_epochs: Number of representative dataset epochs to train. - optimizer: Optimizer to use. - optimizer_rest: Optimizer to use for bias and quantizer parameters. loss: The loss to use. See 'multiple_tensors_mse_loss' for the expected interface. - log_function: Function to log information about the GPTQ process. + optimizer: Optimizer to use. + optimizer_rest: Default optimizer to use for bias and quantizer parameters. train_bias: Whether to update the bias during the training or not. - rounding_type: An enum that defines the rounding type. - use_hessian_based_weights: Whether to use Hessian-based weights for weighted average loss. - optimizer_quantization_parameter: Optimizer to override the rest optimizer for quantizer parameters. - optimizer_bias: Optimizer to override the rest optimizer for bias. - regularization_factor: A floating point number that defines the regularization factor. hessian_weights_config: A configuration that include all necessary arguments to run a computation of Hessian scores for the GPTQ loss. gradual_activation_quantization_config: A configuration for Gradual Activation Quantization. + regularization_factor: A floating point number that defines the regularization factor. + rounding_type: An enum that defines the rounding type. + optimizer_quantization_parameter: Optimizer to override the rest optimizer for quantizer parameters. + optimizer_bias: Optimizer to override the rest optimizer for bias. + log_function: Function to log information about the GPTQ process. gptq_quantizer_params_override: A dictionary of parameters to override in GPTQ quantizer instantiation. """ n_epochs: int + loss: Callable optimizer: Any - optimizer_rest: Any = None - loss: Callable = None - log_function: Callable = None - train_bias: bool = True + optimizer_rest: Any + train_bias: bool + hessian_weights_config: Optional[GPTQHessianScoresConfig] + gradual_activation_quantization_config: Optional[GradualActivationQuantizationConfig] + regularization_factor: float rounding_type: RoundingType = RoundingType.SoftQuantizer - use_hessian_based_weights: bool = True optimizer_quantization_parameter: Any = None optimizer_bias: Any = None - regularization_factor: float = REG_DEFAULT - hessian_weights_config: GPTQHessianScoresConfig = field(default_factory=GPTQHessianScoresConfig) - gradual_activation_quantization_config: Optional[GradualActivationQuantizationConfig] = None + log_function: Callable = None gptq_quantizer_params_override: Dict[str, Any] = field(default_factory=dict) diff --git a/model_compression_toolkit/gptq/common/gptq_constants.py b/model_compression_toolkit/gptq/common/gptq_constants.py index 3451d9732..50b3304c7 100644 --- a/model_compression_toolkit/gptq/common/gptq_constants.py +++ b/model_compression_toolkit/gptq/common/gptq_constants.py @@ -14,6 +14,7 @@ MIM_TEMP = 0.5 MAX_TEMP = 1.0 REG_DEFAULT = 0.01 +REG_DEFAULT_SLA = 10 MAX_LSB_CHANGE = 1 # Soft rounding arguments values @@ -27,6 +28,5 @@ # GPTQ learning hyperparameters LR_DEFAULT = 3e-2 LR_REST_DEFAULT = 1e-4 -LR_BIAS_DEFAULT = 1e-3 -LR_QUANTIZATION_PARAM_DEFAULT = 1e-3 +LR_BIAS_DEFAULT = 1e-4 GPTQ_MOMENTUM = 0.9 diff --git a/model_compression_toolkit/gptq/common/gptq_training.py b/model_compression_toolkit/gptq/common/gptq_training.py index 32fc85163..04a39e2be 100644 --- a/model_compression_toolkit/gptq/common/gptq_training.py +++ b/model_compression_toolkit/gptq/common/gptq_training.py @@ -75,7 +75,7 @@ def __init__(self, fw_info=self.fw_info) self.fxp_model, self.gptq_user_info = self.build_gptq_model() - if self.gptq_config.use_hessian_based_weights: + if self.gptq_config.hessian_weights_config: if not isinstance(hessian_info_service, HessianInfoService): Logger.critical(f"When using Hessian-based approximations for sensitivity evaluation, " f"an 'HessianInfoService' object must be provided, but received: {hessian_info_service}.") # pragma: no cover diff --git a/model_compression_toolkit/gptq/keras/gptq_training.py b/model_compression_toolkit/gptq/keras/gptq_training.py index 4ba44e8fe..166778b77 100644 --- a/model_compression_toolkit/gptq/keras/gptq_training.py +++ b/model_compression_toolkit/gptq/keras/gptq_training.py @@ -139,7 +139,7 @@ def _get_total_grad_steps(): def _get_compare_points_loss_weights(self): """ Get compare points weights for the distillation loss. """ - if self.gptq_config.use_hessian_based_weights: + if self.gptq_config.hessian_weights_config: hess_dataloader = data_gen_to_dataloader(self.representative_data_gen_fn, batch_size=self.gptq_config.hessian_weights_config.hessian_batch_size) return self.compute_hessian_based_weights(hess_dataloader) diff --git a/model_compression_toolkit/gptq/keras/quantization_facade.py b/model_compression_toolkit/gptq/keras/quantization_facade.py index ead11c941..3233e1e28 100644 --- a/model_compression_toolkit/gptq/keras/quantization_facade.py +++ b/model_compression_toolkit/gptq/keras/quantization_facade.py @@ -21,7 +21,7 @@ from model_compression_toolkit.gptq.common.gptq_constants import REG_DEFAULT, LR_DEFAULT, LR_REST_DEFAULT, \ LR_BIAS_DEFAULT, GPTQ_MOMENTUM from model_compression_toolkit.logger import Logger -from model_compression_toolkit.constants import TENSORFLOW, ACT_HESSIAN_DEFAULT_BATCH_SIZE +from model_compression_toolkit.constants import TENSORFLOW, ACT_HESSIAN_DEFAULT_BATCH_SIZE, GPTQ_HESSIAN_NUM_SAMPLES from model_compression_toolkit.verify_packages import FOUND_TF from model_compression_toolkit.core.common.user_info import UserInformation from model_compression_toolkit.gptq.common.gptq_config import GradientPTQConfig, GPTQHessianScoresConfig, \ @@ -117,16 +117,20 @@ def get_keras_gptq_config(n_epochs: int, raise TypeError(f'gradual_activation_quantization argument should be bool or ' f'GradualActivationQuantizationConfig, received {type(gradual_activation_quantization)}') - return GradientPTQConfig(n_epochs, - optimizer, + hessian_weights_config = None + if use_hessian_based_weights: + hessian_weights_config = GPTQHessianScoresConfig(per_sample=False, + hessians_num_samples=GPTQ_HESSIAN_NUM_SAMPLES, + hessian_batch_size=hessian_batch_size) + return GradientPTQConfig(n_epochs=n_epochs, + optimizer=optimizer, optimizer_rest=optimizer_rest, loss=loss, log_function=log_function, train_bias=True, optimizer_bias=bias_optimizer, - use_hessian_based_weights=use_hessian_based_weights, regularization_factor=regularization_factor, - hessian_weights_config=GPTQHessianScoresConfig(hessian_batch_size=hessian_batch_size), + hessian_weights_config=hessian_weights_config, gradual_activation_quantization_config=gradual_quant_config) diff --git a/model_compression_toolkit/gptq/pytorch/gptq_training.py b/model_compression_toolkit/gptq/pytorch/gptq_training.py index ff510e6a6..ee91a36d3 100644 --- a/model_compression_toolkit/gptq/pytorch/gptq_training.py +++ b/model_compression_toolkit/gptq/pytorch/gptq_training.py @@ -116,7 +116,7 @@ def _get_total_grad_steps(): trainable_threshold) hessian_cfg = self.gptq_config.hessian_weights_config - self.use_sample_layer_attention = hessian_cfg.per_sample + self.use_sample_layer_attention = hessian_cfg and hessian_cfg.per_sample if self.use_sample_layer_attention: # normalization is currently not supported, make sure the config reflects it. if hessian_cfg.norm_scores or hessian_cfg.log_norm or hessian_cfg.scale_log_norm: @@ -178,7 +178,7 @@ def _prepare_train_dataloader_for_non_sla(self, data_gen_fn: Callable[[], Genera dataset = IterableDatasetFromGenerator(data_gen_fn) num_nodes = len(self.compare_points) - if self.gptq_config.use_hessian_based_weights: + if self.gptq_config.hessian_weights_config: hess_dataloader = DataLoader(dataset, batch_size=self.gptq_config.hessian_weights_config.hessian_batch_size) loss_weights = torch.from_numpy(self.compute_hessian_based_weights(hess_dataloader)) else: diff --git a/model_compression_toolkit/gptq/pytorch/quantization_facade.py b/model_compression_toolkit/gptq/pytorch/quantization_facade.py index 220fbb3ec..dbe964ee0 100644 --- a/model_compression_toolkit/gptq/pytorch/quantization_facade.py +++ b/model_compression_toolkit/gptq/pytorch/quantization_facade.py @@ -15,7 +15,7 @@ import copy from typing import Callable, Union -from model_compression_toolkit.constants import ACT_HESSIAN_DEFAULT_BATCH_SIZE, PYTORCH +from model_compression_toolkit.constants import ACT_HESSIAN_DEFAULT_BATCH_SIZE, PYTORCH, GPTQ_HESSIAN_NUM_SAMPLES from model_compression_toolkit.core import CoreConfig from model_compression_toolkit.core.analyzer import analyzer_model_quantization from model_compression_toolkit.core.common.mixed_precision.mixed_precision_quantization_config import \ @@ -27,7 +27,7 @@ from model_compression_toolkit.gptq.common.gptq_config import ( GradientPTQConfig, GPTQHessianScoresConfig, GradualActivationQuantizationConfig) from model_compression_toolkit.gptq.common.gptq_constants import REG_DEFAULT, LR_DEFAULT, LR_REST_DEFAULT, \ - LR_BIAS_DEFAULT, GPTQ_MOMENTUM + LR_BIAS_DEFAULT, GPTQ_MOMENTUM, REG_DEFAULT_SLA from model_compression_toolkit.gptq.runner import gptq_runner from model_compression_toolkit.logger import Logger from model_compression_toolkit.metadata import create_model_metadata @@ -55,10 +55,10 @@ def get_pytorch_gptq_config(n_epochs: int, loss: Callable = None, log_function: Callable = None, use_hessian_based_weights: bool = True, - regularization_factor: float = REG_DEFAULT, + regularization_factor: float = None, hessian_batch_size: int = ACT_HESSIAN_DEFAULT_BATCH_SIZE, - use_hessian_sample_attention: bool = False, - gradual_activation_quantization: Union[bool, GradualActivationQuantizationConfig] = False, + use_hessian_sample_attention: bool = True, + gradual_activation_quantization: Union[bool, GradualActivationQuantizationConfig] = True, ) -> GradientPTQConfig: """ Create a GradientPTQConfig instance for Pytorch models. @@ -94,25 +94,26 @@ def get_pytorch_gptq_config(n_epochs: int, """ optimizer = optimizer or Adam([torch.Tensor([])], lr=LR_DEFAULT) optimizer_rest = optimizer_rest or Adam([torch.Tensor([])], lr=LR_REST_DEFAULT) - + # TODO this contradicts the docstring for optimizer_rest bias_optimizer = torch.optim.SGD([torch.Tensor([])], lr=LR_BIAS_DEFAULT, momentum=GPTQ_MOMENTUM) + if regularization_factor is None: + regularization_factor = REG_DEFAULT_SLA if use_hessian_sample_attention else REG_DEFAULT + + loss = loss or multiple_tensors_mse_loss + hessian_weights_config = None if use_hessian_sample_attention: if not use_hessian_based_weights: # pragma: no cover raise ValueError('use_hessian_based_weights must be set to True in order to use Sample Layer Attention.') - hessian_weights_config = GPTQHessianScoresConfig( - hessians_num_samples=None, - norm_scores=False, - log_norm=False, - scale_log_norm=False, - hessian_batch_size=hessian_batch_size, - per_sample=True, - ) + hessian_weights_config = GPTQHessianScoresConfig(per_sample=True, + hessians_num_samples=None, + hessian_batch_size=hessian_batch_size) loss = loss or sample_layer_attention_loss - else: - hessian_weights_config = GPTQHessianScoresConfig(hessian_batch_size=hessian_batch_size) - loss = loss or multiple_tensors_mse_loss + elif use_hessian_based_weights: + hessian_weights_config = GPTQHessianScoresConfig(per_sample=False, + hessians_num_samples=GPTQ_HESSIAN_NUM_SAMPLES, + hessian_batch_size=hessian_batch_size) if isinstance(gradual_activation_quantization, bool): gradual_quant_config = GradualActivationQuantizationConfig() if gradual_activation_quantization else None @@ -122,12 +123,16 @@ def get_pytorch_gptq_config(n_epochs: int, raise TypeError(f'gradual_activation_quantization argument should be bool or ' f'GradualActivationQuantizationConfig, received {type(gradual_activation_quantization)}') - return GradientPTQConfig(n_epochs, optimizer, optimizer_rest=optimizer_rest, loss=loss, - log_function=log_function, train_bias=True, optimizer_bias=bias_optimizer, - use_hessian_based_weights=use_hessian_based_weights, + return GradientPTQConfig(n_epochs=n_epochs, + loss=loss, + optimizer=optimizer, + optimizer_rest=optimizer_rest, + optimizer_bias=bias_optimizer, + train_bias=True, regularization_factor=regularization_factor, hessian_weights_config=hessian_weights_config, - gradual_activation_quantization_config=gradual_quant_config) + gradual_activation_quantization_config=gradual_quant_config, + log_function=log_function) def pytorch_gradient_post_training_quantization(model: Module, representative_data_gen: Callable, diff --git a/tests/keras_tests/feature_networks_tests/feature_networks/gptq/gptq_test.py b/tests/keras_tests/feature_networks_tests/feature_networks/gptq/gptq_test.py index 0b7c60acc..d16eb8a16 100644 --- a/tests/keras_tests/feature_networks_tests/feature_networks/gptq/gptq_test.py +++ b/tests/keras_tests/feature_networks_tests/feature_networks/gptq/gptq_test.py @@ -59,8 +59,10 @@ def build_model(in_input_shape: List[int]) -> keras.Model: class GradientPTQBaseTest(BaseKerasFeatureNetworkTest): def __init__(self, unit_test, quant_method=QuantizationMethod.SYMMETRIC, rounding_type=RoundingType.STE, per_channel=True, input_shape=(1, 16, 16, 3), - hessian_weights=True, log_norm_weights=True, scaled_log_norm=False, - quantization_parameter_learning=True, num_calibration_iter=GPTQ_HESSIAN_NUM_SAMPLES): + hessian_weights=True, log_norm_weights=True, scaled_log_norm=False, norm_scores=False, + quantization_parameter_learning=True, num_calibration_iter=GPTQ_HESSIAN_NUM_SAMPLES, + hessian_num_samples=GPTQ_HESSIAN_NUM_SAMPLES, per_sample=False, + reg_factor=1, grad_act_quant_cfg=None): super().__init__(unit_test, input_shape=input_shape, num_calibration_iter=num_calibration_iter) @@ -68,15 +70,24 @@ def __init__(self, unit_test, quant_method=QuantizationMethod.SYMMETRIC, roundin self.quant_method = quant_method self.rounding_type = rounding_type self.per_channel = per_channel - self.hessian_weights = hessian_weights - self.log_norm_weights = log_norm_weights - self.scaled_log_norm = scaled_log_norm + + self.hessian_weights_config = None + if hessian_weights: + self.hessian_weights_config = GPTQHessianScoresConfig(per_sample=per_sample, + norm_scores=norm_scores, + log_norm=log_norm_weights, + scale_log_norm=scaled_log_norm, + hessians_num_samples=hessian_num_samples) + + self.grad_act_quant_cfg = grad_act_quant_cfg + self.reg_factor = reg_factor + if rounding_type == RoundingType.SoftQuantizer: self.override_params = {QUANT_PARAM_LEARNING_STR: quantization_parameter_learning} elif rounding_type == RoundingType.STE: self.override_params = {MAX_LSB_STR: DefaultDict(default_value=1)} else: - self.override_params = None + raise ValueError('unknown rounding type', rounding_type) def get_tpc(self): return get_tpc("gptq_test", 16, 16, self.quant_method) @@ -87,12 +98,13 @@ def get_quantization_config(self): relu_bound_to_power_of_2=True, weights_bias_correction=False) def get_gptq_config(self): - return GradientPTQConfig(5, optimizer=tf.keras.optimizers.Adam( - learning_rate=0.0001), optimizer_rest=tf.keras.optimizers.Adam( - learning_rate=0.0001), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, - use_hessian_based_weights=self.hessian_weights, - hessian_weights_config=GPTQHessianScoresConfig(log_norm=self.log_norm_weights, - scale_log_norm=self.scaled_log_norm), + return GradientPTQConfig(5, + optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), + optimizer_rest=tf.keras.optimizers.Adam(learning_rate=0.0001), + loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + hessian_weights_config=self.hessian_weights_config, + gradual_activation_quantization_config=self.grad_act_quant_cfg, + regularization_factor=self.reg_factor, gptq_quantizer_params_override=self.override_params) def create_networks(self): @@ -144,9 +156,13 @@ def compare(self, quantized_model, float_model, input_x=None, quantization_info= class GradientPTQNoTempLearningTest(GradientPTQBaseTest): def get_gptq_config(self): - return GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam( - learning_rate=0.0001), optimizer_rest=tf.keras.optimizers.Adam( - learning_rate=0.0001), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + return GradientPTQConfig(1, + optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), + optimizer_rest=tf.keras.optimizers.Adam(learning_rate=0.0001), + loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + hessian_weights_config=self.hessian_weights_config, + gradual_activation_quantization_config=self.grad_act_quant_cfg, + regularization_factor=self.reg_factor, gptq_quantizer_params_override=self.override_params) def compare(self, quantized_model, float_model, input_x=None, quantization_info=None): @@ -159,9 +175,13 @@ def compare(self, quantized_model, float_model, input_x=None, quantization_info= class GradientPTQWeightsUpdateTest(GradientPTQBaseTest): def get_gptq_config(self): - return GradientPTQConfig(20, optimizer=tf.keras.optimizers.Adam( - learning_rate=1e-2), optimizer_rest=tf.keras.optimizers.Adam( - learning_rate=1e-1), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + return GradientPTQConfig(20, + optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2), + optimizer_rest=tf.keras.optimizers.Adam(learning_rate=1e-1), + loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + hessian_weights_config=self.hessian_weights_config, + gradual_activation_quantization_config=self.grad_act_quant_cfg, + regularization_factor=self.reg_factor, gptq_quantizer_params_override=self.override_params) def compare(self, quantized_model, quantized_gptq_model, input_x=None, quantization_info=None): @@ -182,9 +202,13 @@ def compare(self, quantized_model, quantized_gptq_model, input_x=None, quantizat class GradientPTQLearnRateZeroTest(GradientPTQBaseTest): def get_gptq_config(self): - return GradientPTQConfig(1, optimizer=tf.keras.optimizers.SGD( - learning_rate=0.0), optimizer_rest=tf.keras.optimizers.SGD( - learning_rate=0.0), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + return GradientPTQConfig(1, + optimizer=tf.keras.optimizers.SGD(learning_rate=0.0), + optimizer_rest=tf.keras.optimizers.SGD(learning_rate=0.0), + loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + hessian_weights_config=self.hessian_weights_config, + gradual_activation_quantization_config=self.grad_act_quant_cfg, + regularization_factor=self.reg_factor, gptq_quantizer_params_override=self.override_params) def compare(self, quantized_model, quantized_gptq_model, input_x=None, quantization_info=None): @@ -200,16 +224,17 @@ def compare(self, quantized_model, quantized_gptq_model, input_x=None, quantizat class GradientPTQWeightedLossTest(GradientPTQBaseTest): + def __init__(self, unit_test, **kwargs): + super().__init__(unit_test, norm_scores=False, hessian_num_samples=16, **kwargs) def get_gptq_config(self): - return GradientPTQConfig(5, optimizer=tf.keras.optimizers.Adam( - learning_rate=0.0001), optimizer_rest=tf.keras.optimizers.Adam( - learning_rate=0.0001), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, - use_hessian_based_weights=True, - hessian_weights_config=GPTQHessianScoresConfig(hessians_num_samples=16, - norm_scores=False, - log_norm=self.log_norm_weights, - scale_log_norm=self.scaled_log_norm), + return GradientPTQConfig(5, + optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), + optimizer_rest=tf.keras.optimizers.Adam(learning_rate=0.0001), + loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, + hessian_weights_config=self.hessian_weights_config, + gradual_activation_quantization_config=self.grad_act_quant_cfg, + regularization_factor=self.reg_factor, gptq_quantizer_params_override=self.override_params) def compare(self, quantized_model, float_model, input_x=None, quantization_info=None): diff --git a/tests/keras_tests/feature_networks_tests/test_features_runner.py b/tests/keras_tests/feature_networks_tests/test_features_runner.py index 59336b057..b11be7c04 100644 --- a/tests/keras_tests/feature_networks_tests/test_features_runner.py +++ b/tests/keras_tests/feature_networks_tests/test_features_runner.py @@ -710,7 +710,7 @@ def test_gptq(self): per_channel=True, hessian_weights=True, log_norm_weights=True, scaled_log_norm=True).run_test() GradientPTQWeightedLossTest(self, rounding_type=RoundingType.SoftQuantizer, - per_channel=True, hessian_weights=True, log_norm_weights=True, + per_channel=True, log_norm_weights=True, scaled_log_norm=True).run_test() GradientPTQNoTempLearningTest(self, rounding_type=RoundingType.SoftQuantizer).run_test() diff --git a/tests/keras_tests/function_tests/test_get_gptq_config.py b/tests/keras_tests/function_tests/test_get_gptq_config.py index fbdf99921..f7d2a0e0a 100644 --- a/tests/keras_tests/function_tests/test_get_gptq_config.py +++ b/tests/keras_tests/function_tests/test_get_gptq_config.py @@ -66,7 +66,10 @@ def setUp(self): weights_bias_correction=False) # disable bias correction when working with GPTQ self.cc = CoreConfig(quantization_config=self.qc) - test_hessian_weights_config = GPTQHessianScoresConfig(hessians_num_samples=2, + default_reg_factor = 1 + default_hessian_cfg = GPTQHessianScoresConfig(per_sample=False, hessians_num_samples=2) + test_hessian_weights_config = GPTQHessianScoresConfig(per_sample=False, + hessians_num_samples=2, norm_scores=False, log_norm=True, scale_log_norm=True) @@ -75,35 +78,51 @@ def setUp(self): optimizer_rest=tf.keras.optimizers.RMSprop(), train_bias=True, loss=multiple_tensors_mse_loss, - rounding_type=RoundingType.SoftQuantizer), + rounding_type=RoundingType.SoftQuantizer, + hessian_weights_config=default_hessian_cfg, + regularization_factor=default_reg_factor, + gradual_activation_quantization_config=None), GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam(), optimizer_rest=tf.keras.optimizers.Adam(), train_bias=True, loss=multiple_tensors_mse_loss, - rounding_type=RoundingType.SoftQuantizer), + rounding_type=RoundingType.SoftQuantizer, + hessian_weights_config=default_hessian_cfg, + regularization_factor=default_reg_factor, + gradual_activation_quantization_config=None), GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam(), optimizer_rest=tf.keras.optimizers.Adam(), train_bias=True, loss=multiple_tensors_mse_loss, rounding_type=RoundingType.SoftQuantizer, - regularization_factor=15), + hessian_weights_config=default_hessian_cfg, + regularization_factor=15, + gradual_activation_quantization_config=None), GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam(), optimizer_rest=tf.keras.optimizers.Adam(), train_bias=True, loss=multiple_tensors_mse_loss, rounding_type=RoundingType.SoftQuantizer, + hessian_weights_config=default_hessian_cfg, + regularization_factor=default_reg_factor, + gradual_activation_quantization_config=None, gptq_quantizer_params_override={QUANT_PARAM_LEARNING_STR: True}), GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam(), optimizer_rest=tf.keras.optimizers.Adam(), train_bias=True, loss=multiple_tensors_mse_loss, rounding_type=RoundingType.SoftQuantizer, - hessian_weights_config=test_hessian_weights_config), + hessian_weights_config=test_hessian_weights_config, + regularization_factor=default_reg_factor, + gradual_activation_quantization_config=None), GradientPTQConfig(1, optimizer=tf.keras.optimizers.Adam(), optimizer_rest=tf.keras.optimizers.Adam(), train_bias=True, loss=multiple_tensors_mse_loss, rounding_type=RoundingType.STE, + hessian_weights_config=default_hessian_cfg, + regularization_factor=default_reg_factor, + gradual_activation_quantization_config=None, gptq_quantizer_params_override={ MAX_LSB_STR: DefaultDict(default_value=1)}), get_keras_gptq_config(n_epochs=1, @@ -112,7 +131,6 @@ def setUp(self): optimizer=tf.keras.optimizers.Adam(), regularization_factor=0.001)] - pot_tp = generate_test_tp_model({'weights_quantization_method': QuantizationMethod.POWER_OF_TWO}) self.pot_weights_tpc = generate_keras_tpc(name="gptq_pot_config_test", tp_model=pot_tp) @@ -153,10 +171,12 @@ def test_get_keras_gptq_config_symmetric(self): tf.config.run_functions_eagerly(False) def test_gradual_activation_quantization_custom_config(self): - custom_config = mct.gptq.GradualActivationQuantizationConfig(q_fraction_scheduler_policy=mct.gptq.QFractionLinearAnnealingConfig(initial_q_fraction=0.2, - target_q_fraction=0.8, - start_step=1, - end_step=2)) + custom_config = mct.gptq.GradualActivationQuantizationConfig( + q_fraction_scheduler_policy=mct.gptq.QFractionLinearAnnealingConfig(initial_q_fraction=0.2, + target_q_fraction=0.8, + start_step=1, + end_step=2) + ) config = get_keras_gptq_config(n_epochs=5, gradual_activation_quantization=custom_config) self.assertEqual(config.gradual_activation_quantization_config, custom_config) diff --git a/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py b/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py index 1746de6ec..0c1e8ce8f 100644 --- a/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py +++ b/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py @@ -112,6 +112,7 @@ def test_adding_holder_with_gradual_act_quantization(self): QFractionLinearAnnealingConfig(initial_q_fraction=0.1, target_q_fraction=0.9, start_step=100, end_step=500) ) gptq_cfg = mct.gptq.get_pytorch_gptq_config(1, use_hessian_based_weights=False, + use_hessian_sample_attention=False, gradual_activation_quantization=gradual_act_quant_cfg) gptq_model = self._get_gptq_model(INPUT_SHAPE, BasicModel(), gptq_cfg) activation_holders = self._get_holders_with_validation(gptq_model, exp_n_holders=3) @@ -153,7 +154,9 @@ def _get_gptq_model(self, input_shape, in_model, gptq_cfg=None): qc=qc) graph = set_bit_widths(mixed_precision_enable=False, graph=graph) - gptq_cfg = gptq_cfg or mct.gptq.get_pytorch_gptq_config(1, use_hessian_based_weights=False) + gptq_cfg = gptq_cfg or mct.gptq.get_pytorch_gptq_config(1, use_hessian_based_weights=False, + use_hessian_sample_attention=False, + gradual_activation_quantization=False) trainer = PytorchGPTQTrainer(graph, graph, gptq_cfg, diff --git a/tests/pytorch_tests/model_tests/feature_models/gptq_test.py b/tests/pytorch_tests/model_tests/feature_models/gptq_test.py index 602d7c839..3b8b2230c 100644 --- a/tests/pytorch_tests/model_tests/feature_models/gptq_test.py +++ b/tests/pytorch_tests/model_tests/feature_models/gptq_test.py @@ -57,25 +57,30 @@ def __init__(self, unit_test, weights_bits=8, weights_quant_method=QuantizationM hessian_weights=True, norm_scores=True, log_norm_weights=True, scaled_log_norm=False, params_learning=True, num_calibration_iter=GPTQ_HESSIAN_NUM_SAMPLES, gradual_activation_quantization=False, hessian_num_samples=GPTQ_HESSIAN_NUM_SAMPLES, sample_layer_attention=False, - loss=multiple_tensors_mse_loss, hessian_batch_size=1): + loss=multiple_tensors_mse_loss, hessian_batch_size=1, reg_factor=1): super().__init__(unit_test, input_shape=(3, 16, 16), num_calibration_iter=num_calibration_iter) self.seed = 0 self.rounding_type = rounding_type self.weights_bits = weights_bits self.weights_quant_method = weights_quant_method self.per_channel = per_channel - self.hessian_weights = hessian_weights - self.norm_scores = norm_scores - self.log_norm_weights = log_norm_weights - self.scaled_log_norm = scaled_log_norm - self.override_params = {QUANT_PARAM_LEARNING_STR: params_learning} if \ - rounding_type == RoundingType.SoftQuantizer else {MAX_LSB_STR: DefaultDict(default_value=1)} \ - if rounding_type == RoundingType.STE else None + if rounding_type == RoundingType.SoftQuantizer: + self.override_params = {QUANT_PARAM_LEARNING_STR: params_learning} + elif rounding_type == RoundingType.STE: + self.override_params = {MAX_LSB_STR: DefaultDict(default_value=1)} + else: + raise ValueError('unknown rounding_type', rounding_type) self.gradual_activation_quantization = gradual_activation_quantization - self.hessian_num_samples = hessian_num_samples - self.sample_layer_attention = sample_layer_attention self.loss = loss - self.hessian_batch_size = hessian_batch_size + self.reg_factor = reg_factor + self.hessian_cfg = None + if hessian_weights: + self.hessian_cfg = GPTQHessianScoresConfig(per_sample=sample_layer_attention, + norm_scores=norm_scores, + log_norm=log_norm_weights, + scale_log_norm=scaled_log_norm, + hessians_num_samples=hessian_num_samples, + hessian_batch_size=hessian_batch_size) def get_quantization_config(self): return mct.core.QuantizationConfig(mct.core.QuantizationErrorMethod.NOCLIPPING, @@ -145,16 +150,11 @@ def get_gptq_config(self): return GradientPTQConfig(5, optimizer=torch.optim.Adam([torch.Tensor([])], lr=1e-4), optimizer_rest=torch.optim.Adam([torch.Tensor([])], lr=1e-4), loss=self.loss, train_bias=True, rounding_type=self.rounding_type, - use_hessian_based_weights=self.hessian_weights, optimizer_bias=torch.optim.Adam([torch.Tensor([])], lr=0.4), - hessian_weights_config=GPTQHessianScoresConfig(log_norm=self.log_norm_weights, - scale_log_norm=self.scaled_log_norm, - norm_scores=self.norm_scores, - per_sample=self.sample_layer_attention, - hessians_num_samples=self.hessian_num_samples, - hessian_batch_size=self.hessian_batch_size), + hessian_weights_config=self.hessian_cfg, gptq_quantizer_params_override=self.override_params, - gradual_activation_quantization_config=gradual_act_cfg) + gradual_activation_quantization_config=gradual_act_cfg, + regularization_factor=self.reg_factor) def gptq_compare(self, ptq_model, gptq_model, input_x=None): ptq_weights = torch_tensor_to_numpy(list(ptq_model.parameters())) @@ -171,7 +171,9 @@ def get_gptq_config(self): optimizer_rest=torch.optim.Adam([torch.Tensor([])], lr=0.5), loss=multiple_tensors_mse_loss, train_bias=True, rounding_type=self.rounding_type, gradual_activation_quantization_config=gradual_act_cfg, - gptq_quantizer_params_override=self.override_params) + gptq_quantizer_params_override=self.override_params, + regularization_factor=self.reg_factor, + hessian_weights_config=self.hessian_cfg) def compare(self, ptq_model, gptq_model, input_x=None, max_change=None): ptq_weights = torch_tensor_to_numpy(list(ptq_model.parameters())) @@ -194,7 +196,9 @@ def get_gptq_config(self): optimizer_rest=torch.optim.Adam([torch.Tensor([])], lr=0), loss=multiple_tensors_mse_loss, train_bias=False, rounding_type=self.rounding_type, gradual_activation_quantization_config=gradual_act_cfg, - gptq_quantizer_params_override=self.override_params) + gptq_quantizer_params_override=self.override_params, + regularization_factor=self.reg_factor, + hessian_weights_config=self.hessian_cfg) def gptq_compare(self, ptq_model, gptq_model, input_x=None): ptq_out = torch_tensor_to_numpy(ptq_model(input_x)) diff --git a/tests_pytest/keras/gptq/test_gradual_act_quantization.py b/tests_pytest/keras/gptq/test_gradual_act_quantization.py index 9eceff8bc..cab0a78c7 100644 --- a/tests_pytest/keras/gptq/test_gradual_act_quantization.py +++ b/tests_pytest/keras/gptq/test_gradual_act_quantization.py @@ -94,7 +94,8 @@ def test_factory_linear_common_case(self, x): def _run_factory_test(self, qdrop_cfg, get_grad_steps_fn): # Mocks are used to just pass anything - gptq_cfg = GradientPTQConfig(n_epochs=5, optimizer=Mock(), loss=Mock(), + gptq_cfg = GradientPTQConfig(n_epochs=5, optimizer=Mock(), optimizer_rest=Mock(), loss=Mock(), + hessian_weights_config=None, train_bias=False, regularization_factor=1, gradual_activation_quantization_config=qdrop_cfg) factory = get_gradual_activation_quantizer_wrapper_factory(gptq_cfg, get_grad_steps_fn, KerasLinearAnnealingScheduler) quantizer = Quantizer() diff --git a/tests_pytest/pytorch/gptq/test_gradual_act_quantization.py b/tests_pytest/pytorch/gptq/test_gradual_act_quantization.py index 08c440d6c..02173a7ec 100644 --- a/tests_pytest/pytorch/gptq/test_gradual_act_quantization.py +++ b/tests_pytest/pytorch/gptq/test_gradual_act_quantization.py @@ -92,7 +92,8 @@ def test_factory_linear_common_case(self, x): def _run_factory_test(self, qdrop_cfg, get_grad_steps_fn): # Mocks are used to just pass anything - gptq_cfg = GradientPTQConfig(n_epochs=5, optimizer=Mock(), loss=Mock(), + gptq_cfg = GradientPTQConfig(n_epochs=5, optimizer=Mock(), loss=Mock(), optimizer_rest=Mock(), + hessian_weights_config=None, train_bias=False, regularization_factor=1, gradual_activation_quantization_config=qdrop_cfg) factory = get_gradual_activation_quantizer_wrapper_factory(gptq_cfg, get_grad_steps_fn, PytorchLinearAnnealingScheduler) quantizer = Quantizer() From 95fc53254508ace11b0bd75a8f8d19c88c82b6e6 Mon Sep 17 00:00:00 2001 From: Chizkiyahu Raful <37312901+Chizkiyahu@users.noreply.github.com> Date: Wed, 13 Nov 2024 09:27:24 +0200 Subject: [PATCH 5/8] torch 2.5 and python 3.12 support (#1264) Add workflows to run tests with Pytorch 2.5 to officially support torch 2.5. Extend current versions tests with missing Python 3.12. Update actions dependencies version unittest - update deprecated self.assertEquals to self.assertEqual --- .github/workflows/link_checker.yml | 4 ++-- .github/workflows/nightly.yml | 4 ++-- .github/workflows/publish_release.yml | 4 ++-- .../run_keras_sony_custom_layers.yml | 4 ++-- .github/workflows/run_keras_tests.yml | 4 ++-- .github/workflows/run_pytorch_tests.yml | 4 ++-- .../run_tests_python310_pytorch25.yml | 19 +++++++++++++++++++ .../run_tests_python311_pytorch25.yml | 19 +++++++++++++++++++ .../run_tests_python312_pytorch22.yml | 19 +++++++++++++++++++ .../run_tests_python312_pytorch23.yml | 19 +++++++++++++++++++ .../run_tests_python312_pytorch24.yml | 19 +++++++++++++++++++ .../run_tests_python312_pytorch25.yml | 19 +++++++++++++++++++ .../run_tests_python39_pytorch25.yml | 19 +++++++++++++++++++ .../workflows/run_tests_suite_coverage.yml | 4 ++-- .github/workflows/stale.yml | 2 +- .github/workflows/test_whl_install.yml | 4 ++-- .github/workflows/tests_common.yml | 4 ++-- README.md | 17 ++++++++++------- .../test_hessian_info_calculator.py | 2 +- ...est_activation_quantization_holder_gptq.py | 6 +++--- 20 files changed, 166 insertions(+), 30 deletions(-) create mode 100644 .github/workflows/run_tests_python310_pytorch25.yml create mode 100644 .github/workflows/run_tests_python311_pytorch25.yml create mode 100644 .github/workflows/run_tests_python312_pytorch22.yml create mode 100644 .github/workflows/run_tests_python312_pytorch23.yml create mode 100644 .github/workflows/run_tests_python312_pytorch24.yml create mode 100644 .github/workflows/run_tests_python312_pytorch25.yml create mode 100644 .github/workflows/run_tests_python39_pytorch25.yml diff --git a/.github/workflows/link_checker.yml b/.github/workflows/link_checker.yml index 4ef2c1435..cfe681591 100644 --- a/.github/workflows/link_checker.yml +++ b/.github/workflows/link_checker.yml @@ -14,9 +14,9 @@ jobs: link-checker: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: 3.10.* - name: Install dependencies diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml index d0a31d5d5..6311eb887 100644 --- a/.github/workflows/nightly.yml +++ b/.github/workflows/nightly.yml @@ -9,9 +9,9 @@ jobs: if: github.repository == 'sony/model_optimization' # Don't do this in forks runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: 3.8 diff --git a/.github/workflows/publish_release.yml b/.github/workflows/publish_release.yml index 1ad00ed48..632aeaefc 100644 --- a/.github/workflows/publish_release.yml +++ b/.github/workflows/publish_release.yml @@ -6,9 +6,9 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: 3.8 - name: Install dependencies diff --git a/.github/workflows/run_keras_sony_custom_layers.yml b/.github/workflows/run_keras_sony_custom_layers.yml index 8047c70fb..92c0a6d7b 100644 --- a/.github/workflows/run_keras_sony_custom_layers.yml +++ b/.github/workflows/run_keras_sony_custom_layers.yml @@ -15,9 +15,9 @@ jobs: run-tensorflow-tests: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install dependencies diff --git a/.github/workflows/run_keras_tests.yml b/.github/workflows/run_keras_tests.yml index fd4498139..073a72bb3 100644 --- a/.github/workflows/run_keras_tests.yml +++ b/.github/workflows/run_keras_tests.yml @@ -14,9 +14,9 @@ jobs: run-tensorflow-tests: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: ${{ inputs.python-version }} - name: Install dependencies diff --git a/.github/workflows/run_pytorch_tests.yml b/.github/workflows/run_pytorch_tests.yml index a62572b6c..f976d2d31 100644 --- a/.github/workflows/run_pytorch_tests.yml +++ b/.github/workflows/run_pytorch_tests.yml @@ -14,9 +14,9 @@ jobs: run-pytorch-tests: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: ${{ inputs.python-version }} - name: Install dependencies diff --git a/.github/workflows/run_tests_python310_pytorch25.yml b/.github/workflows/run_tests_python310_pytorch25.yml new file mode 100644 index 000000000..31e63c5e8 --- /dev/null +++ b/.github/workflows/run_tests_python310_pytorch25.yml @@ -0,0 +1,19 @@ +name: Python 3.10, Pytorch 2.5 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.10" + torch-version: "2.5.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python311_pytorch25.yml b/.github/workflows/run_tests_python311_pytorch25.yml new file mode 100644 index 000000000..72dd479e8 --- /dev/null +++ b/.github/workflows/run_tests_python311_pytorch25.yml @@ -0,0 +1,19 @@ +name: Python 3.11, Pytorch 2.5 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.11" + torch-version: "2.5.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python312_pytorch22.yml b/.github/workflows/run_tests_python312_pytorch22.yml new file mode 100644 index 000000000..e40fb32b5 --- /dev/null +++ b/.github/workflows/run_tests_python312_pytorch22.yml @@ -0,0 +1,19 @@ +name: Python 3.12, Pytorch 2.2 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.12" + torch-version: "2.2.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python312_pytorch23.yml b/.github/workflows/run_tests_python312_pytorch23.yml new file mode 100644 index 000000000..c261924d0 --- /dev/null +++ b/.github/workflows/run_tests_python312_pytorch23.yml @@ -0,0 +1,19 @@ +name: Python 3.12, Pytorch 2.3 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.12" + torch-version: "2.3.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python312_pytorch24.yml b/.github/workflows/run_tests_python312_pytorch24.yml new file mode 100644 index 000000000..abf35ba87 --- /dev/null +++ b/.github/workflows/run_tests_python312_pytorch24.yml @@ -0,0 +1,19 @@ +name: Python 3.12, Pytorch 2.4 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.12" + torch-version: "2.4.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python312_pytorch25.yml b/.github/workflows/run_tests_python312_pytorch25.yml new file mode 100644 index 000000000..9c75338ce --- /dev/null +++ b/.github/workflows/run_tests_python312_pytorch25.yml @@ -0,0 +1,19 @@ +name: Python 3.2, Pytorch 2.5 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.12" + torch-version: "2.5.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_python39_pytorch25.yml b/.github/workflows/run_tests_python39_pytorch25.yml new file mode 100644 index 000000000..78f9a6bd2 --- /dev/null +++ b/.github/workflows/run_tests_python39_pytorch25.yml @@ -0,0 +1,19 @@ +name: Python 3.9, Pytorch 2.5 +on: + workflow_dispatch: # Allow manual triggers + schedule: + - cron: 0 0 * * * + pull_request: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main' }} + +jobs: + run-tests: + uses: ./.github/workflows/run_pytorch_tests.yml + with: + python-version: "3.9" + torch-version: "2.5.*" \ No newline at end of file diff --git a/.github/workflows/run_tests_suite_coverage.yml b/.github/workflows/run_tests_suite_coverage.yml index ef7e6e7ba..287854741 100644 --- a/.github/workflows/run_tests_suite_coverage.yml +++ b/.github/workflows/run_tests_suite_coverage.yml @@ -18,9 +18,9 @@ jobs: env: COVERAGE_THRESHOLD: 98 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: '3.10' diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 595ca175a..d3cf32eaf 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -18,7 +18,7 @@ jobs: pull-requests: write steps: - - uses: actions/stale@v3 + - uses: actions/stale@v9 with: repo-token: ${{ secrets.GITHUB_TOKEN }} stale-issue-message: 'Stale issue message' diff --git a/.github/workflows/test_whl_install.yml b/.github/workflows/test_whl_install.yml index 3df651911..32ae5a736 100644 --- a/.github/workflows/test_whl_install.yml +++ b/.github/workflows/test_whl_install.yml @@ -11,11 +11,11 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: ${{ inputs.python_version }} - name: Install dependencies diff --git a/.github/workflows/tests_common.yml b/.github/workflows/tests_common.yml index 0a716da04..89090d949 100644 --- a/.github/workflows/tests_common.yml +++ b/.github/workflows/tests_common.yml @@ -15,9 +15,9 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Install Python 3 - uses: actions/setup-python@v1 + uses: actions/setup-python@v5 with: python-version: 3.11 - name: Install dependencies diff --git a/README.md b/README.md index 8c3479381..6b898678e 100644 --- a/README.md +++ b/README.md @@ -51,16 +51,19 @@ for hands-on learning. For example: Currently, MCT is being tested on various Python, Pytorch and TensorFlow versions: -| | PyTorch 2.1 | PyTorch 2.2 | PyTorch 2.3 | PyTorch 2.4 | -|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Python 3.9 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml) | -| Python 3.10 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml) | -| Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml) | +| | PyTorch 2.1 | PyTorch 2.2 | PyTorch 2.3 | PyTorch 2.4 | PyTorch 2.5 | +|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Python 3.9 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch25.yml) | +| Python 3.10 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml) | +| Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml) | +| Python 3.12 | | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml) | -| | TensorFlow 2.12 | TensorFlow 2.13 | TensorFlow 2.14 | TensorFlow 2.15 | -|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + + +| | TensorFlow 2.12 | TensorFlow 2.13 | TensorFlow 2.14 | TensorFlow 2.15 | +|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Python 3.9 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras212.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras212.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras213.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras213.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras214.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras214.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras215.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_keras215.yml) | | Python 3.10 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras212.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras212.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras213.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras213.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras214.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras214.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras215.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras215.yml) | | Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras212.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras212.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras213.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras213.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras214.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras214.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras215.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras215.yml) | diff --git a/tests/keras_tests/function_tests/test_hessian_info_calculator.py b/tests/keras_tests/function_tests/test_hessian_info_calculator.py index 62202a057..6ab6a58fb 100644 --- a/tests/keras_tests/function_tests/test_hessian_info_calculator.py +++ b/tests/keras_tests/function_tests/test_hessian_info_calculator.py @@ -127,7 +127,7 @@ def _test_hessian_scores(self, hessian_info, target_nodes, repr_dataset, granula info = hessian_info.fetch_hessian(request) self.assertTrue(isinstance(info, dict)) - self.assertEquals(set(info.keys()), {n.name for n in target_nodes}) + self.assertEqual(set(info.keys()), {n.name for n in target_nodes}) for target_node in target_nodes: node_score = info[target_node.name] kernel_attr_name = [w for w in target_node.weights if KERNEL in w] diff --git a/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py b/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py index 0c1e8ce8f..a475f2921 100644 --- a/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py +++ b/tests/pytorch_tests/function_tests/test_activation_quantization_holder_gptq.py @@ -75,10 +75,10 @@ def test_adding_holder_instead_quantize_wrapper(self): activation_quantization_holders_in_model = self._get_holders_with_validation(gptq_model, exp_n_holders=3) for a in activation_quantization_holders_in_model: self.assertTrue(isinstance(a.activation_holder_quantizer, STESymmetricActivationTrainableQuantizer)) - self.assertEquals(a.activation_holder_quantizer.identifier, TrainingMethod.STE) + self.assertEqual(a.activation_holder_quantizer.identifier, TrainingMethod.STE) # activation quantization params for gptq should be frozen (non-learnable) self.assertTrue(a.activation_holder_quantizer.freeze_quant_params is True) - self.assertEquals(a.activation_holder_quantizer.get_trainable_variables(VariableGroup.QPARAMS), []) + self.assertEqual(a.activation_holder_quantizer.get_trainable_variables(VariableGroup.QPARAMS), []) for name, module in gptq_model.named_modules(): if isinstance(module, PytorchQuantizationWrapper): @@ -130,7 +130,7 @@ def test_adding_holder_with_gradual_act_quantization(self): quantizer = a.activation_holder_quantizer.quantizer self.assertTrue(isinstance(quantizer, STESymmetricActivationTrainableQuantizer)) self.assertTrue(quantizer.freeze_quant_params is True) - self.assertEquals(quantizer.get_trainable_variables(VariableGroup.QPARAMS), []) + self.assertEqual(quantizer.get_trainable_variables(VariableGroup.QPARAMS), []) def _get_holders_with_validation(self, gptq_model, exp_n_holders): last_module = list(gptq_model.named_modules())[-1][1] From 0bcf7ab0c6a7524531921d0fd87832910e45bac3 Mon Sep 17 00:00:00 2001 From: Amir Servi <131249114+ServiAmirPM@users.noreply.github.com> Date: Thu, 14 Nov 2024 14:29:22 +0200 Subject: [PATCH 6/8] Updated readme file with better design, less text (#1265) - Re-structure - changed the way the document is structured (table of content), including changing titles names - Redesigned the header - with better look and feel, with as much information in the header. - Removed tutorials from the tutorials section in the readme - will add some back after re-structuring the tutorials. - Added visuals to the page for better look and feel, and to better explain the product. - improved the quantization flow table and added 4 tutorials to it. - Re-ordered the Resources section, making it clear that API is per quantization method - Hidden (collapsed) the supported version table. users can open it on demand - Results - removed text, still requires updating the numbers, add mixed precision and the update graph - Troubleshooting and community - added community to this section. - Updated the license section with text and a badge --- README.md | 160 +++++++++++++++++++---------- docsrc/images/Classification.png | Bin 0 -> 39642 bytes docsrc/images/ObjDet.png | Bin 0 -> 51804 bytes docsrc/images/PoseEst.png | Bin 0 -> 73996 bytes docsrc/images/SemSeg.png | Bin 0 -> 77214 bytes docsrc/images/colab-badge.svg | 1 + docsrc/images/mctDiagramFlow.svg | 1 + docsrc/images/mctDiagram_clean.svg | 1 + docsrc/images/mctFlow.png | Bin 0 -> 187647 bytes 9 files changed, 106 insertions(+), 57 deletions(-) create mode 100644 docsrc/images/Classification.png create mode 100644 docsrc/images/ObjDet.png create mode 100644 docsrc/images/PoseEst.png create mode 100644 docsrc/images/SemSeg.png create mode 100644 docsrc/images/colab-badge.svg create mode 100644 docsrc/images/mctDiagramFlow.svg create mode 100644 docsrc/images/mctDiagram_clean.svg create mode 100644 docsrc/images/mctFlow.png diff --git a/README.md b/README.md index 6b898678e..a5065aac1 100644 --- a/README.md +++ b/README.md @@ -1,66 +1,101 @@ -# Model Compression Toolkit (MCT) - -Model Compression Toolkit (MCT) is an open-source project for neural network model optimization under efficient, constrained hardware. +
-This project provides researchers, developers, and engineers tools for optimizing and deploying state-of-the-art neural networks on efficient hardware. - -Specifically, this project aims to apply quantization to compress neural networks. +# Model Compression Toolkit (MCT) - +**Open-source project for neural network model optimization, providing researchers, developers, and engineers with advanced quantization and compression tools for deploying state-of-the-art neural networks on efficient, constrained hardware.** +______________________________________________________________________ + +
+
+

+ Getting Started • + Tutorials • + High level features and techniques • + Resources • + Community • + License +

+

+ + + + + + +

+
+ +__________________________________________________________________________________________________________ -MCT is developed by researchers and engineers working at Sony Semiconductor Israel. +## Getting Started +### Quick Installation +Pip install the model compression toolkit package in a Python>=3.9 environment with PyTorch>=2.1 or Tensorflow>=2.12. +``` +pip install model-compression-toolkit +``` +For installing the nightly version or installing from source, refer to the [installation guide](https://github.com/sony/model_optimization/blob/main/INSTALLATION.md). +**Important note**: In order to use MCT, you’ll need to provide a floating point .pt or .keras model as an input. +### Tutorials and Examples -## Table of Contents +Our [tutorials](https://github.com/sony/model_optimization/blob/main/tutorials/README.md) section will walk you through the basics of the MCT tool, covering various compression techniques for both Keras and PyTorch models. +Access interactive notebooks for hands-on learning with popular models/tasks or move on to [Resources](#resources) section. -- [Getting Started](https://github.com/sony/model_optimization?tab=readme-ov-file#getting-started) -- [Supported features](https://github.com/sony/model_optimization?tab=readme-ov-file#supported-features) -- [Results](https://github.com/sony/model_optimization?tab=readme-ov-file#results) -- [Troubleshooting](https://github.com/sony/model_optimization?tab=readme-ov-file#trouble-shooting) -- [Contributions](https://github.com/sony/model_optimization?tab=readme-ov-file#contributions) -- [License](https://github.com/sony/model_optimization?tab=readme-ov-file#license) +### Supported Quantization Methods +MCT supports various quantization methods as appears below. +
+

+ Quantization Method | Complexity | Computational Cost | Tutorial +-------------------- | -----------|--------------------|--------- +PTQ (Post Training Quantization) | Low | Low (~1-10 CPU minutes) | +GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | +QAT (Quantization Aware Training) | High | High (~12-36 GPU hours) | -## Getting Started +

+
-This section provides an installation and a quick starting guide. +For each flow, **Quantization core** utilizes various algorithms and hyper-parameters for optimal [hardware-aware](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/target_platform_capabilities/README.md) quantization results. +For further details, please see [Supported features and algorithms](#supported-features). -### Installation +Required input: +- Floating point model - 32bit model in either .pt or .keras format +- Representative dataset - can be either provided by the user, or generated utilizing the [Data Generation](#data-generation-) capability -To install the latest stable release of MCT, run the following command: -``` -pip install model-compression-toolkit -``` +
+

-For installing the nightly version or installing from source, refer to the [installation guide](https://github.com/sony/model_optimization/blob/main/INSTALLATION.md). + +

+
+### Resources +* [User Guide](https://sony.github.io/model_optimization/docs/index.html) contains detailed information about MCT and guides you from installation through optimizing models for your edge AI applications. -### Quick start & tutorials +* MCT's [API Docs](https://sony.github.io/model_optimization/docs/api/api_docs/) is seperated per quantization methods: -Explore the Model Compression Toolkit (MCT) through our tutorials, -covering compression techniques for Keras and PyTorch models. Access interactive [notebooks](https://github.com/sony/model_optimization/blob/main/tutorials/README.md) -for hands-on learning. For example: -* [Keras MobileNetV2 post training quantization](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb) -* [Post training quantization with PyTorch](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb) -* [Data Generation for ResNet18 with PyTorch](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb). + * [Post-training quantization](https://sony.github.io/model_optimization/docs/api/api_docs/index.html#ptq) | PTQ API docs + * [Gradient-based post-training quantization](https://sony.github.io/model_optimization/docs/api/api_docs/index.html#gptq) | GPTQ API docs + * [Quantization-aware training](https://sony.github.io/model_optimization/docs/api/api_docs/index.html#qat) | QAT API docs + +* [Debug](https://sony.github.io/model_optimization/docs/guidelines/visualization.html) – modify optimization process or generate explainable report + +* [Release notes](https://github.com/sony/model_optimization/releases) ### Supported Versions Currently, MCT is being tested on various Python, Pytorch and TensorFlow versions: +
+ Supported Versions Table - -| | PyTorch 2.1 | PyTorch 2.2 | PyTorch 2.3 | PyTorch 2.4 | PyTorch 2.5 | -|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Python 3.9 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml) | [![Run 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Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml) | -| Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch21.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml) | -| Python 3.12 | | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml) | - - - - +| | PyTorch 2.2 | PyTorch 2.3 | PyTorch 2.4 | PyTorch 2.5 | +|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Python 3.9 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python39_pytorch25.yml) | +| Python 3.10 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_pytorch25.yml) | +| Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_pytorch25.yml) | +| Python 3.12 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch22.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch23.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch24.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python312_pytorch25.yml) | | | TensorFlow 2.12 | TensorFlow 2.13 | TensorFlow 2.14 | TensorFlow 2.15 | |-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| @@ -68,6 +103,7 @@ Currently, MCT is being tested on various Python, Pytorch and TensorFlow version | Python 3.10 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras212.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras212.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras213.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras213.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras214.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras214.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras215.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python310_keras215.yml) | | Python 3.11 | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras212.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras212.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras213.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras213.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras214.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras214.yml) | [![Run Tests](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras215.yml/badge.svg)](https://github.com/sony/model_optimization/actions/workflows/run_tests_python311_keras215.yml) | +
## Supported Features MCT offers a range of powerful features to optimize neural network models for efficient deployment. These supported features include: @@ -85,9 +121,9 @@ MCT supports different quantization methods: | Quantization Method | Complexity | Computational Cost | |-----------------------------------------------|------------|-----------------------------| -| PTQ | Low | Low (order of minutes) | -| GPTQ (parameters fine-tuning using gradients) | Mild | Mild (order of 2-3 hours) | -| QAT | High | High (order of 12-36 hours) | +| PTQ | Low | Low (~CPU minutes) | +| GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | +| QAT | High | High (~12-36 GPU hours) | In addition, MCT supports different quantization schemes for quantizing weights and activations: @@ -133,15 +169,14 @@ For more details, we highly recommend visiting our project website where experim ## Results -### Keras -Graph of [MobileNetV2](https://keras.io/api/applications/mobilenet/) accuracy on ImageNet vs average bit-width of weights, using -single-precision quantization, mixed-precision quantization, and mixed-precision quantization with GPTQ. - - -For more results, please see [1] +

+ + + + -### Pytorch + ### Pytorch We quantized classification networks from the torchvision library. In the following table we present the ImageNet validation results for these models: @@ -151,6 +186,14 @@ In the following table we present the ImageNet validation results for these mode | ResNet-18 [3] | 69.86 | 69.63 |69.53| | SqueezeNet 1.1 [3] | 58.128 | 57.678 || +### Keras +MCT can quantize an existing 32-bit floating-point model to an 8-bit fixed-point (or less) model without compromising accuracy. +Below is a graph of [MobileNetV2](https://keras.io/api/applications/mobilenet/) accuracy on ImageNet vs average bit-width of weights (X-axis), using +single-precision quantization, mixed-precision quantization, and mixed-precision quantization with GPTQ. + + + +For more results, please see [1] #### Pruning Results @@ -161,23 +204,26 @@ Results for applying pruning to reduce the parameters of the following models by | ResNet50 [2] | 75.1 | 72.4 | | DenseNet121 [3] | 74.44 | 71.71 | +## Troubleshooting and Community -## Trouble Shooting +If you encountered large accuracy degradation with MCT, check out the [Quantization Troubleshooting](https://github.com/sony/model_optimization/tree/main/quantization_troubleshooting.md) +for common pitfalls and some tools to improve quantized model's accuracy. -If the accuracy degradation of the quantized model is too large for your application, check out the [Quantization Troubleshooting](https://github.com/sony/model_optimization/tree/main/quantization_troubleshooting.md) -for common pitfalls and some tools to improve quantization accuracy. +Check out the [FAQ](https://github.com/sony/model_optimization/tree/main/FAQ.md) for common issues. -Check out the [FAQ](https://github.com/sony/model_optimization/tree/main/FAQ.md) for common issues. +You are welcome to ask questions and get support on our [issues section](https://github.com/sony/model_optimization/issues) and manage community discussions under [discussions section](https://github.com/sony/model_optimization/discussions). ## Contributions MCT aims at keeping a more up-to-date fork and welcomes contributions from anyone. -*You will find more information about contributions in the [Contribution guide](https://github.com/sony/model_optimization/blob/main/CONTRIBUTING.md). +*Checkout our [Contribution guide](https://github.com/sony/model_optimization/blob/main/CONTRIBUTING.md) for more details. ## License -[Apache License 2.0](https://github.com/sony/model_optimization/blob/main/LICENSE.md). +MCT is licensed under Apache License Version 2.0. 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zO_fXaxgL#km+@#nJ`%mVNu-LnsDQf9}E3zgV@itG?~@*xLK8E-;;Ir zvdSurvuyhFkcgYA%1Ev=o8v`^oruU!5j8*_9v*iDLc`XUo1Q0xs^IxlN>AJtawZKP|k+gZ}tHIay zT$OIz00+DQUX6qLC{=Weyh)7wVrA_PD`yz%_KZ}r=f9vqSkpLVxA`;m&BsvFo!Y&nJmtu z>F3Ws-<+r`<-DqJYQQe!h{msden#eRb6cCW(Q6%3J+QEiA(4>F5|2LoGGt+4txyIO z^WLi_=V^>2qI=R46Basv$@rv?1lAaGt(fcGD3rp|5_8m)xCL--yR`v1a!Ln1+nIUP z+~7T3(8H_t{G-R%`t~9tFK65W>KoyT&inHZBY~*TZ+f!R(s&m^Q?Fe;1056XDouFw Fe*pCGMWp}$ literal 0 HcmV?d00001 From 95524fc0f757b0e895f8ec522594a57ffd3bba94 Mon Sep 17 00:00:00 2001 From: Amir Servi <131249114+ServiAmirPM@users.noreply.github.com> Date: Thu, 14 Nov 2024 20:20:09 +0200 Subject: [PATCH 7/8] Design header (#1268) replaced the header from text to designed header (with the same text) When clicking on the header - it will open the MCT github.io website --- README.md | 9 +++++---- docsrc/images/mctHeader-cropped.svg | 1 + 2 files changed, 6 insertions(+), 4 deletions(-) create mode 100644 docsrc/images/mctHeader-cropped.svg diff --git a/README.md b/README.md index a5065aac1..0def30d30 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@

- -# Model Compression Toolkit (MCT) - -**Open-source project for neural network model optimization, providing researchers, developers, and engineers with advanced quantization and compression tools for deploying state-of-the-art neural networks on efficient, constrained hardware.** +

+ + +

+ ______________________________________________________________________
diff --git a/docsrc/images/mctHeader-cropped.svg b/docsrc/images/mctHeader-cropped.svg new file mode 100644 index 000000000..166c9d493 --- /dev/null +++ b/docsrc/images/mctHeader-cropped.svg @@ -0,0 +1 @@ + \ No newline at end of file From c72cd0fcdeb7e57699993b170ff08471e9075d63 Mon Sep 17 00:00:00 2001 From: Amir Servi <131249114+ServiAmirPM@users.noreply.github.com> Date: Mon, 18 Nov 2024 14:23:04 +0200 Subject: [PATCH 8/8] Update README.md (#1270) Round 2 of updaing readme. fixed, designed and repositioned "Supported Feature" section Added dividers between features in features section added API column to the quantization methods table added links to relevant notebooks in relevant places made images of tasks (under results) a bit smaller Fixed Headers issue (some headers were in wrong level.) added more capabilities under debug feature - Xquant & network editor (including links to tutorial centered all section titles (makes the page more clean in my opinion) fixed according to review: -- - added tutorials to qadvanced quatntization & Quantization parameter search. -- - Updated EPTQ paper title and added E. Cohen as contributer -- - changed type (on the how to -> on how to) --- README.md | 143 ++++++++++++++++++++++-------------------------------- 1 file changed, 59 insertions(+), 84 deletions(-) diff --git a/README.md b/README.md index 0def30d30..e83541610 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ ______________________________________________________________________

Getting Started • Tutorials • - High level features and techniques • + High level features and techniques • Resources • Community • License @@ -28,7 +28,7 @@ ______________________________________________________________________ __________________________________________________________________________________________________________ -## Getting Started +##

Getting Started
### Quick Installation Pip install the model compression toolkit package in a Python>=3.9 environment with PyTorch>=2.1 or Tensorflow>=2.12. ``` @@ -48,17 +48,17 @@ MCT supports various quantization methods as appears below.

- Quantization Method | Complexity | Computational Cost | Tutorial --------------------- | -----------|--------------------|--------- -PTQ (Post Training Quantization) | Low | Low (~1-10 CPU minutes) | -GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | -QAT (Quantization Aware Training) | High | High (~12-36 GPU hours) | + Quantization Method | Complexity | Computational Cost | API | Tutorial +-------------------- | -----------|--------------------|---------|-------- +PTQ (Post Training Quantization) | Low | Low (~1-10 CPU minutes) | [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_post_training_quantization.html) / [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_post_training_quantization.html) | +GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_gradient_post_training_quantization.html) / [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_gradient_post_training_quantization.html) | +QAT (Quantization Aware Training) | High | High (~12-36 GPU hours) | [QAT API](https://sony.github.io/model_optimization/docs/api/api_docs/index.html#qat) |

For each flow, **Quantization core** utilizes various algorithms and hyper-parameters for optimal [hardware-aware](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/target_platform_capabilities/README.md) quantization results. -For further details, please see [Supported features and algorithms](#supported-features). +For further details, please see [Supported features and algorithms](#high-level-features-and-techniques). Required input: - Floating point model - 32bit model in either .pt or .keras format @@ -71,7 +71,45 @@ Required input:

-### Resources +##
High level features and techniques
+ +MCT offers a range of powerful features to optimize models for efficient edge deployment. These supported features include: + +### Quantization Core Features + +🏆 **Mixed-precision search** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb). Assigning optimal quantization bit-width per layer (for weights/activations) + +📈 **Graph optimizations**. +Transforming the model to be best fitted for quantization process. + +🔎 **Quantization parameter search** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_threshold_search.ipynb). Minimizing expected quantization-noise during thresholds search using methods such as MSE, No-Clipping and MAE. + +🧮 **Advanced quantization algorithms** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_z_score_threshold.ipynb). Enhancing quantization performance for advanced cases is available with some algorithms that can be applied, such as Shift negative correction, Outliers filtering and clustering. +__________________________________________________________________________________________________________ +### Hardware-aware optimization + +🎯 **TPC (Target Platform Capabilities)**. Describes the target hardware’s constrains, for which the model optimization is targeted. See [TPC Readme](./model_compression_toolkit/target_platform_capabilities/README.md) for more information. +__________________________________________________________________________________________________________ +### Data-free quantization (Data Generation) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb) +Generates synthetic images based on the statistics stored in the model's batch normalization layers, according to your specific needs, for when image data isn’t available. See [Data Generation Library](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/data_generation/README.md) for more. +__________________________________________________________________________________________________________ +### Structured Pruning [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_pruning_mnist.ipynb) +Reduces model size/complexity and ensures better channels utilization by removing redundant input channels from layers and reconstruction of layer weights. Read more ([Pytorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_pruning_experimental.html) / [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_pruning_experimental.html)). +__________________________________________________________________________________________________________ +### **Debugging and Visualization** +**🎛️ Network Editor (Modify Quantization Configurations)** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_network_editor.ipynb). +Modify your model's quantization configuration for specific layers or apply a custom edit rule (e.g adjust layer's bit-width) using MCT’s network editor + +**🖥️ Visualization**. Observe useful information for troubleshooting the quantized model's performance using TensorBoard. [Read more](https://sony.github.io/model_optimization/docs/guidelines/visualization.html). + +**🔑 XQuant (Explainable Quantization)** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb). Get valuable insights regarding the quality and success of the quantization process of your model. The report includes histograms and similarity metrics between the original float model and the quantized model in key points of the model. The report can be visualized using TensorBoard. +__________________________________________________________________________________________________________ +### Enhanced Post-Training Quantization (EPTQ) +As part of the GPTQ capability, we provide an advanced optimization algorithm called EPTQ. +The specifications of the algorithm are detailed in the paper: _"**EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization**"_ [4]. +More details on how to use EPTQ via MCT can be found in the [GPTQ guidelines](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/gptq/README.md). + +##
Resources
* [User Guide](https://sony.github.io/model_optimization/docs/index.html) contains detailed information about MCT and guides you from installation through optimizing models for your edge AI applications. * MCT's [API Docs](https://sony.github.io/model_optimization/docs/api/api_docs/) is seperated per quantization methods: @@ -85,7 +123,7 @@ Required input: * [Release notes](https://github.com/sony/model_optimization/releases) -### Supported Versions +##
Supported Versions
Currently, MCT is being tested on various Python, Pytorch and TensorFlow versions:
@@ -106,76 +144,13 @@ Currently, MCT is being tested on various Python, Pytorch and TensorFlow version
-## Supported Features -MCT offers a range of powerful features to optimize neural network models for efficient deployment. These supported features include: - -### Data Generation [*](https://github.com/sony/model_optimization?tab=readme-ov-file#experimental-features) -MCT provides tools for generating synthetic images based on the statistics stored in a model's batch normalization layers. These generated images are valuable for various compression tasks where image data is required, such as quantization and pruning. -You can customize data generation configurations to suit your specific needs. [Go to the Data Generation page.](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/data_generation/README.md) - -### Quantization -MCT supports different quantization methods: -* Post-training quantization (PTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_post_training_quantization.html), [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_post_training_quantization.html) -* Gradient-based post-training quantization (GPTQ): [Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_gradient_post_training_quantization.html), [PyTorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_gradient_post_training_quantization.html) -* Quantization-aware training (QAT) [*](https://github.com/sony/model_optimization?tab=readme-ov-file#experimental-features) - - -| Quantization Method | Complexity | Computational Cost | -|-----------------------------------------------|------------|-----------------------------| -| PTQ | Low | Low (~CPU minutes) | -| GPTQ (parameters fine-tuning using gradients) | Moderate | Moderate (~1-3 GPU hours) | -| QAT | High | High (~12-36 GPU hours) | - - -In addition, MCT supports different quantization schemes for quantizing weights and activations: - -* Power-Of-Two (hardware-friendly quantization [1]) -* Symmetric -* Uniform - -Main features: -* Graph optimizations: Transforming the model to an equivalent (yet, more efficient) model (for example, batch-normalization layer folding to its preceding linear layer). -* Quantization parameter search: Different methods can be used to minimize the expected added quantization-noise during thresholds search (by default, we use Mean-Square-Error, but other metrics can be used such as No-Clipping, Mean-Average-Error, and more). -* Advanced quantization algorithms: To prevent a performance degradation some algorithms are applied such as: - * Shift negative correction: Symmetric activation quantization can hurt the model's performance when some layers output both negative and positive activations, but their range is asymmetric. For more details please visit [1]. - * Outliers filtering: Computing z-score for activation statistics to detect and remove outliers. -* Clustering: Using non-uniform quantization grid to quantize the weights and activations to match their distributions.[*](https://github.com/sony/model_optimization?tab=readme-ov-file#experimental-features) -* Mixed-precision search: Assigning quantization bit-width per layer (for weights/activations), based on the layer's sensitivity to different bit-widths. -* Visualization: You can use TensorBoard to observe useful information for troubleshooting the quantized model's performance (for example, the model in different phases of the quantization, collected statistics, similarity between layers of the float and quantized model and bit-width configuration for mixed-precision quantization). For more details, please read the [visualization documentation](https://sony.github.io/model_optimization/docs/guidelines/visualization.html). -* Target Platform Capabilities: The Target Platform Capabilities (TPC) describes the target platform (an edge device with dedicated hardware). For more details, please read the [TPC README](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/target_platform_capabilities/README.md). - -### Enhanced Post-Training Quantization (EPTQ) -As part of the GPTQ we provide an advanced optimization algorithm called EPTQ. - -The specifications of the algorithm are detailed in the paper: _"**EPTQ: Enhanced Post-Training Quantization via Label-Free Hessian**"_ [4]. - -More details on the how to use EPTQ via MCT can be found in the [EPTQ guidelines](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/gptq/README.md). - - -### Structured Pruning [*](https://github.com/sony/model_optimization?tab=readme-ov-file#experimental-features) -MCT introduces a structured and hardware-aware model pruning. -This pruning technique is designed to compress models for specific hardware architectures, -taking into account the target platform's Single Instruction, Multiple Data (SIMD) capabilities. -By pruning groups of channels (SIMD groups), our approach not only reduces model size -and complexity, but ensures that better utilization of channels is in line with the SIMD architecture -for a target Resource Utilization of weights memory footprint. -[Keras API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/keras_pruning_experimental.html) -[Pytorch API](https://sony.github.io/model_optimization/docs/api/api_docs/methods/pytorch_pruning_experimental.html) - -#### Experimental features - -Some features are experimental and subject to future changes. - -For more details, we highly recommend visiting our project website where experimental features are mentioned as experimental. - - -## Results +##
Results

- - - - + + + + ### Pytorch We quantized classification networks from the torchvision library. @@ -196,7 +171,7 @@ single-precision quantization, mixed-precision quantization, and mixed-precision For more results, please see [1] -#### Pruning Results +### Pruning Results Results for applying pruning to reduce the parameters of the following models by 50%: @@ -205,7 +180,7 @@ Results for applying pruning to reduce the parameters of the following models by | ResNet50 [2] | 75.1 | 72.4 | | DenseNet121 [3] | 74.44 | 71.71 | -## Troubleshooting and Community +##

Troubleshooting and Community
If you encountered large accuracy degradation with MCT, check out the [Quantization Troubleshooting](https://github.com/sony/model_optimization/tree/main/quantization_troubleshooting.md) for common pitfalls and some tools to improve quantized model's accuracy. @@ -215,18 +190,18 @@ Check out the [FAQ](https://github.com/sony/model_optimization/tree/main/FAQ.md) You are welcome to ask questions and get support on our [issues section](https://github.com/sony/model_optimization/issues) and manage community discussions under [discussions section](https://github.com/sony/model_optimization/discussions). -## Contributions +##
Contributions
MCT aims at keeping a more up-to-date fork and welcomes contributions from anyone. *Checkout our [Contribution guide](https://github.com/sony/model_optimization/blob/main/CONTRIBUTING.md) for more details. -## License +##
License
MCT is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms. -## References +##
References
[1] Habi, H.V., Peretz, R., Cohen, E., Dikstein, L., Dror, O., Diamant, I., Jennings, R.H. and Netzer, A., 2021. [HPTQ: Hardware-Friendly Post Training Quantization. arXiv preprint](https://arxiv.org/abs/2109.09113). @@ -234,4 +209,4 @@ MCT is licensed under Apache License Version 2.0. By contributing to the project [3] [TORCHVISION.MODELS](https://pytorch.org/vision/stable/models.html) -[4] Gordon, O., Habi, H. V., & Netzer, A., 2023. [EPTQ: Enhanced Post-Training Quantization via Label-Free Hessian. arXiv preprint](https://arxiv.org/abs/2309.11531) +[4] Gordon, O., Cohen, E., Habi, H. V., & Netzer, A., 2024. [EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization. arXiv preprint](https://arxiv.org/abs/2309.11531)