mirror of
https://github.com/pytorch/pytorch.git
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45c62a3377
Summary: same function in observer and quantize, consolidated to a single function. Note the definitions were slightly different, I've changed the definition to be maximally inclusive so that the name of the function is more accurate Test Plan: python test/test_public_bindings.py python test/test_quantization.py Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D40709276](https://our.internmc.facebook.com/intern/diff/D40709276) Pull Request resolved: https://github.com/pytorch/pytorch/pull/87520 Approved by: https://github.com/jcaip
145 lines
4.5 KiB
Python
145 lines
4.5 KiB
Python
# flake8: noqa: F403
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from .fake_quantize import * # noqa: F403
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from .fuse_modules import fuse_modules # noqa: F403
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from .fuse_modules import fuse_modules_qat # noqa: F403
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from .fuser_method_mappings import * # noqa: F403
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from .observer import * # noqa: F403
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from .qconfig import * # noqa: F403
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from .qconfig_mapping import * # noqa: F403
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from .quant_type import * # noqa: F403
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from .quantization_mappings import * # type: ignore[no-redef]
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from .quantize import * # noqa: F403
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from .quantize_jit import * # noqa: F403
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from .stubs import * # noqa: F403
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__all__ = [
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"DeQuantStub",
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"FakeQuantize",
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"FakeQuantizeBase",
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"FixedQParamsFakeQuantize",
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"FixedQParamsObserver",
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"FusedMovingAvgObsFakeQuantize",
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"HistogramObserver",
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"MatchAllNode",
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"MinMaxObserver",
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"MovingAverageMinMaxObserver",
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"MovingAveragePerChannelMinMaxObserver",
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"NoopObserver",
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"ObserverBase",
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"Pattern",
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"PerChannelMinMaxObserver",
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"PlaceholderObserver",
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"QConfig",
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"QConfigAny",
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"QConfigDynamic",
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"QConfigMapping",
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"QuantStub",
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"QuantType",
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"QuantWrapper",
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"RecordingObserver",
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"ReuseInputObserver",
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"UniformQuantizationObserverBase",
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"add_observer_",
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"add_quant_dequant",
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"convert",
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"convert_dynamic_jit",
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"convert_jit",
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"default_affine_fixed_qparams_fake_quant",
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"default_affine_fixed_qparams_observer",
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"default_debug_observer",
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"default_dynamic_fake_quant",
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"default_dynamic_quant_observer",
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"default_embedding_fake_quant",
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"default_embedding_fake_quant_4bit",
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"default_eval_fn",
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"default_fake_quant",
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"default_fixed_qparams_range_0to1_fake_quant",
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"default_fixed_qparams_range_0to1_observer",
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"default_fixed_qparams_range_neg1to1_fake_quant",
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"default_fixed_qparams_range_neg1to1_observer",
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"default_float_qparams_observer",
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"default_float_qparams_observer_4bit",
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"default_fused_act_fake_quant",
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"default_fused_per_channel_wt_fake_quant",
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"default_fused_wt_fake_quant",
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"default_histogram_fake_quant",
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"default_histogram_observer",
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"default_observer",
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"default_per_channel_weight_fake_quant",
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"default_per_channel_weight_observer",
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"default_placeholder_observer",
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"default_reuse_input_observer",
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"default_symmetric_fixed_qparams_fake_quant",
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"default_symmetric_fixed_qparams_observer",
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"default_weight_fake_quant",
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"default_weight_observer",
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"disable_fake_quant",
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"disable_observer",
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"enable_fake_quant",
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"enable_observer",
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"fuse_conv_bn",
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"fuse_conv_bn_jit",
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"fuse_conv_bn_relu",
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"fuse_convtranspose_bn",
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"fuse_linear_bn",
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"fuse_modules",
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"fuse_modules_qat",
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"fused_per_channel_wt_fake_quant_range_neg_127_to_127",
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"fused_wt_fake_quant_range_neg_127_to_127",
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"get_combined_dict",
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"get_default_compare_output_module_list",
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"get_default_custom_config_dict",
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"get_default_dynamic_quant_module_mappings",
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"get_default_dynamic_sparse_quant_module_mappings",
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"get_default_float_to_quantized_operator_mappings",
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"get_default_qat_module_mappings",
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"get_default_qat_qconfig",
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"get_default_qat_qconfig_dict",
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"get_default_qat_qconfig_mapping",
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"get_default_qconfig",
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"get_default_qconfig_dict",
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"get_default_qconfig_mapping",
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"get_default_qconfig_propagation_list",
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"get_default_static_quant_module_mappings",
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"get_default_static_quant_reference_module_mappings",
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"get_default_static_sparse_quant_module_mappings",
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"get_dynamic_quant_module_class",
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"get_embedding_qat_module_mappings",
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"get_embedding_static_quant_module_mappings",
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"get_fuser_method",
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"get_fuser_method_new",
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"get_observer_dict",
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"get_observer_state_dict",
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"get_quantized_operator",
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"get_static_quant_module_class",
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"get_unique_devices_",
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"load_observer_state_dict",
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"no_observer_set",
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"per_channel_weight_observer_range_neg_127_to_127",
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"prepare",
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"prepare_dynamic_jit",
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"prepare_jit",
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"prepare_qat",
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"propagate_qconfig_",
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"qconfig_equals",
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"quantize",
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"quantize_dynamic",
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"quantize_dynamic_jit",
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"quantize_jit",
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"quantize_qat",
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"register_activation_post_process_hook",
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"script_qconfig",
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"script_qconfig_dict",
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"swap_module",
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"weight_observer_range_neg_127_to_127",
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]
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def default_eval_fn(model, calib_data):
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r"""
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Default evaluation function takes a torch.utils.data.Dataset or a list of
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input Tensors and run the model on the dataset
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"""
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for data, target in calib_data:
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model(data)
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