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https://github.com/saymrwulf/pytorch.git
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dc8b5db5f8
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/62348 Originally we have a supported_dtypes check for linear and conv, but it's only valid for non reference option, this PR removes the constraint when is_reference=True and enables producing reference patterns for the dtype combinations that's not supported by fbgemm/qnnpack, for example qint8 activation dtypes Test Plan: python test/test_quantization.py TestQuantizeFx.test_linear_qint8_activation Imported from OSS Reviewed By: vkuzo Differential Revision: D29968675 fbshipit-source-id: 2abe37940eb62e16fcf0cbb700c174de49719223
62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
from .quantize 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 .fake_quantize import * # noqa: F403
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from .fuse_modules import fuse_modules
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from .stubs import * # noqa: F403
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from .quant_type import * # noqa: F403
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from .quantize_jit import * # noqa: F403
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# from .quantize_fx import *
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from .quantization_mappings import * # noqa: F403
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from .fuser_method_mappings import * # noqa: F403
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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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_all__ = [
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'QuantWrapper', 'QuantStub', 'DeQuantStub',
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# Top level API for eager mode quantization
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'quantize', 'quantize_dynamic', 'quantize_qat',
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'prepare', 'convert', 'prepare_qat',
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# Top level API for graph mode quantization on TorchScript
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'quantize_jit', 'quantize_dynamic_jit',
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# Top level API for graph mode quantization on GraphModule(torch.fx)
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# 'fuse_fx', 'quantize_fx', # TODO: add quantize_dynamic_fx
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# 'prepare_fx', 'prepare_dynamic_fx', 'convert_fx',
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'QuantType', 'quant_type_to_str', # quantization type
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# custom module APIs
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'get_default_static_quant_module_mappings', 'get_static_quant_module_class',
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'get_default_dynamic_quant_module_mappings',
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'get_default_qat_module_mappings',
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'get_default_qconfig_propagation_list',
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'get_default_compare_output_module_list',
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'get_quantized_operator',
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'get_fuser_method',
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# Sub functions for `prepare` and `swap_module`
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'propagate_qconfig_', 'add_quant_dequant', 'add_observer_', 'swap_module',
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'default_eval_fn', 'get_observer_dict',
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'register_activation_post_process_hook',
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# Observers
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'ObserverBase', 'WeightObserver', 'HistogramObserver',
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'observer', 'default_observer',
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'default_weight_observer', 'default_placeholder_observer',
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'default_per_channel_weight_observer',
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# FakeQuantize (for qat)
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'default_fake_quant', 'default_weight_fake_quant',
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'default_symmetric_fixed_qparams_fake_quant',
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'default_affine_fixed_qparams_fake_quant',
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'default_per_channel_weight_fake_quant',
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'default_histogram_fake_quant',
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# QConfig
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'QConfig', 'default_qconfig', 'default_dynamic_qconfig', 'float16_dynamic_qconfig',
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'float_qparams_weight_only_qconfig',
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# QAT utilities
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'default_qat_qconfig', 'prepare_qat', 'quantize_qat',
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# module transformations
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'fuse_modules',
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]
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