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https://github.com/zebrajr/pytorch.git
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b1e8e01e50
Applies some autofixes from the ruff PYI rules to improve the typing of PyTorch. I haven't enabled most of these ruff rules yet as they do not have autofixes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/107521 Approved by: https://github.com/ezyang
42 lines
2.9 KiB
Python
42 lines
2.9 KiB
Python
# pyre-strict
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from typing import Tuple, List, Dict
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class QueryTensorQparam:
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def __init__(self, scale: float, zero_point: int, precision: int) -> None: ...
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def scale(self) -> float: ...
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def zero_point(self) -> int: ...
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def precision(self) -> int: ...
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def min(self) -> float: ...
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def max(self) -> float: ...
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class HistogramNetObserver:
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...
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class OutputColumnMaxHistogramNetObserver:
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...
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class RegisterQuantizationParamsWithHistogramNetObserver:
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...
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def ClearNetObservers() -> None: ...
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def ObserveMinMaxOfOutput(min_max_file_name: str, dump_freq: int = -1, delimiter: str = " ") -> None: ...
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def ObserveHistogramOfOutput(out_file_name: str, dump_freq: int = -1, mul_nets: bool = False, op_filter: str = "", delimiter: str = " ") -> None: ...
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def DumpHistogramFile(ob: HistogramNetObserver) -> None: ...
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def AddHistogramObserver(net_name: str, out_file_name: str, dump_freq: int = -1, mul_nets: bool = False, delimiter: str = " ") -> HistogramNetObserver: ...
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def DumpOutputColumnMaxHistogramFile(ob: OutputColumnMaxHistogramNetObserver) -> None: ...
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def AddOutputColumnMaxHistogramObserver(net_name: str, out_file_name: str, observe_column_max_for_blobs: List[str], dump_freq: int = -1, bin_nums: int = 16, mul_nets: bool = False, delimiter: str = " ") -> OutputColumnMaxHistogramNetObserver: ...
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def ChooseQuantizationParams(blob_name: str) -> Tuple[float, int]: ...
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def RegisterQuantizationParams(min_max_file_name: str, is_weight: bool = False, qparams_output_file_name: str = "") -> None: ...
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def RegisterQuantizationParamsWithHistogram(histogram_file_name: str, is_weight: bool = False, qparams_output_file_name: str = "") -> None: ...
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def AddRegisterQuantizationParamsWithHistogramObserver(net_name: str, histogram_file_name: str, is_weight: bool = False, qparams_output_file_name: str = "") -> RegisterQuantizationParamsWithHistogramNetObserver: ...
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def AddScaleZeroOffsetArgumentsWithHistogram(net_def_bytes: bytes, histogram_file_name: str) -> bytes: ...
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def get_fakefp16_mapping(use_fp16_acc: bool, use_nnpi: bool) -> Dict[str, str]: ...
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def freeze_quantization_params(net_def_bytes: bytes) -> bytes: ...
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def ChooseStaticQuantizationParams(min: float, max: float, bins: List[int], preserve_sparsity: bool = True, precision: int = 8, quant_scheme: str = "min_max", p99_threshold: float = 0.99, is_weight: bool = False) -> QueryTensorQparam: ...
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def ObserveFp16FCPackedWeights(blob_name: str, weights_out_file: str) -> None: ...
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def ObserveInt8FCPackedWeights(blob_name: str, weights_out_file: str) -> None: ...
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def CreateInt8QuantSchemeBlob(quant_scheme_blob_name: str, quantization_kind: str, preserve_sparsity: bool) -> None: ...
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def CreateInt8QuantParamsBlob(quant_param_blob_name: str, scale: float, zero_point: int) -> None: ...
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def ObserveInt8QuantParamsBlob(quant_params_blob_name: str) -> Tuple[float, int]: ...
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def ObserveInt8QuantSchemeBlob(quant_scheme_blob_name: str) -> Tuple[str, bool]: ...
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