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https://github.com/pytorch/pytorch.git
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7f65a20884
This enables a check that which a class which only inherits from immutable classes like str, tuple, and NamedTuple, also defined `__slots__` so they don't allocate memory unnecessarily. This also ensure contributors think about how they define their classes with subclass NamedTuples and str, of which we have many in our codebase Pull Request resolved: https://github.com/pytorch/pytorch/pull/146276 Approved by: https://github.com/aorenste
701 lines
24 KiB
Python
701 lines
24 KiB
Python
# mypy: allow-untyped-defs
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import copy
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import warnings
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from collections import namedtuple
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from typing import Any, Optional, Union
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from typing_extensions import deprecated
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import torch
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import torch.nn as nn
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from torch.ao.quantization.fake_quantize import (
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default_dynamic_fake_quant,
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default_embedding_fake_quant,
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default_embedding_fake_quant_4bit,
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default_fake_quant,
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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_per_channel_weight_fake_quant,
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default_weight_fake_quant,
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FakeQuantize,
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FakeQuantizeBase,
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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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FusedMovingAvgObsFakeQuantize,
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)
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from .observer import (
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_PartialWrapper,
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default_debug_observer,
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default_dynamic_quant_observer,
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default_float_qparams_observer,
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default_float_qparams_observer_4bit,
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default_observer,
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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_weight_observer,
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HistogramObserver,
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MinMaxObserver,
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MovingAverageMinMaxObserver,
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NoopObserver,
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ObserverBase,
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per_channel_weight_observer_range_neg_127_to_127,
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PlaceholderObserver,
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ReuseInputObserver,
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weight_observer_range_neg_127_to_127,
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)
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__all__ = [
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"QConfig",
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# TODO: deprecated, remove
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"QConfigDynamic",
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"default_qconfig",
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"default_debug_qconfig",
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"default_per_channel_qconfig",
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"default_dynamic_qconfig",
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"float16_dynamic_qconfig",
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"float16_static_qconfig",
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"per_channel_dynamic_qconfig",
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"float_qparams_weight_only_qconfig",
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"float_qparams_weight_only_qconfig_4bit",
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"default_quint8_weight_qconfig",
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"default_qat_qconfig",
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"default_dynamic_qat_qconfig",
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"default_weight_only_qconfig",
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"default_activation_only_qconfig",
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"default_qat_qconfig_v2",
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"default_reuse_input_qconfig",
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"default_symmetric_qnnpack_qconfig",
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"default_per_channel_symmetric_qnnpack_qconfig",
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"default_symmetric_qnnpack_qat_qconfig",
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"default_per_channel_symmetric_qnnpack_qat_qconfig",
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"default_embedding_qat_qconfig",
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"default_embedding_qat_qconfig_4bit",
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"get_default_qconfig",
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"get_default_qat_qconfig",
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"get_default_qconfig_dict",
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"get_default_qat_qconfig_dict",
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"QConfigAny",
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"qconfig_equals",
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]
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class QConfig(namedtuple("QConfig", ["activation", "weight"])):
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"""
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Describes how to quantize a layer or a part of the network by providing
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settings (observer classes) for activations and weights respectively.
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Note that QConfig needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
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instances on invocation, not the concrete observer instances themselves.
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Quantization preparation function will instantiate observers multiple times for each of the layers.
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Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
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method (that behaves like functools.partial)::
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my_qconfig = QConfig(
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activation=MinMaxObserver.with_args(dtype=torch.qint8),
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weight=default_observer.with_args(dtype=torch.qint8))
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"""
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__slots__ = ()
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def __new__(cls, activation, weight):
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# catch common mistakes
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if isinstance(activation, nn.Module) or isinstance(weight, nn.Module):
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raise ValueError(
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"QConfig received observer instance, please pass observer class instead. "
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+ "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
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)
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return super().__new__(cls, activation, weight)
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@deprecated(
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"`QConfigDynamic` is going to be deprecated in PyTorch 1.12, please use `QConfig` instead",
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category=FutureWarning,
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)
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class QConfigDynamic(namedtuple("QConfigDynamic", ["activation", "weight"])):
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"""
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Describes how to dynamically quantize a layer or a part of the network by providing
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settings (observer classes) for weights.
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It's like QConfig, but for dynamic quantization.
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Note that QConfigDynamic needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
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instances on invocation, not the concrete observer instances themselves.
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Quantization function will instantiate observers multiple times for each of the layers.
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Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
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method (that behaves like functools.partial)::
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my_qconfig = QConfigDynamic(weight=default_observer.with_args(dtype=torch.qint8))
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"""
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__slots__ = ()
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def __new__(cls, activation=torch.nn.Identity, weight=torch.nn.Identity):
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# catch common mistakes
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if isinstance(weight, nn.Module):
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raise ValueError(
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"QConfigDynamic received observer instance, please pass observer class instead. "
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+ "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
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)
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return super().__new__(cls, activation, weight)
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default_qconfig = QConfig(activation=default_observer, weight=default_weight_observer)
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"""
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Default qconfig configuration.
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"""
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default_debug_qconfig = QConfig(
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weight=default_weight_observer, activation=default_debug_observer
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)
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"""
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Default qconfig configuration for debugging.
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"""
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default_per_channel_qconfig = QConfig(
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activation=default_observer, weight=default_per_channel_weight_observer
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)
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"""
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Default qconfig configuration for per channel weight quantization.
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"""
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default_dynamic_qconfig = QConfig(
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activation=default_dynamic_quant_observer, weight=default_weight_observer
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)
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"""
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Default dynamic qconfig.
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"""
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float16_dynamic_qconfig = QConfig(
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activation=PlaceholderObserver.with_args(dtype=torch.float16, is_dynamic=True),
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weight=PlaceholderObserver.with_args(dtype=torch.float16),
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)
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"""
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Dynamic qconfig with weights quantized to `torch.float16`.
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"""
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float16_static_qconfig = QConfig(
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activation=PlaceholderObserver.with_args(dtype=torch.float16),
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weight=PlaceholderObserver.with_args(dtype=torch.float16),
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)
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"""
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Dynamic qconfig with both activations and weights quantized to `torch.float16`.
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"""
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per_channel_dynamic_qconfig = QConfig(
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activation=default_dynamic_quant_observer,
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weight=default_per_channel_weight_observer,
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)
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"""
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Dynamic qconfig with weights quantized per channel.
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"""
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float_qparams_weight_only_qconfig = QConfig(
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activation=default_placeholder_observer, weight=default_float_qparams_observer
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)
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"""
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Dynamic qconfig with weights quantized with a floating point zero_point.
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"""
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float_qparams_weight_only_qconfig_4bit = QConfig(
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activation=default_placeholder_observer, weight=default_float_qparams_observer_4bit
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)
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default_qat_qconfig = QConfig(
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activation=default_fake_quant, weight=default_weight_fake_quant
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)
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"""
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Default qconfig for QAT.
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"""
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default_dynamic_qat_qconfig = QConfig(
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activation=default_dynamic_fake_quant, weight=default_weight_fake_quant
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)
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"""
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Default qconfig for dynamic QAT.
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"""
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default_weight_only_qconfig = QConfig(
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activation=torch.nn.Identity, weight=default_weight_fake_quant
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)
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"""
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Default qconfig for quantizing weights only.
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"""
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default_activation_only_qconfig = QConfig(
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activation=default_fake_quant, weight=torch.nn.Identity
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)
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"""
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Default qconfig for quantizing activations only.
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"""
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# QAT config that uses a fused observer + fake quant modules for optimized training performance.
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# to modify the activation/weight observers, the default entries in fake_quantize.py can be modified.
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default_qat_qconfig_v2 = QConfig(
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activation=default_fused_act_fake_quant, weight=default_fused_wt_fake_quant
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)
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"""
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Fused version of `default_qat_config`, has performance benefits.
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"""
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default_reuse_input_qconfig = QConfig(
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activation=default_reuse_input_observer, weight=NoopObserver
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)
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"""
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Default qconfig for operators that reuse the observers from input Tensor, e.g. reshape
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"""
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def get_default_qconfig(backend="x86", version=0):
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"""
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Returns the default PTQ qconfig for the specified backend.
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Args:
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* `backend` (str): a string representing the target backend. Currently supports
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`x86` (default), `fbgemm`, `qnnpack` and `onednn`.
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Return:
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qconfig
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"""
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supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
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if backend not in supported_backends:
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raise AssertionError(
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"backend: "
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+ str(backend)
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+ f" not supported. backend must be one of {supported_backends}"
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)
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if version == 0:
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if backend == "fbgemm":
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qconfig = QConfig(
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activation=HistogramObserver.with_args(reduce_range=True),
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weight=default_per_channel_weight_observer,
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)
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elif backend == "qnnpack":
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# TODO: make this compatible with xnnpack constraints
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qconfig = QConfig(
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activation=HistogramObserver.with_args(reduce_range=False),
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weight=default_weight_observer,
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)
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elif backend == "onednn":
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if not torch.cpu._is_vnni_supported():
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warnings.warn(
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"Default qconfig of oneDNN backend with reduce_range of false may have accuracy issues "
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"on CPU without Vector Neural Network Instruction support."
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)
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qconfig = QConfig(
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activation=HistogramObserver.with_args(reduce_range=False),
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weight=default_per_channel_weight_observer,
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)
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elif backend == "x86":
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qconfig = QConfig(
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activation=HistogramObserver.with_args(reduce_range=True),
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weight=default_per_channel_weight_observer,
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)
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else:
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# won't reach
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qconfig = default_qconfig
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else:
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raise AssertionError(
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"Version number: "
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+ str(version)
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+ " in get_default_qconfig is not supported. Version number must be 0"
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)
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return qconfig
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"""
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Default, symmetric PTQ qconfig for the specified backend. And a per_channel
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variant of the same.
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Symmetric here applies to signed weights with zero point = 0, and additional
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value restrictions. The activations are also signed 8-bit integers with this
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qconfig.
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* Once this change is merged [as of 3/17/22], with backend or qengine =
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'qnnpack', some quantized operators with this symmetric qconfig may use
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operators from xnnpack library.
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** Support to use xnnpack ops with `qnnpack` backed for asymmetric
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qconfig (returned by get_default_qconfig()) is not available yet.
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* This qconfig uses signed activations and weights. Weights have added
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restrictions such as zero point is forced to be 0, making the weights
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symmetric, hence the name. And the 8-bit quantized values are
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restricting to to [-127, +127], excluding -128.
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* xnnpack has a requantization scale value restriction, 0x1p-32 <=
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requantization_scale < 256.0 where, `requantization_scale = (input_scale
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* kernel_scale) / (output_scale)`. Using this eps (w/ assumed max value
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of 256) is to prevent requantization_scale to go below xnnpack lower
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threshold.
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"""
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default_symmetric_qnnpack_qconfig = QConfig(
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activation=HistogramObserver.with_args(
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dtype=torch.qint8, reduce_range=False, eps=2**-12
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),
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weight=weight_observer_range_neg_127_to_127,
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)
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default_per_channel_symmetric_qnnpack_qconfig = QConfig(
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activation=HistogramObserver.with_args(
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dtype=torch.qint8, reduce_range=False, eps=2**-12
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),
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weight=per_channel_weight_observer_range_neg_127_to_127,
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)
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default_embedding_qat_qconfig = QConfig(
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activation=NoopObserver.with_args(dtype=torch.float32),
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weight=default_embedding_fake_quant,
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)
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default_embedding_qat_qconfig_4bit = QConfig(
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activation=NoopObserver.with_args(dtype=torch.float32),
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weight=default_embedding_fake_quant_4bit,
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)
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default_quint8_weight_qconfig = QConfig(
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activation=HistogramObserver, weight=MinMaxObserver
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)
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def get_default_qat_qconfig(backend="x86", version=1):
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"""
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Returns the default QAT qconfig for the specified backend.
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Args:
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* `backend` (str): a string representing the target backend. Currently supports
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`x86` (default), `fbgemm`, `qnnpack` and `onednn`.
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* `version`: version, for backwards compatibility. Can be `None` or `1`.
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Return:
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qconfig
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"""
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supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
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if backend not in supported_backends:
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raise AssertionError(
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"backend: "
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+ str(backend)
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+ f" not supported. backend must be one of {supported_backends}"
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)
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# Histogram observer is too slow for quantization aware training
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if version == 0:
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if backend == "fbgemm":
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qconfig = QConfig(
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activation=FakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=True,
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),
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weight=default_per_channel_weight_fake_quant,
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)
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elif backend == "qnnpack":
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qconfig = QConfig(
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activation=FakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=False,
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),
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weight=default_weight_fake_quant,
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)
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elif backend == "onednn":
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qconfig = QConfig(
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activation=FakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
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),
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weight=default_per_channel_weight_fake_quant,
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)
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elif backend == "x86":
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qconfig = QConfig(
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activation=FakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=True,
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),
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weight=default_per_channel_weight_fake_quant,
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)
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else:
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qconfig = default_qat_qconfig
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# Use the fused observe + fake_quant modules for doing QAT.
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elif version == 1:
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if backend == "fbgemm":
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qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=True,
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),
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weight=default_fused_per_channel_wt_fake_quant,
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)
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elif backend == "qnnpack":
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# TODO: make this compatible with xnnpack constraints
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qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=False,
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),
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weight=default_fused_wt_fake_quant,
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)
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elif backend == "onednn":
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qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
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),
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weight=default_fused_per_channel_wt_fake_quant,
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)
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elif backend == "x86":
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qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=0,
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quant_max=255,
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reduce_range=True,
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),
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weight=default_fused_per_channel_wt_fake_quant,
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)
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else:
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qconfig = default_qat_qconfig_v2
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else:
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raise AssertionError(
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"Version number: "
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+ str(version)
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+ "in get_default_qat_qconfig is not supported. Version number must be 0 or 1"
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)
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return qconfig
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"""
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Default symmetric QAT qconfig for qnnpack. And its per channel weight variant.
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"""
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default_symmetric_qnnpack_qat_qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=-128,
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quant_max=127,
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dtype=torch.qint8,
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reduce_range=False,
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eps=2**-12,
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),
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weight=fused_wt_fake_quant_range_neg_127_to_127,
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)
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default_per_channel_symmetric_qnnpack_qat_qconfig = QConfig(
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activation=FusedMovingAvgObsFakeQuantize.with_args(
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observer=MovingAverageMinMaxObserver,
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quant_min=-128,
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quant_max=127,
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dtype=torch.qint8,
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reduce_range=False,
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eps=2**-12,
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),
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weight=fused_per_channel_wt_fake_quant_range_neg_127_to_127,
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)
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_default_fp32_placeholder_qconfig = QConfig(
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activation=PlaceholderObserver.with_args(dtype=torch.float32),
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weight=PlaceholderObserver.with_args(dtype=torch.float32),
|
|
)
|
|
|
|
_default_quint8_placeholder_qconfig = QConfig(
|
|
activation=PlaceholderObserver.with_args(dtype=torch.quint8),
|
|
# operators using this qconfig doesn't have weights
|
|
weight=None,
|
|
)
|
|
|
|
|
|
@deprecated(
|
|
"`torch.ao.quantization.get_default_qconfig_dict` is deprecated and will be removed in "
|
|
"a future version. Please use `torch.ao.quantization.get_default_qconfig_mapping` instead.",
|
|
category=FutureWarning,
|
|
)
|
|
def get_default_qconfig_dict(backend="x86", version=0):
|
|
return torch.ao.quantization.get_default_qconfig_mapping(backend, version).to_dict()
|
|
|
|
|
|
@deprecated(
|
|
"`torch.ao.quantization.get_default_qat_qconfig_dict` is deprecated and will be removed in "
|
|
"a future version. Please use `torch.ao.quantization.get_default_qat_qconfig_mapping` instead.",
|
|
category=FutureWarning,
|
|
)
|
|
def get_default_qat_qconfig_dict(backend="x86", version=1):
|
|
return torch.ao.quantization.get_default_qat_qconfig_mapping(
|
|
backend, version
|
|
).to_dict()
|
|
|
|
|
|
def _assert_valid_qconfig(qconfig: Optional[QConfig], mod: torch.nn.Module) -> None:
|
|
"""
|
|
Verifies that this `qconfig` is valid.
|
|
"""
|
|
if qconfig is None:
|
|
return
|
|
is_conv_transpose_mod = isinstance(
|
|
mod,
|
|
(torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d),
|
|
)
|
|
if is_conv_transpose_mod:
|
|
if qconfig.weight is None:
|
|
# for now, we assume that any qconfig for ConvTranspose without a weight is valid
|
|
return
|
|
example_observer = qconfig.weight()
|
|
is_per_channel = isinstance(
|
|
example_observer,
|
|
(
|
|
torch.ao.quantization.PerChannelMinMaxObserver,
|
|
torch.ao.quantization.MovingAveragePerChannelMinMaxObserver,
|
|
),
|
|
)
|
|
assert (
|
|
not is_per_channel
|
|
), "Per channel weight observer is not supported yet for ConvTranspose{n}d."
|
|
|
|
|
|
QConfigAny = Optional[QConfig]
|
|
QConfigAny.__module__ = "torch.ao.quantization.qconfig"
|
|
|
|
|
|
def _add_module_to_qconfig_obs_ctr(
|
|
qconfig: QConfigAny, module: Optional[nn.Module]
|
|
) -> Any:
|
|
r"""This is a helper function for use in quantization prepare that updates a qconfig so that
|
|
the constructors stored in the qconfig will create observers on the same device that
|
|
'module' is on. This is intended to be used when the qconfigs are propagated to each
|
|
module in order to avoid potential device alignment issues.
|
|
|
|
Args:
|
|
qconfig: QConfig with obs constructors stored in activation and weight
|
|
module: module which the qconfig is related to
|
|
|
|
Return:
|
|
qconfig: configured so that obs constructors set to construct on the same device as module
|
|
"""
|
|
|
|
if module is None or qconfig is None or qconfig._fields != ("activation", "weight"):
|
|
return qconfig
|
|
|
|
def get_factory_kwargs_based_on_module_device():
|
|
assert isinstance(module, torch.nn.Module)
|
|
devices = {p.device for p in module.parameters()} | {
|
|
p.device for p in module.buffers()
|
|
}
|
|
device = next(iter(devices)) if len(devices) > 0 else None
|
|
return None if device is None else {"device": device}
|
|
|
|
def configure_constructor_to_put_obs_on_module_device(original_constructor):
|
|
try:
|
|
# check if constructor can accept factory_kwargs
|
|
check = original_constructor.with_args(factory_kwargs=None)
|
|
check()
|
|
return original_constructor.with_callable_args(
|
|
factory_kwargs=get_factory_kwargs_based_on_module_device
|
|
)
|
|
except AttributeError: # qconfig doesn't have activation or weight
|
|
return original_constructor
|
|
except TypeError: # the class doesn't accept factory_kwargs argument
|
|
return original_constructor
|
|
|
|
activation = configure_constructor_to_put_obs_on_module_device(qconfig.activation)
|
|
weight = configure_constructor_to_put_obs_on_module_device(qconfig.weight)
|
|
|
|
return QConfig(activation, weight)
|
|
|
|
|
|
_ObserverOrFakeQuantizeConstructor = Union[
|
|
_PartialWrapper, type[ObserverBase], type[FakeQuantizeBase]
|
|
]
|
|
|
|
|
|
def _obs_or_fq_ctr_equals(
|
|
obs_or_fq1: _ObserverOrFakeQuantizeConstructor,
|
|
obs_or_fq2: _ObserverOrFakeQuantizeConstructor,
|
|
):
|
|
if isinstance(obs_or_fq1, _PartialWrapper) and isinstance(
|
|
obs_or_fq2, _PartialWrapper
|
|
):
|
|
return _partial_wrapper_equals(obs_or_fq1, obs_or_fq2)
|
|
return obs_or_fq1 == obs_or_fq2
|
|
|
|
|
|
def _partial_wrapper_equals(obs_or_fq1: _PartialWrapper, obs_or_fq2: _PartialWrapper):
|
|
"""
|
|
Return whether the two partial wrappers are equal,
|
|
"""
|
|
# functools.partial has no __eq__ operator defined so '==' defaults to 'is'
|
|
obs_or_fq1_keywords = copy.copy(obs_or_fq1.p.keywords)
|
|
obs_or_fq2_keywords = copy.copy(obs_or_fq2.p.keywords)
|
|
keywords_equal = True
|
|
# compare observer constructor with _obs_or_fq_ctr_equals since direct compare would fail
|
|
if "observer" in obs_or_fq1_keywords and "observer" in obs_or_fq2_keywords:
|
|
keywords_equal = keywords_equal and _obs_or_fq_ctr_equals(
|
|
obs_or_fq1_keywords["observer"], obs_or_fq2_keywords["observer"]
|
|
)
|
|
obs_or_fq1_keywords.pop("observer")
|
|
obs_or_fq2_keywords.pop("observer")
|
|
keywords_equal = keywords_equal and obs_or_fq1_keywords == obs_or_fq2_keywords
|
|
return (
|
|
obs_or_fq1.p.func == obs_or_fq2.p.func
|
|
and obs_or_fq1.p.args == obs_or_fq2.p.args
|
|
and keywords_equal
|
|
)
|
|
|
|
|
|
def qconfig_equals(q1: QConfigAny, q2: QConfigAny):
|
|
"""
|
|
Returns `True` if `q1` equals `q2`, and `False` otherwise.
|
|
"""
|
|
if q1 is None or q2 is None:
|
|
return q1 == q2
|
|
else:
|
|
assert q1 is not None and q2 is not None
|
|
try:
|
|
# Qconfig weight and activation can be either a partial wrapper,
|
|
# or an observer class. Special handling is required (above) for
|
|
# comparing partial wrappers.
|
|
activation_same = _obs_or_fq_ctr_equals(q1.activation, q2.activation)
|
|
weight_same = _obs_or_fq_ctr_equals(q1.weight, q2.weight)
|
|
return activation_same and weight_same
|
|
except AttributeError:
|
|
return q1 == q2
|
|
|
|
|
|
def _activation_is_memoryless(qconfig: QConfig):
|
|
"""
|
|
Return whether the observer for activations defined in the given QConfig is memoryless.
|
|
This means a MovingAverage observer with averaging constant equal to 1.
|
|
"""
|
|
|
|
def _is_memoryless(observer):
|
|
return (
|
|
hasattr(observer, "averaging_constant") and observer.averaging_constant == 1
|
|
)
|
|
|
|
act = qconfig.activation()
|
|
if isinstance(act, FakeQuantizeBase) and hasattr(act, "activation_post_process"):
|
|
return _is_memoryless(act.activation_post_process)
|
|
else:
|
|
return _is_memoryless(act)
|
|
|
|
|
|
def _is_reuse_input_qconfig(qconfig: Optional[QConfig]):
|
|
return (
|
|
qconfig is not None
|
|
and isinstance(qconfig.activation(), ReuseInputObserver)
|
|
and isinstance(qconfig.weight(), NoopObserver)
|
|
)
|