torch/ao/quantization/quantization_mappings.py

import copy

import torch
from torch import nn

import torch.nn.functional as F
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nniq
import torch.nn.intrinsic.quantized.dynamic as nniqd
import torch.nn.intrinsic.qat as nniqat
import torch.ao.nn.quantized as nnq
import torch.ao.nn.quantized.reference as nnqr
import torch.ao.nn.quantized.dynamic as nnqd
import torch.ao.nn.qat as nnqat
import torch.ao.nn.qat.dynamic as nnqatd

from typing import Optional, Union, Dict, Set, Callable, Any

# Because `torch.ao.nn` uses lazy imports, we need to make
# sure we import the contents explicitly here.
import torch.ao.nn.sparse
import torch.ao.nn as ao_nn
from torch.ao.quantization.stubs import QuantStub, DeQuantStub
from torch.ao.quantization.fake_quantize import (
    default_fixed_qparams_range_0to1_fake_quant,
    default_fixed_qparams_range_neg1to1_fake_quant,
)
from torch.ao.quantization.utils import get_combined_dict
from torch.nn.utils.parametrize import type_before_parametrizations

# Default map for swapping float module to reference quantized modules
DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    QuantStub: nnq.Quantize,
    DeQuantStub: nnq.DeQuantize,
    nn.Linear: nnqr.Linear,
    nn.Conv1d: nnqr.Conv1d,
    nn.Conv2d: nnqr.Conv2d,
    nn.Conv3d: nnqr.Conv3d,
    nn.ConvTranspose1d: nnqr.ConvTranspose1d,
    nn.ConvTranspose2d: nnqr.ConvTranspose2d,
    nn.ConvTranspose3d: nnqr.ConvTranspose3d,
    nn.Embedding: nnqr.Embedding,
    nn.EmbeddingBag: nnqr.EmbeddingBag,
    nn.GRUCell: nnqr.GRUCell,
    nn.LSTMCell: nnqr.LSTMCell,
    nn.RNNCell: nnqr.RNNCell,
    nn.LSTM: nnqr.LSTM,
}

# Default map for swapping float module to quantized ones
DEFAULT_STATIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    QuantStub: nnq.Quantize,
    DeQuantStub: nnq.DeQuantize,
    nn.BatchNorm2d: nnq.BatchNorm2d,
    nn.BatchNorm3d: nnq.BatchNorm3d,
    nn.Dropout: nnq.Dropout,
    nn.Conv1d: nnq.Conv1d,
    nn.Conv2d: nnq.Conv2d,
    nn.Conv3d: nnq.Conv3d,
    nn.ConvTranspose1d: nnq.ConvTranspose1d,
    nn.ConvTranspose2d: nnq.ConvTranspose2d,
    nn.ConvTranspose3d: nnq.ConvTranspose3d,
    nn.ELU: nnq.ELU,
    nn.Embedding: nnq.Embedding,
    nn.EmbeddingBag: nnq.EmbeddingBag,
    nn.GroupNorm: nnq.GroupNorm,
    nn.Hardswish: nnq.Hardswish,
    nn.InstanceNorm1d: nnq.InstanceNorm1d,
    nn.InstanceNorm2d: nnq.InstanceNorm2d,
    nn.InstanceNorm3d: nnq.InstanceNorm3d,
    nn.LayerNorm: nnq.LayerNorm,
    nn.LeakyReLU: nnq.LeakyReLU,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnq.Linear,
    nn.Linear: nnq.Linear,
    nn.ReLU6: nnq.ReLU6,
    nn.Dropout: nnq.Dropout,
    nn.PReLU: nnq.PReLU,
    # Wrapper Modules:
    nnq.FloatFunctional: nnq.QFunctional,
    # Intrinsic modules:
    nni.BNReLU2d: nniq.BNReLU2d,
    nni.BNReLU3d: nniq.BNReLU3d,
    nni.ConvReLU1d: nniq.ConvReLU1d,
    nni.ConvReLU2d: nniq.ConvReLU2d,
    nni.ConvReLU3d: nniq.ConvReLU3d,
    nni.LinearReLU: nniq.LinearReLU,
    nniqat.ConvBn1d: nnq.Conv1d,
    nniqat.ConvBn2d: nnq.Conv2d,
    nniqat.ConvBn3d: nnq.Conv3d,
    nniqat.ConvBnReLU1d: nniq.ConvReLU1d,
    nniqat.ConvBnReLU2d: nniq.ConvReLU2d,
    nniqat.ConvBnReLU3d: nniq.ConvReLU3d,
    nniqat.ConvReLU2d: nniq.ConvReLU2d,
    nniqat.ConvReLU3d: nniq.ConvReLU3d,
    nniqat.LinearReLU: nniq.LinearReLU,
    nniqat.LinearBn1d: nnq.Linear,
    # QAT modules:
    nnqat.Linear: nnq.Linear,
    nnqat.Conv2d: nnq.Conv2d,
    nnqat.Conv3d: nnq.Conv3d,
}

# Default map for swapping float module to qat modules
DEFAULT_QAT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Conv2d: nnqat.Conv2d,
    nn.Conv3d: nnqat.Conv3d,
    nn.Linear: nnqat.Linear,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqat.Linear,
    # Intrinsic modules:
    nni.ConvBn1d: nniqat.ConvBn1d,
    nni.ConvBn2d: nniqat.ConvBn2d,
    nni.ConvBn3d: nniqat.ConvBn3d,
    nni.ConvBnReLU1d: nniqat.ConvBnReLU1d,
    nni.ConvBnReLU2d: nniqat.ConvBnReLU2d,
    nni.ConvBnReLU3d: nniqat.ConvBnReLU3d,
    nni.ConvReLU2d: nniqat.ConvReLU2d,
    nni.ConvReLU3d: nniqat.ConvReLU3d,
    nni.LinearReLU: nniqat.LinearReLU,
    nni.LinearBn1d: nniqat.LinearBn1d,
}

# Default map for swapping dynamic modules
DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.GRUCell: nnqd.GRUCell,
    nn.Linear: nnqd.Linear,
    nnqatd.Linear: nnqd.Linear,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqd.Linear,
    nn.LSTM: nnqd.LSTM,
    nn.GRU: nnqd.GRU,
    nn.LSTMCell: nnqd.LSTMCell,
    nn.RNNCell: nnqd.RNNCell,
    nni.LinearReLU: nniqd.LinearReLU,
    nn.EmbeddingBag: nnq.EmbeddingBag,
    nn.Embedding: nnq.Embedding,
    # Don't want to enable these by default because the numerical
    # accuracy is poor compared to other dynamic ops
    # nn.Conv1d: nnqd.Conv1d,
    # nn.Conv2d: nnqd.Conv2d,
    # nn.Conv3d: nnqd.Conv3d,
    # nn.ConvTranspose1d: nnqd.ConvTranspose1d,
    # nn.ConvTranspose2d: nnqd.ConvTranspose2d,
    # nn.ConvTranspose3d: nnqd.ConvTranspose3d,
}

# Allowlist for propagating the qconfig
_INCLUDE_QCONFIG_PROPAGATE_LIST : Set[Callable] = {
    nn.Sequential,
}

# Default mapping from floating point function or torch ops to quantized ops
# TODO: merge with default static mapping
DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS : Dict[Union[Callable, str], Callable] = {
    F.elu: torch.ops.quantized.elu,
    F.hardswish: torch.ops.quantized.hardswish,
    F.instance_norm: torch.ops.quantized.instance_norm,
    F.layer_norm: torch.ops.quantized.layer_norm,
    F.leaky_relu: torch.ops.quantized.leaky_relu,
    F.dropout: torch.ops.quantized.dropout,
}

# mapping from module to output activation post process class
DEFAULT_MODULE_TO_ACT_POST_PROCESS : Dict[Callable, Callable] = {
    nn.Hardsigmoid: default_fixed_qparams_range_0to1_fake_quant,
    nn.Sigmoid: default_fixed_qparams_range_0to1_fake_quant,
    nn.Softmax: default_fixed_qparams_range_0to1_fake_quant,
    nn.Tanh: default_fixed_qparams_range_neg1to1_fake_quant,
}

# Default map for swapping float module to static sparse quantized ones
DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Linear: ao_nn.sparse.quantized.Linear
}

# Default map for swapping float module to dynamic sparse quantized ones
DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Linear: ao_nn.sparse.quantized.dynamic.Linear
}

def no_observer_set() -> Set[Any]:
    r"""These modules cannot have observers inserted by default."""
    no_observers = set([
        nn.quantizable.LSTM,
        nn.quantizable.MultiheadAttention
    ])
    return no_observers

def get_default_static_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training static quantization
    '''
    return copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)

def get_default_static_quant_reference_module_mappings() -> Dict[Callable, Any]:
    ''' Get reference module mapping for post training static quantization
    '''
    return copy.deepcopy(DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS)

def get_embedding_static_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping, including mapping for embedding QAT
    '''
    mapping = copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
    mapping[nnqat.EmbeddingBag] = nnq.EmbeddingBag
    mapping[nnqat.Embedding] = nnq.Embedding
    return mapping

def get_default_static_sparse_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training static sparse quantization
    '''
    return copy.deepcopy(DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS)

def get_static_quant_module_class(
        float_module_class: Callable,
        additional_static_quant_mapping: Optional[Dict[Callable, Any]] = None,
        is_reference: bool = False) -> Any:
    r"""n Get the statically quantized module class corresponding to
    the floating point module class
    """
    if additional_static_quant_mapping is None:
        additional_static_quant_mapping = {}
    all_mappings = get_combined_dict(
        DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS if is_reference
        else DEFAULT_STATIC_QUANT_MODULE_MAPPINGS, additional_static_quant_mapping)
    static_quant_module_class = all_mappings.get(float_module_class, None)
    assert static_quant_module_class is not None, \
        "Floating point module class {}".format(str(float_module_class)) + \
        " does not have a corresponding quantized module class"
    return copy.deepcopy(static_quant_module_class)

def get_dynamic_quant_module_class(
        float_module_class: Callable,
        additional_dynamic_quant_mapping: Optional[Dict[Callable, Any]] = None) -> Any:
    r"""n Get the dynamically quantized module class corresponding to
    the floating point module class
    """
    if additional_dynamic_quant_mapping is None:
        additional_dynamic_quant_mapping = {}
    all_mappings = get_combined_dict(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS, additional_dynamic_quant_mapping)
    dynamic_quant_module_class = all_mappings.get(float_module_class, None)
    assert dynamic_quant_module_class is not None, \
        "Floating point module class {}".format(str(float_module_class)) + \
        " does not have a corresponding quantized module class"
    return copy.deepcopy(dynamic_quant_module_class)

def get_default_qat_module_mappings() -> Dict[Callable, Any]:
    ''' Get default module mapping for quantization aware training
    '''
    return copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)

def get_embedding_qat_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for quantization aware training
        This is includes default values in addition to
        enabling qat for embeddings.
    '''
    mapping = copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
    mapping[nn.EmbeddingBag] = nnqat.EmbeddingBag
    mapping[nn.Embedding] = nnqat.Embedding
    return mapping

def get_default_dynamic_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training dynamic quantization
    '''
    return DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS

def get_default_dynamic_sparse_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training dynamic sparse quantization
    '''
    return DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS

def get_default_qconfig_propagation_list() -> Set[Callable]:
    ''' Get the default list of module types that we'll attach qconfig
    attribute to in prepare
    '''
    QCONFIG_PROPAGATE_MODULE_CLASS_LIST = (
        (set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys()) |
         set(DEFAULT_QAT_MODULE_MAPPINGS.keys()) |
         set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys()) |
         _INCLUDE_QCONFIG_PROPAGATE_LIST)
    )
    return copy.deepcopy(QCONFIG_PROPAGATE_MODULE_CLASS_LIST)

def get_default_compare_output_module_list() -> Set[Callable]:
    ''' Get list of module class types that we will record output
    in numeric suite
    '''
    NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST = (
        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.values())
        | set(DEFAULT_QAT_MODULE_MAPPINGS.values())
        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.values())
        | set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
        | _INCLUDE_QCONFIG_PROPAGATE_LIST
    )
    return copy.deepcopy(NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST)

def get_default_float_to_quantized_operator_mappings(
) -> Dict[Union[Callable, str], Callable]:
    return copy.deepcopy(DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS)

# TODO: merge with get_static_quant_module_class
def get_quantized_operator(float_op: Union[Callable, str]) -> Callable:
    ''' Get the quantized operator corresponding to the float operator
    '''
    quantized_op = DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS.get(float_op, None)
    assert quantized_op is not None, \
        'Operator {} does not have corresponding quantized op'.format(str(float_op))
    return quantized_op

def _get_special_act_post_process(module: torch.nn.Module) -> Optional[Callable]:
    r""" Get the special activation post process for `module`, this has
    higher priority than the activation post process in `qconfig`
    e.g.
    input: torch.nn.Sigmoid
    output: default_affine_fixed_qparam_fake_quant
    """
    return DEFAULT_MODULE_TO_ACT_POST_PROCESS.get(type_before_parametrizations(module), None)

def _has_special_act_post_process(module: torch.nn.Module) -> bool:
    return module.training and type(module) in DEFAULT_MODULE_TO_ACT_POST_PROCESS