Spaces:
Running
Running
File size: 14,226 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 |
import copy
import torch
from torch import nn
import torch.nn.functional as F
import torch.ao.nn.intrinsic as nni
import torch.ao.nn.intrinsic.quantized as nniq
import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
import torch.ao.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
__all__ = [
"DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS",
"DEFAULT_STATIC_QUANT_MODULE_MAPPINGS",
"DEFAULT_QAT_MODULE_MAPPINGS",
"DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS",
"DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS",
"DEFAULT_MODULE_TO_ACT_POST_PROCESS",
"DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS",
"DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS",
"no_observer_set",
"get_default_static_quant_module_mappings",
"get_default_static_quant_reference_module_mappings",
"get_embedding_static_quant_module_mappings",
"get_default_static_sparse_quant_module_mappings",
"get_static_quant_module_class",
"get_dynamic_quant_module_class",
"get_default_qat_module_mappings",
"get_embedding_qat_module_mappings",
"get_default_dynamic_quant_module_mappings",
"get_default_dynamic_sparse_quant_module_mappings",
"get_default_qconfig_propagation_list",
"get_default_compare_output_module_list",
"get_default_float_to_quantized_operator_mappings",
"get_quantized_operator",
]
# 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.ConvAdd2d: nniq.ConvAdd2d,
nni.ConvAddReLU2d: nniq.ConvAddReLU2d,
nni.LinearReLU: nniq.LinearReLU,
nni.LinearLeakyReLU: nniq.LinearLeakyReLU,
nni.LinearTanh: nniq.LinearTanh,
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 = {
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, \
f"Floating point module class {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, \
f"Floating point module class {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, \
f'Operator {str(float_op)} does not have corresponding quantized 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
|