Spaces:
Running
Running
File size: 26,610 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 |
"""
Utils shared by different modes of quantization (eager/graph)
"""
import functools
import warnings
from collections import OrderedDict
from inspect import getfullargspec, signature
from typing import Any, Callable, Dict, Optional, Tuple, Union
import torch
from torch.ao.quantization.quant_type import QuantType
from torch.fx import Node
from torch.nn.utils.parametrize import is_parametrized
NodePattern = Union[Tuple[Node, Node], Tuple[Node, Tuple[Node, Node]], Any]
NodePattern.__module__ = "torch.ao.quantization.utils"
# This is the Quantizer class instance from torch/quantization/fx/quantize.py.
# Define separately to prevent circular imports.
# TODO(future PR): improve this.
# make this public once fixed (can't be public as is because setting the module directly
# doesn't work)
QuantizerCls = Any
# Type for fusion patterns, it can be more complicated than the following actually,
# see pattern.md for docs
# TODO: not sure if typing supports recursive data types
Pattern = Union[
Callable, Tuple[Callable, Callable], Tuple[Callable, Tuple[Callable, Callable]], Any
]
Pattern.__module__ = "torch.ao.quantization.utils"
# TODO: maybe rename this to MatchInputNode
class MatchAllNode:
""" A node pattern that matches all nodes, used in defining
fusion patterns in FX Graph Mode Quantization
"""
pass
module_type_list = {
torch.nn.ReLU,
torch.nn.ReLU6,
torch.nn.AdaptiveAvgPool1d,
torch.nn.AdaptiveAvgPool2d,
torch.nn.AdaptiveAvgPool3d,
torch.nn.AvgPool1d,
torch.nn.AvgPool2d,
torch.nn.AvgPool3d,
torch.nn.MaxPool1d,
torch.nn.MaxPool2d,
torch.nn.MaxPool3d,
torch.nn.Identity,
torch.nn.Hardsigmoid,
torch.nn.Sigmoid,
torch.nn.Tanh,
}
func_list = {
torch.nn.functional.adaptive_avg_pool1d,
torch.nn.functional.adaptive_avg_pool2d,
torch.nn.functional.adaptive_avg_pool3d,
torch.nn.functional.elu,
torch.nn.functional.hardswish,
torch.nn.functional.instance_norm,
torch.nn.functional.layer_norm,
torch.nn.functional.leaky_relu,
torch.nn.functional.silu,
torch.nn.functional.mish,
torch.nn.functional.dropout,
torch.nn.functional.max_pool1d,
torch.nn.functional.max_pool2d,
torch.nn.functional.max_pool3d,
torch.nn.functional.relu,
torch.nn.functional.hardtanh,
torch.nn.functional.hardtanh_,
torch.nn.functional.hardsigmoid,
torch.nn.functional.sigmoid,
torch.transpose,
torch.repeat_interleave,
torch.sigmoid,
torch.squeeze,
torch.stack,
torch.sum,
torch.tanh,
torch.unsqueeze,
torch.cat,
}
method_list = {
torch.mean,
'relu',
'relu_',
'contiguous',
'detach',
'detach_',
'hardsigmoid',
'hardsigmoid_',
'permute',
'repeat',
'repeat_interleave',
'reshape',
'resize_',
'shape',
'sigmoid',
'sigmoid_',
'size',
'squeeze',
'squeeze_',
'tanh',
'tanh_',
'transpose',
'unsqueeze',
'unsqueeze_',
'view',
}
# TODO: not used now, remove
def check_node(node, modules):
# TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py
is_call_function = node.op == "call_function" and node.target in func_list
is_call_method = node.op == "call_method" and node.target in method_list
is_call_module = node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
return is_call_function, is_call_method, is_call_module
def get_combined_dict(default_dict, additional_dict):
d = default_dict.copy()
d.update(additional_dict)
return d
def is_per_tensor(qscheme):
return qscheme == torch.per_tensor_affine or \
qscheme == torch.per_tensor_symmetric
def is_per_channel(qscheme):
return qscheme in [torch.per_channel_affine,
torch.per_channel_affine_float_qparams,
torch.per_channel_symmetric]
def getattr_from_fqn(obj: Any, fqn: str) -> Any:
"""
Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz.
"""
return functools.reduce(getattr, fqn.split("."), obj)
def to_underlying_dtype(qdtype):
DTYPE_MAPPING = {
torch.quint8: torch.uint8,
torch.qint8: torch.int8,
torch.qint32: torch.int32,
torch.quint4x2: torch.uint8,
torch.quint2x4: torch.uint8,
torch.uint8: torch.uint8,
torch.int8: torch.int8,
torch.int16: torch.int16,
torch.int32: torch.int32,
}
assert qdtype in DTYPE_MAPPING, "Unsupported dtype: " + str(qdtype)
return DTYPE_MAPPING[qdtype]
def get_qparam_dict(observer_or_fake_quant):
from torch.ao.quantization.observer import PlaceholderObserver
qscheme = getattr(observer_or_fake_quant, "qscheme", None)
dtype = observer_or_fake_quant.dtype
qparams = {"qscheme": qscheme, "dtype": dtype}
if not qscheme or isinstance(observer_or_fake_quant, PlaceholderObserver):
return {"qscheme": None, "dtype": dtype}
if is_per_tensor(qscheme):
qscheme = torch.per_tensor_affine
elif is_per_channel(qscheme):
# change symmetric to affine since we do not have symmetric
# quantized Tensor
if qscheme == torch.per_channel_symmetric:
qscheme = torch.per_channel_affine
qparams["axis"] = observer_or_fake_quant.ch_axis
else:
raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
# update qscheme, since we don't have symmetric quant qscheme
# in quantized Tensor
qparams["qscheme"] = qscheme
scale, zero_point = observer_or_fake_quant.calculate_qparams()
qparams["scale"] = scale
qparams["zero_point"] = zero_point
if hasattr(observer_or_fake_quant, "quant_min"):
qparams["quant_min"] = observer_or_fake_quant.quant_min
if hasattr(observer_or_fake_quant, "quant_max"):
qparams["quant_max"] = observer_or_fake_quant.quant_max
return qparams
def get_swapped_custom_module_class(custom_module, custom_module_class_mapping, qconfig):
""" Get the observed/quantized custom module class that we need
to swap `custom_module` to
Input:
custom_module: input, can be an instance of either a float or observed custom module
custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
qconfig: qconfig configured for the custom module
Output:
corresponding observed/quantized custom module class for input custom module instance
"""
quant_type = get_quant_type(qconfig)
class_mapping = custom_module_class_mapping.get(quant_type, {})
assert type(custom_module) in class_mapping, "did not find corresponding observed " \
f"module class for {type(custom_module)} in mapping: {class_mapping}"
return class_mapping[type(custom_module)]
def activation_dtype(qconfig):
assert qconfig is not None
activation = qconfig.activation()
return activation.dtype
def weight_dtype(qconfig):
assert qconfig is not None
weight = qconfig.weight()
return weight.dtype
def activation_is_statically_quantized(qconfig):
""" Given a qconfig, decide if the activation needs to be
quantized or not, this includes quantizing to quint8, qint8 and qint32 and float16
"""
return (
activation_dtype(qconfig) in [
torch.quint8,
torch.qint8,
torch.qint32,
torch.float16,
torch.uint8,
torch.int8,
torch.int16,
torch.int32
]
and (not activation_is_dynamically_quantized(qconfig))
)
def activation_is_dynamically_quantized(qconfig):
""" Given a qconfig, decide if the activation needs to be
dynamically quantized or not, this includes dynamically quantizing to
quint8, qint8 and float16
"""
activation_dtype, _, activation_is_dynamic = \
get_qconfig_dtypes(qconfig)
return activation_is_dynamic
def activation_is_int8_quantized(qconfig):
""" Given a qconfig, decide if the activation needs to be
quantized to int8 or not, this includes quantizing to quint8, qint8
"""
return activation_dtype(qconfig) in [torch.quint8, torch.qint8, torch.uint8, torch.int8]
def activation_is_int32_quantized(qconfig):
""" Given a qconfig, decide if the activation needs to be
quantized to int32 or not
"""
return activation_dtype(qconfig) in [torch.qint32, torch.int32]
def weight_is_quantized(qconfig):
""" Given a qconfig, decide if the weight needs to be
quantized or not
"""
return weight_dtype(qconfig) in [
torch.quint8,
torch.qint8,
torch.float16,
torch.quint4x2,
torch.uint8,
torch.int8,
torch.int16,
torch.int32
]
def weight_is_statically_quantized(qconfig):
""" Given a qconfig, decide if the weight needs to be statically
quantized or not
"""
return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.uint8, torch.int8]
def op_is_int8_dynamically_quantized(qconfig) -> bool:
""" Given a qconfig, returns True if this op is using int8 dynamic
quantization
"""
activation_dtype, weight_dtype, activation_is_dynamic = \
get_qconfig_dtypes(qconfig)
return (
activation_dtype in [torch.quint8, torch.uint8] and
# for now, the lines below assume fbgemm or qnnpack
weight_dtype in [torch.qint8, torch.int8] and
activation_is_dynamic
)
def get_qconfig_dtypes(qconfig):
r""" returns the qconfig tuple for qconfig:
(activation_dtype, weight_dtype, activation_is_dynamic)
"""
assert qconfig is not None
activation = qconfig.activation()
weight = qconfig.weight()
act_is_dynamic = getattr(activation, "is_dynamic", False)
return (activation.dtype, weight.dtype, act_is_dynamic)
def get_quant_type(qconfig):
assert qconfig is not None
activation = qconfig.activation()
weight = qconfig.weight()
static_dtypes = [torch.quint8, torch.qint8, torch.quint4x2, torch.qint32, torch.uint8, torch.int8, torch.int16, torch.int32]
if weight.dtype in static_dtypes:
if hasattr(activation, 'is_dynamic') and activation.is_dynamic:
return QuantType.DYNAMIC
elif activation.dtype in static_dtypes:
return QuantType.STATIC
else:
return QuantType.WEIGHT_ONLY
if weight.dtype == torch.float16:
if hasattr(activation, 'is_dynamic') and activation.is_dynamic:
return QuantType.DYNAMIC
elif activation.dtype == torch.float16:
return QuantType.STATIC
raise Exception(f"Unrecognized dtype combination in get_quant_type: activation({activation.dtype}),"
f"weight({weight.dtype})")
def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
""" Checks if the given minimum and maximum values are valid, meaning that
they exist and the min value is less than the max value.
"""
if min_val.numel() == 0 or max_val.numel() == 0:
warnings.warn(
"must run observer before calling calculate_qparams. " +
"Returning default values."
)
return False
if min_val.dim() == 0 or max_val.dim() == 0:
if min_val == float("inf") and max_val == float("-inf"):
warnings.warn(
"must run observer before calling calculate_qparams. " +
"Returning default values."
)
return False
assert min_val <= max_val, f"min {min_val} should be less than max {max_val}"
else:
assert torch.all(
min_val <= max_val
), f"min {min_val} should be less than max {max_val}"
return True
def calculate_qmin_qmax(quant_min: int, quant_max: int, has_customized_qrange: bool, dtype: torch.dtype,
reduce_range: bool) -> Tuple[int, int]:
r"""Calculates actual qmin and qmax based on the quantization range,
observer datatype and if range is reduced.
"""
# TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted.
if has_customized_qrange:
# This initialization here is to be resolve TorchScript compilation issues and allow
# using of refinement to decouple initial_qmin and initial_qmax from quantization range.
# The actual values of initial_qmin and initial_qmax will be reset below.
if dtype in [torch.qint32, torch.int32]:
initial_quant_min, initial_quant_max = 0, 2**32 - 1
else:
initial_quant_min, initial_quant_max = 0, 255
# The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
# attribute from Optional valid integers for use, based on TorchScript's requirements.
custom_quant_min, custom_quant_max = quant_min, quant_max
if custom_quant_min is not None and custom_quant_max is not None:
initial_quant_min, initial_quant_max = (
custom_quant_min,
custom_quant_max,
)
qrange_len = initial_quant_max - initial_quant_min + 1
if dtype in [torch.qint8, torch.int8]:
assert (
0 < qrange_len <= 256
), "quantization range should be positive and not exceed the maximum bit range (=256)."
elif dtype in [torch.qint32, torch.int32]:
assert (
0 < qrange_len <= 2**32
), "quantization range should be positive and not exceed the maximum bit range (=4294967296)."
if reduce_range:
quant_min, quant_max = quant_min // 2, quant_max // 2
else:
# Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
if dtype in [torch.qint8, torch.int8]:
if reduce_range:
quant_min, quant_max = -64, 63
else:
quant_min, quant_max = -128, 127
elif dtype in [torch.quint8, torch.uint8]:
if reduce_range:
quant_min, quant_max = 0, 127
else:
quant_min, quant_max = 0, 255
elif dtype in [torch.qint32, torch.int32]:
quant_min, quant_max = -1 * (2 ** 31), (2 ** 31) - 1
else:
quant_min, quant_max = 0, 15
return quant_min, quant_max
def _parent_name(target):
"""
Turn 'foo.bar' into ['foo', 'bar']
"""
r = target.rsplit('.', 1)
if len(r) == 1:
return '', r[0]
else:
return r[0], r[1]
def has_no_children_ignoring_parametrizations(module):
"""
Checks if module._modules is empty or
if module is a parametrization, checks that module._modules only has
the 'parametrizations' module
"""
if len(module._modules) == 0:
return True
elif is_parametrized(module):
return len(module._modules) == 1 and 'parametrizations' in module._modules
else:
return False
def _get_path_of_module(root: torch.nn.Module, submodule: torch.nn.Module) -> Optional[str]:
""" Get the path (fully qualified name) of a submodule
Example::
>> class M(torch.nn.Module):
def __init__(self):
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
return self.linear(x)
>> m = M()
>> l = m.linear
>> _get_path_of_module(m, l)
"linear"
"""
for n, p in root.named_modules():
if submodule is p:
return n
return None
def _get_signature_locals(f: Callable, loc: Dict[str, Any]) -> Dict[str, Any]:
""" Get local keyword arguments
Example::
>> def f(self, a, b=9):
pass
>> loc = {"a": 6, "c": 7}
>> _get_signature_locals(f, loc)
{"a": 6}
"""
return {k: v for k, v in loc.items() if k in signature(f).parameters}
def _get_default_kwargs(f: Callable) -> "OrderedDict[str, Any]":
""" Get all default keyword arguments from function signature
Example::
>> def f(self, a, b=9):
pass
>> _get_default_kwargs(f)
{"b": 9}
"""
kwargs = {}
for name, param in signature(f).parameters.items():
if param.default is not param.empty:
kwargs[name] = param.default
elif param.kind is param.VAR_POSITIONAL:
kwargs[name] = ()
elif param.kind is param.VAR_KEYWORD:
kwargs[name] = {}
return OrderedDict(kwargs)
def _normalize_kwargs(func: Callable, loc: Dict[str, Any]) -> "OrderedDict[str, Any]":
""" Given a function and local function arguments, normalize the keyword
arguments by filling in default arguments from function signature
Example::
>> def f(self, key1=3, key2=3):
pass
>> loc = {"key2": 6}
>> _normalize_kwargs(f, loc)
{"key1": 3, "key2": 6}
"""
default_kwargs = _get_default_kwargs(func)
local_kwargs = _get_signature_locals(func, loc)
normalized_kwargs = default_kwargs.copy()
for attr, val in local_kwargs.items():
if attr in normalized_kwargs:
# override the default keyword arguments
normalized_kwargs[attr] = val
return normalized_kwargs
def validate_qmin_qmax(quant_min: int, quant_max: int) -> None:
r"""Validates that the user-specified quantization range is properly initialized
and within the given bound supported by the observer dtype.
To accommodate lower-bit quantization with respect to the existing torch.qint8 and
torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
fake quantization. These estimates are compared against parameters learned through backpropagation.
The related literatures for scale and zero point via backpropagation are as follows:
Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
"""
# The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
# based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
assert (
quant_min <= 0 <= quant_max
), "Used-specified quantization range must include 0."
assert (
quant_min < quant_max
), "qmin must be strictly less than qmax for user-specified quantization range."
# Functionally equivalent to '_calculate_qparams' in observer.py. Observers must be torchscriptable however and qscheme
# as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
# to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code seems unlikey to change
# (last update over 1 year ago) and when torchscript is fully deprecated we can refactor. TODO(jakeszwe, jerryzh168)
def determine_qparams(
min_val: torch.Tensor, max_val: torch.Tensor, quant_min: int, quant_max: int,
dtype: torch.dtype, eps: torch.Tensor, has_customized_qrange: bool,
qscheme: torch.qscheme = torch.per_tensor_affine) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Calculates the quantization parameters, given min and max
value tensors. Works for both per tensor and per channel cases
Args:
min_val: Minimum values per channel
max_val: Maximum values per channel
Returns:
scales: Scales tensor of shape (#channels,)
zero_points: Zero points tensor of shape (#channels,)
"""
if not check_min_max_valid(min_val, max_val):
return torch.tensor([1.0], device=min_val.device.type), torch.tensor([0], device=min_val.device.type)
min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
device = min_val_neg.device
scale = torch.ones(min_val_neg.size(), dtype=torch.double, device=device)
zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
if (
qscheme == torch.per_tensor_symmetric
or qscheme == torch.per_channel_symmetric
):
max_val_pos = torch.max(-min_val_neg, max_val_pos)
scale = max_val_pos / (float(quant_max - quant_min) / 2)
scale = torch.max(scale, eps)
if dtype in [torch.uint8, torch.quint8]:
if has_customized_qrange:
# When customized quantization range is used, down-rounded midpoint of the range is chosen.
zero_point = zero_point.new_full(
zero_point.size(), (quant_min + quant_max) // 2
)
else:
zero_point = zero_point.new_full(zero_point.size(), 128)
elif qscheme == torch.per_channel_affine_float_qparams:
scale = (max_val - min_val) / float(quant_max - quant_min)
scale = torch.where(scale > eps, scale, torch.ones_like(scale))
# We use the quantize function
# xq = Round(Xf * inv_scale + zero_point),
# setting zero_point to (-1 * min *inv_scale) we get
# Xq = Round((Xf - min) * inv_scale)
zero_point = -1 * min_val / scale
else:
scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
scale = torch.max(scale, eps)
zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
zero_point = torch.clamp(zero_point, quant_min, quant_max)
# For scalar values, cast them to Tensors of size 1 to keep the shape
# consistent with default values in FakeQuantize.
if len(scale.shape) == 0:
# TODO: switch to scale.item() after adding JIT support
scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
if len(zero_point.shape) == 0:
# TODO: switch to zero_point.item() after adding JIT support
zero_point = torch.tensor(
[int(zero_point)], dtype=zero_point.dtype, device=device
)
if qscheme == torch.per_channel_affine_float_qparams:
zero_point = torch.tensor(
[float(zero_point)], dtype=zero_point.dtype, device=device
)
return scale.to(torch.double), zero_point.to(torch.int64)
def _get_num_pos_args(f: Callable) -> int:
""" Get number of positional args for a function
Example::
>> def f(self, key1=3, key2=3):
pass
>> _get_num_pos_args(f)
3
"""
return len(getfullargspec(f).args)
def get_fqn_to_example_inputs(
model: torch.nn.Module,
example_inputs: Tuple[Any, ...]
) -> Dict[str, Tuple[Any, ...]]:
""" Given a model and its example inputs, return a dictionary from
fully qualified name of submodules to example_inputs for that submodule,
e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,),
"sub.linear1": (tensor4,), ...}
Used to make quantizing submodules easier now that FX Graph Mode Quantization requires
example inputs.
Also works for keyword arguments with default values, we would flatten keyword
arguments as positional arguments and fill in the missing keyword args with default
values, e.g. if we have a forward function:
def forward(self, x, key1=3, key2=3):
...
and we call it with self.submodule(x, key2=6)
we'll get example_inputs: (x, 3, 6)
user can also override `key1` with positional arguments as well:
for self.submodule(x, 5, key2=6)
we'll get: (x, 5, 6)
variable positional arguments and variable positional keyword arguments in forward
function are not supported currently, so please make sure no submodules is using
them.
"""
root = model
fqn_to_example_inputs = {}
def _patched_module_call(self, *args, **kwargs):
submodule_example_inputs = list(args).copy()
normalized_kwargs = _normalize_kwargs(self.forward, kwargs)
# minus 1 to skipping counting `self`
num_args = _get_num_pos_args(self.forward) - 1
num_to_pop = num_args - len(submodule_example_inputs)
while num_to_pop and normalized_kwargs:
normalized_kwargs.popitem(last=False)
num_to_pop -= 1
submodule_example_inputs.extend(normalized_kwargs.values())
submodule_example_inputs_tuple = tuple(submodule_example_inputs)
fqn = _get_path_of_module(root, self)
if fqn is not None:
fqn_to_example_inputs[fqn] = submodule_example_inputs_tuple
return orig_module_call(self, *args, **kwargs)
orig_module_call = torch.nn.Module.__call__
torch.nn.Module.__call__ = _patched_module_call # type: ignore[method-assign]
try:
model(*example_inputs)
finally:
# restore the module call even if there is an exception
torch.nn.Module.__call__ = orig_module_call # type: ignore[method-assign]
return fqn_to_example_inputs
__all__ = [
"NodePattern",
"Pattern",
"MatchAllNode",
"check_node",
"get_combined_dict",
"is_per_tensor",
"is_per_channel",
"getattr_from_fqn",
"get_qparam_dict",
"get_swapped_custom_module_class",
"activation_dtype",
"weight_dtype",
"activation_is_statically_quantized",
"activation_is_dynamically_quantized",
"activation_is_int8_quantized",
"activation_is_int32_quantized",
"weight_is_quantized",
"weight_is_statically_quantized",
"op_is_int8_dynamically_quantized",
"get_qconfig_dtypes",
"get_quant_type",
"check_min_max_valid",
"calculate_qmin_qmax",
"has_no_children_ignoring_parametrizations",
"get_fqn_to_example_inputs",
"to_underlying_dtype",
"determine_qparams",
"validate_qmin_qmax",
]
|