def get_native_quant_patterns(
additional_quant_patterns: Dict[Pattern, QuantizerCls] = None
) -> Dict[Pattern, QuantizerCls]:
"""
Return a map from pattern to quantize handlers based on the default patterns and the native backend_config_dict.
The returned map is sorted such that longer patterns will be encountered first when iterating through it.
"""
patterns = get_default_quant_patterns()
if additional_quant_patterns is not None:
patterns = get_combined_dict(patterns, additional_quant_patterns)
# TODO: currently we just extend the quantize handlers generated from
# `get_native_backend_config_dict`
# in the future we can just assign backend_config_dict when everything is defined
for pattern, quantize_handler in get_pattern_to_quantize_handlers(
get_native_backend_config_dict()).items():
patterns[pattern] = quantize_handler
return sorted_patterns_dict(patterns)
def convert(
model: GraphModule, is_reference: bool = False,
convert_custom_config_dict: Dict[str, Any] = None,
is_standalone_module: bool = False,
_remove_qconfig_flag: bool = True,
convert_qconfig_dict: Dict[str, Any] = None,
backend_config_dict: Optional[Dict[str, Any]] = None) -> torch.nn.Module:
"""
We will convert an observed model (a module with observer calls) to a reference
quantized model, the rule is simple:
1. for each observer module call in the graph, we'll convert it to calls to
quantize and dequantize functions based on the observer instance
2. for weighted operations like linear/conv, we need to convert them to reference
quantized module, this requires us to know whether the dtype configured for the
weight is supported in the backend, this is done in prepare step and the result
is stored in observed_node_names, we can decide whether we need to swap the
module based on this set
standalone_module means it a submodule that is not inlined in
parent module, and will be quantized separately as one unit.
Returns a quantized standalone module, whether input/output is quantized is
specified by prepare_custom_config_dict, with
input_quantized_idxs, output_quantized_idxs, please
see docs for prepare_fx for details
"""
if convert_custom_config_dict is None:
convert_custom_config_dict = {}
node_name_to_scope, prepare_custom_config_dict, observed_node_names = restore_state(model)
qconfig_map: Dict[str, QConfigAny] = model._qconfig_map # type: ignore[assignment]
# TODO this should be removed now that gpu support for quantization is being supported.
# however in practice, as of 7/22/2021, certain functions that get called by convert expect
# only cpu arguments.
# As an example, in TestQuantizeFxModels.test_qat_functional_linear when device='cuda',
# fold_weight will call quantized::linear_prepack which doesn't support QuantizedCuda backend.
if not is_reference:
model.cpu()
# mapping from fully qualified module name to module instance
# for example,
# {
# '': Model(...),
# 'linear': Linear(...),
# 'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
# }
# We use remove_duplicate=False here because torch.cat uses
# the same activation_post_process module instance but different names
modules = dict(model.named_modules(remove_duplicate=False))
# TODO refactor this code once we update the prepare logic to have additional information on
# which graph nodes have been observed and share that with convert to decide which observers to ignore.
if convert_qconfig_dict:
prepare_qconfig_dict: Dict[str, Dict[Any, Any]] = model._qconfig_dict # type: ignore[assignment]
modules_copy = copy.deepcopy(modules)
convert_dict_to_ordered_dict(convert_qconfig_dict)
if model._is_qat:
convert_qconfig_dict = update_qconfig_for_qat(convert_qconfig_dict, {})
convert_qconfig_dict = update_qconfig_for_fusion(model, convert_qconfig_dict)
compare_prepare_convert_qconfig_dict(prepare_qconfig_dict, convert_qconfig_dict) # type: ignore[arg-type]
convert_qconfig_map = generate_qconfig_map(model, modules_copy, model.graph, convert_qconfig_dict, node_name_to_scope)
# check the convert_qconfig_map generated and ensure that all the values either match what was set in prepare qconfig_map
# or are set to None in the convert_qconfig_map.
for k, v in qconfig_map.items():
assert k in convert_qconfig_map, 'Expected key {} in convert qconfig_map'.format(k)
if convert_qconfig_map[k] is not None:
assert qconfig_equals(v, convert_qconfig_map[k]), 'Expected k {} to have the same value in prepare qconfig_dict \
and convert qconfig_dict, found {} updated to {}.'.format(k, v, convert_qconfig_map[k])
qconfig_map = convert_qconfig_map
custom_module_classes = get_custom_module_class_keys(
convert_custom_config_dict,
"observed_to_quantized_custom_module_class")
custom_module_class_mapping = convert_custom_config_dict.get("observed_to_quantized_custom_module_class", {})
if model._equalization_qconfig_map is not None:
# If we want to do equalization then do the following:
# Calculate the equalization scale, update the observers with the scaled
# inputs, and scale the weight
weight_eq_obs_dict = update_obs_for_equalization(model, modules)
convert_eq_obs(model, modules, weight_eq_obs_dict)
# always run weight observers in the top level forward method
# for dynamic quant ops or weight only quant ops
run_weight_observers(model)
graph_inputs: List[str] = []
for node in model.graph.nodes:
if node.op == 'placeholder':
graph_inputs.append(node.name)
# TODO: move this outside of this function
def replace_observer_with_quantize_dequantize_node(
model: torch.nn.Module,
graph: Graph,
node: Node,
modules: Dict[str, torch.nn.Module],
node_name_to_scope: Dict[str, Tuple[str, type]],
qconfig_map: Dict[str, QConfigAny]) -> None:
""" Replace activation_post_process module call node with quantize and
dequantize node
Before:
... -> observer_0(x) -> ...
After:
... -> torch.quantize_per_tensor(x, ...) -> x.dequantize() -> ...
"""
assert modules is not None
assert isinstance(node.target, str)
module_path, prefix = get_module_path_and_prefix(node, node_name_to_scope, qconfig_map)
observer_module = modules[node.target]
maybe_quantize_node_info = get_quantize_node_info(observer_module)
# Skip replacing observers to quant/dequant nodes if the qconfigs of all
# consumers and producers of this observer are None
skip_replacement = all([
has_none_qconfig(n, qconfig_map) for n in
list(node.args) + list(node.users.keys())])
if skip_replacement or maybe_quantize_node_info is None:
# didn't find correponding quantize op and info for the observer_module
# so we just remove the observer
with graph.inserting_before(node):
node.replace_all_uses_with(node.args[0])
graph.erase_node(node)
else:
# otherwise, we can convert the observer moduel call to quantize/dequantize node
node_type, quantize_op, qparams = maybe_quantize_node_info
# replace observer node with quant - dequant node
with graph.inserting_before(node):
input_node = node.args[0]
inputs = [input_node]
for key, value in qparams.items():
# TODO: we can add the information of whether a value needs to
# be registered as an attribute in qparams dict itself
if key in ['_scale_', '_zero_point_']:
# For scale and zero_point values we register them as buffers in the root module.
# TODO: maybe need more complex attr name here
qparam_node = create_getattr_from_value(model, graph, module_path + prefix + key, value)
inputs.append(qparam_node)
else:
# for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
inputs.append(value)
quantized_node = graph.create_node(node_type, quantize_op, tuple(inputs), {})
dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
node.replace_all_uses_with(dequantized_node)
graph.erase_node(node)
# this is a temporary hack for custom module, we may want to implement
# this properly after the custom module class design is finalized
def replace_observer_with_dequantize_node(node: Node, graph: Graph):
call_custom_module_node = node.args[0]
assert isinstance(call_custom_module_node, Node), \
f"Expecting the for call custom module node to be a Node, but got {call_custom_module_node}"
node.replace_all_uses_with(call_custom_module_node)
graph.erase_node(node)
insert_dequantize_node(call_custom_module_node, graph)
# additional state to override inputs to be quantized, if specified
# by the user
placeholder_node_seen_cnt = 0
input_quantized_idxs: List[int] = prepare_custom_config_dict.get(
"input_quantized_idxs", [])
output_quantized_idxs: List[int] = prepare_custom_config_dict.get(
"output_quantized_idxs", [])
if backend_config_dict is None:
backend_config_dict = get_native_backend_config_dict()
root_module_to_quantized_reference_module = get_root_module_to_quantized_reference_module(backend_config_dict)
# convert tuples so that it can work with isinstance(module, tuple_of_classes)
root_module_classes = tuple(root_module_to_quantized_reference_module.keys())
qat_module_classes = get_qat_module_classes(backend_config_dict)
fused_module_classes = get_fused_module_classes(backend_config_dict)
statically_quantized_custom_module_nodes: Set[Node] = set()
for node in list(model.graph.nodes):
if node.op == 'placeholder':
cur_placeholder_node_idx = placeholder_node_seen_cnt
placeholder_node_seen_cnt += 1
if cur_placeholder_node_idx in input_quantized_idxs:
# Inputs are assumed to be quantized if the user specifid the
# input_quantized_idxs override.
# we need to dequantize the inputs since all operators took
# floating point inputs in reference quantized models
insert_dequantize_node(node, model.graph)
elif node.op == "output":
# If the argument is empty we don't need to do anything
if len(output_quantized_idxs) == 0:
continue
# Result are kept quantized if the user specified the
# output_quantized_idxs override.
# Remove the dequantize operator for the node in the end if any
return_node = node
output = node.args[0]
# outputs can be Node, list, tuple, dict, other cases are not supported yet
if isinstance(output, (list, tuple)):
for idx in output_quantized_idxs:
maybe_recursive_remove_dequantize(output[idx], return_node, model.graph)
elif isinstance(output, (Node, dict)):
# we treat dict as a single argument currently, but it can be extended
# to support {"key": dtype} after we change output_quantized_idxs to
# dict
if 0 in output_quantized_idxs:
maybe_recursive_remove_dequantize(output, return_node, model.graph)
else:
warnings.warn(f"Unsupported node type for output_quantized_idxs: {type(output)}")
elif node.op == "call_module":
if is_activation_post_process(modules[node.target]):
observed_node = node.args[0]
if observed_node in statically_quantized_custom_module_nodes:
replace_observer_with_dequantize_node(node, model.graph)
else:
replace_observer_with_quantize_dequantize_node(
model, model.graph, node, modules, node_name_to_scope,
qconfig_map)
elif is_observed_standalone_module(modules[node.target]):
convert_standalone_module(
node, modules, model, is_reference, backend_config_dict)
elif type(modules[node.target]) in set(
root_module_classes).union(qat_module_classes).union(fused_module_classes):
# extra check for fused module classes to make sure they are fused module classes
# of target modules
if type(modules[node.target]) in fused_module_classes and \
type(modules[node.target][0]) not in root_module_classes:
continue
convert_weighted_module(
node, modules, observed_node_names, qconfig_map, backend_config_dict)
elif type(modules[node.target]) in custom_module_classes:
convert_custom_module(
node, model.graph, modules, custom_module_class_mapping,
statically_quantized_custom_module_nodes)
preserved_attributes = set(convert_custom_config_dict.get("preserved_attributes", []))
model = QuantizedGraphModule(model, copy.deepcopy(model.graph), preserved_attributes)
# remove deadcode after converting observers to quant/dequant ops
model.graph.eliminate_dead_code()
model.recompile()
# TODO: maybe move this to quantize_fx.py
if not is_reference:
model = duplicate_dequantize_node(model)
model = duplicate_quantize_dynamic_node(model)
model = lower_to_fbgemm(model, qconfig_map, node_name_to_scope)
model = remove_quant_dequant_pairs(model)
model = remove_extra_dequantize(model)
# TODO: this looks hacky, we want to check why we need this and see if we can
# remove this
# removes qconfig and activation_post_process modules
if _remove_qconfig_flag:
_remove_qconfig(model)
return model
def get_base_name_to_sets_of_related_ops() -> Dict[str, Set[NSNodeTargetType]]:
# note: this set is modified below by items from backend_config_dict
sets_of_related_ops: List[Set[NSNodeTargetType]] = [
# conv modules
set([
nn.Conv1d,
]),
set([
nn.Conv2d,
]),
set([
nn.Conv3d,
]),
# conv functionals
set([
F.conv1d,
]),
set([
F.conv2d,
]),
set([
F.conv3d,
]),
# linear modules
set([
nn.Linear,
]),
# linear functionals
set([
F.linear,
]),
# average pool
set([
nn.AvgPool1d,
torch.avg_pool1d,
]),
set([
nn.AvgPool2d,
torch._C._nn.avg_pool2d,
]),
set([
nn.AvgPool3d,
torch._C._nn.avg_pool3d,
]),
# adaptive average pool
set([
nn.AdaptiveAvgPool1d,
F.adaptive_avg_pool1d,
]),
set([
nn.AdaptiveAvgPool2d,
F.adaptive_avg_pool2d,
]),
set([
nn.AdaptiveAvgPool3d,
F.adaptive_avg_pool3d,
]),
# LSTM
set([
nn.LSTM,
]),
# add
set([
torch.add,
operator.add, # x + y
]),
# cat
set([
torch.cat,
]),
# mul
set([
torch.mul,
operator.mul,
]),
# relu
set([
F.relu,
nn.ReLU,
'relu',
'relu_',
torch.relu,
]),
# maxpool
set([
nn.MaxPool1d,
F.max_pool1d,
]),
set([
nn.MaxPool2d,
F.max_pool2d,
]),
set([
nn.MaxPool3d,
F.max_pool3d,
]),
# sigmoid
set([
torch.sigmoid,
'sigmoid',
'sigmoid_',
nn.Sigmoid,
F.sigmoid,
]),
# BatchNorm
set([
nn.BatchNorm2d,
]),
set([
nn.BatchNorm3d,
]),
# ConvTranspose
set([
nn.ConvTranspose1d,
]),
set([
nn.ConvTranspose2d,
]),
set([
nn.ConvTranspose3d,
]),
# ELU
set([
nn.ELU,
]),
# Embedding
set([
nn.Embedding,
]),
# EmbeddingBag
set([
nn.EmbeddingBag,
]),
# GroupNorm
set([
nn.GroupNorm,
]),
# Hardswish
set([
nn.Hardswish,
]),
# InstanceNorm
set([
nn.InstanceNorm1d,
]),
set([
nn.InstanceNorm2d,
]),
set([
nn.InstanceNorm3d,
]),
# LayerNorm
set([
nn.LayerNorm,
]),
# LeakyReLU
set([
nn.LeakyReLU,
]),
# ReLU6
set([
nn.ReLU6,
F.relu6,
]),
# F.elu
set([
F.elu,
]),
# F.hardswish
set([
F.hardswish,
]),
# F.instance_norm
set([
F.instance_norm,
]),
# F.layer_norm
set([
F.layer_norm,
]),
# F.leaky_relu
set([
F.leaky_relu,
]),
# F.silu
set([
nn.SiLU,
F.silu,
]),
# F.mish
set([
nn.Mish,
F.mish,
]),
# F.tanh
set([
nn.Tanh,
F.tanh,
torch.tanh,
'tanh_',
'tanh',
]),
# F.hardsigmoid
set([
'hardsigmoid_',
'hardsigmoid',
F.hardsigmoid,
nn.Hardsigmoid,
]),
# F.hardtanh
set([
nn.Hardtanh,
F.hardtanh,
F.hardtanh_,
]),
# floordiv
set([
operator.floordiv,
]),
# unsqueeze
set([
torch.unsqueeze,
]),
# stack
set([
torch.stack,
]),
# squeeze
set([
torch.squeeze,
]),
# sort
set([
torch.sort,
]),
# repeat_interleave
set([
torch.repeat_interleave,
]),
# min
set([
torch.min,
]),
# mean
set([
torch.mean,
]),
# max
set([
torch.max,
]),
# transpose
set([
torch.transpose,
]),
# flatten
set([
torch.flatten,
]),
# clamp
set([
torch.clamp,
]),
# chunk
set([
torch.chunk,
]),
# interpolate
set([
torch.nn.functional.interpolate,
]),
# dropout
set([
nn.Dropout,
]),
# F.dropout
set([
F.dropout,
]),
# matmul
set([
torch.matmul,
]),
# Softmax
set([
nn.Softmax,
]),
]
# for each floating point op, add versions of the op added by
# backend_config_dict
backend_config_dict = get_native_backend_config_dict()
new_connections = [
# technical debt edge case
(nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear),
]
for config in backend_config_dict['configs']:
if 'pattern' not in config:
continue
# format: (c, (b, a))
pattern = config['pattern']
first_element = pattern
# look from the end, because pattern is in reverse order
while isinstance(first_element, (list, tuple)):
first_element = first_element[-1]
if 'fused_module' in config:
# case 1: pattern fuses a pattern of ops into an op
# example: nn.Conv1d, nn.ReLU fused into nni.ConvReLU1d
new_connections.append((first_element, config['fused_module']))
if 'qat_module' in config:
# case 2: pattern swaps a module into a QAT module
# example: nni.ConvReLU1d swapped into nniqat.ConvReLU1d
new_connections.append((first_element, config['qat_module']))
if 'reference_quantized_module_for_root' in config:
# case 3: reference version of floating point module, such as
# nn.Conv2d and nnqr.Conv2d
new_connections.append(
(first_element, config['reference_quantized_module_for_root']))
#
# Add reference module swaps from default lowering path
#
for source_to_target in (
_lower_to_native_backend.STATIC_LOWER_MODULE_MAP,
_lower_to_native_backend.DYNAMIC_LOWER_MODULE_MAP,
_lower_to_native_backend.WEIGHT_ONLY_LOWER_MODULE_MAP,
_lower_to_native_backend.SPECIAL_PATTERN_LOWER_MODULE_MAP,
):
for source, target in source_to_target.items(
): # type: ignore[attr-defined]
new_connections.append((source, target))
for source_to_double_target in (
_lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_MAP,
_lower_to_native_backend.DYNAMIC_LOWER_FUSED_MODULE_MAP,
):
for source, (target1, target2) in source_to_double_target.items(
): # type: ignore[attr-defined]
new_connections.append((source, target1))
new_connections.append((source, target2))
#
# Add function swaps from default lowering path
#
for source, (target1, target2) in \
_lower_to_native_backend.STATIC_LOWER_FUNCTIONAL_MAP.items():
new_connections.append((source, target1))
new_connections.append((source, target2))
for source_to_target in (
_lower_to_native_backend.QBIN_OP_MAPPING,
_lower_to_native_backend.QBIN_RELU_OP_MAPPING,
quantization_mappings.DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
):
for source, target in source_to_target.items():
new_connections.append((source, target))
#
# Add other swaps, ideally in the future this could be removed
# after the lowering code stops using these.
#
for source_to_target in (
quantization_mappings.DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS, ):
for source, target in source_to_target.items():
new_connections.append((source, target))
# add the new connections from backend_config_dict
for item1, item2 in new_connections:
for set_of_related_ops in sets_of_related_ops:
if item1 in set_of_related_ops or item2 in set_of_related_ops:
set_of_related_ops.add(item1)
set_of_related_ops.add(item2)
break
base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]] = {}
counter = 0
for set_of_related_ops in sets_of_related_ops:
base_name = str(counter)
counter += 1
base_name_to_sets_of_related_ops[base_name] = set_of_related_ops
return base_name_to_sets_of_related_ops
import os.path
# Create a directory for the images, if it doesn't exist
QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH = os.path.join(
os.path.realpath(os.path.join(__file__, "..")),
"quantization_backend_configs"
)
if not os.path.exists(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH):
os.mkdir(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH)
output_path = os.path.join(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH, "default_backend_config.txt")
with open(output_path, "w") as f:
native_backend_config_dict = get_native_backend_config_dict()
configs = native_backend_config_dict['configs']
def _sort_key_func(entry):
pattern = entry['pattern']
while isinstance(pattern, tuple):
pattern = pattern[-1]
pattern = remove_boolean_dispatch_from_name(pattern)
if not isinstance(pattern, str):
# methods are already strings
pattern = torch.typename(pattern)
# we want
#
"""
This script will generate default values of quantization configs.
These are for use in the documentation.
"""
from torch.ao.quantization.backend_config import get_native_backend_config_dict
import os.path
from pprint import pprint
# Create a directory for the images, if it doesn't exist
QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH = os.path.join(
os.path.realpath(os.path.join(__file__, "..")),
"quantization_backend_configs"
)
if not os.path.exists(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH):
os.mkdir(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH)
output_path = os.path.join(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH, "default_backend_config.txt")
with open(output_path, "w") as f:
pprint(get_native_backend_config_dict(), stream=f)
