from typing import Dict, Any, List, Callable, Tuple, Optional, Set import torch from torch.fx import GraphModule from torch.fx._symbolic_trace import Tracer from torch.fx.node import Target, Node, Argument from torch.nn.intrinsic import _FusedModule from .fx import fuse # noqa: F401 from .fx import prepare # noqa: F401 from .fx.convert import convert from .backend_config import get_tensorrt_backend_config_dict # noqa: F401 from .fx.graph_module import ObservedGraphModule from .fx.qconfig_utils import ( check_is_valid_convert_custom_config_dict, check_is_valid_fuse_custom_config_dict, check_is_valid_prepare_custom_config_dict, check_is_valid_qconfig_dict, ) from .fx.utils import graph_pretty_str # noqa: F401 from .fx.utils import get_custom_module_class_keys # noqa: F401 def _check_is_graph_module(model: torch.nn.Module) -> None: if not isinstance(model, GraphModule): raise ValueError( "input model must be a GraphModule, " + "Got type:" + str(type(model)) + " Please make " + "sure to follow the tutorials." ) def _swap_ff_with_fxff(model: torch.nn.Module) -> None: r""" Swap FloatFunctional with FXFloatFunctional """ modules_to_swap = [] for name, module in model.named_children(): if isinstance(module, torch.nn.quantized.FloatFunctional): modules_to_swap.append(name) else: _swap_ff_with_fxff(module) for name in modules_to_swap: del model._modules[name] model._modules[name] = torch.nn.quantized.FXFloatFunctional() def _fuse_fx( graph_module: GraphModule, is_qat: bool, fuse_custom_config_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, ) -> GraphModule: r""" Internal helper function to fuse modules in preparation for quantization Args: graph_module: GraphModule object from symbolic tracing (torch.fx.symbolic_trace) """ _check_is_graph_module(graph_module) return fuse( graph_module, is_qat, fuse_custom_config_dict, backend_config_dict) # type: ignore[operator] class Scope(object): """ Scope object that records the module path and the module type of a module. Scope is used to track the information of the module that contains a Node in a Graph of GraphModule. For example:: class Sub(torch.nn.Module): def forward(self, x): # This will be a call_method Node in GraphModule, # scope for this would be (module_path="sub", module_type=Sub) return x.transpose(1, 2) class M(torch.nn.Module): def __init__(self): self.sub = Sub() def forward(self, x): # This will be a call_method Node as well, # scope for this would be (module_path="", None) x = x.transpose(1, 2) x = self.sub(x) return x """ def __init__(self, module_path: str, module_type: Any): super().__init__() self.module_path = module_path self.module_type = module_type class ScopeContextManager(object): """ A context manager to track the Scope of Node during symbolic tracing. When entering a forward function of a Module, we'll update the scope information of the current module, and when we exit, we'll restore the previous scope information. """ def __init__( self, scope: Scope, current_module: torch.nn.Module, current_module_path: str ): super().__init__() self.prev_module_type = scope.module_type self.prev_module_path = scope.module_path self.scope = scope self.scope.module_path = current_module_path self.scope.module_type = type(current_module) def __enter__(self): return def __exit__(self, *args): self.scope.module_path = self.prev_module_path self.scope.module_type = self.prev_module_type return class QuantizationTracer(Tracer): def __init__( self, skipped_module_names: List[str], skipped_module_classes: List[Callable] ): super().__init__() self.skipped_module_names = skipped_module_names self.skipped_module_classes = skipped_module_classes # NB: initialized the module_type of top level module to None # we are assuming people won't configure the model with the type of top level # module here, since people can use "" for global config # We can change this if there is a use case that configures # qconfig using top level module type self.scope = Scope("", None) self.node_name_to_scope: Dict[str, Tuple[str, type]] = {} self.record_stack_traces = True def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool: return ( ( m.__module__.startswith("torch.nn") and not isinstance(m, torch.nn.Sequential) ) or module_qualified_name in self.skipped_module_names or type(m) in self.skipped_module_classes or isinstance(m, _FusedModule) ) def call_module( self, m: torch.nn.Module, forward: Callable[..., Any], args: Tuple[Any, ...], kwargs: Dict[str, Any], ) -> Any: module_qualified_name = self.path_of_module(m) # Creating scope with information of current module # scope will be restored automatically upon exit with ScopeContextManager(self.scope, m, module_qualified_name): return super().call_module(m, forward, args, kwargs) def create_node( self, kind: str, target: Target, args: Tuple[Argument, ...], kwargs: Dict[str, Argument], name: Optional[str] = None, type_expr: Optional[Any] = None, ) -> Node: node = super().create_node(kind, target, args, kwargs, name, type_expr) self.node_name_to_scope[node.name] = ( self.scope.module_path, self.scope.module_type, ) return node def _prepare_fx( model: torch.nn.Module, qconfig_dict: Any, is_qat: bool, prepare_custom_config_dict: Optional[Dict[str, Any]] = None, equalization_qconfig_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, is_standalone_module: bool = False, ) -> ObservedGraphModule: r""" Internal helper function for prepare_fx Args: `model`, `qconfig_dict`, `prepare_custom_config_dict`, `equalization_qonfig_dict`: see docs for :func:`~torch.ao.quantization.prepare_fx` `is_standalone_module`: a boolean flag indicates whether we are quantizing a standalone module or not, a standalone module is a submodule of the parent module that is not inlined in the forward graph of the parent module, the way we quantize standalone module is described in: :func:`~torch.ao.quantization._prepare_standalone_module_fx` """ if prepare_custom_config_dict is None: prepare_custom_config_dict = {} if equalization_qconfig_dict is None: equalization_qconfig_dict = {} check_is_valid_qconfig_dict(qconfig_dict) check_is_valid_prepare_custom_config_dict(prepare_custom_config_dict) check_is_valid_qconfig_dict(equalization_qconfig_dict) skipped_module_names = prepare_custom_config_dict.get( "non_traceable_module_name", [] ) skipped_module_classes = prepare_custom_config_dict.get( "non_traceable_module_class", [] ) # swap FloatFunctional with FXFloatFunctional _swap_ff_with_fxff(model) # symbolically trace the model if not is_standalone_module: # standalone module and custom module config are applied in top level module standalone_module_name_configs = prepare_custom_config_dict.get( "standalone_module_name", [] ) skipped_module_names += [config[0] for config in standalone_module_name_configs] standalone_module_class_configs = prepare_custom_config_dict.get( "standalone_module_class", [] ) skipped_module_classes += [ config[0] for config in standalone_module_class_configs ] float_custom_module_classes = get_custom_module_class_keys( prepare_custom_config_dict, "float_to_observed_custom_module_class" ) skipped_module_classes += float_custom_module_classes preserved_attributes = prepare_custom_config_dict.get("preserved_attributes", []) tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) graph_module = GraphModule(model, tracer.trace(model)) for attr_name in preserved_attributes: setattr(graph_module, attr_name, getattr(model, attr_name)) graph_module = _fuse_fx( graph_module, is_qat, prepare_custom_config_dict, backend_config_dict) prepared = prepare( graph_module, qconfig_dict, is_qat, tracer.node_name_to_scope, prepare_custom_config_dict=prepare_custom_config_dict, equalization_qconfig_dict=equalization_qconfig_dict, backend_config_dict=backend_config_dict, is_standalone_module=is_standalone_module, ) # type: ignore[operator] for attr_name in preserved_attributes: setattr(prepared, attr_name, getattr(model, attr_name)) return prepared def _prepare_standalone_module_fx( model: torch.nn.Module, qconfig_dict: Any, is_qat: bool, prepare_custom_config_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, ) -> GraphModule: r""" [Internal use only] Prepare a standalone module, so that it can be used when quantizing the parent module. standalone_module means it a submodule that is not inlined in parent module, and will be quantized separately as one unit. How the standalone module is observed is specified by `input_quantized_idxs` and `output_quantized_idxs` in the prepare_custom_config for the standalone module Returns: * model(GraphModule): prepared standalone module. It has these attributes: * `_standalone_module_input_quantized_idxs(List[Int])`: a list of indexes for the graph input that is expected to be quantized, same as input_quantized_idxs configuration provided for the standalone module * `_standalone_module_output_quantized_idxs(List[Int])`: a list of indexs for the graph output that is quantized same as input_quantized_idxs configuration provided for the standalone module """ return _prepare_fx( model, qconfig_dict, is_qat, prepare_custom_config_dict, backend_config_dict=backend_config_dict, is_standalone_module=True, ) def fuse_fx( model: torch.nn.Module, fuse_custom_config_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, ) -> GraphModule: r""" Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode. Fusion rules are defined in torch.quantization.fx.fusion_pattern.py Args: * `model`: a torch.nn.Module model * `fuse_custom_config_dict`: Dictionary for custom configurations for fuse_fx, e.g.:: fuse_custom_config_dict = { # Attributes that are not used in forward function will # be removed when constructing GraphModule, this is a list of attributes # to preserve as an attribute of the GraphModule even when they are # not used in the code, these attributes will also persist through deepcopy "preserved_attributes": ["preserved_attr"], } Example:: from torch.ao.quantization import fuse_fx m = Model().eval() m = fuse_fx(m) """ torch._C._log_api_usage_once("quantization_api.quantize_fx.fuse_fx") check_is_valid_fuse_custom_config_dict(fuse_custom_config_dict) graph_module = torch.fx.symbolic_trace(model) preserved_attributes: Set[str] = set() if fuse_custom_config_dict: preserved_attributes = set( fuse_custom_config_dict.get("preserved_attributes", []) ) for attr_name in preserved_attributes: setattr(graph_module, attr_name, getattr(model, attr_name)) return _fuse_fx(graph_module, False, fuse_custom_config_dict, backend_config_dict) def prepare_fx( model: torch.nn.Module, qconfig_dict: Any, prepare_custom_config_dict: Optional[Dict[str, Any]] = None, equalization_qconfig_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, ) -> ObservedGraphModule: r""" Prepare a model for post training static quantization Args: * `model`: torch.nn.Module model, must be in eval mode * `qconfig_dict`: qconfig_dict is a dictionary with the following configurations:: qconfig_dict = { # optional, global config "": qconfig?, # optional, used for module and function types # could also be split into module_types and function_types if we prefer "object_type": [ (torch.nn.Conv2d, qconfig?), (torch.nn.functional.add, qconfig?), ..., ], # optional, used for module names "module_name": [ ("foo.bar", qconfig?) ..., ], # optional, matched in order, first match takes precedence "module_name_regex": [ ("foo.*bar.*conv[0-9]+", qconfig?) ..., ], # optional, used for matching object type invocations in a submodule by # order # TODO(future PR): potentially support multiple indices ('0,1') and/or # ranges ('0:3'). "module_name_object_type_order": [ # fully_qualified_name, object_type, index, qconfig ("foo.bar", torch.nn.functional.linear, 0, qconfig?), ], # priority (in increasing order): # global, object_type, module_name_regex, module_name, # module_name_object_type_order # qconfig == None means fusion and quantization should be skipped for anything # matching the rule } * `prepare_custom_config_dict`: customization configuration dictionary for quantization tool:: prepare_custom_config_dict = { # optional: specify the path for standalone modules # These modules are symbolically traced and quantized as one unit "standalone_module_name": [ # module_name, qconfig_dict, prepare_custom_config_dict ("submodule.standalone", None, # qconfig_dict for the prepare function called in the submodule, # None means use qconfig from parent qconfig_dict {"input_quantized_idxs": [], "output_quantized_idxs": []}), # prepare_custom_config_dict {} # backend_config_dict, TODO: point to README doc when it's ready ], "standalone_module_class": [ # module_class, qconfig_dict, prepare_custom_config_dict (StandaloneModule, None, # qconfig_dict for the prepare function called in the submodule, # None means use qconfig from parent qconfig_dict {"input_quantized_idxs": [0], "output_quantized_idxs": [0]}, # prepare_custom_config_dict {}) # backend_config_dict, TODO: point to README doc when it's ready ], # user will manually define the corresponding observed # module class which has a from_float class method that converts # float custom module to observed custom module # (only needed for static quantization) "float_to_observed_custom_module_class": { "static": { CustomModule: ObservedCustomModule } }, # the qualified names for the submodule that are not symbolically traceable "non_traceable_module_name": [ "non_traceable_module" ], # the module classes that are not symbolically traceable # we'll also put dynamic/weight_only custom module here "non_traceable_module_class": [ NonTraceableModule ], # By default, inputs and outputs of the graph are assumed to be in # fp32. Providing `input_quantized_idxs` will set the inputs with the # corresponding indices to be quantized. Providing # `output_quantized_idxs` will set the outputs with the corresponding # indices to be quantized. "input_quantized_idxs": [0], "output_quantized_idxs": [0], # Attributes that are not used in forward function will # be removed when constructing GraphModule, this is a list of attributes # to preserve as an attribute of the GraphModule even when they are # not used in the code, these attributes will also persist through deepcopy "preserved_attributes": ["preserved_attr"], } * `equalization_qconfig_dict`: equalization_qconfig_dict is a dictionary with a similar structure as qconfig_dict except it will contain configurations specific to equalization techniques such as input-weight equalization. * `backend_config_dict`: a dictionary that specifies how operators are quantized in a backend, this includes how the operaetors are observed, supported fusion patterns, how quantize/dequantize ops are inserted, supported dtypes etc. The structure of the dictionary is still WIP and will change in the future, please don't use right now. Return: A GraphModule with observer (configured by qconfig_dict), ready for calibration Example:: import torch from torch.ao.quantization import get_default_qconfig from torch.ao.quantization import prepare_fx float_model.eval() qconfig = get_default_qconfig('fbgemm') def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) qconfig_dict = {"": qconfig} prepared_model = prepare_fx(float_model, qconfig_dict) # Run calibration calibrate(prepared_model, sample_inference_data) """ torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_fx") return _prepare_fx( model, qconfig_dict, False, # is_qat prepare_custom_config_dict, equalization_qconfig_dict, backend_config_dict, ) def prepare_qat_fx( model: torch.nn.Module, qconfig_dict: Any, prepare_custom_config_dict: Optional[Dict[str, Any]] = None, backend_config_dict: Optional[Dict[str, Any]] = None, ) -> ObservedGraphModule: r""" Prepare a model for quantization aware training Args: * `model`: torch.nn.Module model, must be in train mode * `qconfig_dict`: see :func:`~torch.ao.quantization.prepare_fx` * `prepare_custom_config_dict`: see :func:`~torch.ao.quantization.prepare_fx` * `backend_config_dict`: see :func:`~torch.ao.quantization.prepare_fx` Return: A GraphModule with fake quant modules (configured by qconfig_dict), ready for quantization aware training Example:: import torch from torch.ao.quantization import get_default_qat_qconfig from torch.ao.quantization import prepare_fx qconfig = get_default_qat_qconfig('fbgemm') def train_loop(model, train_data): model.train() for image, target in data_loader: ... float_model.train() qconfig_dict = {"": qconfig} prepared_model = prepare_fx(float_model, qconfig_dict) # Run calibration train_loop(prepared_model, train_loop) """ torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_qat_fx") return _prepare_fx( model, qconfig_dict, True, # is_qat prepare_custom_config_dict, backend_config_dict=backend_config_dict, ) def _convert_fx( graph_module: GraphModule, is_reference: bool, convert_custom_config_dict: Optional[Dict[str, Any]] = None, is_standalone_module: bool = False, _remove_qconfig: bool = True, qconfig_dict: Dict[str, Any] = None, backend_config_dict: Dict[str, Any] = None, ) -> torch.nn.Module: """ `is_standalone_module`: see docs in :func:`~torch.ao.quantization.prepare_standalone_module_fx` """ if convert_custom_config_dict is None: convert_custom_config_dict = {} _check_is_graph_module(graph_module) check_is_valid_convert_custom_config_dict(convert_custom_config_dict) quantized = convert( graph_module, is_reference, convert_custom_config_dict, is_standalone_module, _remove_qconfig_flag=_remove_qconfig, convert_qconfig_dict=qconfig_dict, backend_config_dict=backend_config_dict, ) preserved_attributes = convert_custom_config_dict.get("preserved_attributes", []) for attr_name in preserved_attributes: setattr(quantized, attr_name, getattr(graph_module, attr_name)) return quantized def convert_fx( graph_module: GraphModule, is_reference: bool = False, convert_custom_config_dict: Optional[Dict[str, Any]] = None, _remove_qconfig: bool = True, qconfig_dict: Dict[str, Any] = None, backend_config_dict: Dict[str, Any] = None, ) -> torch.nn.Module: r""" Convert a calibrated or trained model to a quantized model Args: * `graph_module`: A prepared and calibrated/trained model (GraphModule) * `is_reference`: flag for whether to produce a reference quantized model, which will be a common interface between pytorch quantization with other backends like accelerators * `convert_custom_config_dict`: dictionary for custom configurations for convert function:: convert_custom_config_dict = { # user will manually define the corresponding quantized # module class which has a from_observed class method that converts # observed custom module to quantized custom module "observed_to_quantized_custom_module_class": { "static": { ObservedCustomModule: QuantizedCustomModule }, "dynamic": { ObservedCustomModule: QuantizedCustomModule }, "weight_only": { ObservedCustomModule: QuantizedCustomModule } }, # Attributes that are not used in forward function will # be removed when constructing GraphModule, this is a list of attributes # to preserve as an attribute of the GraphModule even when they are # not used in the code "preserved_attributes": ["preserved_attr"], } * `_remove_qconfig`: Option to remove the qconfig attributes in the model after convert. * `qconfig_dict`: qconfig_dict with either same keys as what is passed to the qconfig_dict in `prepare_fx` API, with same values or `None`, or additional keys with values set to `None` For each entry whose value is set to None, we skip quantizing that entry in the model:: qconfig_dict = { # used for object_type, skip quantizing torch.nn.functional.add "object_type": [ (torch.nn.functional.add, None), (torch.nn.functional.linear, qconfig_from_prepare) ..., ], # sed for module names, skip quantizing "foo.bar" "module_name": [ ("foo.bar", None) ..., ], } * `backend_config_dict`: A configuration for the backend which describes how operators should be quantized in the backend, this includes quantization mode support (static/dynamic/weight_only), dtype support (quint8/qint8 etc.), observer placement for each operators and fused operators. Detailed documentation can be found in torch/ao/quantization/backend_config/README.md Return: A quantized model (GraphModule) Example:: # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training quantized_model = convert_fx(prepared_model) """ torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_fx") return _convert_fx( graph_module, is_reference, convert_custom_config_dict, _remove_qconfig=_remove_qconfig, qconfig_dict=qconfig_dict, backend_config_dict=backend_config_dict, ) def _convert_standalone_module_fx( graph_module: GraphModule, is_reference: bool = False, convert_custom_config_dict: Optional[Dict[str, Any]] = None, ) -> torch.nn.Module: r""" [Internal use only] Convert a model produced by :func:`~torch.ao.quantization.prepare_standalone_module_fx` and convert it to a quantized model 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 """ return _convert_fx( graph_module, is_reference, convert_custom_config_dict, is_standalone_module=True, )