# mypy: allow-untyped-defs import copy import warnings from dataclasses import asdict from typing import Any, Optional, Union import torch from torch._subclasses import FakeTensor from torch.ao.quantization import ( _DerivedObserverOrFakeQuantize, FixedQParamsFakeQuantize, FixedQParamsObserver, ObserverBase, ObserverOrFakeQuantize, PlaceholderObserver, ) from torch.ao.quantization.backend_config import ( BackendConfig, DTypeConfig, get_native_backend_config, ) from torch.ao.quantization.backend_config.utils import ( get_fusion_pattern_to_root_node_getter, get_module_to_qat_module, get_pattern_to_dtype_configs, ) from torch.ao.quantization.observer import _is_activation_post_process, _PartialWrapper from torch.ao.quantization.qconfig import _is_reuse_input_qconfig, QConfigAny from torch.ao.quantization.qconfig_mapping import QConfigMapping from torch.ao.quantization.quantize import convert, propagate_qconfig_ from torch.ao.quantization.quantizer import ( DerivedQuantizationSpec, EdgeOrNode, FixedQParamsQuantizationSpec, QuantizationSpec, QuantizationSpecBase, SharedQuantizationSpec, ) from torch.ao.quantization.utils import ( _parent_name, get_qconfig_dtypes, get_swapped_custom_module_class, NodePattern, Pattern, ) from torch.fx import GraphModule from torch.fx.graph import Graph, Node from torch.fx.node import Argument from ._equalize import is_equalization_observer, node_supports_equalization from .custom_config import PrepareCustomConfig, StandaloneModuleConfigEntry from .match_utils import _find_matches, _MatchResultWithQConfig from .pattern_utils import _sorted_patterns_dict from .qconfig_mapping_utils import ( _generate_node_name_to_qconfig, _get_flattened_qconfig_dict, _update_qconfig_for_fusion, _update_qconfig_for_qat, ) from .quantize_handler import ( _default_root_node_getter, _get_pattern_to_quantize_handlers, QuantizeHandler, ) from .utils import ( _insert_dequant_stubs_for_custom_module_lstm_output, _is_custom_module_lstm, _maybe_get_custom_module_lstm_from_node_arg, _qconfig_satisfies_dtype_config_constraints, all_node_args_have_no_tensors, assert_and_get_unique_device, get_custom_module_class_keys, get_new_attr_name_with_prefix, get_non_observable_arg_indexes_and_types, node_arg_is_bias, node_arg_is_weight, NON_QUANTIZABLE_WEIGHT_OPS, ObservedGraphModuleAttrs, ) __all__ = [ "insert_observers_for_model", "prepare", "propagate_dtypes_for_known_nodes", ] # list of dtypes to not add observers to _DO_NOT_OBS_DTYPE_LIST = [int, float, torch.bool, None] _OBS_DTYPE_LIST = [ torch.quint8, torch.qint8, torch.qint32, torch.float16, torch.uint8, torch.int8, torch.int16, torch.int32, torch.float8_e5m2, torch.float8_e4m3fn, ] _DEFAULT_FP32_OBS_OR_FQ_CTR = PlaceholderObserver.with_args(dtype=torch.float) # note: the following default target dtype info dicts are temporary, # should be moved to the new programmable API class soon _DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO = { "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation, "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation, } _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO = { "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation, "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation, } def _get_observer_kwargs( quant_spec: Union[QuantizationSpec, FixedQParamsQuantizationSpec] ): kwargs_dict = asdict(quant_spec) return copy.deepcopy(kwargs_dict) def _get_qspec_for_arg( arg: Node, input_qspec_map: dict[Node, QuantizationSpecBase], named_modules: dict[str, torch.nn.Module], ) -> Optional[QuantizationSpecBase]: while _is_activation_post_process_node(arg, named_modules): arg = arg.args[0] # type: ignore[assignment] return input_qspec_map.get(arg, None) def _create_obs_or_fq_from_qspec( quantization_spec: Optional[QuantizationSpecBase], obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ): """Create observer or fake quantize objects based on quantization spec Args: quantization_spec: used to store parameters to create the observer or fake quantizer obs_or_fq_map: this is a map from edge/output to the corresponding observer/fake_quant instance, it may be reused for different edge/output depending on configuration """ if quantization_spec is None: return None if isinstance(quantization_spec, SharedQuantizationSpec): edge_or_node = quantization_spec.edge_or_node assert edge_or_node in obs_or_fq_map, ( "please make sure only refer to edge or node that has " f"observer/fake_quant inserted: '{edge_or_node}' not in\n{obs_or_fq_map.keys()}" ) return obs_or_fq_map[edge_or_node] elif isinstance(quantization_spec, DerivedQuantizationSpec): # can't use asdict, so not calling get_observer_kwargs here kwargs = { "dtype": quantization_spec.dtype, "derive_qparams_fn": quantization_spec.derive_qparams_fn, "quant_min": quantization_spec.quant_min, "quant_max": quantization_spec.quant_max, "qscheme": quantization_spec.qscheme, "ch_axis": quantization_spec.ch_axis, } edge_or_nodes = quantization_spec.derived_from obs_or_fqs = [obs_or_fq_map[k] for k in edge_or_nodes] kwargs["obs_or_fqs"] = obs_or_fqs return _DerivedObserverOrFakeQuantize.with_args(**kwargs)() elif isinstance(quantization_spec, FixedQParamsQuantizationSpec): kwargs = _get_observer_kwargs(quantization_spec) observer_ctr = FixedQParamsObserver.with_args(**kwargs) if is_qat: return FixedQParamsFakeQuantize.with_args(observer=observer_ctr)() else: return observer_ctr() assert isinstance(quantization_spec, QuantizationSpec) observer_or_fake_quant_ctr = quantization_spec.observer_or_fake_quant_ctr kwargs = _get_observer_kwargs(quantization_spec) kwargs.pop("observer_or_fake_quant_ctr") # we will remove is_dynamic from QuantizationSpec because # it seems that dynamic range quantization obs_or_fq_class = observer_or_fake_quant_ctr if isinstance(observer_or_fake_quant_ctr, _PartialWrapper): obs_or_fq_class = observer_or_fake_quant_ctr.p.func # type: ignore[union-attr, assignment] if "PerChannel" not in obs_or_fq_class.__name__: # type: ignore[operator, union-attr] kwargs.pop("ch_axis") return observer_or_fake_quant_ctr.with_args(**kwargs)() def _needs_obs_or_fq( prev_output_dtype: Any, prev_output_is_dynamic: bool, cur_target_dtype: Any, cur_target_is_dynamic: bool, reuse_input_obs_or_fq: bool, is_zeroth_arg: bool = False, ) -> bool: """ note: we will treat "not specified" as torch.float for now utility function that checks if we should insert an observer or fake quant node base on the requested dtype for the nodes from user is_zeroth_arg: we only dynamically quantize the first arg of the node right now this should be removed when we enable configuring dynamic quantization for a specific argument, this can be removed if we deprecate fx graph mode quantization """ # need to insert placeholder observer for dynamic quantization so that it can # be converted to choose_qparams -> q -> dq in convert step if cur_target_is_dynamic: assert ( cur_target_dtype in _OBS_DTYPE_LIST ), f"Expected cur_target_dtype to be torch.float, but got: {cur_target_dtype}" assert prev_output_dtype not in _DO_NOT_OBS_DTYPE_LIST return is_zeroth_arg if reuse_input_obs_or_fq: return False # non dynamic quantization if cur_target_dtype in _OBS_DTYPE_LIST: return ( prev_output_dtype in _OBS_DTYPE_LIST + [torch.float] and cur_target_dtype != prev_output_dtype ) # lots of error checking are skipped here for now return False def _is_activation_post_process_node( node: Node, named_modules: dict[str, torch.nn.Module] ) -> bool: return ( isinstance(node, torch.fx.Node) and node.op == "call_module" and _is_activation_post_process(named_modules[str(node.target)]) ) def _get_dtype_and_is_dynamic( obs_or_fq: Optional[ObserverOrFakeQuantize], ) -> tuple[Optional[torch.dtype], bool]: """Given a constructor for observer or fake quant module, returns a Tuple of dtype and is_dynamic """ # TODO: instead of instantiating the instance, we can use inspect to get the default args if obs_or_fq is None: return None, False else: return obs_or_fq.dtype, getattr(obs_or_fq, "is_dynamic", False) # type: ignore[return-value] def _is_input_arg_dtype_supported_by_backend( arg: Argument, node: Node, qconfig: QConfigAny, dtype_config: DTypeConfig, backend_config: BackendConfig, ) -> bool: """Check if the configured qconfig for the argument is supported by the backend or not """ if isinstance(arg, (list, tuple)): return all( _is_input_arg_dtype_supported_by_backend( a, node, qconfig, dtype_config, backend_config ) for a in arg ) if not isinstance(arg, Node): return True # TODO: support check for standalone module is_weight = node_arg_is_weight(node, arg) is_bias = node_arg_is_bias(node, arg) is_activation = not is_weight and not is_bias if is_activation: input_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get( "input_act_obs_or_fq_ctr" ) input_act_obs_or_fq = ( input_act_obs_or_fq_ctr() if input_act_obs_or_fq_ctr else None ) qconfig_dtype, qconfig_is_dynamic = _get_dtype_and_is_dynamic( input_act_obs_or_fq ) # TODO(future PR): remove the cast to bool below after figuring # out why backend_config has is_dynamic set to None in some cases. return (dtype_config.input_dtype is None) or ( dtype_config.input_dtype == qconfig_dtype and bool(dtype_config.is_dynamic) == bool(qconfig_is_dynamic) and _qconfig_satisfies_dtype_config_constraints( qconfig, dtype_config.input_dtype_with_constraints ) ) elif is_weight: # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well weight_obs_or_fq_ctr = node.meta["target_dtype_info"].get( "weight_obs_or_fq_ctr", None ) weight_obs_or_fq = weight_obs_or_fq_ctr() if weight_obs_or_fq_ctr else None qconfig_weight_dtype, _ = _get_dtype_and_is_dynamic(weight_obs_or_fq) backend_config_weight_dtype = dtype_config.weight_dtype dtype_matches = qconfig_weight_dtype == backend_config_weight_dtype qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints( qconfig, dtype_config.weight_dtype_with_constraints, is_activation=False ) return backend_config_weight_dtype is None or ( dtype_matches and qconfig_satisfies_constraints ) else: # bias # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well bias_obs_or_fq_ctr = node.meta["target_dtype_info"].get( "bias_obs_or_fq_ctr", None ) bias_obs_or_fq = bias_obs_or_fq_ctr() if bias_obs_or_fq_ctr else None qconfig_bias_dtype, _ = _get_dtype_and_is_dynamic(bias_obs_or_fq) backend_config_bias_dtype = dtype_config.bias_dtype return ( backend_config_bias_dtype is None or qconfig_bias_dtype == backend_config_bias_dtype ) def _is_output_dtype_supported_by_backend( node: Node, qconfig: QConfigAny, dtype_config: DTypeConfig, ) -> bool: """Check if the configured qconfig for the output is supported by the backend or not """ # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well backend_config_output_dtype = dtype_config.output_dtype # TODO: we should check is_dynamic here as well, the code from _is_input_arg_dtype_supported_by_backend # from input activation check can be reused here qconfig_output_dtype = None output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get( "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR ) output_act_obs_or_fq = ( output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None ) qconfig_output_dtype, qconfig_output_is_dynamic = _get_dtype_and_is_dynamic( output_act_obs_or_fq ) # TODO: this is a hack because we can only specify one activation_obs_or_fq for # qconfig (qconfig.activation), and we are only supporting dynamically quantized # linear op which has fp32 output dtype, this should be removed if we generalize # the structure of qconfig in the future if qconfig_output_is_dynamic: qconfig_output_dtype = torch.float32 dtype_matches = qconfig_output_dtype == backend_config_output_dtype qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints( qconfig, dtype_config.output_dtype_with_constraints ) return backend_config_output_dtype is None or ( dtype_matches and qconfig_satisfies_constraints ) def _is_observer_in_same_graph( node: Node, named_modules: dict[str, torch.nn.Module], obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat, ): """Check if observer in same graph when the node output is not fp32 and input is 'placeholder' the input is assumed to be quantized, so it is observed in a different place rather than not observed. """ node_output_dtype = _get_arg_target_dtype_as_output( node, named_modules, obs_or_fq_map, is_qat ) if len(node.args) > 0 and isinstance(node.args[0], Node): if ( node_output_dtype in [torch.quint8, torch.uint8] and node.args[0].op == "placeholder" ): return False return True def _is_pattern_dtype_config_and_qconfig_supported_by_backend( pattern: Optional[Pattern], matched_node_pattern: Optional[list[Node]], qconfig: QConfigAny, backend_config: BackendConfig, ) -> bool: """Check if the dtype configuration of a pattern is supported by the backend or not, and whether the qconfig satisfies constraints specified in the corresponding dtype config. """ if backend_config is None or pattern is None: return True assert matched_node_pattern is not None and len(matched_node_pattern) >= 1 pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config) dtype_configs: list[DTypeConfig] = pattern_to_dtype_configs.get(pattern, []) pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(backend_config) root_node_getter = pattern_to_root_node_getter.get( pattern, _default_root_node_getter ) root_node = root_node_getter(matched_node_pattern) input_node = root_node output_node = matched_node_pattern[0] for dtype_config in dtype_configs: # check if arg dtype are supported supported = True for arg in list(input_node.args) + list(input_node.kwargs.values()): supported = supported and _is_input_arg_dtype_supported_by_backend( arg, input_node, qconfig, dtype_config, backend_config ) # check if output dtype is supported supported = supported and _is_output_dtype_supported_by_backend( output_node, qconfig, dtype_config ) if supported: return True return False def _get_standalone_module_configs( node: Node, named_modules: dict[str, torch.nn.Module], prepare_custom_config: PrepareCustomConfig, parent_qconfig: QConfigAny, parent_backend_config: Optional[BackendConfig], ) -> tuple[ QConfigMapping, tuple[Any, ...], PrepareCustomConfig, Optional[BackendConfig] ]: """ Returns the standalone module QConfigMapping and PrepareCustomConfig for `node`, assuming that the module pointed to by `node` is a standalone modules. """ module_name = str(node.target) module_type = type(named_modules[module_name]) # type: ignore[index] # name config has precedence over type config config_entry = StandaloneModuleConfigEntry(None, (), None, None) config_entry = prepare_custom_config.standalone_module_classes.get( module_type, config_entry ) config_entry = prepare_custom_config.standalone_module_names.get( module_name, config_entry ) # fallback to use parent module's qconfig if user didn't specify qconfig dict qconfig_mapping = config_entry.qconfig_mapping or QConfigMapping().set_global( parent_qconfig ) example_inputs = config_entry.example_inputs prepare_custom_config = config_entry.prepare_custom_config or PrepareCustomConfig() backend_config = config_entry.backend_config or parent_backend_config return (qconfig_mapping, example_inputs, prepare_custom_config, backend_config) def _qat_swap_modules( root: torch.nn.Module, module_to_qat_module: dict[Pattern, type[torch.nn.Module]] ) -> None: convert(root, mapping=module_to_qat_module, inplace=True, remove_qconfig=False) def _add_matched_node_name_to_set(matched_node_pattern: NodePattern, s: set[str]): if isinstance(matched_node_pattern, Node): s.add(matched_node_pattern.name) elif isinstance(matched_node_pattern, (list, tuple)): for maybe_node in matched_node_pattern: _add_matched_node_name_to_set(maybe_node, s) def _insert_obs_or_fq( node: Node, obs_or_fq: ObserverOrFakeQuantize, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, ) -> Node: """ Attaches `obs_or_fq` to `model`, and creates a node which calls `obs_or_fq` on the output of `node`. obs_or_fq: an instance of Observer or FakeQuantize module """ model_device = assert_and_get_unique_device(model) if model_device: obs_or_fq.to(model_device) # add obs_or_fq module as attribute if is_equalization_observer(obs_or_fq): prefix = node.name + "_equalization_process_" else: prefix = "activation_post_process_" get_new_obs_or_fq_name = get_new_attr_name_with_prefix(prefix) obs_or_fq_name = get_new_obs_or_fq_name(model) setattr(model, obs_or_fq_name, obs_or_fq) named_modules[obs_or_fq_name] = obs_or_fq with graph.inserting_after(node): new_obs = graph.create_node("call_module", obs_or_fq_name, (node,), {}) return new_obs def _set_target_dtype_info_for_matched_node_pattern( matched_node_pattern: NodePattern, last_node: Node, qconfig: QConfigAny, qhandler: Optional[QuantizeHandler], backend_config: BackendConfig, named_modules: dict[str, torch.nn.Module], cache_for_no_tensor_check: dict[Node, bool], processed_nodes: set[Node], ) -> None: """Sets the target_dtype_info for each node in matched_node_pattern Note: processed_nodes is used to ensure we only process each node once """ if isinstance(matched_node_pattern, (list, tuple)): for node_pattern in matched_node_pattern: _set_target_dtype_info_for_matched_node_pattern( node_pattern, last_node, qconfig, qhandler, backend_config, named_modules, cache_for_no_tensor_check, processed_nodes, ) # set target_dtype_info if matched_node_pattern is a Node # other types of matched object, e.g. int, float literals, are ignored elif isinstance(matched_node_pattern, Node): # for pyre assert isinstance(matched_node_pattern, Node) node = matched_node_pattern if node in processed_nodes: return processed_nodes.add(node) if qconfig is None: return # TODO: refactor the following code in terms of apply a qconfig to a pattern # e.g. for a pattern with op1 -> op2 -> op3, and qconfig = QConfig(input_act=obs0, output_act=obs1) # we set the input_obs_or_fq_ctr for the arguments of op1 to based on qconfig.input_act, # and set output_obs_or_fq_ctr based on qconfig.output_act # this also requires we extend the structure of QConfig to support more fine # grained configurations target_dtype_info: dict[str, Any] = _get_target_activation_dtype_for_node( node, qconfig, qhandler, named_modules, backend_config, cache_for_no_tensor_check, ) node.meta["target_dtype_info"] = target_dtype_info def _get_target_activation_dtype_for_node( node: Node, qconfig: QConfigAny, qhandler: Optional[QuantizeHandler], named_modules: dict[str, torch.nn.Module], backend_config: BackendConfig, cache_for_no_tensor_check: dict[Node, bool], ) -> dict[str, Any]: """ For each op attribute in the op's input activation, output activation, weight, bias - returns the settings of dtype and is_dynamic we expect for the `quantize` call in the reference model representation, or None if there is no `quantize` call needed. For example, if we have a node corresponding to `op0` in x0 -> op0 -> x1 And we want a reference quantized representation to be x0 -> quant_static -> dequant -> op0 -> quant_dynamic -> dequant -> x1 Then this function will return { "input_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False), "output_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False), } TODO(future PR, if needed): explicitly spell out the non-Tensor dtypes. """ args_have_no_tensors = all_node_args_have_no_tensors( node, named_modules, cache_for_no_tensor_check ) if args_have_no_tensors: return { "input_act_obs_or_fq_ctr": None, "output_act_obs_or_fq_ctr": None, } # get qconfig to determine the eventual dtype of this node if qconfig is not None: act_dtype, weight_dtype, input_act_is_dynamic = get_qconfig_dtypes(qconfig) # Currently `QConfig` only has one `activation` field. # For static quantization, it is reused for both input # and output activation. For dynamic quantization, this # field is currently only used for the input activation, # with the output activation being in fp32. # In the future this may change as we add more fields # to the `QConfig` object. bias_dtype = ( torch.float16 if ( act_dtype == torch.float16 and weight_dtype == torch.float16 and (not input_act_is_dynamic) ) else torch.float ) is_general_tensor_value_op = ( qhandler is not None and qhandler.is_general_tensor_value_op() ) _is_standalone_module = qhandler is not None and qhandler.is_standalone_module() weight_index = None if ( isinstance(node, Node) and node.op == "call_function" and node.target in backend_config._pattern_complex_format_to_config ): weight_index = backend_config._pattern_complex_format_to_config[ node.target ]._input_type_to_index.get("weight") bias_index = None if ( isinstance(node, Node) and node.op == "call_function" and node.target in backend_config._pattern_complex_format_to_config ): bias_index = backend_config._pattern_complex_format_to_config[ node.target ]._input_type_to_index.get("bias") return { "input_act_obs_or_fq_ctr": qconfig.activation, "weight_obs_or_fq_ctr": qconfig.weight, "bias_obs_or_fq_ctr": PlaceholderObserver.with_args(dtype=bias_dtype), "weight_index": weight_index, "bias_index": bias_index, "output_act_obs_or_fq_ctr": qconfig.activation, "reuse_input_obs_or_fq": _is_reuse_input_qconfig(qconfig), "input_output_share_observers": is_general_tensor_value_op, "_is_standalone_module": _is_standalone_module, } return copy.copy(_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO) def _get_output_act_obs_or_fq( arg: Node, named_modules: dict[str, torch.nn.Module], obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ) -> Optional[ObserverOrFakeQuantize]: """Get the constructor for observer or fake quant object for the argument in the original graph as the output of previous node, skipping inserted observers We are assuming that the observers are inserted correctly, and the dtype for argument in quantized graph will match what is specified by the qconfig """ assert isinstance(arg, Node) if "quantization_annotation" in arg.meta: return _create_obs_or_fq_from_qspec( arg.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat ) # Custom module LSTM output is a tuple that we broke down into the internal nodes in order # to insert DeQuantStubs (see `_insert_dequant_stubs_for_custom_module_lstm_output`). # Since we modified the graph in this case, we must trace back from the args through # the specific nodes we added in order to reach the original LSTM node. Otherwise, we would # not be able to accurately detect whether this node is a consumer of custom module LSTM. custom_module_lstm_node = _maybe_get_custom_module_lstm_from_node_arg( arg, named_modules ) output_act_obs_or_fq_ctr = None if custom_module_lstm_node is not None: output_act_obs_or_fq_ctr = custom_module_lstm_node.meta["target_dtype_info"][ "output_act_obs_or_fq_ctr" ] output_act_obs_or_fq = ( output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None ) elif _is_activation_post_process_node(arg, named_modules): observed_arg = arg.args[0] assert isinstance( observed_arg, Node ), "Currently we only support observing Node" if "quantization_annotation" in observed_arg.meta: output_act_obs_or_fq = _create_obs_or_fq_from_qspec( observed_arg.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat, ) else: assert "target_dtype_info" in observed_arg.meta output_act_obs_or_fq_ctr = observed_arg.meta["target_dtype_info"][ "output_act_obs_or_fq_ctr" ] output_act_obs_or_fq = ( output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None ) else: if "target_dtype_info" in arg.meta: output_act_obs_or_fq_ctr = arg.meta["target_dtype_info"].get( "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR ) else: output_act_obs_or_fq_ctr = _DEFAULT_FP32_OBS_OR_FQ_CTR output_act_obs_or_fq = ( output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None ) return output_act_obs_or_fq def _get_arg_target_dtype_as_output( arg: Node, named_modules: dict[str, torch.nn.Module], obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ) -> Optional[torch.dtype]: arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq( arg, named_modules, obs_or_fq_map, is_qat ) arg_as_output_target_dtype, _ = _get_dtype_and_is_dynamic( arg_as_output_act_obs_or_fq ) return arg_as_output_target_dtype def _get_arg_as_input_act_obs_or_fq( arg: Node, node: Node, named_modules: dict[str, torch.nn.Module], obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ) -> Optional[ObserverOrFakeQuantize]: """Get the observer or fake quant constructor for the Argument `arg`, as input to Node `node` """ assert isinstance(arg, Node) # "input_qspec_map" is the more general design we'll use for pt2e path # it is a map from input argument node to observer or fake quant constructor, for example # for the following graph: # x -> conv -> output # # we may annotate conv node like the following: # conv.meta[...] = QuantizationAnnotation("input_qspec_map": {x: MinMaxObserver.with_args(dtype=torch.qint8)}, ...) # if "quantization_annotation" in node.meta: input_qspec_map = node.meta["quantization_annotation"].input_qspec_map input_arg_qspec = _get_qspec_for_arg(arg, input_qspec_map, named_modules) if input_arg_qspec is None: input_arg_obs_or_fq = _DEFAULT_FP32_OBS_OR_FQ_CTR() else: input_arg_obs_or_fq = _create_obs_or_fq_from_qspec( input_arg_qspec, obs_or_fq_map, is_qat ) return input_arg_obs_or_fq # we can remove the following path in the future if fx graph mode quantization is # no longer used is_weight = node_arg_is_weight(node, arg) is_bias = node_arg_is_bias(node, arg) is_activation = not is_weight and not is_bias obs_or_fq_ctr = None if is_activation: obs_or_fq_ctr = node.meta["target_dtype_info"].get( "input_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR ) elif is_weight: if node.target not in NON_QUANTIZABLE_WEIGHT_OPS: obs_or_fq_ctr = node.meta["target_dtype_info"].get( "weight_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR ) else: obs_or_fq_ctr = node.meta["target_dtype_info"].get( "bias_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR ) return obs_or_fq_ctr() if obs_or_fq_ctr else None def _maybe_insert_input_observer_for_arg_or_kwarg( node: Union[Node, Any], arg: Argument, qconfig: QConfigAny, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, qhandler: Optional[QuantizeHandler], prepare_custom_config: PrepareCustomConfig, obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, backend_config: Optional[BackendConfig] = None, ) -> Argument: """ Given a `node` and an `arg`, inserts an input observer between `node` and `arg` if necessary. """ # for ops such as torch.cat([x0, x1]), # traverse through the list if isinstance(arg, (list, tuple)): new_arg_to_return = [] for inner_arg in arg: new_inner_arg = _maybe_insert_input_observer_for_arg_or_kwarg( node, inner_arg, qconfig, model, named_modules, graph, qhandler, prepare_custom_config, obs_or_fq_map, is_qat, backend_config, ) new_arg_to_return.append(new_inner_arg) return type(arg)(new_arg_to_return) if not isinstance(arg, Node): return arg assert isinstance(arg, Node) # default (no observer) new_arg = arg is_standalone_module = qhandler is not None and qhandler.is_standalone_module() # TODO: move this to a separate function if not is_standalone_module: # Note: qconfig can be None in this branch this we are getting act/fq from # node.meta now # regular flow for most nodes, except standalone modules if "quantization_annotation" in node.meta: reuse_input_obs_or_fq = node.meta[ "quantization_annotation" ]._reuse_input_obs_or_fq else: assert "target_dtype_info" in node.meta # TODO: we are assuming "target_dtype_info" exists here, maybe # a default value also need to be provided here target_dtype_info = node.meta["target_dtype_info"] # for nodes that doesn't have `reuse_input_obs_or_fq` configured, # we'll default to False, this makes configuring this field optional for users reuse_input_obs_or_fq = target_dtype_info.get( "reuse_input_obs_or_fq", False ) arg_as_input_act_obs_or_fq = _get_arg_as_input_act_obs_or_fq( arg, node, named_modules, obs_or_fq_map, is_qat ) ( arg_as_input_target_dtype, arg_as_input_target_is_dynamic, ) = _get_dtype_and_is_dynamic(arg_as_input_act_obs_or_fq) arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq( arg, named_modules, obs_or_fq_map, is_qat ) ( arg_as_output_target_dtype, arg_as_output_target_is_dynamic, ) = _get_dtype_and_is_dynamic(arg_as_output_act_obs_or_fq) needs_obs_or_fq = _needs_obs_or_fq( arg_as_output_target_dtype, arg_as_output_target_is_dynamic, arg_as_input_target_dtype, arg_as_input_target_is_dynamic, reuse_input_obs_or_fq, is_zeroth_arg=len(node.args) > 0 and arg is node.args[0], ) else: assert qconfig is not None # custom flow for standalone modules _, _, sm_prepare_custom_config, _ = _get_standalone_module_configs( node, named_modules, prepare_custom_config, qconfig, backend_config ) sm_input_quantized_idxs = sm_prepare_custom_config.input_quantized_indexes # for args, this is set to the index of the current arg # for kwargs, this is left at None cur_input_idx = None for arg_idx, arg_to_check in enumerate(node.args): if arg_to_check is arg: cur_input_idx = arg_idx break if cur_input_idx is None: needs_obs_or_fq = False else: arg_as_output_target_dtype = _get_arg_target_dtype_as_output( arg, named_modules, obs_or_fq_map, is_qat ) arg_as_input_target_dtype = ( torch.quint8 if cur_input_idx in sm_input_quantized_idxs else torch.float ) needs_obs_or_fq = ( arg_as_output_target_dtype != arg_as_input_target_dtype ) and (arg_as_input_target_dtype != torch.float) act_post_process_ctr = qconfig.activation arg_as_input_act_obs_or_fq = ( act_post_process_ctr() if act_post_process_ctr else None ) if needs_obs_or_fq: existing_obs_node = None # Before using the new observer, check if an observer # of the correct type already exists. If it does, use it. # This prevents duplicate observer insertions if a node is # used by multiple nodes. # TODO: this is looking into how the value is used in the future # we should remove this # removing this means we insert one observer for each use, even if they # have the same dtype, we can have an extra pass that removes the extra observers for maybe_obs_node in arg.users.keys(): if maybe_obs_node.op == "call_module": maybe_obs_mod = named_modules[maybe_obs_node.target] # type: ignore[index] if ( type(maybe_obs_mod) == type(arg_as_input_act_obs_or_fq) and maybe_obs_mod.dtype == arg_as_input_target_dtype # type: ignore[possibly-undefined] ): arg_as_input_act_obs_or_fq = maybe_obs_mod # type: ignore[assignment] existing_obs_node = maybe_obs_node break assert arg_as_input_act_obs_or_fq is not None obs_or_fq_map[(arg, node)] = arg_as_input_act_obs_or_fq if existing_obs_node is None: new_obs_node = _insert_obs_or_fq( arg, arg_as_input_act_obs_or_fq, model, named_modules, graph ) # override this arg to be the observed arg new_arg = new_obs_node else: new_arg = existing_obs_node return new_arg def _maybe_insert_input_observers_for_node( node: Node, qconfig: QConfigAny, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, qhandler: Optional[QuantizeHandler], prepare_custom_config: PrepareCustomConfig, obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, backend_config: Optional[BackendConfig] = None, ) -> None: """ If needed, inserts observers to the input args and kwargs of `node`. Note: modifies `node` inplace. For example, if cur_node needs an observer after prev_node, we change from prev_node -> cur_node To prev_node -> obs -> cur_node Note: backend_config only needed for standalone_module node """ # Look through every input arg. If that arg's target dtype does not # match the current node's target dtype, insert an observer. new_args = [] for arg in node.args: new_arg = _maybe_insert_input_observer_for_arg_or_kwarg( node, arg, qconfig, model, named_modules, graph, qhandler, prepare_custom_config, obs_or_fq_map, is_qat, backend_config, ) new_args.append(new_arg) new_kwargs = {} for k, kwarg in node.kwargs.items(): new_kwarg = _maybe_insert_input_observer_for_arg_or_kwarg( node, kwarg, qconfig, model, named_modules, graph, qhandler, prepare_custom_config, obs_or_fq_map, is_qat, backend_config, ) new_kwargs[k] = new_kwarg # assign the new args and kwargs to the node, inplace node.args = tuple(new_args) node.kwargs = new_kwargs def _maybe_insert_input_equalization_observers_for_node( node: Node, equalization_qconfig: Any, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, is_branch: bool, ) -> None: """ If `node` needs to be equalized, find the input/weight observers it needs in `equalization_qconfig`, creates them, and inserts it into `graph`. If `node` does not need an equalization observer, returns None. """ if equalization_qconfig is None or not node_supports_equalization( node, named_modules ): return if is_branch: warnings.warn(f"Cannot equalize {node} because it is part of a branch.") return new_args = [] for arg in node.args: if not isinstance(arg, Node) or node_arg_is_bias(node, arg): new_args.append(arg) continue is_weight = node_arg_is_weight(node, arg) act_eq_process_ctr = ( equalization_qconfig.weight if is_weight else equalization_qconfig.input_activation ) new_eq_obs_mod = act_eq_process_ctr() new_eq_obs_node = _insert_obs_or_fq( arg, new_eq_obs_mod, model, named_modules, graph ) new_args.append(new_eq_obs_node) # assign the new args and kwargs to the node, inplace node.args = tuple(new_args) def _maybe_insert_output_observer_for_node( node: Node, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ) -> Optional[Node]: """ If `node` needs an output observer, creates it, inserts it into `graph` and returns it. If `node` does not need an output observer, returns None. Note: inserting dynamic quantization ops for output is not supported in fx graph mode quantization code path right now """ assert node.op != "output", "observer insertion for outputs is handled elsewhere" is_standalone_module = False if "quantization_annotation" in node.meta: output_act_obs_or_fq = _create_obs_or_fq_from_qspec( node.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat ) else: assert "target_dtype_info" in node.meta is_standalone_module = node.meta["target_dtype_info"].get( "_is_standalone_module", False ) output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get( "output_act_obs_or_fq_ctr" ) output_act_obs_or_fq = ( output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None ) target_dtype, target_is_dynamic = _get_dtype_and_is_dynamic(output_act_obs_or_fq) # uncomment after we support reuse_input_obs_or_fq properly by having separate # implemntations for this key instead of reusing the input_output_share_observers # code # reuse_input_obs_or_fq = node.meta["target_dtype_info"].get("reuse_input_obs_or_fq", False) # for now we set this to False since reuse_input_obs_or_fq for # the output of a node is implementation in the same code path as observer sharing, # we should refactor this part to make it clearer in the future # and we would be able to read this from config directly reuse_input_obs_or_fq = False # Note: prev_output_dtype = torch.float and prev_output_is_dynamic=False # because the prev_output is the output of an fp32 op, althought technically # we should get the dtype of the output from node.meta["val"] in the future # if we deprecate fx graph mode quantization needs_obs_or_fq = _needs_obs_or_fq( torch.float, False, target_dtype, target_is_dynamic, reuse_input_obs_or_fq ) # currently the activation in QConfig(activation=...,) is for both input # and output, and when the activation is configured to be dynamic quantization # e.g. PlaceholderObserver(dtype=torch.quint8, is_dynamic=True, ...), it means # the input should by dynamically quantized, but output should not be quantized # # there is no way we can specify different observer/fq for input and output # activation through QConfig today, this limitation is lifted in the # quantizer/annotation API in pytorch 2.0 export quantization code path, # but since this code is reused, annotating output to be dynamically quantized # would not work either for that. # we can change QConfig to support input/output activation if we want # to remove the following check, or if we can deprecate fx graph mode quantization if target_is_dynamic: needs_obs_or_fq = False # we never insert observers to output of standalone module, we assume # if needed, they are inserted inside the standalone module needs_obs_or_fq = needs_obs_or_fq and (not is_standalone_module) if needs_obs_or_fq: obs_or_fq_map[node] = output_act_obs_or_fq return _insert_obs_or_fq( node, output_act_obs_or_fq, model, named_modules, graph ) else: return None def _maybe_insert_observers_before_graph_output( graph_output_node: Node, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize], is_qat: bool, ) -> None: """ If the output needs to be quantized and there are any nodes in the output which are not already observed, inserts observers for those nodes. """ def _recursive_maybe_replace_node_with_obs( maybe_node: Argument, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], graph: Graph, ) -> Argument: """ Navigate an arbitrary data structure of lists, tuples, dicts. For each container type, recurse on all inputs. Once any Node is found, insert an observer if needed and do not recurse further. For example, given a structure of {'foo1': [[bar1]], 'foo2': {'foo3': [[[bar3]]]}} we recurse down to bar1 and bar3, observe them if necessary, and if we inserted an observer then replace the original node with its observer. Returns the data structure with all nodes needing observation being replaced by their observers. """ if isinstance(maybe_node, Node): # check dtype of this node arg_as_output_target_dtype = _get_arg_target_dtype_as_output( maybe_node, named_modules, obs_or_fq_map, is_qat ) observer_mod = None arg_as_input_target_dtype = torch.float if "target_dtype_info" in maybe_node.meta: observer_cls = maybe_node.meta["target_dtype_info"].get( "input_act_obs_or_fq_ctr", None ) if observer_cls is not None: observer_mod = observer_cls() arg_as_input_target_dtype = observer_mod.dtype # TODO: this does not handle dynamic quantization yet need_obs = ( arg_as_output_target_dtype != arg_as_input_target_dtype and arg_as_input_target_dtype != torch.float ) if need_obs: assert observer_mod is not None # insert observer observer_node = _insert_obs_or_fq( maybe_node, observer_mod, model, named_modules, graph ) return observer_node else: return maybe_node elif isinstance(maybe_node, (list, tuple)): results = [ _recursive_maybe_replace_node_with_obs( inner_node, model, named_modules, graph ) for inner_node in maybe_node ] if isinstance(maybe_node, list): return results else: return tuple(results) elif isinstance(maybe_node, dict): results_dict = {} for k, inner_v in maybe_node.items(): results_dict[k] = _recursive_maybe_replace_node_with_obs( inner_v, model, named_modules, graph ) return results_dict elif maybe_node is None: return None else: raise Exception( # noqa: TRY002 "Unhandled type for returned node:", maybe_node ) new_args = [ _recursive_maybe_replace_node_with_obs(old_arg, model, named_modules, graph) for old_arg in graph_output_node.args ] graph_output_node.args = tuple(new_args) # type: ignore[assignment] def _maybe_propagate_dtype_for_node( node: Node, target_dtype: Union[torch.dtype, type], node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig], ) -> None: """ Assigns `target_dtype` to `node`, setting `is_dynamic` to False. If `node` is a general tensor shape op, also call this function recursively on the first argument, to propagate the dtype to the caller. """ node.meta["target_dtype_info"]["input_act_obs_or_fq_ctr"] = None node.meta["target_dtype_info"]["output_act_obs_or_fq_ctr"] = None # if this is a copy node, propagate to first arg ( _root_node, _, _pattern, qhandler, _qconfig, ) = node_name_to_match_result_with_qconfig.get( node.name, (None, None, None, None, None) ) # TODO: probably need to remove `is_general_tensor_value_op` if qhandler is not None and qhandler.is_general_tensor_value_op(): prev_node = node.args[0] if isinstance(prev_node, Node): _maybe_propagate_dtype_for_node( prev_node, target_dtype, node_name_to_match_result_with_qconfig ) def propagate_dtypes_for_known_nodes( graph: Graph, node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig], ) -> None: """ Currently we assume that inputs to the graph are either `torch.float` or `torch.quint8`, which is not always correct. For ops such as `x.masked_fill(mask, value)`, we know that the dtype of `mask` is a `BoolTensor`. Propagate this information throughout the graph. Note: not all dtypes in the graph will be correct after this pass, but a higher percentage of them will be correct. Hopefully in the future we can replace this with a better way to reason about dtypes of tensors. """ for node in graph.nodes: non_observable_arg_dict = get_non_observable_arg_indexes_and_types(node) for arg_type in non_observable_arg_dict: non_observable_indices = non_observable_arg_dict[arg_type](node) for index in non_observable_indices: arg = node.args[index] # when an argument is a tuple, it does not show up as another node so we need to go through # all elements of the tuple manually if isinstance(arg, (tuple, list)): arg_list = list(arg) else: arg_list = [arg] for cur_arg in arg_list: # hard coded arguments show up but aren't `Node` typed and do not need dtype propagated if isinstance(cur_arg, torch.fx.node.Node): _maybe_propagate_dtype_for_node( cur_arg, arg_type, node_name_to_match_result_with_qconfig ) def _maybe_make_input_output_share_observers( node: Node, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module], ) -> bool: """ Ensures that we share an observer for all input arguments as well as the output argument. In detail, given a graph of x0 -> obs0 -> op -> x2 / x1 -> obs1 / where node obs0 points to observer instance observer0, obs1 points to observer1 and obs2 points to observer2, we make nodes obs1 and ob2 point to observer0. Returns: whether the operation succeeded or not """ first_arg = None # find the first non-Tensor arg for i in range(len(node.args)): if isinstance(node.args[i], (Node, list, tuple)): first_arg = node.args[i] break # if there is no non-Tensor arg, return directly if first_arg is None: return False if isinstance(first_arg, (list, tuple)): first_arg_arg = first_arg[0] elif isinstance(first_arg, Node): first_arg_arg = first_arg else: return False # if we have a graph such as # observed_node -> non_observed_node -> cat # we need to navigate up to the first observer iteration_guard = 0 while not _is_activation_post_process_node(first_arg_arg, named_modules): if not isinstance(first_arg_arg, Node): return False # did not find an activation_post_process for the op if first_arg_arg.op == "placeholder": return False # trace back the args until we found the first Tensor/Node trace_back_node = None for i in range(len(first_arg_arg.args)): trace_back_node = first_arg_arg.args[i] if isinstance(trace_back_node, Node): break if trace_back_node is None: return False first_arg_arg = trace_back_node iteration_guard += 1 if iteration_guard > 10000: raise AssertionError("Unable to find observer of previous node") assert isinstance(first_arg_arg, Node) target_to_use = first_arg_arg.target assert isinstance(target_to_use, str) obs_mod_to_use = named_modules[target_to_use] if isinstance(first_arg, (list, tuple)): # set all other input observer nodes to use that module for input_idx, input_arg in enumerate(first_arg): if input_idx == 0: continue iteration_guard = 0 while not _is_activation_post_process_node(input_arg, named_modules): # failed to trace back since no input arg for the current node if len(input_arg.args) < 1: return False input_arg = input_arg.args[0] iteration_guard += 1 if iteration_guard > 10000: raise AssertionError("Unable to find observer of previous node") parent_name, name = _parent_name(input_arg.target) setattr(named_modules[parent_name], name, obs_mod_to_use) # set the output observer node to use that module for output_obs_node in node.users.keys(): assert _is_activation_post_process_node(output_obs_node, named_modules) parent_name, name = _parent_name(output_obs_node.target) setattr(named_modules[parent_name], name, obs_mod_to_use) # TODO(future PR): delete the orphaned observer modules return True def _remove_output_observer( node: Node, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module] ): items = list(node.users.items()) for output_obs_node, _ in items: assert _is_activation_post_process_node(output_obs_node, named_modules) output_obs_node.replace_all_uses_with(node) model.graph.erase_node(output_obs_node) # type: ignore[union-attr, operator] def _swap_custom_module_to_observed( node: Node, qconfig: QConfigAny, named_modules: dict[str, torch.nn.Module], prepare_custom_config: PrepareCustomConfig, ): custom_module = named_modules[node.target] # type: ignore[index] custom_module_class_mapping = prepare_custom_config.float_to_observed_mapping observed_custom_module_class = get_swapped_custom_module_class( custom_module, custom_module_class_mapping, qconfig ) observed_custom_module = observed_custom_module_class.from_float(custom_module) parent_name, name = _parent_name(node.target) setattr(named_modules[parent_name], name, observed_custom_module) def insert_observers_for_model( model: GraphModule, node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig], node_name_to_qconfig: dict[str, QConfigAny], prepare_custom_config: PrepareCustomConfig, equalization_config_map: dict[str, Any], backend_config: BackendConfig, observed_node_names: set[str], is_qat: bool, ) -> Optional[Node]: """ Inserts observers, using the following high level algorithm: For each node in the graph: 1. determine the target dtype of this node in the quantized graph, and save it for future steps 2. determine the target dtype or all args and kwargs of this node 3. if any arg or kwarg's target dtype does not match the current node's dtype, insert an observer 4. if the current node needs an output observer, insert it For example: - starting graph: x0 -> linear -> x1 - observed graph after processing x0: x0(fp32) - observed graph after processing linear: x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8) - observed graph after processing x1: x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8) -> x1 After a node is processed, the naive observer placement is guaranteed to be complete for that node and all of its predecessors. There can be future passes which optimize the graph by deduplicating observers, etc. """ # node.meta["target_dtype_info"] stores the target dtype information # that's derived from qconfig for the Node, for example, if we have # a conv2d node that has a qconfig # qconfig = QConfig(activation=..., weight=...) # # information for input and bias node omitted # # for getattr node # # weight = getattr(self, 'weight') # weight.meta["target_dtype_info"] = { # 'output_act_obs_or_fq_ctr': qconfig.weight, # } # # for conv2d node # # conv2d = call_function[target=torch.nn.functional.conv2d]( # # args=(input, weight, bias)) # conv2d.meta["target_dtype_info"] = { # 'input_act_obs_or_fq_ctr': qconfig.activation # 'weight_obs_or_fq_ctr': qconfig.weight, # 'bias_obs_or_fq_ctr': PlaceholderObserver.with_args(dtype=torch.float32), # 'output_act_obs_or_fq_ctr': qconfig.activation, # } # cache_for_no_tensor_check: dict[Node, bool] = {} # first, populate the dtype map based only on qconfig and qhandler # this assumes: # graph inputs are fp32 by default, and int8 where overriden # other nodes output dtype is specified by the qconfig named_modules = dict(model.named_modules(remove_duplicate=False)) input_quantized_idxs: list[int] = prepare_custom_config.input_quantized_indexes output_quantized_idxs: list[int] = prepare_custom_config.output_quantized_indexes processed_nodes: set[Node] = set() # initialize target_dtype_info for node in model.graph.nodes: node.meta["target_dtype_info"] = copy.copy( _DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO ) inputs_seen_counter = 0 outputs_seen_counter = 0 placeholder_node_to_input_index: dict[Node, int] = {} # TODO: we probably don't need this counter since each graph will only have # one output node? output_node_to_output_index: dict[Node, int] = {} for node in model.graph.nodes: if node.op == "placeholder": placeholder_node_to_input_index[node] = inputs_seen_counter inputs_seen_counter += 1 if node.op == "output": output_node_to_output_index[node] = outputs_seen_counter outputs_seen_counter += 1 # Step 1, set the observer or fake quantize module constructor for each node in the # matched_node_pattern for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values(): ( last_node, matched_node_pattern, pattern, qhandler, qconfig, ) = match_res_with_qconfig assert qhandler is not None _set_target_dtype_info_for_matched_node_pattern( matched_node_pattern, last_node, qconfig, qhandler, backend_config, named_modules, cache_for_no_tensor_check, processed_nodes, ) # Step 2. Special cases for some operators, we might be able to remove them # in the future if we know dtype information of each node better # Step 2.1. some settings are not based on patterns, we need to process each node # instead for node in model.graph.nodes: if ( node.op == "placeholder" and placeholder_node_to_input_index[node] in input_quantized_idxs ): # users are not supposed to call calculate_qparams on PlaceholderObserver, and # this is OK because we are using this as a way to encode the dtypes of input # tensor, we won't actually insert these observers in the graph and won't # actually call calculate_qparams node.meta["target_dtype_info"] = copy.copy( _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO ) elif node.op in ("call_module", "call_method", "call_function"): args_have_no_tensors = all_node_args_have_no_tensors( node, named_modules, cache_for_no_tensor_check ) if args_have_no_tensors: node.meta["target_dtype_info"] = { "input_act_obs_or_fq_ctr": None, "output_act_obs_or_fq_ctr": None, } elif ( node.op == "output" and output_node_to_output_index[node] in output_quantized_idxs ): # TODO(future PR): update the output_quantized_idxs API to match # arbitrary data structures. There is always a single output, and # that output can have arbitrary nesting of values. List[int] is # not the right data type for this. # TODO(future PR): support more dtypes in model outputs, if necessary node.meta["target_dtype_info"] = copy.copy( _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO ) # Step 2.2, for nodes with known input dtypes, propagate them throughout the # graph. For example, if there is a call such as # x1 = x0.masked_fill(mask, 1) # we propagate the type of mask to be torch.bool propagate_dtypes_for_known_nodes( model.graph, node_name_to_match_result_with_qconfig ) # Step 3, check if the requested target_dtype_info is supported by backend or not # if not, we'll reset the target_dtye_info to use the default (float Tensor) # reset the counters and set of processed_nodes processed_nodes: set[Node] = set() for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values(): ( last_node, matched_node_pattern, pattern, qhandler, qconfig, ) = match_res_with_qconfig is_supported_by_backend = ( _is_pattern_dtype_config_and_qconfig_supported_by_backend( pattern, matched_node_pattern, qconfig, backend_config ) ) assert qhandler is not None # get output_act_dtype so that we don't also reset the special typed nodes # TODO: we might want to handle these more uniformly with the default path # this can be improved if we can use node.meta["val"] output_act_or_fq_ctr = node.meta["target_dtype_info"][ "output_act_obs_or_fq_ctr" ] output_act_or_fq = output_act_or_fq_ctr() if output_act_or_fq_ctr else None output_act_dtype, _ = _get_dtype_and_is_dynamic(output_act_or_fq) if not is_supported_by_backend and output_act_dtype not in [ None, int, float, torch.bool, ]: # restore target_dtype_info to default if it is not supported by backend _set_target_dtype_info_for_matched_node_pattern( matched_node_pattern, last_node, torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig, None, backend_config, named_modules, cache_for_no_tensor_check, processed_nodes, ) # After this point, the current node and all of its arguments # have a target_dtype_info assigned. Now, we insert observers for inputs # of this node (if needed for this node), and the output of this node # (if needed for this node). # Since we are mutating the graph as we go, we iterate over the original # nodes before observer insertion, instead of model.graph.nodes. nodes_before_observation = list(model.graph.nodes) # Avoid duplicates custom module swaps for multiple nodes with same target. custom_module_names_already_swapped: set[str] = set() # TODO: reuse placeholder_node_to_input_index and output_node_to_output_index # reset inputs/outputs counters inputs_seen_counter = 0 outputs_seen_counter = 0 results_node = None obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize] = {} # TODO: change this to insert obs/fq by pattern instead of by node for node in nodes_before_observation: if node.op == "placeholder": # if a graph input is in fp32, it does not need observation # if a graph input is in int8, we assume the observation happens # outside of the graph, and no additional observation is needed pass elif node.op in ("call_module", "call_method", "call_function", "output"): # check for matches ( last_node, matched_node_pattern, pattern, qhandler, qconfig, ) = node_name_to_match_result_with_qconfig.get( # type: ignore[assignment] node.name, (None, None, None, None, None) ) equalization_qconfig = equalization_config_map.get(node.name, None) this_node_dtype_info = node.meta["target_dtype_info"] if "val" in node.meta: output_is_a_tensor = this_node_dtype_info is not None and isinstance( node.meta["val"], FakeTensor ) else: output_is_a_tensor = this_node_dtype_info is not None skip_inserting_observers = ( (qconfig is None) or not output_is_a_tensor ) and (not node.op == "output") # TODO: take a closer look to see if we can remove this check # right now it is here because of `observed_node_names`, we are using # it as an indicator for swapping the modules to reference modules in # convert is_supported_by_backend = ( _is_pattern_dtype_config_and_qconfig_supported_by_backend( pattern, matched_node_pattern, qconfig, backend_config ) ) if not skip_inserting_observers and is_supported_by_backend: named_modules = dict(model.named_modules(remove_duplicate=False)) if node.op != "output": assert matched_node_pattern is not None # add matched nodes to the observed node name set _add_matched_node_name_to_set( matched_node_pattern, observed_node_names ) # This is currently only used for equalization. # Checks if the current node is in a branch in which the two # first layers are both being quantized. # # ex. conv2 # / # x -> conv1 # # If this is the case, we will not apply equalization to the # initial two layers. is_quantized_branch = False if ( len(node.args) > 0 and isinstance(node.args[0], Node) and len(node.args[0].users) > 1 ): for user in node.args[0].users: # Checks if there exists another user being quantized is_user_quantized = node_name_to_qconfig.get( user.name, None ) is not None or ( user.op == "call_module" and isinstance( named_modules[str(user.target)], ObserverBase ) ) if user != node and is_user_quantized: is_quantized_branch = True pattern_to_root_node_getter = ( get_fusion_pattern_to_root_node_getter(backend_config) ) root_node_getter = pattern_to_root_node_getter.get( pattern, _default_root_node_getter ) root_node = root_node_getter(matched_node_pattern) is_input_node_of_the_pattern = node is root_node if is_input_node_of_the_pattern: # this modifies node inplace _maybe_insert_input_observers_for_node( node, qconfig, model, named_modules, model.graph, qhandler, prepare_custom_config, obs_or_fq_map, is_qat, backend_config, ) # insert equalization input observers if needed _maybe_insert_input_equalization_observers_for_node( node, equalization_qconfig, model, named_modules, model.graph, is_quantized_branch, ) is_last_node_of_pattern = node is last_node input_output_share_observers = node.meta["target_dtype_info"].get( "input_output_share_observers", False ) reuse_input_obs_or_fq = node.meta["target_dtype_info"].get( "reuse_input_obs_or_fq", False ) if is_last_node_of_pattern: if _is_custom_module_lstm( node, named_modules, qconfig, qhandler ): # Currently custom module outputs are assumed to be already quantized, # so we need to insert a DeQuantStub after the output. For custom module # LSTM specifically, the outputs are also a nested tuple, so we must first # break down the tuple to insert DeQuantStubs after the internal nodes. # TODO: This currently diverges from how custom modules are handled today, # where we insert observers after the output instead of DeQuantStubs, and # replace these observers with "dequantize" nodes during convert. Conceptually, # these output observers are the same as DeQuantStubs. In the future, we # should resolve this inconsistency by inserting DeQuantStubs for all custom # modules, not just for LSTM. _insert_dequant_stubs_for_custom_module_lstm_output( node, model, named_modules, model.graph ) if node.target not in custom_module_names_already_swapped: custom_module_names_already_swapped.add(node.target) _swap_custom_module_to_observed( node, qconfig, named_modules, prepare_custom_config ) else: # this returns the new observer node if it was needed maybe_output_obs_node = ( _maybe_insert_output_observer_for_node( node, model, named_modules, model.graph, obs_or_fq_map, is_qat, ) ) if maybe_output_obs_node is not None: # Update users of original node to use the output observer # instead. For example, change # # next_node # / # cur_node -> obs # # to # # next_node # / # cur_node -> obs # # We need to save orig users before updating uses because # the list of users will change as we update uses orig_users = list(node.users.keys()) for user_node in orig_users: if user_node is maybe_output_obs_node: continue user_node.replace_input_with( node, maybe_output_obs_node ) _is_observer_in_same_graph_ = ( _is_observer_in_same_graph( node, named_modules, obs_or_fq_map, is_qat ) ) # for ops whose inputs and outputs share observer/fqs, we modify the graph # to make all inputs and outputs use the first input's # observer/fq if ( input_output_share_observers and _is_observer_in_same_graph_ ) or reuse_input_obs_or_fq: if not _maybe_make_input_output_share_observers( node, model, named_modules ): _remove_output_observer( node, model, named_modules ) if qhandler is not None and qhandler.is_custom_module(): if ( node.target not in custom_module_names_already_swapped ): custom_module_names_already_swapped.add( node.target ) _swap_custom_module_to_observed( node, qconfig, named_modules, prepare_custom_config, ) else: # output _maybe_insert_observers_before_graph_output( node, model, named_modules, model.graph, obs_or_fq_map, is_qat ) # # After this point, the current node has input and output observers # that it needs for itself inserted. # # increment the counters, so future inputs and outputs are assigned # correct dtypes if node.op == "placeholder": inputs_seen_counter += 1 elif node.op == "output": outputs_seen_counter += 1 results_node = node return results_node def _run_prepare_fx_on_standalone_modules( model: torch.nn.Module, is_qat: bool, named_modules: dict[str, torch.nn.Module], node_name_to_match_result_with_qconfig: Any, prepare_custom_config: PrepareCustomConfig, backend_config: BackendConfig, ) -> None: """ Runs prepare_fx on each standalone module. Note: this does not modify the graph, it just replaces the unobserved modules with their observed versions. """ for ( root_node, _, _pattern, qhandler, qconfig, ) in node_name_to_match_result_with_qconfig.values(): if qhandler is None: continue elif not qhandler.is_standalone_module(): continue ( sm_qconfig_mapping, sm_example_inputs, sm_prepare_custom_config, sm_backend_config, ) = _get_standalone_module_configs( root_node, named_modules, prepare_custom_config, qconfig, backend_config ) standalone_module = named_modules[root_node.target] prepare = ( torch.ao.quantization.quantize_fx._prepare_standalone_module_fx ) # type: ignore[attr-defined] observed_standalone_module = prepare( standalone_module, sm_qconfig_mapping, is_qat, example_inputs=sm_example_inputs, prepare_custom_config=sm_prepare_custom_config, backend_config=sm_backend_config, ) parent_name, name = _parent_name(root_node.target) setattr(named_modules[parent_name], name, observed_standalone_module) named_modules[root_node.target] = observed_standalone_module def _save_state( observed: GraphModule, node_name_to_qconfig: dict[str, QConfigAny], node_name_to_scope: dict[str, tuple[str, type]], prepare_custom_config: PrepareCustomConfig, equalization_node_name_to_qconfig: dict[str, Any], qconfig_mapping: QConfigMapping, is_qat: bool, observed_node_names: set[str], ) -> None: observed.meta["_observed_graph_module_attrs"] = ObservedGraphModuleAttrs( node_name_to_qconfig=node_name_to_qconfig, node_name_to_scope=node_name_to_scope, prepare_custom_config=prepare_custom_config, equalization_node_name_to_qconfig=equalization_node_name_to_qconfig, qconfig_mapping=qconfig_mapping, is_qat=is_qat, observed_node_names=observed_node_names, ) def prepare( model: GraphModule, qconfig_mapping: Union[QConfigMapping, dict[str, Any]], is_qat: bool, node_name_to_scope: dict[str, tuple[str, type]], example_inputs: tuple[Any, ...], prepare_custom_config: Union[PrepareCustomConfig, dict[str, Any], None] = None, _equalization_config: Union[QConfigMapping, dict[str, Any], None] = None, backend_config: Union[BackendConfig, dict[str, Any], None] = None, is_standalone_module: bool = False, ) -> GraphModule: """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 Args: node_name_to_scope: mapping from node name to the scope of the module which contains the node. The scope is a tuple of fully qualified path of the module and the type of the module Returns: model(GraphModule): prepared standalone module attributes related to standalone module in model.meta["_observed_graph_module_attrs"]: is_observed_standalone_module (bool): boolean value that shows whether the current model is a observed standalone module or not 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 """ if prepare_custom_config is None: prepare_custom_config = PrepareCustomConfig() if _equalization_config is None: _equalization_config = QConfigMapping() if isinstance(qconfig_mapping, dict): warnings.warn( "Passing a QConfig dictionary to prepare is deprecated and will not be supported " "in a future version. Please pass in a QConfigMapping instead.", FutureWarning, stacklevel=2, ) qconfig_mapping = QConfigMapping.from_dict(qconfig_mapping) if isinstance(_equalization_config, dict): warnings.warn( "Passing a QConfig dictionary to prepare for equalization is deprecated and will not " "be supported in a future version. Please pass in a QConfigMapping instead.", FutureWarning, stacklevel=2, ) _equalization_config = QConfigMapping.from_dict(_equalization_config) if isinstance(prepare_custom_config, dict): warnings.warn( "Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported " "in a future version. Please pass in a PrepareCustomConfig instead.", FutureWarning, stacklevel=2, ) prepare_custom_config = PrepareCustomConfig.from_dict(prepare_custom_config) if isinstance(backend_config, dict): warnings.warn( "Passing a backend_config_dict to prepare is deprecated and will not be supported " "in a future version. Please pass in a BackendConfig instead.", FutureWarning, stacklevel=2, ) backend_config = BackendConfig.from_dict(backend_config) assert isinstance(qconfig_mapping, QConfigMapping) assert isinstance(_equalization_config, QConfigMapping) qconfig_mapping = copy.deepcopy(qconfig_mapping) _equalization_config = copy.deepcopy(_equalization_config) # mapping from a tuple of nodes in reverse order to uninitialized # QuantizeHandler subclass. For example, # { # # match a single node # (: # ), # # match multiple nodes in reverse order # ((, ): # ), # } pattern_to_quantize_handler: dict[Pattern, QuantizeHandler] = {} if backend_config is None: backend_config = get_native_backend_config() pattern_to_quantize_handler = _get_pattern_to_quantize_handlers(backend_config) pattern_to_quantize_handler = _sorted_patterns_dict(pattern_to_quantize_handler) root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config) _update_qconfig_for_fusion(model, qconfig_mapping) _update_qconfig_for_fusion(model, _equalization_config) flattened_qconfig_dict = _get_flattened_qconfig_dict(qconfig_mapping) # TODO: support regex as well propagate_qconfig_(model, flattened_qconfig_dict, prepare_custom_config.to_dict()) if is_qat: module_to_qat_module = get_module_to_qat_module(backend_config) _qat_swap_modules(model, module_to_qat_module) _update_qconfig_for_qat(qconfig_mapping, backend_config) # mapping from fully qualified module name to module instance # for example, # { # '': Model(...), # 'linear': Linear(...), # 'linear.weight_fake_quant': PerChannelMinMaxObserver(...), # } named_modules = dict(model.named_modules(remove_duplicate=False)) # fill node_name_to_qconfig, a map from node name to qconfig, used in _find_matches equalization_node_name_to_qconfig = _generate_node_name_to_qconfig( model, named_modules, model.graph, _equalization_config, node_name_to_scope ) node_name_to_qconfig = _generate_node_name_to_qconfig( model, named_modules, model.graph, qconfig_mapping, node_name_to_scope ) # match the patterns that will get quantized standalone_module_names = list(prepare_custom_config.standalone_module_names.keys()) standalone_module_classes = list( prepare_custom_config.standalone_module_classes.keys() ) custom_module_classes = get_custom_module_class_keys( prepare_custom_config.float_to_observed_mapping ) matches_without_qconfig = _find_matches( model.graph, named_modules, pattern_to_quantize_handler, root_node_getter_mapping, standalone_module_names, standalone_module_classes, custom_module_classes, ) # map qconfig instances to matches node_name_to_match_result_with_qconfig = {} for node_name, match_without_qconfig in matches_without_qconfig.items(): match_with_qconfig = (*match_without_qconfig, node_name_to_qconfig[node_name]) node_name_to_match_result_with_qconfig[node_name] = match_with_qconfig _run_prepare_fx_on_standalone_modules( model, is_qat, named_modules, node_name_to_match_result_with_qconfig, prepare_custom_config, backend_config, ) # record names for the set of observed node, so that in convert step # we know whether we need to convert a floating point module to reference # quantized module or not observed_node_names: set[str] = set() result_node = insert_observers_for_model( model, node_name_to_match_result_with_qconfig, node_name_to_qconfig, prepare_custom_config, equalization_node_name_to_qconfig, backend_config, observed_node_names, is_qat, ) model = GraphModule(model, model.graph) _save_state( model, node_name_to_qconfig, node_name_to_scope, prepare_custom_config, equalization_node_name_to_qconfig, qconfig_mapping, is_qat, observed_node_names, ) if is_standalone_module: assert result_node is not None assert isinstance(result_node.args[0], Node), ( "standalone module only supports returning simple value currently" "(not tuple, dict etc.)" ) # these inputs are observed in parent # converting List[int] to Tensor since module attribute is # Union[Tensor, Module] input_quantized_idxs: list[int] = prepare_custom_config.input_quantized_indexes output_quantized_idxs: list[ int ] = prepare_custom_config.output_quantized_indexes observed_graph_module_attrs = model.meta["_observed_graph_module_attrs"] # inplace modification observed_graph_module_attrs.is_observed_standalone_module = True observed_graph_module_attrs.standalone_module_input_quantized_idxs = ( input_quantized_idxs ) observed_graph_module_attrs.standalone_module_output_quantized_idxs = ( output_quantized_idxs ) return model