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297805fd8f
This word appears often in class descriptions and is not consistently spelled. Update comments and some function names to use the correct spelling consistently. Facilitates searching the codebase. Pull Request resolved: https://github.com/pytorch/pytorch/pull/155944 Approved by: https://github.com/Skylion007
2190 lines
86 KiB
Python
2190 lines
86 KiB
Python
# mypy: allow-untyped-defs
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import copy
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import warnings
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from dataclasses import asdict
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from typing import Any, Optional, Union
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import torch
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from torch._subclasses import FakeTensor
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from torch.ao.quantization import (
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_DerivedObserverOrFakeQuantize,
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FixedQParamsFakeQuantize,
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FixedQParamsObserver,
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ObserverBase,
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ObserverOrFakeQuantize,
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PlaceholderObserver,
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)
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from torch.ao.quantization.backend_config import (
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BackendConfig,
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DTypeConfig,
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get_native_backend_config,
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)
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from torch.ao.quantization.backend_config.utils import (
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get_fusion_pattern_to_root_node_getter,
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get_module_to_qat_module,
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get_pattern_to_dtype_configs,
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)
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from torch.ao.quantization.observer import _is_activation_post_process, _PartialWrapper
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from torch.ao.quantization.qconfig import _is_reuse_input_qconfig, QConfigAny
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from torch.ao.quantization.qconfig_mapping import QConfigMapping
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from torch.ao.quantization.quantize import convert, propagate_qconfig_
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from torch.ao.quantization.quantizer import (
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DerivedQuantizationSpec,
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EdgeOrNode,
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FixedQParamsQuantizationSpec,
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QuantizationSpec,
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QuantizationSpecBase,
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SharedQuantizationSpec,
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)
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from torch.ao.quantization.utils import (
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_parent_name,
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get_qconfig_dtypes,
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get_swapped_custom_module_class,
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NodePattern,
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Pattern,
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)
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from torch.fx import GraphModule
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from torch.fx.graph import Graph, Node
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from torch.fx.node import Argument
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from ._equalize import is_equalization_observer, node_supports_equalization
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from .custom_config import PrepareCustomConfig, StandaloneModuleConfigEntry
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from .match_utils import _find_matches, _MatchResultWithQConfig
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from .pattern_utils import _sorted_patterns_dict
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from .qconfig_mapping_utils import (
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_generate_node_name_to_qconfig,
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_get_flattened_qconfig_dict,
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_update_qconfig_for_fusion,
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_update_qconfig_for_qat,
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)
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from .quantize_handler import (
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_default_root_node_getter,
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_get_pattern_to_quantize_handlers,
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QuantizeHandler,
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)
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from .utils import (
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_insert_dequant_stubs_for_custom_module_lstm_output,
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_is_custom_module_lstm,
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_maybe_get_custom_module_lstm_from_node_arg,
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_qconfig_satisfies_dtype_config_constraints,
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all_node_args_have_no_tensors,
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assert_and_get_unique_device,
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get_custom_module_class_keys,
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get_new_attr_name_with_prefix,
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get_non_observable_arg_indexes_and_types,
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node_arg_is_bias,
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node_arg_is_weight,
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NON_QUANTIZABLE_WEIGHT_OPS,
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ObservedGraphModuleAttrs,
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)
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__all__ = [
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"insert_observers_for_model",
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"prepare",
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"propagate_dtypes_for_known_nodes",
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]
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# list of dtypes to not add observers to
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_DO_NOT_OBS_DTYPE_LIST = [int, float, torch.bool, None]
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_OBS_DTYPE_LIST = [
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torch.quint8,
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torch.qint8,
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torch.qint32,
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torch.float16,
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torch.uint8,
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torch.int8,
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torch.int16,
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torch.int32,
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torch.float8_e5m2,
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torch.float8_e4m3fn,
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]
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_DEFAULT_FP32_OBS_OR_FQ_CTR = PlaceholderObserver.with_args(dtype=torch.float)
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# note: the following default target dtype info dicts are temporary,
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# should be moved to the new programmable API class soon
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_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO = {
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"input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
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"output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
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}
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_DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO = {
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"input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
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"output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
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}
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def _get_observer_kwargs(
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quant_spec: Union[QuantizationSpec, FixedQParamsQuantizationSpec]
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):
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kwargs_dict = asdict(quant_spec)
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return copy.deepcopy(kwargs_dict)
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def _get_qspec_for_arg(
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arg: Node,
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input_qspec_map: dict[Node, QuantizationSpecBase],
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named_modules: dict[str, torch.nn.Module],
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) -> Optional[QuantizationSpecBase]:
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while _is_activation_post_process_node(arg, named_modules):
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arg = arg.args[0] # type: ignore[assignment]
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return input_qspec_map.get(arg, None)
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def _create_obs_or_fq_from_qspec(
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quantization_spec: Optional[QuantizationSpecBase],
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obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
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is_qat: bool,
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):
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"""Create observer or fake quantize objects based on quantization spec
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Args:
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quantization_spec: used to store parameters to create the observer or fake quantizer
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obs_or_fq_map: this is a map from edge/output to the corresponding observer/fake_quant
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instance, it may be reused for different edge/output depending on configuration
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"""
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if quantization_spec is None:
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return None
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if isinstance(quantization_spec, SharedQuantizationSpec):
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edge_or_node = quantization_spec.edge_or_node
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assert edge_or_node in obs_or_fq_map, (
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"please make sure only refer to edge or node that has "
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f"observer/fake_quant inserted: '{edge_or_node}' not in\n{obs_or_fq_map.keys()}"
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)
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return obs_or_fq_map[edge_or_node]
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elif isinstance(quantization_spec, DerivedQuantizationSpec):
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# can't use asdict, so not calling get_observer_kwargs here
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kwargs = {
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"dtype": quantization_spec.dtype,
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"derive_qparams_fn": quantization_spec.derive_qparams_fn,
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"quant_min": quantization_spec.quant_min,
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"quant_max": quantization_spec.quant_max,
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"qscheme": quantization_spec.qscheme,
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"ch_axis": quantization_spec.ch_axis,
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}
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edge_or_nodes = quantization_spec.derived_from
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obs_or_fqs = [obs_or_fq_map[k] for k in edge_or_nodes]
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kwargs["obs_or_fqs"] = obs_or_fqs
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return _DerivedObserverOrFakeQuantize.with_args(**kwargs)()
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elif isinstance(quantization_spec, FixedQParamsQuantizationSpec):
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kwargs = _get_observer_kwargs(quantization_spec)
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observer_ctr = FixedQParamsObserver.with_args(**kwargs)
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if is_qat:
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return FixedQParamsFakeQuantize.with_args(observer=observer_ctr)()
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else:
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return observer_ctr()
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assert isinstance(quantization_spec, QuantizationSpec)
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observer_or_fake_quant_ctr = quantization_spec.observer_or_fake_quant_ctr
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kwargs = _get_observer_kwargs(quantization_spec)
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kwargs.pop("observer_or_fake_quant_ctr")
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# we will remove is_dynamic from QuantizationSpec because
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# it seems that dynamic range quantization
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obs_or_fq_class = observer_or_fake_quant_ctr
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if isinstance(observer_or_fake_quant_ctr, _PartialWrapper):
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obs_or_fq_class = observer_or_fake_quant_ctr.p.func # type: ignore[union-attr, assignment]
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if "PerChannel" not in obs_or_fq_class.__name__: # type: ignore[operator, union-attr]
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kwargs.pop("ch_axis")
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return observer_or_fake_quant_ctr.with_args(**kwargs)()
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def _needs_obs_or_fq(
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prev_output_dtype: Any,
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prev_output_is_dynamic: bool,
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cur_target_dtype: Any,
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cur_target_is_dynamic: bool,
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reuse_input_obs_or_fq: bool,
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is_zeroth_arg: bool = False,
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) -> bool:
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"""
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note: we will treat "not specified" as torch.float for now
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utility function that checks if we should insert an observer or fake quant node
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base on the requested dtype for the nodes from user
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is_zeroth_arg: we only dynamically quantize the first arg of the node right now
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this should be removed when we enable configuring dynamic quantization
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for a specific argument, this can be removed if we deprecate fx graph mode
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quantization
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"""
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# need to insert placeholder observer for dynamic quantization so that it can
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# be converted to choose_qparams -> q -> dq in convert step
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if cur_target_is_dynamic:
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assert (
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cur_target_dtype in _OBS_DTYPE_LIST
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), f"Expected cur_target_dtype to be torch.float, but got: {cur_target_dtype}"
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assert prev_output_dtype not in _DO_NOT_OBS_DTYPE_LIST
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return is_zeroth_arg
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if reuse_input_obs_or_fq:
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return False
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# non dynamic quantization
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if cur_target_dtype in _OBS_DTYPE_LIST:
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return (
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prev_output_dtype in _OBS_DTYPE_LIST + [torch.float]
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and cur_target_dtype != prev_output_dtype
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)
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# lots of error checking are skipped here for now
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return False
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def _is_activation_post_process_node(
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node: Node, named_modules: dict[str, torch.nn.Module]
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) -> bool:
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return (
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isinstance(node, torch.fx.Node)
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and node.op == "call_module"
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and _is_activation_post_process(named_modules[str(node.target)])
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)
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def _get_dtype_and_is_dynamic(
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obs_or_fq: Optional[ObserverOrFakeQuantize],
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) -> tuple[Optional[torch.dtype], bool]:
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"""Given a constructor for observer or fake quant module, returns
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a Tuple of dtype and is_dynamic
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"""
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# TODO: instead of instantiating the instance, we can use inspect to get the default args
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if obs_or_fq is None:
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return None, False
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else:
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return obs_or_fq.dtype, getattr(obs_or_fq, "is_dynamic", False) # type: ignore[return-value]
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def _is_input_arg_dtype_supported_by_backend(
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arg: Argument,
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node: Node,
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qconfig: QConfigAny,
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dtype_config: DTypeConfig,
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backend_config: BackendConfig,
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) -> bool:
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"""Check if the configured qconfig for the argument
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is supported by the backend or not
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"""
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if isinstance(arg, (list, tuple)):
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return all(
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_is_input_arg_dtype_supported_by_backend(
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a, node, qconfig, dtype_config, backend_config
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)
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for a in arg
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)
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if not isinstance(arg, Node):
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return True
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# TODO: support check for standalone module
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is_weight = node_arg_is_weight(node, arg)
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is_bias = node_arg_is_bias(node, arg)
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is_activation = not is_weight and not is_bias
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if is_activation:
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input_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
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"input_act_obs_or_fq_ctr"
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)
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input_act_obs_or_fq = (
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input_act_obs_or_fq_ctr() if input_act_obs_or_fq_ctr else None
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)
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qconfig_dtype, qconfig_is_dynamic = _get_dtype_and_is_dynamic(
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input_act_obs_or_fq
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)
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# TODO(future PR): remove the cast to bool below after figuring
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# out why backend_config has is_dynamic set to None in some cases.
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return (dtype_config.input_dtype is None) or (
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dtype_config.input_dtype == qconfig_dtype
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and bool(dtype_config.is_dynamic) == bool(qconfig_is_dynamic)
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and _qconfig_satisfies_dtype_config_constraints(
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qconfig, dtype_config.input_dtype_with_constraints
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)
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)
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elif is_weight:
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# TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
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weight_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
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"weight_obs_or_fq_ctr", None
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)
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weight_obs_or_fq = weight_obs_or_fq_ctr() if weight_obs_or_fq_ctr else None
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qconfig_weight_dtype, _ = _get_dtype_and_is_dynamic(weight_obs_or_fq)
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backend_config_weight_dtype = dtype_config.weight_dtype
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dtype_matches = qconfig_weight_dtype == backend_config_weight_dtype
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qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
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qconfig, dtype_config.weight_dtype_with_constraints, is_activation=False
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)
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return backend_config_weight_dtype is None or (
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dtype_matches and qconfig_satisfies_constraints
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)
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else: # bias
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# TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
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bias_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
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"bias_obs_or_fq_ctr", None
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)
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bias_obs_or_fq = bias_obs_or_fq_ctr() if bias_obs_or_fq_ctr else None
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qconfig_bias_dtype, _ = _get_dtype_and_is_dynamic(bias_obs_or_fq)
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backend_config_bias_dtype = dtype_config.bias_dtype
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return (
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backend_config_bias_dtype is None
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or qconfig_bias_dtype == backend_config_bias_dtype
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)
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def _is_output_dtype_supported_by_backend(
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node: Node,
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qconfig: QConfigAny,
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dtype_config: DTypeConfig,
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) -> bool:
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"""Check if the configured qconfig for the output
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is supported by the backend or not
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"""
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# TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
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backend_config_output_dtype = dtype_config.output_dtype
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# TODO: we should check is_dynamic here as well, the code from _is_input_arg_dtype_supported_by_backend
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# from input activation check can be reused here
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qconfig_output_dtype = None
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output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
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"output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
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)
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output_act_obs_or_fq = (
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output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
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)
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qconfig_output_dtype, qconfig_output_is_dynamic = _get_dtype_and_is_dynamic(
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output_act_obs_or_fq
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)
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# TODO: this is a hack because we can only specify one activation_obs_or_fq for
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# qconfig (qconfig.activation), and we are only supporting dynamically quantized
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# linear op which has fp32 output dtype, this should be removed if we generalize
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# the structure of qconfig in the future
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if qconfig_output_is_dynamic:
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qconfig_output_dtype = torch.float32
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dtype_matches = qconfig_output_dtype == backend_config_output_dtype
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qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
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qconfig, dtype_config.output_dtype_with_constraints
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)
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return backend_config_output_dtype is None or (
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dtype_matches and qconfig_satisfies_constraints
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)
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|
|
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def _is_observer_in_same_graph(
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node: Node,
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named_modules: dict[str, torch.nn.Module],
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obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
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is_qat,
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):
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"""Check if observer in same graph
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when the node output is not fp32 and input is 'placeholder'
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the input is assumed to be quantized, so it is observed
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in a different place rather than not observed.
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"""
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node_output_dtype = _get_arg_target_dtype_as_output(
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node, named_modules, obs_or_fq_map, is_qat
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)
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if len(node.args) > 0 and isinstance(node.args[0], Node):
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if (
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node_output_dtype in [torch.quint8, torch.uint8]
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and node.args[0].op == "placeholder"
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):
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return False
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return True
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|
|
|
|
def _is_pattern_dtype_config_and_qconfig_supported_by_backend(
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pattern: Optional[Pattern],
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matched_node_pattern: Optional[list[Node]],
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qconfig: QConfigAny,
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|
backend_config: BackendConfig,
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) -> bool:
|
|
"""Check if the dtype configuration of a pattern is supported by
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the backend or not, and whether the qconfig satisfies constraints
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|
specified in the corresponding dtype config.
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"""
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if backend_config is None or pattern is None:
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return True
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assert matched_node_pattern is not None and len(matched_node_pattern) >= 1
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pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config)
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dtype_configs: list[DTypeConfig] = pattern_to_dtype_configs.get(pattern, [])
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pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(backend_config)
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root_node_getter = pattern_to_root_node_getter.get(
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pattern, _default_root_node_getter
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)
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root_node = root_node_getter(matched_node_pattern)
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input_node = root_node
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output_node = matched_node_pattern[0]
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for dtype_config in dtype_configs:
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# check if arg dtype are supported
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supported = True
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for arg in list(input_node.args) + list(input_node.kwargs.values()):
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supported = supported and _is_input_arg_dtype_supported_by_backend(
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arg, input_node, qconfig, dtype_config, backend_config
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)
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# check if output dtype is supported
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supported = supported and _is_output_dtype_supported_by_backend(
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output_node, qconfig, dtype_config
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)
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if supported:
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return True
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return False
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|
|
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def _get_standalone_module_configs(
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node: Node,
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named_modules: dict[str, torch.nn.Module],
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prepare_custom_config: PrepareCustomConfig,
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|
parent_qconfig: QConfigAny,
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parent_backend_config: Optional[BackendConfig],
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) -> tuple[
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QConfigMapping, tuple[Any, ...], PrepareCustomConfig, Optional[BackendConfig]
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]:
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"""
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Returns the standalone module QConfigMapping and PrepareCustomConfig
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for `node`, assuming that the module pointed to by `node` is
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a standalone modules.
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|
"""
|
|
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 overridden
|
|
# 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
|
|
# (<class 'torch.nn.modules.conv.Conv3d'>:
|
|
# <class 'torch.ao.quantization.fx.quantize.ConvRelu'>),
|
|
# # match multiple nodes in reverse order
|
|
# ((<function relu at 0x7f766a7360d0>, <built-in function add>):
|
|
# <class 'torch.ao.quantization.fx.quantize.Add'>),
|
|
# }
|
|
|
|
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
|