import torch import torch.nn as nn import torch.nn.quantized as nnq import torch.nn.quantized.dynamic as nnqd from torch.ao.quantization import prepare from typing import Dict, List, Optional, Any, Union, Callable, Set from torch.ao.quantization.quantization_mappings import ( get_default_compare_output_module_list, ) NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST = { nnqd.Linear, nnq.Linear, nnqd.LSTM, nn.LSTM, } def _find_match( str_list: Union[Dict[str, Any], List[str]], key_str: str, postfix: str, ) -> Optional[str]: split_str = key_str.split(".") if split_str[-1] == postfix: match_string = "".join(key_str.split(".")[0:-1]) for s2 in str_list: pattern1 = "".join(s2.split(".")[0:-1]) pattern2 = "".join(s2.split(".")[0:-2]) if match_string == pattern1: return s2 if match_string == pattern2: return s2 # For matching "fc.weight" and "fc._packed_params._packed_params" if postfix == "_packed_params": match_string = "".join(key_str.split(".")[0:-2]) if len(match_string) == 0: return None for s2 in str_list: pattern1 = "".join(s2.split(".")[0:-1]) pattern2 = "".join(s2.split(".")[0:-2]) if match_string == pattern1: return s2 if match_string == pattern2: return s2 return None else: return None def compare_weights( float_dict: Dict[str, Any], quantized_dict: Dict[str, Any] ) -> Dict[str, Dict[str, torch.Tensor]]: r"""Compare the weights of the float module with its corresponding quantized module. Return a dict with key corresponding to module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the float and quantized weights. This dict can be used to compare and compute the quantization error of the weights of float and quantized models. Example usage:: wt_compare_dict = compare_weights( float_model.state_dict(), qmodel.state_dict()) for key in wt_compare_dict: print( key, compute_error( wt_compare_dict[key]['float'], wt_compare_dict[key]['quantized'].dequantize() ) ) Args: float_dict: state dict of the float model quantized_dict: state dict of the quantized model Return: weight_dict: dict with key corresponding to module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the float and quantized weights """ torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_weights") weight_dict: Dict[str, Dict] = {} for key in quantized_dict: match_key = _find_match(float_dict, key, "weight") if match_key is not None: weight_dict[key] = {} weight_dict[key]["float"] = float_dict[match_key] weight_dict[key]["quantized"] = quantized_dict[key] continue # For matching "fc.weight" and "fc._packed_params._packed_params" match_key = _find_match(float_dict, key, "_packed_params") if match_key is not None: weight_dict[key] = {} weight_dict[key]["float"] = float_dict[match_key] weight_dict[key]["quantized"] = quantized_dict[key][0] # For LSTM split_str = key.split(".") if split_str[-1] == "param" and split_str[-3] == "_all_weight_values": layer = split_str[-2] module_name = ".".join(split_str[:-3]) float_weight_ih_key = module_name + ".weight_ih_l" + layer float_weight_hh_key = module_name + ".weight_hh_l" + layer if float_weight_ih_key in float_dict and float_weight_hh_key in float_dict: weight_dict[key] = {} weight_dict[key]["float"] = float_dict[float_weight_ih_key] weight_dict[key]["quantized"] = ( quantized_dict[key].__getstate__()[0][4][0].__getstate__()[0][0] ) weight_dict[key]["float"] = float_dict[float_weight_hh_key] weight_dict[key]["quantized"] = ( quantized_dict[key].__getstate__()[0][4][1].__getstate__()[0][0] ) return weight_dict def _get_logger_dict_helper( mod: nn.Module, target_dict: Dict[str, Any], prefix: str = "", ) -> None: r"""This is the helper function for get_logger_dict Args: mod: module we want to save all logger stats prefix: prefix for the current module target_dict: the dictionary used to save all logger stats """ def get_prefix(prefix): return prefix if prefix == "" else prefix + "." for name, child in mod.named_children(): if isinstance(child, Logger): target_dict[get_prefix(prefix) + "stats"] = child.stats break for name, child in mod.named_children(): module_prefix = get_prefix(prefix) + name if prefix else name _get_logger_dict_helper(child, target_dict, module_prefix) def get_logger_dict(mod: nn.Module, prefix: str = "") -> Dict[str, Dict]: r"""Traverse the modules and save all logger stats into target dict. This is mainly used for quantization accuracy debug. Type of loggers supported: ShadowLogger: used to log the outputs of the quantized module and its matching float shadow module, OutputLogger: used to log the outputs of the modules Args: mod: module we want to save all logger stats prefix: prefix for the current module Return: target_dict: the dictionary used to save all logger stats """ torch._C._log_api_usage_once("quantization_api._numeric_suite.get_logger_dict") target_dict: Dict[str, Dict] = {} _get_logger_dict_helper(mod, target_dict, prefix) return target_dict class Logger(nn.Module): r"""Base class for stats logging """ def __init__(self): super(Logger, self).__init__() self.stats = {} # We only insert observer if the op is quantized with static quantization, # which is identified by activation_observer.dtype == quint8. This is needed # when attaching Logger as observer for FX mode self.dtype = torch.quint8 def forward(self, x): """ """ # blank docblock to make autodoc happy pass class ShadowLogger(Logger): r"""Class used in Shadow module to record the outputs of the original and shadow modules. """ def __init__(self): super(ShadowLogger, self).__init__() self.stats["float"] = [] self.stats["quantized"] = [] def forward(self, x, y): """ """ # blank docblock to make autodoc happy if len(x) > 1: x = x[0] if len(y) > 1: y = y[0] self.stats["quantized"].append(x.detach()) self.stats["float"].append(y.detach()) class OutputLogger(Logger): r"""Class used to log the outputs of the module """ def __init__(self): super(OutputLogger, self).__init__() self.stats["tensor_val"] = [] def forward(self, x): """ """ # blank docblock to make autodoc happy self.stats["tensor_val"].append(x) return x def _convert_tuple_to_list(t: Any) -> Any: return list(_convert_tuple_to_list(x) for x in t) if type(t) is tuple else t def _dequantize_tensor_list(t: Any) -> Any: return ( list(_dequantize_tensor_list(x) for x in t) if type(t) is list else t.dequantize() if t.is_quantized else t ) class Shadow(nn.Module): r"""Shadow module attaches the float module to its matching quantized module as the shadow. Then it uses Logger module to process the outputs of both modules. Args: q_module: module quantized from float_module that we want to shadow float_module: float module used to shadow q_module logger_cls: type of logger used to process the outputs of q_module and float_module. ShadowLogger or custom loggers can be used. """ def __init__(self, q_module, float_module, logger_cls): super(Shadow, self).__init__() self.orig_module = q_module self.shadow_module = float_module self.dequant = nnq.DeQuantize() self.logger = logger_cls() def forward(self, *x) -> torch.Tensor: """ """ # blank docblock to make autodoc happy xl = _convert_tuple_to_list(x) output = self.orig_module(*xl) xl_float = _dequantize_tensor_list(xl) shadow_output = self.shadow_module(*xl_float) self.logger(output, shadow_output) return output def add(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.add(x, y) x = x.dequantize() y = y.dequantize() shadow_output = self.shadow_module.add(x, y) self.logger(output, shadow_output) return output def add_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.add_scalar(x, y) x = x.dequantize() shadow_output = self.shadow_module.add_scalar(x, y) self.logger(output, shadow_output) return output def mul(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.mul(x, y) x = x.dequantize() y = y.dequantize() shadow_output = self.shadow_module.mul(x, y) self.logger(output, shadow_output) return output def mul_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.mul_scalar(x, y) x = x.dequantize() shadow_output = self.shadow_module.mul_scalar(x, y) self.logger(output, shadow_output) return output def cat(self, x: List[torch.Tensor], dim: int = 0) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.cat(x, dim) x = [y.dequantize() for y in x] shadow_output = self.shadow_module.cat(x, dim) self.logger(output, shadow_output) return output def add_relu(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: """ """ # blank docblock to make autodoc happy output = self.orig_module.add_relu(x, y) x = x.dequantize() y = y.dequantize() shadow_output = self.shadow_module.add_relu(x, y) self.logger(output, shadow_output) return output def prepare_model_with_stubs( float_module: nn.Module, q_module: nn.Module, module_swap_list: Set[type], logger_cls: Callable, ) -> None: r"""Prepare the model by attaching the float module to its matching quantized module as the shadow if the float module type is in module_swap_list. Example usage:: prepare_model_with_stubs(float_model, q_model, module_swap_list, Logger) q_model(data) ob_dict = get_logger_dict(q_model) Args: float_module: float module used to generate the q_module q_module: module quantized from float_module module_swap_list: list of float module types to attach the shadow logger_cls: type of logger to be used in shadow module to process the outputs of quantized module and its float shadow module """ torch._C._log_api_usage_once("quantization_api._numeric_suite.prepare_model_with_stubs") float_module_children = {} for name, mod in float_module.named_children(): float_module_children[name] = mod reassign = {} for name, mod in q_module.named_children(): if name not in float_module_children: continue float_mod = float_module_children[name] if type(float_mod) not in module_swap_list: prepare_model_with_stubs(float_mod, mod, module_swap_list, logger_cls) # Insert shadow module only if the module is not of the same type as # the floating point module if type(float_mod) in module_swap_list and not _is_identical_module_type(mod, float_mod): reassign[name] = Shadow(mod, float_mod, logger_cls) for key, value in reassign.items(): q_module._modules[key] = value def _is_identical_module_type(mod1, mod2): # Compare if two modules have the same dtype mod1_module_types = [type(mod) for mod in mod1.modules()] mod2_module_types = [type(mod) for mod in mod2.modules()] return mod1_module_types == mod2_module_types def compare_model_stub( float_model: nn.Module, q_model: nn.Module, module_swap_list: Set[type], *data, logger_cls=ShadowLogger ) -> Dict[str, Dict]: r"""Compare quantized module in a model with its floating point counterpart, feeding both of them the same input. Return a dict with key corresponding to module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the output tensors of quantized and its matching float shadow module. This dict can be used to compare and compute the module level quantization error. This function first call prepare_model_with_stubs() to swap the quantized module that we want to compare with the Shadow module, which takes quantized module, corresponding float module and logger as input, and creates a forward path inside to make the float module to shadow quantized module sharing the same input. The logger can be customizable, default logger is ShadowLogger and it will save the outputs of the quantized module and float module that can be used to compute the module level quantization error. Example usage:: module_swap_list = [torchvision.models.quantization.resnet.QuantizableBasicBlock] ob_dict = compare_model_stub(float_model,qmodel,module_swap_list, data) for key in ob_dict: print(key, compute_error(ob_dict[key]['float'], ob_dict[key]['quantized'].dequantize())) Args: float_model: float model used to generate the q_model q_model: model quantized from float_model module_swap_list: list of float module types at which shadow modules will be attached. data: input data used to run the prepared q_model logger_cls: type of logger to be used in shadow module to process the outputs of quantized module and its float shadow module """ torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_model_stub") prepare_model_with_stubs(float_model, q_model, module_swap_list, logger_cls) q_model(*data) ob_dict = get_logger_dict(q_model) return ob_dict def get_matching_activations( float_module: nn.Module, q_module: nn.Module, ) -> Dict[str, Dict[str, torch.Tensor]]: r"""Find the matching activation between float and quantized modules. Args: float_module: float module used to generate the q_module q_module: module quantized from float_module Return: act_dict: dict with key corresponding to quantized module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the matching float and quantized activations """ torch._C._log_api_usage_once("quantization_api._numeric_suite.get_matching_activations") float_dict = get_logger_dict(float_module) quantized_dict = get_logger_dict(q_module) act_dict: Dict[str, Dict] = {} for key in quantized_dict: if len(quantized_dict[key]["tensor_val"]) == 0: continue match_key = _find_match(sorted(float_dict, reverse=True), key, "stats") if match_key is not None: act_dict[key] = {} act_dict[key]["float"] = float_dict[match_key]["tensor_val"] act_dict[key]["quantized"] = quantized_dict[key]["tensor_val"] return act_dict def prepare_model_outputs( float_module: nn.Module, q_module: nn.Module, logger_cls=OutputLogger, allow_list=None ) -> None: r"""Prepare the model by attaching the logger to both float module and quantized module if they are in the allow_list. Args: float_module: float module used to generate the q_module q_module: module quantized from float_module logger_cls: type of logger to be attached to float_module and q_module allow_list: list of module types to attach logger """ torch._C._log_api_usage_once("quantization_api._numeric_suite.prepare_model_outputs") if allow_list is None: allow_list = get_default_compare_output_module_list() qconfig_debug = torch.ao.quantization.QConfig(activation=logger_cls, weight=None) float_module.qconfig = qconfig_debug # type: ignore[assignment] prepare(float_module, inplace=True, allow_list=allow_list) q_module.qconfig = qconfig_debug # type: ignore[assignment] prepare( q_module, inplace=True, allow_list=allow_list, observer_non_leaf_module_list=NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST, ) def compare_model_outputs( float_model: nn.Module, q_model: nn.Module, *data, logger_cls=OutputLogger, allow_list=None ) -> Dict[str, Dict[str, torch.Tensor]]: r"""Compare output activations between float and quantized models at corresponding locations for the same input. Return a dict with key corresponding to quantized module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the activations of quantized model and float model at matching locations. This dict can be used to compare and compute the propagation quantization error. Example usage:: act_compare_dict = compare_model_outputs(float_model, qmodel, data) for key in act_compare_dict: print( key, compute_error( act_compare_dict[key]['float'], act_compare_dict[key]['quantized'].dequantize() ) ) Args: float_model: float model used to generate the q_model q_model: model quantized from float_model data: input data used to run the prepared float_model and q_model logger_cls: type of logger to be attached to float_module and q_module allow_list: list of module types to attach logger Return: act_compare_dict: dict with key corresponding to quantized module names and each entry being a dictionary with two keys 'float' and 'quantized', containing the matching float and quantized activations """ torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_model_outputs") if allow_list is None: allow_list = get_default_compare_output_module_list() prepare_model_outputs(float_model, q_model, logger_cls, allow_list) float_model(*data) q_model(*data) act_compare_dict = get_matching_activations(float_model, q_model) return act_compare_dict