import torch
import torch.nn as nn
import torch.ao.nn.quantized as nnq
import torch.ao.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().__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().__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().__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 [_convert_tuple_to_list(x) for x in t] if type(t) is tuple else t
def _dequantize_tensor_list(t: Any) -> Any:
return (
[_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().__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")
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