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import shlex
import subprocess
import torch
from typing import Tuple
def outlier_hook(module, input):
assert isinstance(module, torch.nn.Linear)
tracer = OutlierTracer.get_instance()
hvalue = tracer.get_hvalue(module.weight)
if hvalue not in tracer.hvalue2outlier_idx:
outlier_idx = find_outlier_dims(module.weight)
tracer.outliers.append(outlier_idx)
tracer.hvalues.append(hvalue)
if len(tracer.outliers) > 1:
# assign the current layer the outlier idx found from the weight
# of the previous linear layer
if tracer.outliers[-1].numel() > 0:
assert tracer.outliers[-1].max() < module.weight.shape[1]
tracer.hvalue2outlier_idx[hvalue] = tracer.outliers[-1]
else:
# first layer, we cannot use the weight for outlier detection
# we follow a mixed approach:
# (1) zscore test of std of hidden dimension
# (2) magnitude > 6 test
merged = input[0].view(-1, input[0].shape[-1])
# (1) zscore test of std of hidden dimension
outlier_idx = find_outlier_dims(merged, reduction_dim=1, zscore=3)
# (2) magnitude > 6 test
dims = (torch.abs(input[0])> 6).sum(dim=list(range(len(input[0].shape)-1)))
outlier_idx2 = torch.where(dims > 0)[0]
outlier_idx = torch.cat([outlier_idx, outlier_idx2]).unique()
tracer.hvalue2outlier_idx[hvalue] = outlier_idx
else:
for hook in tracer.hooks:
hook.remove()
class OutlierTracer(object):
_instance = None
def __init__(self):
raise RuntimeError("Call get_instance() instead")
def initialize(self, model):
self.last_w = None
self.current_outlier_dims = None
self.hvalues = []
self.outliers = []
self.hvalue2outlier_idx = {}
self.initialized = True
self.hooks = []
for n, m in model.named_modules():
if isinstance(m, torch.nn.Linear):
self.hooks.append(m.register_forward_pre_hook(outlier_hook))
def is_initialized(self):
return getattr(self, 'initialized', False)
def get_hvalue(self, weight):
return weight.data.storage().data_ptr()
def get_outliers(self, weight):
if not self.is_initialized():
print('Outlier tracer is not initialized...')
return None
hvalue = self.get_hvalue(weight)
if hvalue in self.hvalue2outlier_idx:
return self.hvalue2outlier_idx[hvalue]
else:
return None
@classmethod
def get_instance(cls):
if cls._instance is None:
cls._instance = cls.__new__(cls)
return cls._instance
def find_outlier_dims(weight, reduction_dim=0, zscore=4.0, topk=None, rdm=False):
if rdm:
return torch.randint(0, weight.shape[1], size=(topk,), device=weight.device).long()
m = weight.mean(reduction_dim)
mm = m.mean()
mstd = m.std()
zm = (m-mm)/mstd
std = weight.std(reduction_dim)
stdm = std.mean()
stdstd = std.std()
zstd = (std-stdm)/stdstd
if topk is not None:
val, idx = torch.topk(std.abs(), k=topk, dim=0)
else:
idx = torch.where(zstd > zscore)[0]
return idx
def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
copy_weights (`bool`):
Copy the weights from the old linear module to the new one
post_processing_fun_name (`str`):
A function name of the replacement linear class that is called
after processing.
"""
for name, module in model.named_children():
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
old_module = model._modules[name]
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
)
if copy_weights:
model._modules[name].weight = old_module.weight
model._modules[name].bias = old_module.bias
if post_processing_function is not None:
func = getattr(module, post_processing_function, None)
if func is not None: func(module)
return model
def execute_and_return(command_string: str) -> Tuple[str, str]:
def _decode(subprocess_err_out_tuple):
return tuple(
to_decode.decode("UTF-8").strip()
for to_decode in subprocess_err_out_tuple
)
def execute_and_return_decoded_std_streams(command_string):
return _decode(
subprocess.Popen(
shlex.split(command_string),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
).communicate()
)
std_out, std_err = execute_and_return_decoded_std_streams(command_string)
return std_out, std_err
def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
copy_weights (`bool`):
Copy the weights from the old linear module to the new one
post_processing_fun_name (`str`):
A function name of the replacement linear class that is called
after processing.
"""
for name, module in model.named_children():
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
old_module = model._modules[name]
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
)
if copy_weights:
model._modules[name].weight = old_module.weight
model._modules[name].bias = old_module.bias
if post_processing_function is not None:
func = getattr(module, post_processing_function, None)
if func is not None: func(module)
return model
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