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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from .compress import get_module_name
from .constants import *
from .helper import recursive_getattr
from deepspeed.utils import logger
class compression_scheduler():
'''
Used to schedule different compression methods
'''
def __init__(self, model, compression_config):
self.model = model
self.compression_config = compression_config
self.make_init()
self.training_steps = 0
self.weight_quantization_enabled = False
self.verbose = {
WEIGHT_QUANTIZATION: False,
ACTIVATION_QUANTIZATION: False,
SPARSE_PRUNING: False,
HEAD_PRUNING: False,
ROW_PRUNING: False,
CHANNEL_PRUNING: False
}
def make_init(self):
self.different_compression_methods = {}
for method, method_content in self.compression_config.items():
if LAYER_REDUCTION in method:
continue
self.different_compression_methods[method] = {
TECHNIQUE_ENABLED: False,
SHARED_PARAMETERS: None,
DIFFERENT_GROUPS: []
}
exist_module_name = set()
shared_parameters = method_content[SHARED_PARAMETERS]
self.different_compression_methods[method][TECHNIQUE_ENABLED] = shared_parameters[TECHNIQUE_ENABLED]
self.different_compression_methods[method][SHARED_PARAMETERS] = shared_parameters
for group_name, method_parameters in method_content[DIFFERENT_GROUPS].items():
module_name_list = []
for key_word in method_parameters[DIFFERENT_GROUPS_MODULE_SCOPE]:
module_name, exist_module_name = get_module_name(group_name,
self.model,
key_word,
exist_module_name,
verbose=False)
module_name_list.extend(module_name)
if module_name_list:
self.different_compression_methods[method][DIFFERENT_GROUPS].append(
[group_name, module_name_list,
method_parameters.copy().pop('params')])
def check_weight_quantization(self):
# check weight quantization
wq = self.different_compression_methods[WEIGHT_QUANTIZATION]
if not wq[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = wq[SHARED_PARAMETERS]
if self.training_steps >= shared_parameters[TECHNIQUE_SCHEDULE_OFFSET]:
for group_name, module_name_list, method_parameters in wq[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.weight_quantization_enabled = True
if not self.verbose[WEIGHT_QUANTIZATION]:
logger.info(f'Weight quantization is enabled at step {self.training_steps}')
self.weight_quantization_enabled = True
self.verbose[WEIGHT_QUANTIZATION] = True
def check_activation_quantization(self):
# check activation quantization
aq = self.different_compression_methods[ACTIVATION_QUANTIZATION]
if not aq[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = aq[SHARED_PARAMETERS]
if self.training_steps >= shared_parameters[TECHNIQUE_SCHEDULE_OFFSET]:
for group_name, module_name_list, method_parameters in aq[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.activation_quantization_enabled = True
if not self.verbose[ACTIVATION_QUANTIZATION]:
logger.info(f'Activation quantization is enabled at step {self.training_steps}')
self.verbose[ACTIVATION_QUANTIZATION] = True
def check_sparse_pruning(self):
# check sparse pruning
sp = self.different_compression_methods[SPARSE_PRUNING]
if not sp[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = sp[SHARED_PARAMETERS]
if shared_parameters[TECHNIQUE_SCHEDULE_OFFSET] <= self.training_steps <= shared_parameters[
TECHNIQUE_SCHEDULE_OFFSET_END]:
for group_name, module_name_list, method_parameters in sp[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.sparse_pruning_enabled = True
if not self.verbose[SPARSE_PRUNING]:
logger.info(f'Sparse pruning is enabled at step {self.training_steps}')
self.verbose[SPARSE_PRUNING] = True
def check_head_pruning(self):
# check head pruning
hp = self.different_compression_methods[HEAD_PRUNING]
if not hp[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = hp[SHARED_PARAMETERS]
if self.training_steps >= shared_parameters[TECHNIQUE_SCHEDULE_OFFSET]:
for group_name, module_name_list, method_parameters in hp[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.head_pruning_enabled = True
if not self.verbose[HEAD_PRUNING]:
logger.info(f'Head pruning is enabled at step {self.training_steps}')
self.verbose[HEAD_PRUNING] = True
def check_row_pruning(self):
# check row pruning
rp = self.different_compression_methods[ROW_PRUNING]
if not rp[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = rp[SHARED_PARAMETERS]
if self.training_steps >= shared_parameters[TECHNIQUE_SCHEDULE_OFFSET]:
for group_name, module_name_list, method_parameters in rp[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.row_pruning_enabled = True
if not self.verbose[ROW_PRUNING]:
logger.info(f'Row pruning is enabled at step {self.training_steps}')
self.verbose[ROW_PRUNING] = True
def check_channel_pruning(self):
# check channel pruning
cp = self.different_compression_methods[CHANNEL_PRUNING]
if not cp[TECHNIQUE_ENABLED]:
return
else:
shared_parameters = cp[SHARED_PARAMETERS]
if self.training_steps >= shared_parameters[TECHNIQUE_SCHEDULE_OFFSET]:
for group_name, module_name_list, method_parameters in cp[DIFFERENT_GROUPS]:
for module_name in module_name_list:
module = recursive_getattr(self.model, module_name)
module.channel_pruning_enabled = True
if not self.verbose[CHANNEL_PRUNING]:
logger.info(f'Channel pruning is enabled at step {self.training_steps}')
self.verbose[CHANNEL_PRUNING] = True
def check_all_modules(self):
# check all different compression methods we have
self.check_weight_quantization()
self.check_activation_quantization()
self.check_sparse_pruning()
self.check_head_pruning()
self.check_row_pruning()
self.check_channel_pruning()
def step(self, step_zero_check=False):
if not step_zero_check:
self.training_steps += 1
self.check_all_modules()
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