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Commit c2b62b7f authored by JR_ZZU's avatar JR_ZZU 🌴
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apex/contrib/sparsity/permutation_lib.py deleted 100644 → 0
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import os
import torch
import json
import string
import time
try:
from .permutation_search_kernels import accelerated_search_for_good_permutation, sum_after_2_to_4
print("[ASP][Info] permutation_search_kernels can be imported.")
except ImportError:
print("[ASP][Warning] permutation_search_kernels cannot be imported.")
print("[ASP][Warning] If you want to accelerate the permutation search process by GPU, please build APEX by following the instructions at https://github.com/NVIDIA/apex/blob/master/apex/contrib/sparsity/README.md")
def convert_fx_node_name(fx_node_name):
converted_fx_node_name = fx_node_name
converted_fx_node_name = converted_fx_node_name.replace('_', '.')
return converted_fx_node_name
def get_node_parent_children(fx_node):
# get node parent list, and convert node name to module name
node_parent_name_converted = []
if len(fx_node.all_input_nodes) > 0:
node_parent = fx_node.all_input_nodes
for item in node_parent:
converted_item = convert_fx_node_name(item.name)
node_parent_name_converted.append(converted_item)
else:
node_parent = list('None')
node_parent_name_converted.append('None')
# get node children list, and convert node name to module name
node_children_name_converted = []
if len(list(fx_node.users.keys())) > 0:
node_children = list(fx_node.users.keys())
for item in node_children:
converted_item = convert_fx_node_name(item.name)
node_children_name_converted.append(converted_item)
else:
node_children = list('None')
node_children_name_converted.append('None')
return node_parent_name_converted, node_children_name_converted
class Permutation:
__model = None
__sparse_parameters = []
__allow_permutation = False
__all_parameters = []
__save_permutation_graph = False
__permutation_output_dir = ''
@classmethod
def set_permutation_params_from_asp(cls, model, sparse_parameters, all_parameters):
"""This function is used to set the permutation needed parameters from ASP class."""
print("\n[set_permutation_params_from_asp] Set permutation needed parameters")
cls.__model = model
cls.__sparse_parameters = sparse_parameters
cls.__all_parameters = all_parameters
@classmethod
def set_identical_seed(cls, identical_seed=1):
print("\n[set_identical_seed] Set the identical seed: {:} for all GPUs to make sure the same results generated in permutation search".format(identical_seed))
torch.manual_seed(identical_seed)
torch.cuda.manual_seed_all(identical_seed)
import numpy as np
import random
np.random.seed(identical_seed)
random.seed(identical_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
@classmethod
def set_permutation_saving_params(cls, allow_permutation=False, save_permutation_graph=False, permutation_output_dir='.'):
"""This function is used to set the permutation saving related parameters."""
print("\n[permutation_lib][set_permutation_saving_param] Set permutation saving related parameters")
cls.__allow_permutation = allow_permutation
print("[set_permutation_saving_param]\t Allow permutation: {}".format(cls.__allow_permutation))
cls.__save_permutation_graph = save_permutation_graph
print("[set_permutation_saving_param]\t Save permutation graphs: {}".format(cls.__save_permutation_graph))
cls.__permutation_output_dir = permutation_output_dir
print("[set_permutation_saving_param]\t Permutation graphs saving dir: {}".format(cls.__permutation_output_dir))
@classmethod
def apply_offline_permutation(cls, model, fx_graph):
"""This function is used to offline permutation for each node according to the the whole network graph built with Torch.FX."""
print("\n[apply_offline_permutation] Offline permutation for each node according to the the whole network graph built with Torch.FX")
# Firstly, we should transfer the sparse mask to all-one dense mask
cls.transfer_to_dense_mask()
for node_name in fx_graph.keys():
node_module_type = fx_graph.get(node_name).get('module_type')
# check wheter the current layer can permute as plan, e.g., the flatten layer in VGG will change the shape and broke the permutation chain
# only need to check the 'is_node_real_parents_K_permuted', because the 'is_node_real_parents_C_permuted' has no influence to the children
node_real_parents = fx_graph.get(node_name).get('real_parents')
is_node_real_parents_K_permuted = True
if node_real_parents is not None: # filter out the 'unique_siblings' item
for real_parent_item in node_real_parents:
if fx_graph.get(real_parent_item).get('permutation_type') in ['K', 'KC']:
if fx_graph.get(real_parent_item).get('k_permuted') == 'False':
is_node_real_parents_K_permuted = False
if fx_graph[node_name]['permutation_type'] == 'KC': # intermediate Conv, FC
C_permutation_sequence = cls.fetch_C_permutation_sequence_value(node_name, fx_graph)
K_permutation_sequence = cls.fetch_K_permutation_sequence_value(node_name, fx_graph)
print("\n[apply_offline_permutation] node_name: \'{:}\', node module type: \'{:}\', need to do offline permutation in K and C dims.".format(node_name, node_module_type))
if is_node_real_parents_K_permuted == True:
fx_graph[node_name]['c_permuted'] = str(cls.apply_permutation_in_C_dim(node_name, C_permutation_sequence))
fx_graph[node_name]['k_permuted'] = str(cls.apply_permutation_in_K_dim(node_name, K_permutation_sequence))
else:
print("[apply_offline_permutation][warning] node_name: \'{:}\', its real parents have trouble in permutation in K dim, so skip the offline permutation in C dim.".format(node_name, node_module_type))
fx_graph[node_name]['k_permuted'] = str(cls.apply_permutation_in_K_dim(node_name, K_permutation_sequence))
elif fx_graph[node_name]['permutation_type'] == 'K': # BN, first layer Conv/FC
K_permutation_sequence = cls.fetch_K_permutation_sequence_value(node_name, fx_graph)
print("\n[apply_offline_permutation] node_name: \'{:}\', node module type: \'{:}\', need to do offline permutation in K dim.".format(node_name, node_module_type))
if is_node_real_parents_K_permuted == True:
fx_graph[node_name]['k_permuted'] = str(cls.apply_permutation_in_K_dim(node_name, K_permutation_sequence))
else: # for BN, if the previous Conv cannot do permutation in K dim, then no need to do permutation in K dim for this BN
print("[apply_offline_permutation][warning] node_name: \'{:}\', its real parents have trouble in permutation in K dim, so skip the offline permutation in K dim.".format(node_name, node_module_type))
elif fx_graph[node_name]['permutation_type'] == 'C': # last layer FC/Conv
C_permutation_sequence = cls.fetch_C_permutation_sequence_value(node_name, fx_graph)
print("\n[apply_offline_permutation] node_name: \'{:}\', node module type: \'{:}\', need to do offline permutation in C dim.".format(node_name, node_module_type))
if is_node_real_parents_K_permuted == True:
fx_graph[node_name]['c_permuted'] = str(cls.apply_permutation_in_C_dim(node_name, C_permutation_sequence))
else:
print("[apply_offline_permutation][warning] node_name: \'{:}\', its real parents have trouble in permutation in K dim, so skip the offline permutation in C dim.".format(node_name, node_module_type))
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_apply_offline_permutation.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def transfer_to_dense_mask(cls):
"""Call this method to transfer the sparse mask to all-one dense mask."""
with torch.no_grad():
for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:
mask.fill_(1)
@classmethod
def fetch_C_permutation_sequence_value(cls, node_name, fx_graph):
"""This function is used to fetch the permutation sequence value in C dim from the unique_siblings record."""
# C_permutation_sequence is the corresponding 'permutation_sequence' value stored in the fx_graph.get('unique_siblings') item which contains node_name
unique_siblings_groups = fx_graph.get('unique_siblings').get('name')
unique_siblings_groups_permutation_sequence = fx_graph.get('unique_siblings').get('permutation_sequence')
item_index = 0
fetched_C_permutation_sequence = []
for item in unique_siblings_groups:
if node_name in item:
fetched_C_permutation_sequence = unique_siblings_groups_permutation_sequence[item_index]
item_index = item_index + 1
return fetched_C_permutation_sequence
@classmethod
def fetch_K_permutation_sequence_value(cls, node_name, fx_graph):
"""This function is used to fetch the permutation sequence value in K dim from the unique_siblings record."""
# K_permutation_sequence is its real_children's corresponding 'permutation_sequence' value stored in the fx_graph.get('unique_siblings') item which contains real_children name
# we have the assumption that all the real children are in one unique_sibling group, so should share the same permutation_sequence value
unique_siblings_groups = fx_graph.get('unique_siblings').get('name')
unique_siblings_groups_permutation_sequence = fx_graph.get('unique_siblings').get('permutation_sequence')
node_real_children = fx_graph.get(node_name).get('real_children')
fetched_K_permutation_sequence = []
if len(node_real_children) > 0:
node_representative_child = node_real_children[0]
fetched_K_permutation_sequence = cls.fetch_C_permutation_sequence_value(node_representative_child, fx_graph)
return fetched_K_permutation_sequence
@classmethod
def apply_permutation_in_C_dim(cls, node_name, permutation_sequence):
"""This function is used to permutation for a node in C dim. (Only need to handle the weight of the node) """
print("[apply_permutation_in_C_dim] Permutation for node: \'{:}\' in C dim".format(node_name))
if len(permutation_sequence) == 0:
print("[apply_permutation_in_C_dim] the permutation sequence is empty, fail to apply permutation in C dim.")
return False
is_node_in_sparse_parameters = False
success_permutation = False
for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:
processed_module_name = ''.join(c for c in module_name if c not in string.punctuation).lower()
processed_node_name = ''.join(c for c in node_name if c not in string.punctuation).lower()
distributed_node_name = 'module.' + node_name
processed_distributed_node_name = 'module.' + processed_node_name
if (module_name == node_name) or (module_name == distributed_node_name) or (processed_module_name == processed_node_name) or (processed_module_name == processed_distributed_node_name): # Inception-V3, module_name: Conv2d_2a_3x3.conv, node_name: conv2d.1a.3x3.conv
print("[apply_permutation_in_C_dim] find the node: \'{:}\' in cls.__sparse_parameters, succeed to apply permutation in C dim.".format(node_name))
is_node_in_sparse_parameters = True
temp_weight = torch.zeros_like(p)
temp_weight.copy_(p[:, permutation_sequence, ...])
p.data.copy_(temp_weight)
success_permutation = True
if is_node_in_sparse_parameters == False:
# A special case: if the node itself not in sparse_module_names but one of its real_siblings in sparse_module_names, then the node will not do the permutation search, but it may need to apply the offline permutation in C dim according to the searched permutation sequence from its real_siblings in sparse_module_names
try:
for module_name_from_all_parameters, module_from_all_parameters, p_name_from_all_parameters, p_from_all_parameters in cls.__all_parameters:
if ((node_name == module_name_from_all_parameters) or ('module.' + node_name == module_name_from_all_parameters)) and p_name_from_all_parameters == "weight":
print("[apply_permutation_in_C_dim] cannot find the node: \'{:}\' in cls.__sparse_parameters, but can find in cls.__all_parameters.".format(node_name))
temp_weight = torch.zeros_like(p_from_all_parameters)
temp_weight.copy_(p_from_all_parameters[:, permutation_sequence, ...])
p_from_all_parameters.data.copy_(temp_weight)
success_permutation = True
print("[apply_permutation_in_C_dim] cannot find the node: \'{:}\' in cls.__sparse_parameters, after trying with cls.__all_parameters, succeed to apply permutation in C dim.".format(node_name))
except:
success_permutation = False
print("[apply_permutation_in_C_dim] cannot find the node: \'{:}\' in cls.__sparse_parameters, after trying with cls.__all_parameters, still fail to apply permutation in C dim.".format(node_name))
return success_permutation
@classmethod
def apply_permutation_in_K_dim(cls, node_name, permutation_sequence):
"""This function is used to permutation for a node in K dim. (Need to handle the weight/bias/running_mean/running_var of the node)"""
print("[apply_permutation_in_K_dim] Permutation for node: \'{:}\' in K dim".format(node_name))
if len(permutation_sequence) == 0:
print("[apply_permutation_in_K_dim] the permutation sequence is empty, fail to apply permutation in K dim.")
return False
is_node_in_all_parameters = False
success_permutation = False
for module_name, module, p_name, p in cls.__all_parameters:
processed_module_name = ''.join(c for c in module_name if c not in string.punctuation).lower()
processed_node_name = ''.join(c for c in node_name if c not in string.punctuation).lower()
distributed_node_name = 'module.' + node_name
processed_distributed_node_name = 'module.' + processed_node_name
if (module_name == node_name) or (module_name == distributed_node_name) or (processed_module_name == processed_node_name) or (processed_module_name == processed_distributed_node_name): # Inception-V3, module_name: Conv2d_2a_3x3.conv, node_name: conv2d.1a.3x3.conv
print("[apply_permutation_in_K_dim] find the node: \'{:}\' with \'{:}\' in cls.__all_parameters, may succeed to apply permutation in K dim.".format(node_name, p_name))
is_node_in_all_parameters = True
temp_weight = torch.zeros_like(p)
if p.shape[0] != len(permutation_sequence):
print("[apply_permutation_in_K_dim][warning] the node: \'{:}\' with shape: \'{:}\', cannot match the size of permutation sequence with len: \'{:}\', fail to apply permutation in K dim.".format(node_name, p.shape, len(permutation_sequence)))
success_permutation = False
else:
print("[apply_permutation_in_K_dim] the node: \'{:}\' with shape: \'{:}\', can match the size of permutation sequence with len: \'{:}\', succeed to apply permutation in K dim.".format(node_name, p.shape, len(permutation_sequence)))
temp_weight.copy_(p[permutation_sequence, ...])
p.data.copy_(temp_weight)
success_permutation = True
if is_node_in_all_parameters == False:
print("[apply_permutation_in_K_dim] cannot find the node: \'{:}\' in cls.__all_parameters, fail to apply permutation in K dim.".format(node_name))
success_permutation = False
return success_permutation
@classmethod
def build_offline_permutation_graph(cls, model, dump_fx_graph=False, save_dumped_fx_graph='./model_offline_permutation_graph.json'):
"""This function is used to refine the whole network graph built with Torch.FX with some extra infomation needed for offline permutation."""
print("\n[build_offline_permutation_graph] Further refine the model graph built by Torch.FX for offline permutation")
# extract the output_dir, so all the intermediate fx_graph can be saved under that path
extract_output_dir=os.path.split(save_dumped_fx_graph)[0]
cls.__permutation_output_dir = extract_output_dir
fx_graph, success_in_build_fx_graph = cls.build_fx_graph(model, dump_fx_graph=dump_fx_graph, save_dumped_fx_graph=save_dumped_fx_graph)
if success_in_build_fx_graph:
fx_graph_after_find_real_parents = cls.find_real_parents(fx_graph)
fx_graph_after_find_real_children = cls.find_real_children(fx_graph_after_find_real_parents)
fx_graph_after_find_real_siblings = cls.find_real_siblings(fx_graph_after_find_real_children)
fx_graph_after_extract_all_unique_siblings = cls.extract_all_unique_siblings(fx_graph_after_find_real_siblings)
fx_graph_after_init_permutation_flag = cls.init_permutation_flag(fx_graph_after_extract_all_unique_siblings)
start_time_search_for_good_permutation = time.perf_counter()
fx_graph_after_search_for_good_permutation = cls.search_for_good_permutation(fx_graph_after_init_permutation_flag)
duration_search_for_good_permutation = time.perf_counter() - start_time_search_for_good_permutation
print("\n[build_offline_permutation_graph] Take {:.4f} seconds to finish search_for_good_permutation function.".format(duration_search_for_good_permutation))
else:
fx_graph_after_search_for_good_permutation = {}
return fx_graph_after_search_for_good_permutation, success_in_build_fx_graph
# Please notice the apply_offline_permutation step cannot fold into the above search_for_good_permutation step.
# Because the real_parent node needs to offline permutation in K direction according to the searched permutation sequence from its real_children.
# However, when we search_for_good_permutation for the node, its real_children have not been handled by search_for_good_permutation.
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph_after_search_for_good_permutation, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_build_offline_permutation_graph.json')) # save the intermediate graph as JSON file for debugging
return fx_graph_after_search_for_good_permutation, success_in_build_fx_graph
@classmethod
def search_for_good_permutation(cls, fx_graph):
"""This function is used to:
1. search for the good permutation sequence for each node weight, or each siblings_group weights by calling the permutation search kernels as ASP extension.
2. add the searched permutation sequence for each node according to the whole network graph built with Torch.FX."""
print("\n[search_for_good_permutation] Search for the good permutation sequence for each node according to the whole network graph built with Torch.FX")
unique_siblings_groups = fx_graph.get('unique_siblings').get('name')
unique_siblings_groups_module_type = fx_graph.get('unique_siblings').get('module_type')
unique_siblings_groups_permutation_sequence = []
item_index = 0
for unique_siblings_group in unique_siblings_groups: # loop through all unique siblings groups that must share a permutation sequence
print("\n[search_for_good_permutation] this unique_siblings_group has {:} real siblings: \'{:}\', with module type: \'{:}\'.".format(len(unique_siblings_group), unique_siblings_group, unique_siblings_groups_module_type[item_index]))
item_index = item_index + 1
# concat the weight for layers in the same unique_siblings_group
matrix_group = None
for node_name in unique_siblings_group:
node_module_type = fx_graph.get(node_name).get('module_type')
print("[search_for_good_permutation] try to merge the weight for node: \'{:}\', with module type: \'{:}\'.".format(node_name, node_module_type))
is_node_in_sparse_parameters = False
node_weight = torch.zeros(0)
for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:
processed_module_name = ''.join(c for c in module_name if c not in string.punctuation).lower()
processed_node_name = ''.join(c for c in node_name if c not in string.punctuation).lower()
distributed_node_name = 'module.' + node_name
processed_distributed_node_name = 'module.' + processed_node_name
if (module_name == node_name) or (module_name == distributed_node_name) or (processed_module_name == processed_node_name) or (processed_module_name == processed_distributed_node_name): # Inception-V3, module_name: Conv2d_2a_3x3.conv, node_name: conv2d.1a.3x3.conv
module_type_from_sparse_parameters = str(type(module)) # e.g. <class 'torch.nn.modules.conv.Conv2d'>
module_type_from_sparse_parameters = module_type_from_sparse_parameters[8:-2]
print("[search_for_good_permutation] find the node: \'{:}\' in cls.__sparse_parameters, module type match: \'{:}\'.".format(node_name, node_module_type==module_type_from_sparse_parameters))
is_node_in_sparse_parameters = True
node_weight = torch.zeros_like(p)
node_weight.copy_(p)
# Need to handle the concat for layers with different R & S
shape = node_weight.shape
# 1d-tensor
if len(shape) == 1:
node_weight = node_weight.view(1, shape[0])
# 2d-tensor (in, out)
elif len(shape) == 2:
node_weight = node_weight.view(shape[0], shape[1])
# 3d-tensor (batch, in, out)
elif len(shape) == 3:
node_weight = node_weight.view(shape[0]*shape[1], shape[2])
# 4d-tensor (in, out, h, w)
elif len(shape) == 4:
# convs
node_weight = node_weight.permute(2,3,0,1).contiguous().view(shape[2]*shape[3]*shape[0], shape[1])
if is_node_in_sparse_parameters == False:
print("[search_for_good_permutation] cannot find the node: \'{:}\' in cls.__sparse_parameters, no need to merge its weight for permutation.".format(node_name))
else:
if matrix_group == None:
matrix_group = node_weight
else:
try:
if matrix_group.dim() == node_weight.dim():
matrix_group = torch.cat((matrix_group, node_weight), dim=0) # concat the weights in K dimension, and keep the same C dimension
else: # e.g. when try to merge the Conv and FC layers
print("[search_for_good_permutation] matrix_group dim: {:} is not matched with node_weight dim: {:}.".format(matrix_group.dim(), node_weight.dim()))
print("[search_for_good_permutation] matrix_group shape: \'{:}\' is not matched with node_weight shape: \'{:}\'.".format(matrix_group.size(), node_weight.size()))
if matrix_group.dim() < node_weight.dim():
while node_weight.dim() - matrix_group.dim() > 0:
matrix_group = matrix_group.unsqueeze(matrix_group.dim())
else:
while matrix_group.dim() - node_weight.dim() > 0:
node_weight = node_weight.unsqueeze(node_weight.dim())
print("[search_for_good_permutation] matrix_group shape: \'{:}\' is now matched with node_weight shape: \'{:}\'.".format(matrix_group.size(), node_weight.size()))
matrix_group = torch.cat((matrix_group, node_weight), dim=0) # concat the weights in K dimension, and keep the same C dimension
except:
print("[search_for_good_permutation][warning] cannot merge the weight for node: \'{:}\', with its weight shape: \'{:}\', the matrix_group shape: \'{:}\'.".format(node_name, node_weight.size(), matrix_group.size()))
continue
print("[search_for_good_permutation] have merged the weight for node: \'{:}\', with its weight shape: \'{:}\', the matrix_group shape: \'{:}\'.".format(node_name, node_weight.size(), matrix_group.size()))
if matrix_group == None: # cannot find the node: \'{:}\' in cls.__sparse_parameters
input_channel_num = 0
print("\n[search_for_good_permutation] init the all-zero list with length \'{:}\' for permutation search sequence of this unique_siblings_group.".format(input_channel_num))
print("[search_for_good_permutation] no need to search the permutation_sequence for empty matrix_group.")
permutation_sequence = [0 for n in range(input_channel_num)]
unique_siblings_groups_permutation_sequence.append(permutation_sequence)
continue
else:
input_channel_num = matrix_group.size()[1]
print("\n[search_for_good_permutation] init the all-zero list with length \'{:}\' for permutation search sequence of this unique_siblings_group.".format(input_channel_num))
permutation_sequence = [0 for n in range(input_channel_num)]
# automatic check for skipping the permutation search process
original_magnitude = (torch.abs(matrix_group)).sum(dtype=torch.float64)
pruned_magnitude = sum_after_2_to_4(matrix_group.cpu().detach().numpy())
diff_ratio = abs(original_magnitude - pruned_magnitude)/original_magnitude
epsilon = 1e-3
print("\n[search_for_good_permutation] Original element abs sum: {:}, Pruned element abs sum: {:}, Diff ratio: {:}".format(original_magnitude, pruned_magnitude, diff_ratio))
if diff_ratio < epsilon:
print("[search_for_good_permutation] Original element abs sum is almost same as the pruned element abs sum, further permutation search will not help, skipping!")
print("[search_for_good_permutation] Change the all-zero permutation search sequence to a sequential permutation search sequence.")
permutation_sequence = [n for n in range(input_channel_num)]
unique_siblings_groups_permutation_sequence.append(permutation_sequence)
continue
else:
print("[search_for_good_permutation] Original element abs sum is different from the pruned element abs sum, further permutation search will help, continue with the permutation search!")
# call the permutation search CUDA kernels as ASP extension.
# users can provide prefer search strategy by providing a valid 'search_options' as a dictionary,
# or users can implement their customized 'accelerated_search_for_good_permutation' function.
search_options = {}
# No.1 Strategy: Exhaustive Search
# search_options['strategy'] = 'exhaustive'
# search_options['stripe_group_size'] = 8
# search_options['escape_attempts'] = 100
# No.2 Strategy: Progressive Channel Swap Search
# search_options['strategy'] = 'progressive channel swap'
# search_options['progressive_search_time_limit'] = 10
# search_options['improvement_threshold'] = 1e-9
# No.3 Strategy: User Defined Search
# search_options['strategy'] = 'user defined'
# permutation search time is too long for matrix_group with large channel num
# change from Exhaustive Search to Progressive Channel Swap Search based on input matrix_group size
if input_channel_num > 2048:
search_options['strategy'] = 'progressive channel swap'
search_options['progressive_search_time_limit'] = 120
search_options['improvement_threshold'] = 1e-9
print("[search_for_good_permutation] Change to Progressive Channel Swap Search with {} seconds limitation, because the {} is too large and will leading too long permutation search time with Exhaustive Search.".format(search_options['progressive_search_time_limit'], input_channel_num))
start_time_accelerated_search_for_good_permutation = time.perf_counter()
permutation_sequence = accelerated_search_for_good_permutation(matrix_group, options=search_options)
duration_accelerated_search_for_good_permutation = time.perf_counter() - start_time_accelerated_search_for_good_permutation
print("[search_for_good_permutation] Take {:.4f} seconds to finish accelerated_search_for_good_permutation function.".format(duration_accelerated_search_for_good_permutation))
unique_siblings_groups_permutation_sequence.append(permutation_sequence)
fx_graph['unique_siblings']['permutation_sequence'] = unique_siblings_groups_permutation_sequence
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_search_for_good_permutation.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def init_permutation_flag(cls, fx_graph):
"""This function is used to init the permutation flag for each node according to the whole network graph built with Torch.FX."""
print("\n[init_permutation_flag] Init the permutation flag for each node according to the whole network graph built with Torch.FX")
sparse_module_names = []
processed_sparse_module_names = [] # Inception-V3, module_name: Conv2d_2a_3x3.conv, node_name: conv2d.1a.3x3.conv
for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:
sparse_module_names.append(module_name)
processed_module_name = ''.join(c for c in module_name if c not in string.punctuation).lower()
processed_sparse_module_names.append(processed_module_name)
for node_name in fx_graph.keys():
processed_node_name = ''.join(c for c in node_name if c not in string.punctuation).lower()
distributed_node_name = 'module.' + node_name
processed_distributed_node_name = 'module.' + processed_node_name
node_module_type = fx_graph.get(node_name).get('module_type')
if node_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
node_parents = fx_graph.get(node_name).get('parents')
node_children = fx_graph.get(node_name).get('children')
node_real_parents = fx_graph.get(node_name).get('real_parents')
node_real_children = fx_graph.get(node_name).get('real_children')
node_groups_param = fx_graph.get(node_name).get('groups_param')
is_node_real_children_in_sparse_parameters = False
is_node_real_children_has_group_conv = False
for real_child_item in node_real_children:
processed_real_child_item = ''.join(c for c in real_child_item if c not in string.punctuation).lower()
distributed_real_child_item = 'module.' + real_child_item
processed_distributed_real_child_item = 'module.' + processed_real_child_item
if (real_child_item in sparse_module_names) or (processed_real_child_item in processed_sparse_module_names) or (distributed_real_child_item in sparse_module_names) or (processed_distributed_real_child_item in processed_sparse_module_names):
is_node_real_children_in_sparse_parameters = True
if (fx_graph.get(real_child_item).get('groups_param') not in ['None', '1']):
is_node_real_children_has_group_conv = True
is_node_real_parents_has_group_conv = False
for real_parent_item in node_real_parents:
# notice: we assume the if one item of real_parents need to permute in C or K dim, then the corresponding flag should be set
# so for all items of real_parents, they may not share the same 'permutation_type' (e.g., one item is Group Conv, etc.)
# that's why we also need to judge the 'is_node_real_parents_has_group_conv'
if (fx_graph.get(real_parent_item).get('groups_param') not in ['None', '1']):
is_node_real_parents_has_group_conv = True
# If the node itself is in sparse_module_names or one of its real_children in sparse_module_names, then it may need the offline permutation
if ((node_name in sparse_module_names) or (processed_node_name in processed_sparse_module_names) or (distributed_node_name in sparse_module_names) or (processed_distributed_node_name in processed_sparse_module_names)) or (is_node_real_children_in_sparse_parameters == True):
if node_groups_param not in ['None', '1']:
# for Group Conv, disable the permutation in 'C' and 'K' dim
fx_graph[node_name]['permutation_type'] = 'None'
elif ('x' in node_parents) or ((node_name not in sparse_module_names) and (processed_node_name not in processed_sparse_module_names) and (distributed_node_name not in sparse_module_names) and (processed_distributed_node_name not in processed_sparse_module_names)):
# for the first (due to it is connected to 'x' node or itself is not in sparse_module_names) or not NVIDIA's TC compatiable Conv/FC, only permutate the K direction
if is_node_real_children_has_group_conv == False:
fx_graph[node_name]['permutation_type'] = 'K'
fx_graph[node_name]['k_permuted'] = 'False'
else: # if node real_children contains Group Conv, disable the permutation for node in 'K' dim
fx_graph[node_name]['permutation_type'] = 'None'
elif ('output' in node_children) or (is_node_real_children_in_sparse_parameters == False):
# for the last (due to it is connected to 'output' node or to a node which is not in sparse_module_names) FC/Conv, only permutate the C direction
if is_node_real_parents_has_group_conv == False:
fx_graph[node_name]['permutation_type'] = 'C'
fx_graph[node_name]['c_permuted'] = 'False'
else: # if node real_parents contains Group Conv, disable the permutation for node in 'C' dim
fx_graph[node_name]['permutation_type'] = 'None'
else:
if (is_node_real_parents_has_group_conv == False) and (is_node_real_children_has_group_conv == False):
fx_graph[node_name]['permutation_type'] = 'KC'
fx_graph[node_name]['k_permuted'] = 'False'
fx_graph[node_name]['c_permuted'] = 'False'
elif is_node_real_parents_has_group_conv == True: # TODO: if node real_parents contains Group Conv, disable the permutation for node in 'C' dim
fx_graph[node_name]['permutation_type'] = 'K'
fx_graph[node_name]['k_permuted'] = 'False'
else: # if node real_children contains Group Conv, disable the permutation for node in 'K' dim
fx_graph[node_name]['permutation_type'] = 'C'
fx_graph[node_name]['c_permuted'] = 'False'
else:
fx_graph[node_name]['permutation_type'] = 'None'
elif node_module_type in ['torch.nn.modules.batchnorm.BatchNorm2d']:
node_real_parents = fx_graph.get(node_name).get('real_parents')
is_node_real_parents_need_K_permutation = False
is_node_real_parents_has_group_conv = False
for real_parent_item in node_real_parents:
# notice: we assume the if one item of real_parents need to permute in K dim, then the corresponding flag should be set
# as in most of the cases, BN only follows one Conv, so it should be OK for now
if fx_graph.get(real_parent_item).get('permutation_type') in ['K', 'KC']:
is_node_real_parents_need_K_permutation = True
if (fx_graph.get(real_parent_item).get('groups_param') not in ['None', '1']):
is_node_real_parents_has_group_conv = True
node_real_children = fx_graph.get(node_name).get('real_children')
is_node_real_children_in_sparse_parameters = False
for real_child_item in node_real_children:
processed_real_child_item = ''.join(c for c in real_child_item if c not in string.punctuation).lower()
distributed_real_child_item = 'module.' + real_child_item
processed_distributed_real_child_item = 'module.' + processed_real_child_item
if (real_child_item in sparse_module_names) or (processed_real_child_item in processed_sparse_module_names) or (distributed_real_child_item in sparse_module_names) or (processed_distributed_real_child_item in processed_sparse_module_names):
is_node_real_children_in_sparse_parameters = True
# Firstly, we should make sure the BN is not in the last (due to it is connected to a FC/Conv node which is not in sparse_module_names), then:
# If the real_parents of BN node are in sparse_module_names, then it may need the offline permutation
# Or if the real_parents of BN node just needs to permute in K dim
if (is_node_real_children_in_sparse_parameters == True) and (is_node_real_parents_need_K_permutation == True):
if (is_node_real_parents_has_group_conv == False) and (is_node_real_parents_need_K_permutation == True):
fx_graph[node_name]['permutation_type'] = 'K'
fx_graph[node_name]['k_permuted'] = 'False'
else: # if node real_parents contains Group Conv or does not need permutation in 'K' dim, disable the permutation for node in 'K' dim
fx_graph[node_name]['permutation_type'] = 'None'
else:
fx_graph[node_name]['permutation_type'] = 'None'
else:
fx_graph[node_name]['permutation_type'] = 'None'
# A special case: if the node itself not in sparse_module_names but one of its real_siblings in sparse_module_names, then the node will not do the permutation search, but it may need to apply the offline permutation in C dim according to the searched permutation sequence from its real_siblings in sparse_module_names
# We make it as the post-processing, because if we add this to the previous logic, will make it too complex
# Post-processing Step No.1:
print("\n[init_permutation_flag] Post-processing Step No.1.")
node_change_permutation_due_to_siblings = []
for node_name in fx_graph.keys():
node_real_siblings = fx_graph.get(node_name).get('real_siblings')
if node_real_siblings is not None:
is_node_real_siblings_needs_C_permutation = False
for real_sibling_item in node_real_siblings:
if fx_graph.get(real_sibling_item).get('permutation_type') in ['C', 'KC']:
is_node_real_siblings_needs_C_permutation = True
if is_node_real_siblings_needs_C_permutation == True:
print("[init_permutation_flag] node_name: \'{:}\', one of its real siblings need do offline permutation in C dim.".format(node_name))
node_original_permutation_type = fx_graph.get(node_name).get('permutation_type')
if node_original_permutation_type in ['C', 'KC']:
print("[init_permutation_flag] node_name: \'{:}\', its original permutation: \'{:}\' already includes C dim, no need to do No.1 post-processing change.".format(node_name, node_original_permutation_type))
elif node_original_permutation_type == 'None':
fx_graph[node_name]['permutation_type'] = 'C'
print("[init_permutation_flag] node_name: \'{:}\', change its original permutation: \'{:}\' to new permutation: 'C'.".format(node_name, node_original_permutation_type))
node_change_permutation_due_to_siblings.append(node_name)
elif node_original_permutation_type == 'K':
fx_graph[node_name]['permutation_type'] = 'KC'
print("[init_permutation_flag] node_name: \'{:}\', change its original permutation: \'{:}\' to new permutation: 'KC'.".format(node_name, node_original_permutation_type))
node_change_permutation_due_to_siblings.append(node_name)
# Post-processing Step No.2:
print("\n[init_permutation_flag] Post-processing Step No.2.")
for node_name in fx_graph.keys():
node_real_children = fx_graph.get(node_name).get('real_children')
node_module_type = fx_graph.get(node_name).get('module_type')
if (node_real_children is not None) and (node_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear', 'torch.nn.modules.batchnorm.BatchNorm2d']):
is_node_real_children_has_node_change_permutation = False
for real_child_item in node_real_children:
if real_child_item in node_change_permutation_due_to_siblings:
is_node_real_children_has_node_change_permutation = True
if is_node_real_children_has_node_change_permutation == True:
print("[init_permutation_flag] node_name: \'{:}\', one of its real children has changed permutation due to its siblings.".format(node_name))
node_original_permutation_type = fx_graph.get(node_name).get('permutation_type')
if node_original_permutation_type in ['K', 'KC']:
print("[init_permutation_flag] node_name: \'{:}\', its original permutation: \'{:}\' already includes K dim, no need to do No.2 post-processing change.".format(node_name, node_original_permutation_type))
elif node_original_permutation_type == 'None':
fx_graph[node_name]['permutation_type'] = 'K'
print("[init_permutation_flag] node_name: \'{:}\', change its original permutation: \'{:}\' to new permutation: 'K'.".format(node_name, node_original_permutation_type))
elif node_original_permutation_type == 'C':
fx_graph[node_name]['permutation_type'] = 'KC'
print("[init_permutation_flag] node_name: \'{:}\', change its original permutation: \'{:}\' to new permutation: 'KC'.".format(node_name, node_original_permutation_type))
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_init_permutation_flag.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def extract_all_unique_siblings(cls, fx_graph):
"""This function is used to extrat all unique siblings for the whole network graph built with Torch.FX."""
print("\n[extract_all_unique_siblings] Extract all unique siblings for the whole network graph built with Torch.FX")
all_unique_siblings_name = []
all_unique_siblings_module_type = []
for node_name in fx_graph.keys():
fx_graph[node_name]['node_type'] = 'network_node' # use the 'node_type' to divide the real nodes apart from the auxiliary info node, like 'unique_siblings' node
node_module_type = fx_graph.get(node_name).get('module_type')
node_real_siblings = fx_graph.get(node_name).get('real_siblings')
node_real_siblings_module_type = fx_graph.get(node_name).get('real_siblings_module_type')
if node_real_siblings == []:
print("[extract_all_unique_siblings] node_name: \'{:}\', node module type: \'{:}\', has no real siblings.".format(node_name, node_module_type))
# for the Conv/FC layers without real_siblings, then we should insert itself as an unique_siblings
if node_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
# direct insert will change the real_siblings info for the node in the fx_graph
node_real_siblings_with_node_itself = node_real_siblings.copy()
node_real_siblings_with_node_itself.insert(0, node_name)
node_real_siblings_module_type_with_node_itself = node_real_siblings_module_type.copy()
node_real_siblings_module_type_with_node_itself.insert(0, node_module_type)
all_unique_siblings_name.append(node_real_siblings_with_node_itself)
all_unique_siblings_module_type.append(node_real_siblings_module_type_with_node_itself)
else:
print("[extract_all_unique_siblings] node_name: \'{:}\', node module type: \'{:}\', has {:} real siblings: \'{:}\'.".format(node_name, node_module_type, len(node_real_siblings), node_real_siblings))
# for the two duplicated siblings lists, the node names included should be the same.
# If the node name is already included in one of the unique_siblings_name list, which means the real_siblings of this node is duplicated with the unique_siblings_name list.
# Otherwise, we should insert the [real_siblings + node_name] as a new unique_siblings_name list.
has_include_siblings = False
for unique_siblings_item in all_unique_siblings_name:
if node_name in unique_siblings_item:
has_include_siblings = True
if has_include_siblings == False:
# direct insert will change the real_siblings info for the node in the fx_graph
node_real_siblings_with_node_itself = node_real_siblings.copy()
node_real_siblings_with_node_itself.insert(0, node_name)
node_real_siblings_module_type_with_node_itself = node_real_siblings_module_type.copy()
node_real_siblings_module_type_with_node_itself.insert(0, node_module_type)
all_unique_siblings_name.append(node_real_siblings_with_node_itself)
all_unique_siblings_module_type.append(node_real_siblings_module_type_with_node_itself)
fx_graph['unique_siblings'] = {}
fx_graph['unique_siblings']['name'] = all_unique_siblings_name
fx_graph['unique_siblings']['module_type'] = all_unique_siblings_module_type
fx_graph['unique_siblings']['node_type'] = 'auxiliary_info_node'
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_extract_all_unique_siblings.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def find_real_siblings(cls, fx_graph):
"""This function is used to find all siblings for each node according to the whole network graph built with Torch.FX.
we need to find siblings recursively, because siblings may have siblings via other parents we don't know about.
"""
print("\n[find_real_siblings] Find all siblings for each node according to the whole network graph built with Torch.FX")
for node_name in fx_graph.keys():
node_real_siblings_name = []
node_real_siblings_module_type = []
node_real_parents = fx_graph.get(node_name).get('real_parents')
node_module_type = fx_graph.get(node_name).get('module_type')
if node_module_type not in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
print("[find_real_siblings] node_name: \'{:}\', node module type: \'{:}\', has no real siblings.".format(node_name, node_module_type))
else:
print("[find_real_siblings] node_name: \'{:}\', node module type: \'{:}\', may have real siblings.".format(node_name, node_module_type))
# sibling means the nodes share the same real parent
for real_parent_item in node_real_parents:
for real_child_item in fx_graph.get(real_parent_item).get('real_children'):
if real_child_item != node_name:
sibling_module_type = fx_graph.get(real_child_item).get('module_type')
print("[find_real_siblings] node_name: \'{:}\', has one real sibling: \'{:}\', its real sibling module type: \'{:}\'.".format(node_name, real_child_item, sibling_module_type))
node_real_siblings_name.append(real_child_item)
node_real_siblings_module_type.append(sibling_module_type)
# remove the duplicated real siblings
exclusive_node_real_siblings_name = []
exclusive_node_real_siblings_module_type = []
item_index = 0
duplicated_real_siblings = 0
for item in node_real_siblings_name:
if item not in exclusive_node_real_siblings_name:
exclusive_node_real_siblings_name.append(item)
exclusive_node_real_siblings_module_type.append(node_real_siblings_module_type[item_index])
else:
duplicated_real_siblings = duplicated_real_siblings + 1
item_index = item_index + 1
if duplicated_real_siblings > 0:
print("[find_real_siblings] node_name: \'{:}\', remove {:} duplicated real siblings.".format(node_name, duplicated_real_siblings))
fx_graph[node_name]['real_siblings'] = exclusive_node_real_siblings_name
fx_graph[node_name]['real_siblings_module_type'] = exclusive_node_real_siblings_module_type
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_find_real_siblings.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def recursive_find_real_children(cls, node_name, fx_graph):
"""This function is used to recursively find the real children for each node according to the whole network graph built with Torch.FX.
Used as the sub-function of find_real_children.
"""
node_real_children_name = []
node_real_children_module_type = []
if node_name in fx_graph.keys(): # can be deleted, because node_name is already in the 'children' item in one node of the fx_graph
node_children = fx_graph.get(node_name).get('children')
node_module_type = fx_graph.get(node_name).get('module_type')
has_visit_children_num = 0
has_real_children_num = 0
sub_node_need_recursive_search = []
while has_visit_children_num < len(node_children):
for child_name in node_children:
if child_name != 'output': # 'output' node has no 'module_type'
child_module_type = fx_graph.get(child_name).get('module_type')
if child_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
print("[recursive_find_real_children] node_name: \'{:}\', has one real child: \'{:}\', its real child module type: \'{:}\'.".format(node_name, child_name, child_module_type))
node_real_children_name.append(child_name)
node_real_children_module_type.append(child_module_type)
has_real_children_num = has_real_children_num + 1
else:
print("[recursive_find_real_children] node_name: \'{:}\', its child: \'{:}\' with module type: \'{:}\', needs recursive search.".format(node_name, child_name, child_module_type))
sub_node_need_recursive_search.append(child_name)
else:
print("[recursive_find_real_children] node_name: \'{:}\', its child: \'{:}\' with no module type, is not its real child.".format(node_name, child_name))
has_visit_children_num = has_visit_children_num + 1
if len(sub_node_need_recursive_search) > 0:
for sub_node in sub_node_need_recursive_search:
if fx_graph.get(sub_node).get('real_children') == []:
sub_node_real_children_name, sub_node_real_children_module_type = cls.recursive_find_real_children(sub_node, fx_graph)
else:
# if the sub_node already find the 'real_children', no need to do recursive search
sub_node_real_children_name = fx_graph.get(sub_node).get('real_children')
sub_node_real_children_module_type = fx_graph.get(sub_node).get('real_children_module_type')
node_real_children_name.extend(sub_node_real_children_name)
node_real_children_module_type.extend(sub_node_real_children_module_type)
return node_real_children_name, node_real_children_module_type
@classmethod
def find_real_children(cls, fx_graph):
"""This function is used to find the real children for each node according to the whole network graph built with Torch.FX.
For example:
The real children of Conv is the subsequent Conv/FC.
The real children of BN or other no-need-permutataion layers is the subsequent Conv/FC.
"""
print("\n[find_real_children] Find the real children for each node according to the whole network graph built with Torch.FX")
from sys import version_info
if version_info.major == 3 and version_info.minor >= 8:
reversible_fx_graph_keys = fx_graph.keys()
else: # 'dict_keys' object is not reversible in previous of Python 3.8
reversible_fx_graph_keys = list(fx_graph.keys())
for node_name in reversed(reversible_fx_graph_keys): # as the optimization, we need to find the real children from back to front, to use the already saved 'real_children'
node_real_children_name = []
node_real_children_module_type = []
node_children = fx_graph.get(node_name).get('children')
node_module_type = fx_graph.get(node_name).get('module_type')
if node_module_type not in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
print("\n[find_real_children] node_name: \'{:}\', node module type: \'{:}\', children num: {:}, recursive to find real children.".format(node_name, node_module_type, len(node_children)))
node_real_children_name, node_real_children_module_type = cls.recursive_find_real_children(node_name, fx_graph)
else: # Quick method, but cannot get the real children for no-need-permutataion layers like BN
print("\n[find_real_children] node_name: \'{:}\', node module type: \'{:}\', children num: {:}, can directly find real children.".format(node_name, node_module_type, len(node_children)))
# if the node is in the 'real_parents' list of the other node, then the other node is the real children for this node
for other_node_name in fx_graph.keys():
if (other_node_name != node_name) and (node_name in fx_graph.get(other_node_name).get('real_parents')):
child_module_type = fx_graph.get(other_node_name).get('module_type')
if child_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
print("[find_real_children] node_name: \'{:}\', has one real child: \'{:}\', its real child module type: \'{:}\'.".format(node_name, other_node_name, child_module_type))
node_real_children_name.append(other_node_name)
node_real_children_module_type.append(child_module_type)
# remove the duplicated real children
exclusive_node_real_children_name = []
exclusive_node_real_children_module_type = []
item_index = 0
duplicated_real_children = 0
for item in node_real_children_name:
if item not in exclusive_node_real_children_name:
exclusive_node_real_children_name.append(item)
exclusive_node_real_children_module_type.append(node_real_children_module_type[item_index])
else:
duplicated_real_children = duplicated_real_children + 1
item_index = item_index + 1
if duplicated_real_children > 0:
print("[find_real_children] node_name: \'{:}\', remove {:} duplicated real children.".format(node_name, duplicated_real_children))
fx_graph[node_name]['real_children'] = exclusive_node_real_children_name
fx_graph[node_name]['real_children_module_type'] = exclusive_node_real_children_module_type
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_find_real_children.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def find_real_parents(cls, fx_graph):
"""This function is used to find the real parents for each node according to the whole network graph built with Torch.FX.
For example:
The real parent of BN is the previous Conv/FC.
The real parent of Conv is the previous Conv/FC.
"""
print("\n[find_real_parents] Find the real parents for each node according to the whole network graph built with Torch.FX")
for node_name in fx_graph.keys():
node_real_parents_name = []
node_real_parents_module_type = []
node_parents = fx_graph.get(node_name).get('parents')
print("[find_real_parents] node_name: \'{:}\', parents num: {:}".format(node_name, len(node_parents)))
has_visit_parent_num = 0
while has_visit_parent_num < len(node_parents):
for parent_name in node_parents:
if fx_graph.__contains__(parent_name):
parent_module_type = fx_graph.get(parent_name).get('module_type')
if parent_module_type in ['torch.nn.modules.conv.Conv2d', 'torch.nn.modules.linear.Linear']:
print("[find_real_parents] node_name: \'{:}\', has one real parent: \'{:}\', its real parent module type: \'{:}\'.".format(node_name, parent_name, parent_module_type))
node_real_parents_name.append(parent_name)
node_real_parents_module_type.append(parent_module_type)
else:
print("[find_real_parents] node_name: \'{:}\', has one/several real parent(s): \'{:}\', its real parent module type: \'{:}\'.".format(node_name, fx_graph[parent_name]['real_parents'], fx_graph[parent_name]['real_parents_module_type']))
for real_parent_item in fx_graph[parent_name]['real_parents']:
node_real_parents_name.append(real_parent_item)
for real_parent_module_type_item in fx_graph[parent_name]['real_parents_module_type']:
node_real_parents_module_type.append(real_parent_module_type_item)
else:
print("[find_real_parents] node_name: \'{:}\', has no real parent because this is the first node.".format(node_name))
has_visit_parent_num = has_visit_parent_num + 1
# remove the duplicated real parents
exclusive_node_real_parents_name = []
exclusive_node_real_parents_module_type = []
exclusive_node_real_parents_groups_param = []
item_index = 0
duplicated_real_parents = 0
for item in node_real_parents_name:
if item not in exclusive_node_real_parents_name:
exclusive_node_real_parents_name.append(item)
exclusive_node_real_parents_module_type.append(node_real_parents_module_type[item_index])
exclusive_node_real_parents_groups_param.append(fx_graph.get(item).get('groups_param'))
else:
duplicated_real_parents = duplicated_real_parents + 1
item_index = item_index + 1
if duplicated_real_parents > 0:
print("[find_real_parents] node_name: \'{:}\', remove {:} duplicated real parents.".format(node_name, duplicated_real_parents))
fx_graph[node_name]['real_parents'] = exclusive_node_real_parents_name
fx_graph[node_name]['real_parents_module_type'] = exclusive_node_real_parents_module_type
fx_graph[node_name]['real_parents_groups_param'] = exclusive_node_real_parents_groups_param
if cls.__save_permutation_graph:
cls.save_graph_to_json(fx_graph, save_dumped_graph_path_with_name=os.path.join(cls.__permutation_output_dir, './model_graph_find_real_parent.json')) # save the intermediate graph as JSON file for debugging
return fx_graph
@classmethod
def build_fx_graph(cls, model, dump_fx_graph=False, save_dumped_fx_graph='./model_fx_graph.json'):
"""This function is used to build the whole network graph with Torch.FX features."""
success = True
torch_version = str(torch.__version__)
torch_version_major = int(torch_version.split('.')[0])
torch_version_minor = int(torch_version.split('.')[1])
try:
torch_version_minimum = int(torch_version.split('.')[2])
except ValueError: # support the none standard version
torch_version_minimum = torch_version.split('.')[2]
print("[build_fx_graph] The torch version is: {}, version major is: {}, version minor is: {}, version minimum is: {}".format(torch_version, torch_version_major, torch_version_minor, torch_version_minimum))
if torch_version_major >= 1 and torch_version_minor >= 8:
print("[build_fx_graph] The Torch.FX is supported.")
else: # Torch.FX is introduced in torch 1.8.0
print("[build_fx_graph] The Torch.FX is not supported. So cannot build the Torch.FX graph.")
success = False
network_fx_graph = {}
return network_fx_graph, success
print("\n[build_fx_graph] Print the model structure with pure PyTorch function")
print(model)
print("\n[build_fx_graph] Build the module name and type dictionary")
module_name_type_dict = {}
module_name_group_conv_dict = {}
for name, mod in model.named_modules():
print("[build_fx_graph] module_name: {}, module type: {}".format(name, type(mod)))
module_name_type_dict[name] = str(type(mod)).split("\'")[1]
try:
print("[build_fx_graph] this module has \'group\' param with value: {}".format(mod.groups))
module_name_group_conv_dict[name] = str(mod.groups)
except:
module_name_group_conv_dict[name] = 'None'
continue
graph_module = cls.print_raw_fx_graph(model, print_tabular=True)
# keep track of children and parents for each layer (could be call_module or call_function)
print("\n[build_fx_graph] Print the children and parents relationship for each layer")
network_fx_graph = {}
for node in graph_module.graph.nodes:
if node.op == 'placeholder':
print("[build_fx_graph] This is the \'input\' node: {:}".format(node.target))
continue
elif node.op == 'get_attr':
print("[build_fx_graph] This is the \'get_attr\' node: {:}".format(node.target))
continue
elif node.op == 'call_function': # e.g. 'adaptive.avg.pool2d', 'add', 'cat', 'flatten', 'floordiv', 'getattr', 'getitem', 'hardsigmoid', 'mean', 'mul', 'relu', 'transpose'
node_parent, node_children = get_node_parent_children(node)
converted_node_name=convert_fx_node_name(node.name)
print("[build_fx_graph] This is the \'call_function\' node: {:}, its parent list: {:}, its children list: {:}".format(converted_node_name, node_parent, node_children))
network_fx_graph[converted_node_name] = {}
network_fx_graph[converted_node_name]['parents'] = node_parent
network_fx_graph[converted_node_name]['children'] = node_children
network_fx_graph[converted_node_name]['fx_op'] = 'call_function'
elif node.op == 'call_method': # e.g. 'chunk', 'contiguous', 'mean', 'size', 'unsqueeze', 'view'
node_parent, node_children = get_node_parent_children(node)
converted_node_name=convert_fx_node_name(node.name)
print("[build_fx_graph] This is the \'call_method\' node: {:}, its parent list: {:}, its children list: {:}".format(converted_node_name, node_parent, node_children))
network_fx_graph[converted_node_name] = {}
network_fx_graph[converted_node_name]['parents'] = node_parent
network_fx_graph[converted_node_name]['children'] = node_children
network_fx_graph[converted_node_name]['fx_op'] = 'call_method'
continue
elif node.op == 'call_module':
node_parent, node_children = get_node_parent_children(node)
converted_node_name=convert_fx_node_name(node.name)
# check whether the converted_node_name is same as node.target, especially for ReLU case
if converted_node_name != node.target:
print("[build_fx_graph][warning] The target name from Torch.FX is \'{:}\', the manually converted node name is \'{:}\', not the same one, choose the converted node name".format(node.target, converted_node_name))
# assume the modules share the same target name have the same type, because converted_node_name may not be obtained by model.named_modules(), like some ReLU (defined in forward function)
node_type = module_name_type_dict[node.target]
print("[build_fx_graph] This is the \'call_module\' node: {:}, its parent list: {:}, its children list: {:}, its type: {:}".format(converted_node_name, node_parent, node_children, node_type))
network_fx_graph[converted_node_name] = {}
network_fx_graph[converted_node_name]['parents'] = node_parent
network_fx_graph[converted_node_name]['children'] = node_children
network_fx_graph[converted_node_name]['fx_op'] = 'call_module'
network_fx_graph[converted_node_name]['module_type'] = node_type
network_fx_graph[converted_node_name]['groups_param'] = module_name_group_conv_dict[node.target]
elif node.op == 'output':
print("[build_fx_graph] This is the \'output\' node: {:}".format(node.target))
continue
if dump_fx_graph:
print("\n[build_fx_graph] Dump the overall dict for children and parents relationship into JSON file")
cls.save_graph_to_json(network_fx_graph, save_dumped_graph_path_with_name=save_dumped_fx_graph)
return network_fx_graph, success
@classmethod
def print_raw_fx_graph(cls, model, print_tabular=False, generate_python_code=False):
"""This function is used to print the intermediate representation (IR) - Graph representation with Torch.FX features."""
from torch.fx import symbolic_trace
# Symbolic tracing frontend - captures the semantics of the module
try:
symbolic_traced : torch.fx.GraphModule = symbolic_trace(model)
except:
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
print("\n[print_raw_fx_graph] Meet the fatal fault when trying to symbolic trace the model with Torch.FX")
raise
exit(0)
# High-level intermediate representation (IR) - Graph representation
print("\n[print_raw_fx_graph] Print the intermediate representation (IR) with Torch.FX")
print(symbolic_traced.graph)
if print_tabular:
print("\n[print_raw_fx_graph] Print the intermediate representation (IR) with Torch.FX in a table format")
try:
symbolic_traced.graph.print_tabular()
except AttributeError: # to avoid the AttributeError: 'Graph' object has no attribute 'print_tabular'
print("[print_raw_fx_graph][Warning] \'print_tabular\' function is not supported in current Torch version. Skip!")
# Code generation - valid Python code
if generate_python_code:
print("\n[print_raw_fx_graph] Create valid Python code matching the IR/Graph's semantics with Torch.FX")
print(symbolic_traced.code)
return symbolic_traced
@classmethod
def save_graph_to_json(cls, graph, save_dumped_graph_path_with_name='./model_fx_graph.json'):
"""This function is used to same the graph into JSON file."""
# use dumps to transfer the dict to JSON string
json_graph_str = json.dumps(graph)
with open(save_dumped_graph_path_with_name, 'w', encoding='utf-8') as dumped_graph_file:
dumped_graph_file.write(json_graph_str) # write the transferred JSON string into JSON file
apex/contrib/sparsity/permutation_search_kernels/CUDA_kernels/permutation_search_kernels.cu deleted 100644 → 0
View file @ 2a4864d5
#include <stdio.h>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert %d: %s %s %d\n", (int)code, cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
__device__ float group_2_to_4(float4 vals)
{
vals.x = fabs(vals.x);
vals.y = fabs(vals.y);
vals.z = fabs(vals.z);
vals.w = fabs(vals.w);
float sum0 = vals.x + vals.y;
float sum1 = vals.x + vals.z;
float sum2 = vals.x + vals.w;
float sum3 = vals.y + vals.z;
float sum4 = vals.y + vals.w;
float sum5 = vals.z + vals.w;
float best_sum0 = fmax(sum0, sum1);
float best_sum1 = fmax(sum2, sum3);
float best_sum2 = fmax(sum4, sum5);
float best_sum = fmax(fmax(best_sum0, best_sum1), best_sum2);
return best_sum;
}
inline float* float_ptr_from_numpy(py::array_t<float>& py_float)
{
return (float*)py_float.data();
}
inline unsigned int* uint_ptr_from_numpy(py::array_t<unsigned int>& py_uint)
{
return (unsigned int*)py_uint.data();
}
__global__ void subset_sum_after_2_to_4(float* matrix,
unsigned int rows,
unsigned int cols,
unsigned int start_col,
unsigned int end_col,
float* output)
{
// vectorize
float4* mat4 = (float4*) matrix;
cols /= 4;
start_col /= 4;
end_col /= 4;
// each thread in a block takes some number of rows
size_t num_rows = max((int)ceilf((float)rows / (float)blockDim.x), 1);
size_t row_offset = num_rows * threadIdx.x;
// each block takes some number of columns
size_t num_cols = (end_col - start_col) / gridDim.x;
size_t col_offset = num_cols * blockIdx.x;
start_col += col_offset;
end_col = start_col + num_cols;
float sum = 0.0f;
for ( unsigned int r = row_offset; r < row_offset + num_rows; ++r ) {
if (r < rows) {
for ( unsigned int c = start_col; c < end_col; c++ ) {
sum += group_2_to_4(mat4[r * cols + c]);
}
}
}
atomicAdd(output, sum);
}
// build the entire permute map at once
// each block handles one group of stripes
// each threads in the block handle all handle the same permutation at the same time on different rows before moving to the next permutation
__global__ void build_permute_map(float* matrix,
unsigned int rows,
unsigned int cols,
unsigned int* stripes,
unsigned int group_width,
unsigned int* permutations,
unsigned int num_permutations,
unsigned int perm_length,
float* output,
unsigned int* best_indices)
{
// vectorize
float4* mat4 = (float4*) matrix;
cols /= 4;
// each block handles a group of stripes
unsigned int* stripe_group = (unsigned int*)&stripes[blockIdx.x*group_width];
// shared memory: 32 threads each need 16*2
extern __shared__ float pm_shared[32][32];
float4* local_stripes = (float4*)&pm_shared[threadIdx.x];
float* local_columns = (float*) &pm_shared[threadIdx.x];
float4* permuted_stripes = (float4*) &local_stripes[4];
float* permuted_columns = (float*) &local_columns[16];
// each thread handles all permutations in the row before moving on to the next row
size_t num_rows = max((int)ceilf((float)rows / (float)blockDim.x), 1);
size_t row_offset = num_rows * threadIdx.x;
for ( unsigned int r = row_offset; r < row_offset + num_rows; ++r) {
if (r >= rows)
break;
// load a row into smem
for ( unsigned int s = 0; s < group_width; ++s) {
unsigned int const stripe = stripe_group[s];
local_stripes[s] = mat4[r*cols+stripe];
}
for ( unsigned int p = 0; p < num_permutations; ++p) {
unsigned int* permutation = &permutations[p*perm_length];
float sum = 0.0f;
// permute
#pragma unroll 4
for ( unsigned int c = 0; c < group_width*4; ++c) {
permuted_columns[c] = local_columns[permutation[c]];
}
// sum 2:4
for ( unsigned int s = 0; s < group_width; ++s) {
sum += group_2_to_4(permuted_stripes[s]);
}
// update the running sum for this stripe group's permutation
atomicAdd(&output[blockIdx.x*num_permutations + p], sum);
}
}
// at this point, each permutation's sum in this stripe group has been calculated
// now, find the best option
__syncthreads();
if (threadIdx.x == 0) {
unsigned int best_permutation = 0;
float best_magnitude = output[blockIdx.x*num_permutations];
float base_magnitude = best_magnitude;
//#pragma unroll 32
for (unsigned int p = 1; p < num_permutations; ++p) {
float magnitude = output[blockIdx.x*num_permutations+p];
if (magnitude > best_magnitude) {
best_permutation = p;
best_magnitude = magnitude;
}
}
output[blockIdx.x*num_permutations] = best_magnitude - base_magnitude;
best_indices[blockIdx.x] = best_permutation;
}
}
void free_sum_after_2_to_4_memory(float** dmatrix,
float** dresult)
{
cudaFree(*dmatrix);
cudaFree(*dresult);
}
int set_up_sum_after_2_to_4_memory(float** dmatrix,
unsigned int rows,
unsigned int cols,
float** dresult)
{
static unsigned int setupRows = 0;
static unsigned int setupCols = 0;
static bool allocated = false;
int fresh_allocation = 0;
if (!allocated ||
setupRows != rows ||
setupCols != cols)
{
if (allocated)
free_sum_after_2_to_4_memory(dmatrix, dresult);
gpuErrchk(cudaMalloc( (void**) dmatrix, rows*cols*sizeof(float)));
gpuErrchk(cudaMalloc( (void**) dresult, sizeof(float)));
setupRows = rows;
setupCols = cols;
fresh_allocation = 1;
}
allocated = true;
return fresh_allocation;
}
int run_subset_sum_after_2_to_4(py::array_t<float>& py_matrix,
unsigned int rows,
unsigned int cols,
unsigned int start_col,
unsigned int end_col,
unsigned int blocks,
unsigned int threads,
py::array_t<float>& py_output)
{
static float* d_matrix;
static float* d_result;
int fresh_allocation = set_up_sum_after_2_to_4_memory(&d_matrix, rows, cols, &d_result);
float* matrix = float_ptr_from_numpy(py_matrix);
float* output = float_ptr_from_numpy(py_output);
gpuErrchk(cudaMemcpy( d_matrix, matrix, rows*cols*sizeof(float), cudaMemcpyHostToDevice ));
gpuErrchk(cudaMemset( d_result, 0, sizeof(float)));
subset_sum_after_2_to_4<<<blocks, threads>>>(d_matrix, rows, cols, start_col, end_col, d_result);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaMemcpy( output, d_result, sizeof(float), cudaMemcpyDeviceToHost ));
return 0;
}
void set_up_permute_map_memory(float** dmatrix,
unsigned int rows,
unsigned int cols,
unsigned int** dstripes,
unsigned int num_groups,
unsigned int group_width,
unsigned int** dpermutations,
unsigned int num_permutations,
unsigned int perm_length,
float** doutput,
unsigned int** dindices,
float** hresult,
unsigned int** hindices)
{
static unsigned int setUpRows = 0;
static unsigned int setUpCols = 0;
static unsigned int setUpGroupWidth = 0;
static unsigned int setUpNumGroups = 0;
static unsigned int setUpNumPerms = 0;
static unsigned int setUpPermLength = 0;
if (setUpRows != rows ||
setUpCols != cols) {
if (*dmatrix != NULL) { gpuErrchk(cudaFree(*dmatrix)); *dmatrix = NULL; }
gpuErrchk(cudaMalloc( (void**) dmatrix, rows*cols*sizeof(float)));
}
if (setUpGroupWidth < group_width ||
setUpNumGroups < num_groups) {
if (*dstripes != NULL) { gpuErrchk(cudaFree(*dstripes)); *dstripes = NULL; }
gpuErrchk(cudaMalloc( (void**) dstripes, num_groups*group_width*sizeof(unsigned int)));
if (setUpNumGroups < num_groups) {
if (*dindices != NULL) { gpuErrchk(cudaFree(*dindices)); *dindices = NULL; }
gpuErrchk(cudaMalloc( (void**) dindices, num_groups*sizeof(unsigned int)));
if (*hindices != NULL) { free(*hindices); *hindices = NULL; }
*hindices = (unsigned int*) malloc (num_groups*sizeof(unsigned int));
}
}
if (setUpNumPerms < num_permutations ||
setUpPermLength < perm_length) {
if (*dpermutations != NULL) { gpuErrchk(cudaFree(*dpermutations)); *dpermutations = NULL; }
gpuErrchk(cudaMalloc( (void**) dpermutations, perm_length*num_permutations*sizeof(unsigned int)));
}
if (setUpNumPerms < num_permutations ||
setUpNumGroups < num_groups) {
if (*doutput != NULL) { gpuErrchk(cudaFree(*doutput)); *doutput = NULL; }
gpuErrchk(cudaMalloc( (void**) doutput, num_permutations*num_groups*sizeof(float)));
if (*hresult != NULL) { free(*hresult); *hresult = NULL; }
*hresult = (float*) malloc(num_permutations*num_groups*sizeof(float));
}
setUpRows = rows;
setUpCols = cols;
setUpGroupWidth = group_width;
setUpNumGroups = num_groups;
setUpNumPerms = num_permutations;
setUpPermLength = perm_length;
}
int run_build_permute_map(py::array_t<float>& py_matrix,
unsigned int rows,
unsigned int cols,
py::array_t<unsigned int>& py_stripes,
unsigned int num_groups,
unsigned int group_width,
py::array_t<unsigned int>& py_permutations,
//unsigned int num_permutations,
unsigned int perm_length,
py::array_t<float>& py_improvements,
py::array_t<unsigned int>& py_best_indices)
{
static float* d_matrix = NULL;
static unsigned int* d_stripes = NULL;
static unsigned int* d_permutations = NULL;
static float* d_output = NULL;
static unsigned int* d_indices = NULL;
static float* hresult = NULL;
static unsigned int* hindices = NULL;
//const unsigned int cols = py_matrix.size() / rows;
//const unsigned int num_groups = py_stripes.size() / group_width;
//const unsigned int perm_length = group_width * 4; // 2:4 sparsity - each stripe in the group is 4 elements wide
const unsigned int num_permutations = py_permutations.size() / perm_length;
const unsigned int MAX_GROUPS_PER_LAUNCH = num_permutations <= 5775 ? 1820 : 40;
const unsigned int full_launches = num_groups / MAX_GROUPS_PER_LAUNCH;
const unsigned int final_launch = num_groups % MAX_GROUPS_PER_LAUNCH;
const unsigned int launches = full_launches + (final_launch != 0 ? 1 : 0);
set_up_permute_map_memory(&d_matrix, rows, cols, &d_stripes, min(num_groups,MAX_GROUPS_PER_LAUNCH), group_width, &d_permutations, num_permutations, perm_length, &d_output, &d_indices, &hresult, &hindices);
float* matrix = float_ptr_from_numpy(py_matrix);
unsigned int* stripes = uint_ptr_from_numpy(py_stripes);
unsigned int* permutations = uint_ptr_from_numpy(py_permutations);
float* improvements = float_ptr_from_numpy(py_improvements);
unsigned int* best_indices = uint_ptr_from_numpy(py_best_indices);
gpuErrchk(cudaMemcpy( d_matrix, matrix, rows*cols*sizeof(float), cudaMemcpyHostToDevice ));
gpuErrchk(cudaMemcpy( d_permutations, permutations, num_permutations*perm_length*sizeof(unsigned int), cudaMemcpyHostToDevice ));
unsigned int group_offset = 0;
for (unsigned int l = 0; l < launches; ++l)
{
unsigned int groups_this_launch = (l < full_launches) ? MAX_GROUPS_PER_LAUNCH : final_launch;
gpuErrchk(cudaMemcpy( d_stripes, &stripes[group_offset*group_width], groups_this_launch*group_width*sizeof(unsigned int), cudaMemcpyHostToDevice ));
gpuErrchk(cudaMemset( d_output, 0, groups_this_launch*num_permutations*sizeof(float)));
gpuErrchk(cudaMemset( d_indices, 0, groups_this_launch*sizeof(unsigned int)));
unsigned int shmem = 32*(32)*sizeof(float);
build_permute_map<<<groups_this_launch, 32, shmem>>>(d_matrix, rows, cols, d_stripes, group_width, d_permutations, num_permutations, perm_length, d_output, d_indices);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaMemcpy( hresult, d_output, num_permutations*groups_this_launch*sizeof(float), cudaMemcpyDeviceToHost ));
gpuErrchk(cudaMemcpy( hindices, d_indices, groups_this_launch*sizeof(unsigned int), cudaMemcpyDeviceToHost ));
// thread0 stuck the minimum in the first slot of each group
for (unsigned int g = 0; g < groups_this_launch; ++g) {
improvements[group_offset+g] = hresult[g*num_permutations];
best_indices[group_offset+g] = hindices[g];
}
group_offset += groups_this_launch;
}
return 0;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("sum_after_2_to_4", &run_subset_sum_after_2_to_4, "matrix sum after applying 2:4 (CUDA)");
m.def("build_permute_map", &run_build_permute_map, "optimize stripe groups (CUDA)");
}
\ No newline at end of file
apex/contrib/sparsity/permutation_search_kernels/__init__.py deleted 100644 → 0
View file @ 2a4864d5
from .call_permutation_search_kernels import accelerated_search_for_good_permutation
from .permutation_utilities import sum_after_2_to_4
\ No newline at end of file
apex/contrib/sparsity/permutation_search_kernels/call_permutation_search_kernels.py deleted 100644 → 0
View file @ 2a4864d5
import numpy as np
from .permutation_utilities import *
from .exhaustive_search import Exhaustive_Search
def accelerated_search_for_good_permutation(matrix_group, options=None):
"""This function is used to call the permutation search CUDA kernels.
users can provide prefer search strategy by providing a valid 'options' as a dictionary,
or users can implement their customized 'accelerated_search_for_good_permutation' function.
"""
input_matrix = matrix_group.cpu().detach().numpy()
print("\n[accelerated_search_for_good_permutation] input matrix shape: \'{:}\'.".format(input_matrix.shape))
result = np.copy(input_matrix)
# init a sequential permutation search sequence
input_channel_num = matrix_group.size()[1]
permutation_sequence = [n for n in range(input_channel_num)]
duration = 0.0
if options == None:
options = {}
if 'strategy' not in options: # right now, the default permutation search strategy is: 'exhaustive' search
options['strategy'] = 'exhaustive'
print("[accelerated_search_for_good_permutation] the permutation strategy is: \'{:} search\'.".format(options['strategy']))
# define sub options for each search strategy
if options['strategy'] == 'exhaustive':
# right now, the default options for 'exhaustive' search is: 'exhaustive,8,100'
if 'stripe_group_size' not in options:
options['stripe_group_size'] = 8
if 'escape_attempts' not in options:
options['escape_attempts'] = 100
elif options['strategy'] == 'progressive channel swap':
# just swaps meaningful channels, keeping the good swaps, until the search time limit expires.
if 'progressive_search_time_limit' not in options:
options['progressive_search_time_limit'] = 60
if 'improvement_threshold' not in options:
options['improvement_threshold'] = 1e-9
# execute the requested strategy
if options['strategy'] == 'exhaustive':
result, duration, permutation_sequence = Exhaustive_Search(result, stripe_group_size=options['stripe_group_size'], escape_attempts=options['escape_attempts'])
elif options['strategy'] == 'progressive channel swap':
real_swap_num = 0
start_time = time.perf_counter()
while time.perf_counter() - start_time < options['progressive_search_time_limit']:
src = np.random.randint(result.shape[1])
dst = np.random.randint(result.shape[1])
src_group = int(src/4)
dst_group = int(dst/4)
if src_group == dst_group: # channel swapping within a stripe does nothing
continue
new_sum, improvement = try_swap(result, dst, src)
if improvement > options['improvement_threshold']:
result[...,[src,dst]] = result[...,[dst,src]]
permutation_sequence[src], permutation_sequence[dst] = permutation_sequence[dst], permutation_sequence[src]
real_swap_num += 1
duration = time.perf_counter() - start_time
print("\tFinally swap {} channel pairs until the search time limit expires.".format(real_swap_num))
elif options['strategy'] == 'user defined': # need to get the permutated matrix (result) by applying customized permutation search function
print("[accelerated_search_for_good_permutation] Use the user customized permutation search function!")
else:
print("[accelerated_search_for_good_permutation] Cannot find the implementation of the required strategy!")
print("[accelerated_search_for_good_permutation] Take {:.4f} seconds to search the permutation sequence.".format(duration))
# In the new version of Exhaustive_Search function, there’s no need to use the find_permutation(result, input_matrix) function
# to recover the permutation sequence applied to the input_matrix to get the result separately any more.
#start_time_find_permutation = time.perf_counter()
#permutation_sequence = find_permutation(result, input_matrix)
#duration_find_permutation = time.perf_counter() - start_time_find_permutation
#print("[accelerated_search_for_good_permutation] Take {:.4f} seconds to finish find_permutation function.".format(duration_find_permutation))
#print("[accelerated_search_for_good_permutation] The permutation sequence is: {:}".format(permutation_sequence))
#print("[accelerated_search_for_good_permutation] The length of permutation sequence is: {:}".format(len(permutation_sequence)))
return permutation_sequence
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