Spaces:
Running
Running
File size: 10,841 Bytes
acd7cf4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 |
import random
from collections import defaultdict
from tqdm.asyncio import tqdm as tqdm_async
from graphgen.utils import logger
from graphgen.models import NetworkXStorage, TraverseStrategy
async def _get_node_info(
node_id: str,
graph_storage: NetworkXStorage,
)-> dict:
"""
Get node info
:param node_id: node id
:param graph_storage: graph storage instance
:return: node info
"""
node_data = await graph_storage.get_node(node_id)
return {
"node_id": node_id,
**node_data
}
def _get_level_n_edges_by_max_width(
edge_adj_list: dict,
node_dict: dict,
edges: list,
nodes,
src_edge: tuple,
max_depth: int,
bidirectional: bool,
max_extra_edges: int,
edge_sampling: str,
loss_strategy: str = "only_edge"
) -> list:
"""
Get level n edges for an edge.
n is decided by max_depth in traverse_strategy
:param edge_adj_list
:param node_dict
:param edges
:param nodes
:param src_edge
:param max_depth
:param bidirectional
:param max_extra_edges
:param edge_sampling
:return: level n edges
"""
src_id, tgt_id, _ = src_edge
level_n_edges = []
start_nodes = {tgt_id} if not bidirectional else {src_id, tgt_id}
while max_depth > 0 and max_extra_edges > 0:
max_depth -= 1
candidate_edges = [
edges[edge_id]
for node in start_nodes
for edge_id in edge_adj_list[node]
if not edges[edge_id][2].get("visited", False)
]
if not candidate_edges:
break
if len(candidate_edges) >= max_extra_edges:
if loss_strategy == "both":
er_tuples = [([nodes[node_dict[edge[0]]], nodes[node_dict[edge[1]]]], edge) for edge in candidate_edges]
candidate_edges = _sort_tuples(er_tuples, edge_sampling)[:max_extra_edges]
elif loss_strategy == "only_edge":
candidate_edges = _sort_edges(candidate_edges, edge_sampling)[:max_extra_edges]
else:
raise ValueError(f"Invalid loss strategy: {loss_strategy}")
for edge in candidate_edges:
level_n_edges.append(edge)
edge[2]["visited"] = True
break
max_extra_edges -= len(candidate_edges)
new_start_nodes = set()
for edge in candidate_edges:
level_n_edges.append(edge)
edge[2]["visited"] = True
if not edge[0] in start_nodes:
new_start_nodes.add(edge[0])
if not edge[1] in start_nodes:
new_start_nodes.add(edge[1])
start_nodes = new_start_nodes
return level_n_edges
def _get_level_n_edges_by_max_tokens(
edge_adj_list: dict,
node_dict: dict,
edges: list,
nodes: list,
src_edge: tuple,
max_depth: int,
bidirectional: bool,
max_tokens: int,
edge_sampling: str,
loss_strategy: str = "only_edge"
) -> list:
"""
Get level n edges for an edge.
n is decided by max_depth in traverse_strategy.
:param edge_adj_list
:param node_dict
:param edges
:param nodes
:param src_edge
:param max_depth
:param bidirectional
:param max_tokens
:param edge_sampling
:return: level n edges
"""
src_id, tgt_id, src_edge_data = src_edge
max_tokens -= (src_edge_data["length"] + nodes[node_dict[src_id]][1]["length"]
+ nodes[node_dict[tgt_id]][1]["length"])
level_n_edges = []
start_nodes = {tgt_id} if not bidirectional else {src_id, tgt_id}
temp_nodes = {src_id, tgt_id}
while max_depth > 0 and max_tokens > 0:
max_depth -= 1
candidate_edges = [
edges[edge_id]
for node in start_nodes
for edge_id in edge_adj_list[node]
if not edges[edge_id][2].get("visited", False)
]
if not candidate_edges:
break
if loss_strategy == "both":
er_tuples = [([nodes[node_dict[edge[0]]], nodes[node_dict[edge[1]]]], edge) for edge in candidate_edges]
candidate_edges = _sort_tuples(er_tuples, edge_sampling)
elif loss_strategy == "only_edge":
candidate_edges = _sort_edges(candidate_edges, edge_sampling)
else:
raise ValueError(f"Invalid loss strategy: {loss_strategy}")
for edge in candidate_edges:
max_tokens -= edge[2]["length"]
if not edge[0] in temp_nodes:
max_tokens -= nodes[node_dict[edge[0]]][1]["length"]
if not edge[1] in temp_nodes:
max_tokens -= nodes[node_dict[edge[1]]][1]["length"]
if max_tokens < 0:
return level_n_edges
level_n_edges.append(edge)
edge[2]["visited"] = True
temp_nodes.add(edge[0])
temp_nodes.add(edge[1])
new_start_nodes = set()
for edge in candidate_edges:
if not edge[0] in start_nodes:
new_start_nodes.add(edge[0])
if not edge[1] in start_nodes:
new_start_nodes.add(edge[1])
start_nodes = new_start_nodes
return level_n_edges
def _sort_tuples(er_tuples: list, edge_sampling: str) -> list:
"""
Sort edges with edge sampling strategy
:param er_tuples: [(nodes:list, edge:tuple)]
:param edge_sampling: edge sampling strategy (random, min_loss, max_loss)
:return: sorted edges
"""
if edge_sampling == "random":
er_tuples = random.sample(er_tuples, len(er_tuples))
elif edge_sampling == "min_loss":
er_tuples = sorted(er_tuples, key=lambda x: sum(node[1]["loss"] for node in x[0]) + x[1][2]["loss"])
elif edge_sampling == "max_loss":
er_tuples = sorted(er_tuples, key=lambda x: sum(node[1]["loss"] for node in x[0]) + x[1][2]["loss"],
reverse=True)
else:
raise ValueError(f"Invalid edge sampling: {edge_sampling}")
edges = [edge for _, edge in er_tuples]
return edges
def _sort_edges(edges: list, edge_sampling: str) -> list:
"""
Sort edges with edge sampling strategy
:param edges: total edges
:param edge_sampling: edge sampling strategy (random, min_loss, max_loss)
:return: sorted edges
"""
if edge_sampling == "random":
random.shuffle(edges)
elif edge_sampling == "min_loss":
edges = sorted(edges, key=lambda x: x[2]["loss"])
elif edge_sampling == "max_loss":
edges = sorted(edges, key=lambda x: x[2]["loss"], reverse=True)
else:
raise ValueError(f"Invalid edge sampling: {edge_sampling}")
return edges
async def get_batches_with_strategy( # pylint: disable=too-many-branches
nodes: list,
edges: list,
graph_storage: NetworkXStorage,
traverse_strategy: TraverseStrategy
):
expand_method = traverse_strategy.expand_method
if expand_method == "max_width":
logger.info("Using max width strategy")
elif expand_method == "max_tokens":
logger.info("Using max tokens strategy")
else:
raise ValueError(f"Invalid expand method: {expand_method}")
max_depth = traverse_strategy.max_depth
edge_sampling = traverse_strategy.edge_sampling
# 构建临接矩阵
edge_adj_list = defaultdict(list)
node_dict = {}
processing_batches = []
node_cache = {}
async def get_cached_node_info(node_id: str) -> dict:
if node_id not in node_cache:
node_cache[node_id] = await _get_node_info(node_id, graph_storage)
return node_cache[node_id]
for i, (node_name, _) in enumerate(nodes):
node_dict[node_name] = i
if traverse_strategy.loss_strategy == "both":
er_tuples = [([nodes[node_dict[edge[0]]], nodes[node_dict[edge[1]]]], edge) for edge in edges]
edges = _sort_tuples(er_tuples, edge_sampling)
elif traverse_strategy.loss_strategy == "only_edge":
edges = _sort_edges(edges, edge_sampling)
else:
raise ValueError(f"Invalid loss strategy: {traverse_strategy.loss_strategy}")
for i, (src, tgt, _) in enumerate(edges):
edge_adj_list[src].append(i)
edge_adj_list[tgt].append(i)
for edge in tqdm_async(edges, desc="Preparing batches"):
if "visited" in edge[2] and edge[2]["visited"]:
continue
edge[2]["visited"] = True
_process_nodes = []
_process_edges = []
src_id = edge[0]
tgt_id = edge[1]
_process_nodes.extend([await get_cached_node_info(src_id),
await get_cached_node_info(tgt_id)])
_process_edges.append(edge)
if expand_method == "max_width":
level_n_edges = _get_level_n_edges_by_max_width(
edge_adj_list, node_dict, edges, nodes, edge, max_depth,
traverse_strategy.bidirectional, traverse_strategy.max_extra_edges,
edge_sampling, traverse_strategy.loss_strategy
)
else:
level_n_edges = _get_level_n_edges_by_max_tokens(
edge_adj_list, node_dict, edges, nodes, edge, max_depth,
traverse_strategy.bidirectional, traverse_strategy.max_tokens,
edge_sampling, traverse_strategy.loss_strategy
)
for _edge in level_n_edges:
_process_nodes.append(await get_cached_node_info(_edge[0]))
_process_nodes.append(await get_cached_node_info(_edge[1]))
_process_edges.append(_edge)
# 去重
_process_nodes = list({node['node_id']: node for node in _process_nodes}.values())
_process_edges = list({(edge[0], edge[1]): edge for edge in _process_edges}.values())
processing_batches.append((_process_nodes, _process_edges))
logger.info("Processing batches: %d", len(processing_batches))
# isolate nodes
isolated_node_strategy = traverse_strategy.isolated_node_strategy
if isolated_node_strategy == "add":
processing_batches = await _add_isolated_nodes(nodes, processing_batches, graph_storage)
logger.info("Processing batches after adding isolated nodes: %d", len(processing_batches))
return processing_batches
async def _add_isolated_nodes(
nodes: list,
processing_batches: list,
graph_storage: NetworkXStorage,
) -> list:
visited_nodes = set()
for _process_nodes, _process_edges in processing_batches:
for node in _process_nodes:
visited_nodes.add(node["node_id"])
for node in nodes:
if node[0] not in visited_nodes:
_process_nodes = [await _get_node_info(node[0], graph_storage)]
processing_batches.append((_process_nodes, []))
return processing_batches
|