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tree_search.py
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tree_search.py
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import torch
torch.set_printoptions(profile="full")
import json
from tqdm import tqdm
import sys
from copy import deepcopy
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default="demo-config.json", help='config')
args = parser.parse_args()
print(args)
with open(args.config, 'r') as f:
config = json.load(f)
p = torch.load(config["acceptance_rate_vector"]).cpu()[:-1]
max_branch = p.shape[0] - 1
max_depth = config["max_depth"]
max_budget = config["max_budget"]
T = torch.zeros((max_budget + 1, max_depth + 1, max_branch + 1)).fill_(-torch.inf)
T_max = torch.zeros((max_budget + 1, max_depth + 1))
branch_map = {}
for l in range(1, max_depth + 1):
for b in range(0, max_branch + 1):
if b == 0:
T[1][l][b] = 1.0
branch_map[(1,l,b)] = []
for m in tqdm(range(2, max_budget+1)):
for l in range(2, max_depth + 1):
T[m][l][1] = 1 + p[1] * T[m-1][l-1].max()
if T[m][l][1] > 0:
branch_map[(m,l,1)] = [(m-1, l-1, T[m-1][l-1].argmax(dim=0).item())]
for b in range(2, max_branch + 1):
max_value = -torch.inf
#new_y = -1
for y in range(1, m):
new_value = T[y][l][b-1] + p[b] * T[m-y][l-1].max()
if new_value > max_value:
max_value = new_value
new_y = y
max_value = max(max_value, new_value)
T[m][l][b] = max_value
if max_value >= 0:
new_branch = T[m-new_y][l-1].argmax(dim=0).item()
new_list :list = deepcopy(branch_map[(new_y, l, b-1)])
new_list.append((m-new_y, l-1, new_branch))
branch_map[(m,l,b)] = new_list
results = T.max(dim=2).values
print(results)
draft_inference_time = config['draft_time']
target_verify_time = config['target_time']
valid_budget = config['valid_budget']
dec_time = torch.inf
pairs = None
for i, b in enumerate(valid_budget):
target_time = target_verify_time[i]
for d, ac_len in enumerate(results[b]):
if ac_len < 0:
continue
x = ((d) * draft_inference_time + target_time) / ac_len
if x < dec_time:
dec_time = x
pairs = (b,d)
print(dec_time, target_verify_time[0] / dec_time, pairs)
(m, l) = pairs
b = T[m][l].argmax(dim=0).item()
positions = [0]
states = [(m,l,b)]
active = [True]
depth = [0]
Successors = [[]]
attention_mask = torch.zeros(m,m).long()
parents = [-1]
expand_lists = []
expand_branches = []
num_nodes = 1
while True:
expand = []
expand_branch = []
for i, act in enumerate(active):
if act:
if parents[i] != -1:
attention_mask[i] = attention_mask[parents[i]]
attention_mask[i][i] = 1
expand.append(i)
active[i] = False
(x,y,z) = states[i]
expand_branch.append(z)
positions.extend(list(range(num_nodes, num_nodes + z)))
Successors[i].extend(list(range(num_nodes, num_nodes + z)))
Successors.extend([[] for _ in range(z)])
parents.extend([i for _ in range(z)])
depth.extend([depth[i] + 1 for _ in range(z)])
states.extend(branch_map[(x,y,z)])
assert len(branch_map[(x,y,z)]) == z
num_nodes = num_nodes + z
if len(expand) == 0:
break
expand_lists.append(expand)
expand_branches.append(expand_branch)
active.extend([True for _ in range(sum(expand_branch))])
assert num_nodes == m
assert len(positions) == m
assert len(depth) == m
grow_map = {
"roots": expand_lists,
"branches": expand_branches,
"Successors":Successors,
"mask": attention_mask,
"depth": torch.LongTensor(depth),
"size": num_nodes
}
path = config['dst']
torch.save(grow_map, path)