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[Lint]Style: Convert example to ruff format (#5863)

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### What this PR does / why we need it?
This PR fixes linting issues in the `example/` to align with the
project's Ruff configuration.

- vLLM version: v0.13.0
- vLLM main:
bde38c11df

Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
This commit is contained in:
SILONG ZENG
2026-01-13 20:46:50 +08:00
committed by GitHub
parent f7b904641e
commit 78d5ce3e01
23 changed files with 678 additions and 1037 deletions
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76
examples/eplb/eplb_strategy.py
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@@ -1,4 +1,3 @@
# coding=utf-8
# Copyright (c) Huawei Technologies Co., Ltd. 2025-2025. All rights reserved.
import json
import os
@@ -21,10 +20,7 @@ def save_matrix_to_json(output_path, file_name, deployment):
layer = {"layer_id": i, "device_count": num_cards}
device_list = []
for j in range(num_cards):
device = {
"device_id": j,
"device_expert": deployment[i, j].tolist()
}
device = {"device_id": j, "device_expert": deployment[i, j].tolist()}
device_list.append(device)
layer["device_list"] = device_list
layer_list.append(layer)
@@ -34,7 +30,7 @@ def save_matrix_to_json(output_path, file_name, deployment):
# Save as JSON file
try:
with open(file_name, 'w') as f:
with open(file_name, "w") as f:
json.dump(data, f, indent=4)
except Exception as e:
print(f"write {file_name} failed: {e}")
@@ -63,21 +59,17 @@ def calculate_average(lst):
return total / count
def layer_imblance_polt(y_list, label_names, device_num, output_path,
file_name):
plt.rcParams['font.sans-serif'] = ['Arial']
plt.rcParams['axes.unicode_minus'] = False
def layer_imblance_polt(y_list, label_names, device_num, output_path, file_name):
plt.rcParams["font.sans-serif"] = ["Arial"]
plt.rcParams["axes.unicode_minus"] = False
x = [i for i in range(58)]
for index, y in enumerate(y_list):
plt.plot(x,
y,
label=rf'{label_names[index]}，avg={calculate_average(y)}')
plt.plot(x, y, label=rf"{label_names[index]}，avg={calculate_average(y)}")
plt.legend()
plt.title(rf'Load Distribution (num_gpus={device_num})')
plt.xlabel('layer')
plt.ylabel('Device Load')
plt.title(rf"Load Distribution (num_gpus={device_num})")
plt.xlabel("layer")
plt.ylabel("Device Load")
# Show grid lines
plt.grid(True)
@@ -88,27 +80,23 @@ def layer_imblance_polt(y_list, label_names, device_num, output_path,
plt.close()
def deepseek_deploy(workload, num_redundancy_expert, num_groups, num_nodes,
num_gpus, num_original_expert):
def deepseek_deploy(workload, num_redundancy_expert, num_groups, num_nodes, num_gpus, num_original_expert):
from eplb_deepseek import rebalance_experts
num_replicas = num_original_expert + num_redundancy_expert
hy2log, log2phy, logcnt = rebalance_experts(workload, num_replicas,
num_groups, num_nodes,
num_gpus)
hy2log, log2phy, logcnt = rebalance_experts(workload, num_replicas, num_groups, num_nodes, num_gpus)
# Convert to global_deployment
workload = workload.cpu().numpy()
global_deployment = []
layer_num = log2phy.shape[0]
num_physical_experts_local = (num_original_expert +
num_redundancy_expert) // num_gpus
num_physical_experts_local = (num_original_expert + num_redundancy_expert) // num_gpus
for layer_idx in range(layer_num):
layer_deployment = []
for gpu_idx in range(num_gpus):
local_deployment = hy2log[layer_idx][gpu_idx *
num_physical_experts_local:
(gpu_idx + 1) *
num_physical_experts_local]
local_deployment = hy2log[layer_idx][
gpu_idx * num_physical_experts_local : (gpu_idx + 1) * num_physical_experts_local
]
local_deployment = local_deployment.flatten()
layer_deployment.append(local_deployment.tolist())
global_deployment.append(layer_deployment)
@@ -122,18 +110,15 @@ def deepseek_deploy(workload, num_redundancy_expert, num_groups, num_nodes,
new_value = workload[layer_idx].reshape(num_gpus, -1)
row_sum = np.sum(new_value, axis=1)
original_weights.append(row_sum.max())
average_weights.append((np.sum(workload[layer_idx]) / num_gpus))
average_weights.append(np.sum(workload[layer_idx]) / num_gpus)
opt_workload = np.zeros((num_original_expert + num_redundancy_expert),
dtype=np.float64)
opt_workload = np.zeros((num_original_expert + num_redundancy_expert), dtype=np.float64)
for expert_idx in range(num_original_expert):
physical_expert_idxs = log2phy[layer_idx][expert_idx]
physical_expert_idxs = physical_expert_idxs.flatten()
physical_expert_idxs = physical_expert_idxs[
physical_expert_idxs != -1]
physical_expert_idxs = physical_expert_idxs[physical_expert_idxs != -1]
for physical_expert_idx in physical_expert_idxs:
opt_workload[physical_expert_idx] += workload[layer_idx][
expert_idx] / len(physical_expert_idxs)
opt_workload[physical_expert_idx] += workload[layer_idx][expert_idx] / len(physical_expert_idxs)
opt_workload = opt_workload.reshape(num_gpus, -1)
row_sum = np.sum(opt_workload, axis=1)
max_weights.append(row_sum.max())
@@ -142,8 +127,9 @@ def deepseek_deploy(workload, num_redundancy_expert, num_groups, num_nodes,
return global_deployment, y_list
if __name__ == '__main__':
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--exp_name", type=str, default="gsm8k_temp0.0")
parser.add_argument("--num_original_expert", type=int, default=256)
@@ -165,19 +151,13 @@ if __name__ == '__main__':
num_nodes = args.num_nodes
# NOTE: assume input workload format: [layer_num, num_experts]
workload = torch.load(input_path, map_location=torch.device('cpu'))
global_deployment, y_list = deepseek_deploy(workload,
num_redundancy_expert,
num_groups, num_nodes,
num_devices,
num_original_expert)
workload = torch.load(input_path, map_location=torch.device("cpu"))
global_deployment, y_list = deepseek_deploy(
workload, num_redundancy_expert, num_groups, num_nodes, num_devices, num_original_expert
)
file_name = f"{exp_name}_{num_devices}_{num_redundancy_expert}"
save_matrix_to_json(output_path, file_name, np.array(global_deployment))
label_names = [
'default deployment max load', 'balanced load max load',
'balanced load avg load'
]
label_names = ["default deployment max load", "balanced load max load", "balanced load avg load"]
new_file_name = f"{exp_name}_{num_devices}_{num_redundancy_expert}.png"
layer_imblance_polt(y_list, label_names, num_devices, output_path,
new_file_name)
layer_imblance_polt(y_list, label_names, num_devices, output_path, new_file_name)