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Commit 9afddf86 authored by sharkgene@qq.com's avatar sharkgene@qq.com
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README.md
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# DataAnalysis # DataAnalysis
将大模型推理测试生成的excel数据文件，按照特定方式生成柱状图进行比较
配置文件data_config.json示例说明:
{
"filter": {
"并发数": [],
"模型": []
},
"distinguish": ["模型", "卡类型", "卡数"],
"group_by": [["并发数"], ["输入长度(tokens)", "输出长度(tokens)"]],
"files": [
{
"file": "test100.xlsx",
"sheets": [],
"column_mapping": {
},
"column_replace": {
}
}
]
}
filter: 过滤条件，不设置或者为空则不过滤
distinguish: 选择输出文件的条件，例如["模型", "卡类型", "卡数"]则将相同的模型、卡类型和卡数的所有数据生成以个文件
group_by: 可以进行最多2重分组，每层一个图，第2层以不同柱状显示数据。如果命令行参数指定合并，则所有合成一个图，分组之间间隔开
files: 指定文件信息
## 使用方法
python3 plot_comparison.py --help
usage: plot_comparison.py [-h] [--配置 配置] [--输出目录 输出目录] [--合并分组]
绘制模型性能对比图表
options:
-h, --help show this help message and exit
--配置 配置, -f 配置 数据配置文件路径
--输出目录 输出目录, -d 输出目录 输出图表目录
--合并分组, -m 将第一层分组合并到一张图中
##
data_config.json 0 → 100644
View file @ 9afddf86
{
"filter": {
"并发数": [],
"模型": []
},
"distinguish": ["模型", "卡类型", "卡数"],
"group_by": [["并发数"], ["输入长度(tokens)", "输出长度(tokens)"]],
"files": [
{
"file": "test100.xlsx",
"sheets": [],
"column_mapping": {
},
"column_replace": {
}
}
]
}
plot_comparison.py 0 → 100644
View file @ 9afddf86
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import argparse
import json
import os
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
parser = argparse.ArgumentParser(description='绘制模型性能对比图表')
parser.add_argument('--配置', '-f', type=str, default='data_config.json', help='数据配置文件路径')
parser.add_argument('--输出目录', '-d', type=str, default='charts', help='输出图表目录')
parser.add_argument('--合并分组', '-m', action='store_true', help='将第一层分组合并到一张图中')
args = parser.parse_args()
def load_data_from_files(config):
all_data = []
files_config = config.get('files', [])
for file_config in files_config:
file_path = file_config.get('file')
sheets = file_config.get('sheets', [])
column_mapping = file_config.get('column_mapping', {})
if not os.path.exists(file_path):
print(f"文件不存在: {file_path}, 跳过")
continue
xl = pd.ExcelFile(file_path)
if sheets is None or (isinstance(sheets, list) and len(sheets) == 0):
sheets = xl.sheet_names
else:
sheets = [s for s in sheets if s]
for sheet in sheets:
try:
df = pd.read_excel(file_path, sheet_name=sheet)
df.columns = df.columns.str.replace('\n', '').str.strip()
if column_mapping:
df = df.rename(columns=column_mapping)
column_replace = file_config.get('column_replace', {})
for col, replace_dict in column_replace.items():
if col in df.columns:
df[col] = df[col].replace(replace_dict)
df['source_file'] = file_path
df['source_sheet'] = sheet
all_data.append(df)
print(f"读取: {file_path} - {sheet}, {len(df)} 行")
except Exception as e:
print(f"读取失败: {file_path} - {sheet}: {e}")
if not all_data:
return pd.DataFrame()
combined_df = pd.concat(all_data, ignore_index=True)
return combined_df
def apply_filter(df, filter_dict):
for filter_col, filter_values in filter_dict.items():
if filter_col in df.columns and filter_values:
if isinstance(filter_values, list):
df = df[df[filter_col].isin(filter_values)]
else:
df = df[df[filter_col] == filter_values]
return df
def generate_chart(df_subset, output_path, compare_col, outer_group_cols, inner_group_cols, metric_cols, merge_groups=False):
df_subset = df_subset.copy()
df_subset[compare_col] = df_subset['vLLM版本'].astype(str) + '_' + df_subset['V0/V1 Engine'].astype(str)
all_group_cols = outer_group_cols + inner_group_cols
if all_group_cols:
df_grouped = df_subset[all_group_cols + [compare_col] + metric_cols].groupby(all_group_cols + [compare_col]).mean().reset_index()
else:
df_grouped = df_subset[[compare_col] + metric_cols].groupby([compare_col]).mean().reset_index()
df_grouped[compare_col] = df_grouped.index
if len(df_grouped) == 0:
print(f" 无数据，跳过")
return False
if outer_group_cols:
outer_values = df_grouped.groupby(outer_group_cols).size().reset_index()
else:
outer_values = pd.DataFrame({'': ['all']})
n_outer = len(outer_values)
engine_values = df_grouped[compare_col].unique()
n_engines = len(engine_values)
color_palette = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#3B1F2B', '#95C623', '#7B2D26']
colors = [color_palette[i % len(color_palette)] for i in range(n_engines)]
if merge_groups and n_outer > 1:
fig, axes = plt.subplots(1, 4, figsize=(8 * n_outer + 20, 10))
bar_width = 0.12
bar_spacing = 0.05
group_gap = 3
x_labels_all = None
for col, metric in enumerate(metric_cols):
ax = axes[col]
current_x = 0
for row_idx, (_, outer_row) in enumerate(outer_values.iterrows()):
df_outer = df_grouped.copy()
for gcol in outer_group_cols:
df_outer = df_outer[df_outer[gcol] == outer_row[gcol]]
outer_label_value = '-'.join([str(outer_row[gcol]) for gcol in outer_group_cols])
pt = df_outer.pivot_table(
index=inner_group_cols,
columns=compare_col,
values=metric
).fillna(0)
n_bars_per_group = len(pt)
group_width = n_bars_per_group * n_engines * (bar_width + bar_spacing) + group_gap
group_center = current_x + group_width / 2
x_labels = ['/'.join([str(v) for v in idx]) for idx in pt.index]
if x_labels_all is None:
x_labels_all = x_labels
x = np.arange(len(x_labels)) * (n_engines * (bar_width + bar_spacing)) + current_x
for i, engine in enumerate(engine_values):
if engine in pt.columns:
values = pt[engine].values
offset = i * bar_width
label = f"{engine} ({outer_label_value})"
bars = ax.bar(x + offset, values, bar_width, label=label, color=colors[i], edgecolor='white', linewidth=0.5)
for bar, val in zip(bars, values):
if val > 0:
y_pos = bar.get_height() + bar.get_height()*0.02 if bar.get_height() > 0 else 1
ax.text(bar.get_x() + bar.get_width()/2, y_pos,
f'{val:.1f}', ha='center', va='bottom', fontsize=5, fontweight='bold')
ax.axvline(x=current_x + n_bars_per_group * n_engines * (bar_width + bar_spacing) + group_gap/2, color='gray', linestyle='--', linewidth=1)
ax.text(group_center, ax.get_ylim()[1] * 0.95, outer_label_value,
ha='center', va='top', fontsize=9, fontweight='bold',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
current_x = current_x + n_bars_per_group * n_engines * (bar_width + bar_spacing) + group_gap
total_inner_labels = len(x_labels_all)
inner_positions = []
inner_labels = []
for gi in range(len(outer_values)):
base_x = gi * (total_inner_labels * n_engines * (bar_width + bar_spacing) + group_gap)
for xi in range(total_inner_labels):
center_pos = base_x + xi * n_engines * (bar_width + bar_spacing) + (n_engines * bar_width + (n_engines-1) * bar_spacing) / 2
inner_positions.append(center_pos)
inner_labels.append(x_labels_all[xi])
ax.set_xticks(inner_positions)
ax.set_xticklabels(inner_labels, rotation=45, ha='right', fontsize=6)
ax.set_xlabel('/'.join(inner_group_cols), fontsize=9)
ax.set_ylabel(metric, fontsize=10)
ax.set_title(f'{metric}', fontsize=12, fontweight='bold')
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.legend(fontsize=5, loc='upper right', framealpha=0.9, ncol=1)
else:
fig, axes = plt.subplots(n_outer, 4, figsize=(24, 5 * n_outer))
if n_outer == 1:
axes = axes.reshape(1, -1)
bar_width = 0.2
outer_label = '/'.join(outer_group_cols) if outer_group_cols else '全部'
for row_idx, (_, outer_row) in enumerate(outer_values.iterrows()):
df_outer = df_grouped.copy()
for col in outer_group_cols:
df_outer = df_outer[df_outer[col] == outer_row[col]]
outer_label_value = '-'.join([str(outer_row[col]) for col in outer_group_cols])
for col, metric in enumerate(metric_cols):
ax = axes[row_idx, col]
pt = df_outer.pivot_table(
index=inner_group_cols,
columns=compare_col,
values=metric
).fillna(0)
x_labels = ['/'.join([str(v) for v in idx]) for idx in pt.index]
x = np.arange(len(x_labels))
for i, engine in enumerate(engine_values):
if engine in pt.columns:
values = pt[engine].values
offset = (i - n_engines/2 + 0.5) * bar_width
bars = ax.bar(x + offset, values, bar_width, label=engine, color=colors[i], edgecolor='white', linewidth=0.5)
for bar, val in zip(bars, values):
if val > 0:
y_pos = bar.get_height() + bar.get_height()*0.02 if bar.get_height() > 0 else 1
ax.text(bar.get_x() + bar.get_width()/2, y_pos,
f'{val:.1f}', ha='center', va='bottom', fontsize=7, fontweight='bold')
ax.set_xlabel('/'.join(inner_group_cols), fontsize=9)
ax.set_ylabel(metric, fontsize=10)
ax.set_title(f'{outer_label}={outer_label_value} - {metric}', fontsize=11, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels(x_labels, rotation=45, ha='right', fontsize=7)
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.legend(fontsize=6, loc='upper right', framealpha=0.9, ncol=1)
plt.tight_layout()
plt.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')
plt.close()
return True
print(f"从配置文件加载数据: {args.配置}")
with open(args.配置, 'r', encoding='utf-8') as f:
config = json.load(f)
df = load_data_from_files(config)
if df.empty:
print("未加载到数据")
exit(1)
print(f"\n可用列名: {df.columns.tolist()}")
col_mapping = {}
for std_col, alt_cols in [
('模型', ['模型', 'model', 'Model']),
('卡类型', ['卡类型', 'card_type', '卡']),
('卡数', ['卡数', 'num_cards', '卡数', 'GPU数量']),
('vLLM版本', ['vLLM版本', 'vllm_version', 'vLLM版本']),
('V0/V1 Engine', ['V0/V1 Engine', 'Engine', 'engine']),
('输入长度(tokens)', ['输入长度(tokens)', 'input_length', 'input length', '输入长度']),
('输出长度(tokens)', ['输出长度(tokens)', 'output_length', 'output length', '输出长度']),
('并发数', ['并发数', 'concurrency', '并发', 'num_concurrent']),
('平均首字延时TTFT(ms)', ['平均首字延时TTFT(ms)', 'ttft', 'TTFT', '首字延时']),
('平均生成时间TPOT(ms)', ['平均生成时间TPOT(ms)', 'tpot', 'TPOT', '生成时间']),
('生成吞吐量(tokens/s)', ['生成吞吐量(tokens/s)', 'gen_throughput', '生成吞吐']),
('总吞吐量(tokens/s)', ['总吞吐量(tokens/s)', 'total_throughput', '总吞吐'])
]:
for alt in alt_cols:
if alt in df.columns:
col_mapping[std_col] = alt
print(f"\n列映射: {col_mapping}")
df_renamed = df.rename(columns=col_mapping)
filter_config = config.get('filter', {})
df_renamed = apply_filter(df_renamed, filter_config)
print(f"过滤后数据量: {len(df_renamed)}")
metric_cols = [
'平均首字延时TTFT(ms)',
'平均生成时间TPOT(ms)',
'生成吞吐量(tokens/s)',
'总吞吐量(tokens/s)'
]
dist_cols_config = config.get('distinguish', ['模型', '卡数'])
dist_cols = [col_mapping.get(c, c) for c in dist_cols_config]
dist_cols = [c for c in dist_cols if c in df_renamed.columns]
os.makedirs(args.输出目录, exist_ok=True)
group_by = config.get('group_by', [[], []])
if isinstance(group_by[0], list):
outer_group = group_by[0] if len(group_by) > 0 else []
inner_group = group_by[1] if len(group_by) > 1 else []
else:
outer_group = []
inner_group = group_by
dist_combinations = df_renamed.groupby(dist_cols).size().reset_index()
print(f"\n将生成 {len(dist_combinations)} 个图表...")
chart_count = 0
for idx, (_, dist_row) in enumerate(dist_combinations.iterrows()):
df_subset = df_renamed.copy()
for dist_col in dist_cols:
df_subset = df_subset[df_subset[dist_col] == dist_row[dist_col]]
filter_parts = []
for dist_col in dist_cols:
val = dist_row[dist_col]
safe_col_name = dist_col.replace('/', '_').replace('\\', '_')[:10]
filter_parts.append(f"{safe_col_name}_{val}")
output_filename = '_'.join(filter_parts) + ".png"
output_path = os.path.join(args.输出目录, output_filename)
print(f"[{idx+1}/{len(dist_combinations)}] 生成图表: {output_filename}")
success = generate_chart(df_subset, output_path, 'vLLM_Engine', outer_group, inner_group, metric_cols, args.合并分组)
if success:
chart_count += 1
print(f"\n完成！共生成 {chart_count} 个图表，保存到目录: {args.输出目录}")
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