ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
Build latest book artifacts / build (push) Canceled after 0s
dependency resolution / resolve (3.11) (push) Canceled after 0s
dependency resolution / resolve (3.13) (push) Canceled after 0s
deploy-pages / build (push) Canceled after 0s
deploy-pages / deploy (push) Canceled after 0s
i18n consistency check / check (push) Canceled after 0s
provider adoption tests / test (chapter2/context-compression) (push) Canceled after 0s
provider adoption tests / test (chapter2/prompt-injection) (push) Canceled after 0s
provider adoption tests / test (chapter2/system-hint) (push) Canceled after 0s
provider adoption tests / test (chapter3/log-sanitization) (push) Canceled after 0s
web-search-agent tests / test (push) Canceled after 0s
web-search-agent tests / agentbook (push) Canceled after 0s

This commit is contained in:
2026-08-20 13:12:50 +00:00
commit b119135836
10275 changed files with 3284984 additions and 0 deletions
+67
View File
@@ -0,0 +1,67 @@
"""
从论文中的数据生成两张真实的图表 PNG,放进 Slidev 的 public/ 目录,
供 Proposer 生成的幻灯片直接引用(满足“至少 3 处原图表”的要求,同时
让 Reviewer 的 Vision 检查能真正评估“图片尺寸是否合适”)。
这些图是用 matplotlib 从论文正文里的数字画出来的,属于“论文原始图表”的
程序化复现,而非凭空捏造。
"""
import os
import matplotlib
matplotlib.use("Agg") # 无显示环境
import matplotlib.pyplot as plt
PUBLIC_DIR = os.path.join(os.path.dirname(__file__), "slidev_workspace", "public")
def make_speedup_bar(path):
"""图1FlashAttention 相对标准实现的端到端加速比(论文第 4 节数字)。"""
labels = ["BERT-large\n(seq 512)", "GPT-2\n(seq 1K)", "Long-Range\nArena"]
speedups = [1.15, 3.0, 2.4]
fig, ax = plt.subplots(figsize=(6, 3.4), dpi=150)
bars = ax.bar(labels, speedups, color=["#4C72B0", "#DD8452", "#55A868"])
ax.axhline(1.0, color="gray", linestyle="--", linewidth=1, label="baseline (1x)")
ax.set_ylabel("Speedup vs. standard")
ax.set_title("FlashAttention End-to-End Speedup")
for b, v in zip(bars, speedups):
ax.text(b.get_x() + b.get_width() / 2, v + 0.05, f"{v}x",
ha="center", va="bottom", fontweight="bold")
ax.set_ylim(0, 3.5)
ax.legend(loc="upper left", fontsize=8)
fig.tight_layout()
fig.savefig(path, bbox_inches="tight")
plt.close(fig)
def make_memory_hierarchy(path):
"""图2:GPU 内存层次的带宽对比(论文第 2 节表格,对数坐标)。"""
levels = ["SRAM\n(on-chip)", "HBM\n(main GPU)", "CPU DRAM"]
bandwidth = [19000, 1750, 12.8] # GB/s
fig, ax = plt.subplots(figsize=(6, 3.4), dpi=150)
bars = ax.bar(levels, bandwidth, color=["#C44E52", "#8172B3", "#937860"])
ax.set_yscale("log")
ax.set_ylabel("Bandwidth (GB/s, log scale)")
ax.set_title("GPU Memory Hierarchy (A100)")
for b, v in zip(bars, bandwidth):
ax.text(b.get_x() + b.get_width() / 2, v * 1.15,
f"{v:g}", ha="center", va="bottom", fontweight="bold")
fig.tight_layout()
fig.savefig(path, bbox_inches="tight")
plt.close(fig)
def generate_all():
os.makedirs(PUBLIC_DIR, exist_ok=True)
f1 = os.path.join(PUBLIC_DIR, "speedup_bar.png")
f2 = os.path.join(PUBLIC_DIR, "memory_hierarchy.png")
make_speedup_bar(f1)
make_memory_hierarchy(f2)
return {
"/speedup_bar.png": "FlashAttention 端到端加速比柱状图(BERT 1.15x / GPT-2 3x / LRA 2.4x",
"/memory_hierarchy.png": "A100 GPU 内存层次带宽对比(SRAM 19TB/s / HBM ~1.75TB/s / DRAM 12.8GB/s,对数坐标)",
}
if __name__ == "__main__":
print(generate_all())